News and Agenda Archive

News

Startup Innatera in the news

Innatera is a startup that originated in 2019 in the SPS group and has now grown to 65 employees. Recently they completed a neural network-based microcontroller, based on spiking neural networks. This enables highly power efficient processing (e.g. real-time image recognition) for Internet-of-Things applications.

The chip was demonstrated at the Consumer Electronics Show (CES) in Las Vegas.

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Alle-Jan van der Veen wins EURASIP Technical Achievement Award

Alle-Jan has been awarded the prestigious Technical Achievement Award of EURASIP for his “contributions to subspace-based array signal processing”.

The EURASIP Technical Achievement Award honors a person who, over a period of years, has made outstanding technical contributions to theory or practice in technical areas within the scope of the Society, as demonstrated by publications, patents, or recognized impact in this field.

EURASIP is the European Association for Signal Processing. The award will be presented at EUSIPCO'2024 in August in Lyon.

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Portrait of Medical Delta Professor Johan Frijns (appointed at EEMCS, Bioelectronics group)

Portrait of Medical Delta Professor Johan Frijns 

More than 800,000 people in the Netherlands are hard of hearing. They suffer so much from hearing loss that it limits their daily lives. Prof. Dr. ir. Johan Frijns treats people with hearing loss, conducts research on hearing implants, and gladly shares his knowledge about electrical stimulation of the nervous system. "We shouldn't want to reinvent everything in every little corner. What we learn in one place, we can also use in another."

Johan Frijns is a professor of Otology and Physics of Hearing in the Department of Otorhinolaryngology at LUMC. He heads the Center for Audiology and Hearing Implants Leiden (CAHIL) and the Cochlear Implant Rehabilitation Centre Leiden (CIRCLE). He was recently appointed as a Medical Delta professor with a position at the Faculty of Electrical Engineering, Mathematics, and Computer Science at TU Delft.

Read more: Portrait and video Johan Frijns: “When a deaf child suddenly hears and learns to talk, this also has a huge impact on the people around him.” | Medical Delta

 


New book with contribution of Geert Leus

Sparse Arrays for Radar, Sonar, and Communications

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Science Advances paper co-authored by Geert Leus

"Four-dimensional computational ultrasound imaging of brain hemodynamics" by Michael D. Brown et al. appeared in Science Advances on 17 January, 2024.

Read the paper at the following link.

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Dr. Bahareh Abdi (EEE) awarded the TU Delft Open Education Stimulation Fund

Dr. Bahareh Abdi (EEE) was awarded with the TU Delft Open Education Stimulation Fund for her project “Enhancing Electrical Engineering Education: A Digital Twin and Interactive Manual Approach for Dynamic Hands-On Learning.” Dr. Abdi will receive €20k over one year period to develop interactive textbook equivalent of BSc EE lab manuals with added digital content to improve students’ learning experience.

In this project, Dr. Abdi collaborates with Dr. Seyedmahdi Izadkhast (EEE), Dr. İlke Ercan (EEE), and Prof. Dr. Ir. Alle-Jan van der Veen (SPS) from the Department of Microelectronics, and Dr. Serdar Asut from the Faculty of Architecture who has expertise on the subject. The departure point of the project is Integrated Project-3 in the BSc EE curriculum. The outcome of the project will inform the improvement of other integrated projects and lab courses. 

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Mario Coutino wins best PhD thesis award

Mario Coutino has won the IEEE Signal Processing Society Best PhD Dissertation Award. Mario received his PhD in Apr 2021 with a dissertation titled "Advances in graph signal processing: Graph filtering and network identification" (promotor: Geert Leus (SPS)).

Congratulations!

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Justin Dauwels named IEEE SPS Distinguished Lecturer for 2024

Justin Dauwels has been named IEEE SPS Distinguished Lecturer for 2024. The Distinguished Lecturer Program provides means for IEEE chapters to have access to individuals who are well known educators and authors in the fields of signal processing. The selection is quite rigorous and only 5 persons are appointed as DL each year.

Lecture topics for Justin are:

  • Deep Generative AI
  • Machine Learning for Applications in Neurology
  • Machine Learning for Applications in Psychiatry
  • Perception Error Modelling for Autonomous Driving

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New NWO-TTW project for Geethu and Justin (SPS)

Laser satellite communications is a promising technology to support worldwide access to telecommunications services. A major technological challenge is atmospheric turbulence impacting the propagation of the laser beams. Its effect can be mitigated by adaptive optics and geographic diversity. The DAILSCOM project aims to provide a map of the effective optical channel performance over Europe. This map is needed to design ground network technology and estimate communications service availability. Since our current understanding and ability to estimate the channel performance are limited, we will develop novel physics-informed machine learning algorithms to formulate the optical link performance map.

Project PIs are: Rudolf Saathof (AE), Justin Dauwels, Geethu Joseph, Sukanta Basu (CiTG)

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Justin Dauwels IEEE Signal Processing Magazine Area Editor

Justin Dauwels has been appointed Columns & Forums Area Editor for IEEE Signal Processing Magazine. Congratuations!

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Richard Hendriks Senior Associate Editor

Richard Hendriks has been appointed as Senior Associate Editor of IEEE Transactions on Audio Speech and Language Processing. Congratulations!

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Model-Driven Decisions Lab initiated

On 28 September, a Memorandum of Understanding was signed between the TU Delft and the national police, to collaborate on research around safety and security, and forensic research. Part of this initiative will be a new research lab, the "Model-Driven Decisions Lab". This lab will start up with 5 PhD positions. Scientific director is Justin Dauwels (SPS).

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SPS Summer outing 2023

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Best paper award for Costas Kokke

Costas Kokke received the best contribution award at ISCS, the International Symposium on Computational Sensing. Congratulations!

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IEEE SPS Best Thesis Award for Geethu Joseph

Geethu Joseph received the IEEE Signal Processing Society Best PhD Thesis Award at ICASSP'23. Annually, there are 2 such awards. The 2nd award went to Elvin Isufi, former PhD student of Geert Leus. Congratulations to both!

These awards were installed 3 years ago; until now, there have been 3 recipients.

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Where is everyone?

Greetings from ICASSP'23, Rhodes, Greece!

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Best paper award for Sofia Kotti!

We are proud of our PhD student Sofia-Eirini Kotti who won the best paper award for her work “Modeling nonlinear evoked hemodynamic responses in functional ultrasound” at the Data Science and Learning Workshop during #icassp2023. Special thanks to Aybüke Erol for her contributions to this research!

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Best Paper Award at the SITB'23 symposium

At the 43rd Symposium on Information Theory and Signal Processing in the Benelux, our MSc student Alan Hamo has won the award for Best Student Presentation, for the paper "Machine learning algorithm to predict cardiac output based on arterial pressure measurement". Congratulations!

Paper abstract

Cardiac output (CO) plays a crucial role in determining the delivery of oxygen to tissues and is a key metric in hemodynamic optimization. The gold standard method for measuring cardiac output is through thermodilution using pulmonary artery catheter, but it is an invasive procedure associated with complications during placement and the need for a skilled expert to perform the measurements. An alternative approach is to estimate cardiac output by utilizing arterial blood pressure (ABP) measurements, which is a minimally invasive technique. However, the relationship between ABP and CO is not yet fully understood. In this study, we aim to utilize regression-based machine learning techniques and feature engineering to estimate CO from ABP. Hemodynamics and waveform features, along with demographic information of the patient, were integrated to enhance the  accuracy of the model.

MSc thesis supervisor: Justin Dauwels. Joint work with Drs. Niki Ottenhof and Jan-Wiebe Korstanje of the Erasmus MC. 

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Where is everyone?

43rd Symposium on Information Theory and Signal Processing in the Benelux (SITB 2023)

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Assistant professor Raj Rajan appointed secretary of "De Nederlandse Vereniging voor Ruimtevaart" (Netherlands Space Society)

NVR has approximately 1200 members. They include aerospace professionals, students, and people with a personal interest. As of today, the association has been around for over 70 years. Yet, it remains in touch with the newest Dutch and international developments surrounding space and astronautics. The NVR aims to be an association for all ages, and the network for anyone excited by modern aerospace and space travel.

Raj Rajan has joined the Board of NVR as Secretary, starting Feb 2023.

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IEEE SPS Student Scholarship Program

The IEEE Signal Processing Society (SPS) awards scholarships of up to a total of US$7,000 for up to three years of consecutive support to students who have expressed interest and commitment to pursuing signal processing education and real-world career experiences.

Students and graduate students from all 10 IEEE Regions are encouraged to apply!

This is not a full scholarship but rather a supplementary fund in order to support the signal processing field.  Apply before 30 June 2023.

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Congratulations Class of 2022!

In 2022, SPS (CAS) graduated 48 MSc students, an all-time record for us! In particular, Justin Dauwels (12) and Richard Hendriks (8) had very popular topics, while also biomedical is seeing increasing interest. Four graduates stayed with us to to a PhD.

Congratulations to all the young Engineers!


CAS becomes SPS

The Circuits and Systems (CAS) group was formed over 20 years ago out of a merger of the Electronic Techiques and the Network Theory group. The resulting group had a focus on system theory and digital VLSI design, in particular design and verification tools.

Over these years, the signal processing part of the group has grown, to a point where now it is time to change our name. Starting from 1 January 2023, the group is called Signal Processing Systems (SPS). Happy New Year!

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Nature paper for Gerard Janssen

GPS signals are not available everywhere. Especially in urban environments, the satellites may be blocked or the signals experience multipath, leading to timing errors and inaccurate positions in areas where accuracy matters most.

In the SuperGPS project of Gerard Janssen e.a., it is shown how relatively inaccurate wireless transceivers can be networked; the use of wideband signals and carrier phase recovery techniques results in a localization accuracy at decimeter-scale or better; two orders of magnitude better than GPS in certain situations. The perspective is that such transceivers can be embedded in 4G/5G mobile communication networks (the employed protocols are compatible), such that it is relatively inexpensive to roll out this solution.

In the Nov issue of Nature, the project partners describe experimental results acquired on a testbed at The Green Village at TU Delft. An associated article provides an introduction and a perspective. The topic was also covered by IEEE Spectrum.

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2022 4TU.NIRICT Community Day, November 14

Identified are 3 themes around which we would like to bring NIRICT researchers together to discuss and to explore per theme the opportunities for interdisciplinary collaboration. The themes are related to the societal challenges we are currently facing and require interdisciplinary effort in order to be addressed effectively. We believe that productive discussions during the event could be instrumental in forming new research lines, but also project consortia around various national R&D investment programs addressing the knowledge and innovation agenda’s (KIAs) of the Netherlands (https://www.topsectoren.nl/missiesvoordetoekomst).


Themes of the day:
• Health and ICT People live longer, more healthcare is needed. Work pressure in healthcare is rapidly increasing. More data is collected by smart devices. How can the ICT community contribute solutions to keep our society healthy?
• Energy and ICT The current explosion of the energy prices shows that we are facing an unstable situation with respect to energy production and consumption. We produce more solar energy on one hand and consume more energy due to air conditioners and electric cars. We need smart solutions both in production and consumption. Moreover, due to its significant share in energy consumption, ICT systems from the end user devices to power-hungry data centers need to become more energy efficient.
• Agriculture and ICT We just had another summer with extreme heat waves and droughts. The climate changes we are facing have tremendous consequences for agriculture. The population is growing and the pressure on nature is increasing. What smart solutions do we see to deal with the challenges to keep our planet healthy and at the same time to keep the food production in line with the population growth?
The Community Day starts at 12:30hrs with a walk-in lunch. At 13:30hrs the official part will start with a number of short presentations, followed by several parallel sessions regarding the three themes of the day. You can indicate your preference on the registration form. Afterwards there will be a networking drink from 17:00-18:00hrs.

It would be great to welcome many of you on November 14th, because together we strengthen the ICT community of The Netherlands, so join us for the 4TU.NIRICT Community Day 2022, November 14, in Van der Valk Hotel Utrecht and register here by November 6, 2022.
Your data will only be used for registration and will be discarded within one week after the 4TU.NIRICT Community Day. If you have any questions, don’t hesitate to contact us: m.mommers@tue.nl


Congratulations Ids

CAS PhD student Ids van der Werf won the Best Presentation Award at the 2022 IEEE-SPS / EURASIP Summer School "Data and Graph Driven Learning for Communications and Signal Processing". Congratulations!

Ids started just 3 weeks ago as a PhD student; the presentation was actually based on his MSc thesis work on Sound Field Reconstruction.

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Where is everyone?

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CAS outing 2022

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Congratulations for Miao Sun

PhD defense - 15 June 2022

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New NWO project: GRASPA

Six research projects receive funding within the Open Technology Programme (OTP) this month. The projects receive a total of about 4 million euros from NWO; organisations involved in the research projects invest a total of 1.3 million euros. Among these 6 projects is a project from prof. Geert Leus from the CAS group; Graph Signal Processing in Action (GraSPA).

Graph signal processing (GSP) is the exciting research field that extends concepts from traditional signal processing to signals living in an irregular domain that can be characterized through a graph. GSP is extremely promising for applications in transportation networks, smart grid, wireless communications, social networks, brain science and recommender systems, to name a few. This project focuses on the non-trivial extension of GSP to time-varying or dynamic networks, where either the connections or the nodes can change. We will develop innovative tools to estimate such time-varying graphs from data and devise new graph filtering schemes for denoising, interpolation, and prediction. The developed techniques will be applied to brain activity monitoring, which is crucial to understand the working of the brain, as well as recommender systems, which are omnipresent in our daily lives.

More information: Four million euro for six technological research projects | NWO

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New NWO-TTW project: GraSPA

Graph signal processing (GSP) is the exciting research field that extends concepts from traditional signal processing to signals living in an irregular domain that can be characterized through a graph. GSP is extremely promising for applications in transportation networks, smart grid, wireless communications, social networks, brain science and recommender systems, to name a few. This project focuses on the non-trivial extension of GSP to time-varying or dynamic networks, where either the connections or the nodes can change. We will develop innovative tools to estimate such time-varying graphs from data and devise new graph filtering schemes for denoising, interpolation, and prediction. The developed techniques will be applied to brain activity monitoring, which is crucial to understand the working of the brain, as well as recommender systems, which are omnipresent in our daily lives.

This project will fund 2 PhD students. PIs are Geert Leus and Elvin Isufi.

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Happy Secretaries Day!!!

Our congratulations and best wishes!


Raj Thilak Rajan elected to the IAF-SCAN committee

Raj Thilak Rajan was elected to the Space Communications and Navigation Committee (SCAN) of the International Astronautical Federation (IAF).

The SCAN committee is responsible, within the IAF, for all aspects of satellite-based communication and navigation systems and technology. The committee will organise the SCAN symposium at each IAC and, when appropriate, will organise specialised plenary sessions and Highlight Lectures. It will provide annual inputs to the IAF report to the United Nations.

For more information: see link.

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Justin Dauwels wins the Vincent Bendix Automotive Electronics Engineering Award

A paper by Justin Dauwels (from the time he was in Singapore) has won the Vincent Bendix Automotive Electronics Engineering Award.

The paper is “An Optimal Controller Synthesis for Longitudinal Control of Platoons with Communication Scenarios in Urban Environments and Highways”.

SAE is a foundation for automotive engineers, founded in 1904 by Henri Ford, and with 100,000+ members.

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Alle-Jan van der Veen appointed IEEE Signal Processing Society Vice President

Alle-Jan van der Veen has been appointed IEEE Signal Processing Society Vice President for Technical Directions, for the term 2022-2024.  In this capacity, he is member of ExCom and chairs the Technical Directions Board (comprising the 12 Technical Committees of the SP Society).

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Minister of Education "meets" Jorge

On 20 January, our new Minister of Education Robbert Dijkgraaf interviewed a few young scientists. Jorge Martinez was one of them.

On twitter: "Jonge wetenschappers inspireren mij enorm. Ik sprak ze over hun ideeën voor de toekomst van de wetenschap en hun werkdruk en de werkcultuur. Daarnaast vertelden zij over interne en externe stressfactoren en het pad dat zij voor zich zien, ook buiten de wetenschap. Wordt vervolgd!"

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ISOCC 2021 ISE President Best Paper Award

At this year's edition of ISOCC 2021, our former excellent bachelor and master student, Jun Feng, obtained the "2021 ISE President Best Paper Award" for his paper: "A Versatile and Efficient 01.-to-11 Gb/s CML Transmitter in 40-nm CMOS."

Ph.D. candidates Mohammadreza Beikmirza and Milad Mehrpoo contributed to Jun's outstanding work. Jun is now a Ph.D. candidate at KU-Leuven. We congratulate Jun, Mohammadreza, and Milad for this award and send our best wishes for their future research.


New DAI Labs for Raj Rajan

Raj was awarded a DAI Labs, with 3ME (Manon Kok). This will fund 2 PhD positions for CAS, and generate some TU-wide publicity.

The awarded proposal is "Sensor AI" and investigates sensor networks, sensor fusion, distributed learning, sensor swarms. The 3ME part also looks at human motion estimation and including physical knowledge into learning systems.

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New Medical Delta professors

Congratulations Wouter Serdijn, Natasja de Groot and Andrew Webb on your inauguration as Medical Delta Professor

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Innatera: Spiking neural network architecture

A short video about the Spiking Neural Hardware Architecture Innatera is working on. (Innatera is a startup originating in the CAS group.)

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Welcome Geethu Joseph

The Department of Microelectronics and CAS are welcoming dr. Geethu Joseph, who starts in August 2021 as a new Assistant Professor. Her expertise is in signal processing for communications, in particular machine learning, compressed sensing and sparse recovery on graphs and networks, as well as dynamical systems theory.

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Rising star: Justin Dauwels

As associate professor within the Circuits and Systems group at the faculty of Electrical Engineering, Mathematics and Computer Science, rising star Justin Dauwels is fascinated by how data-driven intelligent systems can tackle many of society’s problems. Neural networks and graphical models are his hammer, and he applies it to topics ranging from autonomous cars to digital health and beyond. But neuroscience has always been a main theme ever since his Erasmus exchange to the Institute of Neuroinformatics in Zürich. ‘I apply the models that I studied as an engineer to help unravel and understand human behaviour,’ he says.

Healthcare for the underprivileged

In one of his long-standing projects, he developed machine learning tools to reliably diagnose epilepsy and other related brain conditions from EEGs – recordings of electrical activity on the scalp – allowing neurologists to spend more time with their patients. He has also used audio and video recordings between psychiatrists and their patients to help diagnose various mental health indications such as depression and schizophrenia as well as the severity of a patient’s symptoms. His drive in developing these tools is to bring world-class healthcare to the underprivileged, such as at Sion’s public hospital in Mumbai which he frequently visited. ‘It serves the local community, with doctors who are severely underequipped and completely overburdened,’ he says. ‘The various intellectual properties have been licensed to a startup focused on brain health to make sure these developments reach the patients.’

We want to be able to answer what-if questions prior to the operation; if I remove some tissue here, what will that accomplish?

Planning epilepsy operations

A more recent project, and one that he will continue to pursue at TU Delft, is to establish a paradigm shift in the planning of operations for epilepsy patients for whom medication alone leads to an insufficient reduction in symptoms. ‘Currently, the decision on where to operate is primarily based on abnormalities observed on a CT-scan,’ he says. ‘We want to be able to answer what-if questions prior to the operation; if I remove some tissue here, what will that accomplish?’ Using advanced imaging, electrical measurements, and anatomical knowledge, Dauwels and collaborators buildt a very simple mathematical model of the patient’s brain. They can subsequently perform virtual resections and run the model to see if it reduces epilepsy attacks. Using only a basic model, this technique was more than 80% correct in a small cohort of patients for whom the outcome of the actually performed surgery was known. ‘We will now improve our model and analyse more patients.’

Next generation deep learning

TU Delft was already on Dauwels’ radar at the time of his master’s thesis, but he chose to first sharpen his skills in Japan, the USA and Singapore. But now, TU Delft is the perfect setting for him. Partially because it is much closer to his parents, who still live in Belgium, and especially because Medical Delta and the ongoing convergence with Erasmus University and ErasmusMC allow him to cooperate with many top-level clinicians. ‘Pretty much everyone in our group has ties with ErasmucMC, meaning there are many opportunities to apply my hammer,’ he says.

But it is not all about applications. He will also delve into the fundamentals of deep learning. ‘People start to realise that neural networks, on their own, won’t be able to address all problems. I have been working on graphical models for more than twenty years and these are a promising path towards next generation deep learning systems. It’s about building a better hammer, one that combines the fast, instinctive thinking of neural networks with the slow thinking of graphical and logical models. Just like in humans, it makes sense to verify an intuitive answer and to correct it if necessary.’

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New TTW project for Richard Hendriks and Jorge Martinez

This week Richard Hendriks received the news that his project proposal 'Personalized Auditory Scene Modification to Assist Hearing Impaired People' was granted funding from NWO TTW. Co-applicant is Jorge Martinez. The project will fund 2 PhD students. Congratulations!

Hearing impairment has become a serious problem for a large and increasing portion of the population as is shown by its prevalence: 11 % of the Dutch population suffers from severe hearing loss. These numbers show that hearing impairments form an important societal problem. It comes with many daily life problems in both private situations and working environments. Moreover, hearing-impaired people are often less confident in practical situations, need more assistance and have a worse quality of life. The underlying reasons are that hearing-impaired people suffer from a) the inability to understand speech in acoustic challenging situations, and, b) the inability to correctly localize sound sources. The goal of this project is to make the hearing impaired user fully benefit from an improved intelligibility and sound localization. To do so, we will develop an algorithm to personalize the presented auditory scene for improved speech intelligibility and sound localization for hearing impaired users.

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The rising stars of the TU Delft, featuring Dante Muratore

After his PhD in what he calls “hardcore analogue microelectronics”, rising star Dante Muratore knew he wanted to continue his career working on systems that are closer to an actual application. A postdoc position at Stanford University, in which he worked on the electronics for an artificial retina to treat medical conditions leading to the loss of vision, brought him just that. Then, wanting to come back to Europe and to continue doing bioelectronics at the highest level possible, an opening at TU Delft crossed his path. ‘It was the easiest choice I ever made,’ he says.

Read more about Dante in the link below!

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Mario: cum laude PhD defense

Today Mario Coutino defended his PhD thesis, and received the title "cum laude". Congratulations!

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"Tech for Health" featuring Natasja de Groot

Please donate to our research on better understanding of cardiac arrhythmia

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Geert Leus wins EURASIP Technical Achievement Award

Geert Leus will be this year's recipient of the European Association for Signal Processing (EURASIP)'s Technical Achievement Award.
The award will be presented at EUSIPCO 2021 Dublin, Ireland.

As noted in the announcement by Maria Sabrina Greco, EURASIP Director for Awards,
The Technical Achievement Award honors a person who, over a period of years, has made outstanding technical contributions to theory or practice in technical areas within the scope of the Society, as demonstrated by publications, patents, or recognized impact in this field.

This year’s recipient is Geert Leus, “For contributions to signal processing for communications and sensing".

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Cardiac mapping of very young children reveals conduction disorders related to atrial fibrillation

Research of Medical Delta professor prof. dr. Natasja de Groot (Erasmus MC and TU Delft)

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New Associate Professor: Justin Dauwels

Per 1 January 2021, Prof. Dauwels starts at CAS. His expertise is in statistical signal processing and machine learning. Welcome!

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EUSIPCO 2020 - virtual event on 18-22 January 2021

The organizing committee of EUSIPCO 2020 has decided that EUSIPCO 2020 will be a full virtual conferencedue to the on-going COVID-19 pandemic and resulting travel restrictions.

We are excited to be able to provide a virtual venue for EUSIPCO 2020 and hope you will join us and learn about the latest developments in research and technology for signal processing.

Please register today for a reduced fee of E 50, or sign up for one of the tutorials for E 25.

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CAS startup Innatera Nanosystems raises €5 million

Innatera, started by CAS members Sumeet, Amir and Rene, is making the next step

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Congratulations for Aydin

On 20 October, Aydin defended his PhD thesis. Congratulations!

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New project: PCaVision

Prostate cancer detection using ultrasound

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SSCS WYE Webinar

Webinar: To Academia, or to Industry, That is the Question. Presented by: Kofi Makinwa and Shin-Lien Lu

Abstract:

You are about to finish graduate school or perhaps a young or seasoned professional, contemplating a career transition. Which is better - a career in academia or industry? What are the pros and cons of one versus the other? How can you start exploring and build up your career accordingly? In this webinar, we will interview Dr. Linus Lu, a professor-turned-industry veteran, and Prof. Kofi Makinwa, an industry veteran-turned-professor, who will share their insights and perspectives from their personal journeys in both academia and industry careers. They will also address what triggered their transitions, how they staged their transitions, and offer their crystal ball projections on present and future career prospects in the solid-state-circuits profession.

REGISTER TODAY!

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Medical Delta Professors appointed

Medical Delta appointed 9 new MD Professors, with joint appointments at LUMC and TU Delft, or Erasmus MC and TU Delft. Three of these are connected to the MicroElectronics Department: Wouter Serdijn, Andrew Webb, and Natasja de Groot.

Prof. Dr. Natasja de Groot (Erasmus MC, TU Delft) researches the use of sensors and catheters to more accurately diagnose and treat cardiac arrhythmias. At TU Delft, she will have an affiliation with CAS and BE.

Prof. Dr. ir. Wouter Serdijn (TU Delft, Erasmus MC) researches the use of bioelectronics in medical research. At EMC, he will have an affiliation with Neuroscience.

Prof. Dr. Andrew Webb (LUMC, TU Delft) researches how imaging can be more widely available for medical purposes. He is a professor in MRI at LUMC, and already had a part-time appointment at CAS.

The new Medical Delta professors introduce themselves and their research in a short video. This can be viewed here:

 

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TU Delft launches first eight TU Delft AI Labs

TU Delft is setting up eight new AI Labs to investigate how artificial intelligence (AI) can accelerate scientific progress. To this end, scientists researching AI will be working together with scientist who use AI in their research. The first of these eight interdisciplinary AI labs will be followed by another sixteen in the course of 2020 and 2021.

Rapid developments in AI, data science and digitalisation can accelerate scientific progress in all fields, ranging from medical science to infrastructural research, and across all levels, from fundamental to applied research. TU Delft is boosting collaboration between AI scientists and scientists in other domains, with the launch of a series of TU Delft AI Labs.

Within the MACHINA Lab, for example, researchers in machine learning work together with materials scientists on the analysis of existing materials and the development of new materials. Within the AidroLab, researchers in geometric deep learning are working with researchers in water management on subjects such as how to improve flood forecasting in the urban environment. Researchers in the CiTyAI-Lab will use a wide variety of data sources to map the impact of the city's 'fabric' on its inhabitants in order to improve the living environment.

Education

AI-related knowledge is indispensable for future generations of engineers and scientists. That is why the labs also aim to strengthen education in the field of AI, data sciences and digitalisation, and to create links with educational programmes in various scientific domains.

Investments

TU Delft plans to double its budget in the field of AI, data & digitalisation to 70 million euros per year. These funds will be used for the recruitment of talented researchers, the establishment of research units, the development of educational programmes on AI, data and digitalization, and on the strengthening of collaborations, partnerships and networks.

DeTAIL: Delft Tensor AI Lab

The DeTAIL research lab was proposed by Bori Hunyadi (CAS) and Kim Batselier (3mE), and studies training and innovation in tensor-based AI methods for biomedical signals.

Real-life biomedical data is often high-dimensional. Current signal processing solutions artificially segment such high-dimensional data into shorter one- or two-dimensional arrays, causing information loss by destroying correlations between these data. At the same time, advances in biomedical sensor and imaging technology – such as substantially larger recording durations of wearable sensor technology and the unprecedented increase in spatial and temporal resolution of the latest neuroimaging techniques – have led to ever increasing data sets. Tensors (multi-dimensional arrays) are the data structure of choice in artificial intelligence research to exploit the full potential of these data in a timely manner.

Within the DeTAIL Lab, we focus on both the development and application of novel low-rank tensor methods for biomedical signal processing, thereby enabling a much faster training of AI models from large datasets without any loss of accuracy.

We will exploit an as of yet unused property of real-life data; the fact that different modes of data may be correlated. Using tensor decompositions, we can find these correlations as well as compress the data, speeding up computations significantly.

Our findings will, for example, be applied to detect events, such as epileptic seizures, through the classification of multichannel time series data based on labelled training data. We also aim to reveal hidden structure, such as functional networks, in neuroimaging data. As biomedical innovation is a defining characteristic of the TU Delft, we will develop an interfaculty elective course on AI tensor methods to satisfy the expected continual increase in demand for such knowledge

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Opening Airport Technology Lab

The Airport Technology Lab (ATL)

This Friday, May 29 at 15:30, the opening of the Airport Technology Lab will be done by Ron Louwerse (Director of the Rotterdam The Hague Airport), Prof. dr. ir. Tim van der Hagen (President of the Board and Rector Magnificus of TU Delft) and Henk Jan Gerzee (Chief Digital Officer of the Royal Schiphol Group).

The Microwave Sensing, Signals, and Systems section (Department of Microelectronics, EEMCS faculty), chaired by Professor DSc. Alexander Yarovoy, participates in the Laboratory and is going to use their professional expertise and advanced radar facilities to develop modern remote sensing techniques to extend the sensing capability of airport radars for constant weather monitoring. This real-time monitoring with high spatial and temporal resolution is aiming to improve air traffic planning (departing/arriving on time, less fuel, less noise, less pollution, etc.) and safety.

The Airport Technology Lab (ATL) is an organization for the development, test, and demonstration of the innovative products and services for airports. The digitization of everything that happens at airports and the application of Artificial Intelligence is increasing exponentially. With all that data and the right IT platform, companies can create and test new services and products. The most successful innovations will help to provide passengers with more comfort and convenience, make the airliners maintain smoother and more efficient flights, and the handlers will optimize their processes. Ultimately, the airport also contributes (extra) to its social responsibility to reduce air pollution, CO2 emissions and noise pollution.


New project: ADACORSA

Autonomous drone flight, beyond visual line of sight, safe and reliable

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Wouter Serdijn appointed theme leader of Delft Health Initiative 2.0's NeuroTech theme

The Delft Health Initiative has laid the foundation for connected health-oriented research at TU Delft and will continue to focus expertise, develop talent and to connect researchers to national and international initiatives. Wouter Serdijn will lead this for Neurotechnology.

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NWA Idea Generator grant for Richard Hendriks

Restored sound localization for hearing impaired people

Dr. Ir. R.C. Hendriks

The inability of hearing impaired people to localize sound has a big impact on their well-being and self- reliance. Compared to normal-hearing people, hearing-impaired people cannot efficiently use the same localization information. In this project will be investigated whether inaudible localization information can be transformed into a different audible form.

With a sum of 50,000 euros each, NWO granted 37 out-of-the-box research ideas with the potential to make an impact in society.

The applicants receive funding from the Idea Generator programme of the Dutch National Research Agenda (NWA). A total of 1.85 million euros was available.

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Congratulations for Miao

Best presenter at the WIC/IEEE Benelux Symposium, Ghent


First Microelectronics Synergy Grants

According to Professor Geert Leus who heads the ME Research Committee, the Synergy Grants are also intended to kick-start the research of young faculty, as it can be quite challenging for them to obtain funding at the beginning of their research careers. The grants cover half the costs of a PhD candidate, with the rest coming from existing research funding. ‘The submitted proposals were carefully evaluated by the ME Research Committee on the basis of their scientific quality, their clarity and feasibility, the synergy between the participating sections, and the relationship to the departmental themes. The ME Management Team (MT) then decided to award Synergy Grants to the top three proposals.’

Changes

The aim of the grants is to encourage newly emerging combinations of technologies and to facilitate cross-overs between them, thus strengthening and broadening the department's research portfolio. This goal fits seamlessly within the research strategy of ME, which has defined itself around the four themes of Health & Wellbeing, XG, Safety & Security and Autonomous Systems to better address societal challenges.

Winners

Last week, the winners were received by the ME MT. They received flowers from the head of the department (Kofi Makinwa) and had the opportunity to briefly present their proposals to the assembled MT. Below are short descriptions of the successful proposals.

Akira Endo & Sten Vollebregt: ‘The aim of our project TANDEM: Terahertz Astronomy with Novel DiElectric Materials is to develop advanced dielectric materials to realize superconducting microstrip lines with very low losses in the frequency ranges of 2-10 GHz and 100-1000 GHz. The PhD candidate will combine the dielectric deposition, characterization, material expertise and facilities of the ECTM group and the Else Kooi Laboratory, and the submillimetre wave device measurement capability of the THz Sensing Group and SRON. The aim is not only to realize low loss dielectrics, but also to understand the underlying physics that governs these losses. If successful, these microstrips will be immediately applied to enhance the sensitivity of the DESHIMA spectrometer on the ASTE telescope in Chile.’

Bori Hunyadi: ‘On one hand, the vast complexity of the human brain (10^11 neurons and 10^14 connections) enables us to process large amounts of information in the fraction of a second. At the same time, imperfections of the wiring in this vast network cause devastating neurological and psychiatric conditions such as epilepsy or schizophrenia. Therefore, understanding brain function is one of the greatest and most important scientific challenges of our times. Brain function manifests as various physical phenomena (electrical or e.g. metabolic) at different spatial and temporal scales. Therefore, the PhD candidate working on this grant will develop a novel multimodal and multiresolution brain imaging paradigm combining EEG and a novel imaging technique, fUS. The specific engineering challenge is to understand and describe the fUS signal characteristics, deal with the large amount of data it records using efficient computational tools; and finally, formulate the specification of a dedicated non-invasive, multimodal, wearable EEG-fUS device.’

Virgilio Valente & Massimo Mastrangeli: ‘The seed money of the Synergy Grant will partially support a joint PhD candidate to investigate the tight integration of an heart-on-chip device with dedicated electronic instrumentation in the same platform. Our aim is to bring sensing and readout electronics as close as possible to a cardiac tissue cultivated within a dedicated micro physiological device. The grant helps promoting the logical convergence between current departmental research activities at ECTM and BE and within the Netherlands Organ-on-Chip Initiative (NOCI) on the development of instrumented organ-on-chip devices.’


New STW project: TOUCAN

Develop cheap 3D ultrasound using compressive sensing

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New postdoc: David Aledo

CAS welcomes David Aledo as a new postdoc, working with Rene van Leuken on the Prystine project. David received his PhD from the Polytechnic University of Madrid (UPM), Spain.

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New project: Medical Delta Cardiac Arrhythmia Lab

with Erasmus MC; Medical Delta will fund 2 PhD students as part of a larger program aimed to unravel and target electropathology related to atrial arrhythmia

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Image formation for future radio telescopes

Radio astronomy is an interesting application area for array signal processing. We developed a new image formation tool called PRIFIRA, inspired by Sparse Bayesian Learning. Featured in ETV Maxwell 22.1.

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New Assistant Professor

CAS welcomes Borbala Hunyadi, a new Assistant Professor, working on Bio Signal Processing

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Guillermo Ortiz selected as "Best 2017/2018 Graduate of EEMCS"

Guillermo Ortiz is selected as "Best 2017/2018 Graduate of EEMCS" by the Dean, and is nominated to compete for the Best Graduate of TU Delft (election on 6 November).

Guillermo did his thesis work on the topic of Graph Signal Processing, which was graded with a 10. Part of his work is already accepted for publication in the GlobalSIP 2018 conference, and has been submitted to an IEEE journal.

Congratulations!

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Shahrzad Naghibzadeh EURASIP 3MT contest finalist

The 3MT (three minute thesis) contest is an international contest for students to explain their PhD thesis within 3 minutes. It is held across many universities and countries. In the EURASIP version, students in Signal Processing are invited to submit their 3-minute video, and the best ones are invited to present their work on stage during the EUSIPCO conference.

In 2018, Shahrzad Naghibzadeh was one of 10 selected students to present her work, in the conference auditorium. By ballot of the over 200 people in the audience, she ended up in the top-three (see picture). The final selection was done by an award committee; the #1 place went to Virginie Ollier.

Congratulations!

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Geert Leus in IEEE ICASSP Top Downloads

An 2015 ICASSP paper co-authored by Geert Leus made it to the Top-20 Downloads list of ICASSP papers over 2015-2017.

The paper is "Compressed Sensing Based Multiuser Millimeter-Wave Systems: How Many Measurements Are Needed?", by Ahmed Alkhateeb, Geert Leus, and Robert Heath. Last year, the related journal paper also won a best (young author) paper award.

The overview of top-downloaded papers was published in IEEE Signal Processing Magazine, July 2018.

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Jie Zhang wins best student paper award

On 10 July, Jie Zhang won one of the three best student paper awards of the IEEE Sensor Array Multichannel Signal Processing workshop (175 att.), for the paper "RATE-DISTRIBUTED BINAURAL LCMV BEAMFORMING FOR ASSISTIVE HEARING IN WIRELESS ACOUSTIC SENSOR NETWORKS", coauthored with Richard Hendriks and Richard Heusdens. Congratulations!


Best paper award for Aydin

Aydin Rajabzadeh was selected as winner of the best student paper presentations at SPIE's Photonics Europe International Symposia held 22-26 April 2018 in Strasbourg, France, with the paper "A computationally efficient approximation of the transfer matrix model for analysis of FBG reflected spectra". Congratulations!

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Leiden Marathon

This weekend, Mario and Tarik ran the half-marathon of Leiden, and Tuomas even the full one!

(This is the event where several people had to be hospitalized because of the heath...)


CAS on the run

Success for Tarik, Mario, Thom and Elvin after the (half) marathon of The Hague on 11 Mar 2018.

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URSI Benelux Forum-2018 poster awards

The Annual Benelux URSI meeting took place on January 25 in Delft. Around 20 posters were presented. Sharzad and Patrick won the first and second Student Poster Award.

Shahrzad Naghibzadeh proposed "PRIFIRA - General regularization using priorconditioning for fast radio interferometric imaging". while Patrick Fuchs presented "Local Maxwell two-dimensional first order electrical properties tomography".


New project: PRYSTINE

Rene van Leuken, together with his team (Sumeet Kumar, Amir Zjajo), and Said Hamdioui at CE acquired part of a new EU project "PRYSTINE". The aim is to design programmable compute hardware for automatic driving functions, across two application targets: data fusion for robust perception; and acceleration of AI frameworks for decision making. The emphasis is on low-power compute platforms.

While the overall budget of the project is 50 ME and spans over 60 partners, Delft will sign up for 1.8 ME.

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Geert Leus Editor-in-Chief of Elsevier Signal Processing

Starting 1 January 2018, Geert is the new Editor-in-Chief of Elsevier Signal Processing (IF: 3.1)

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Best student paper award for Mario Coutino and Elvin Isufi

Mario and Elvin win the Student Paper Award contest (3rd place) at CAMSAP’17 for their paper on "Distributed Edge-Variant Graph Filters".

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3D Ultrasound using a single transducer element

Pim van der Meulen, Geert Leus and our collaborators at Erasmus MC show how this is possible using a phase mask and compressed sensing,

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Tadashi and Geert won best paper award

The IEEE/OES Japan Chapter Young Researcher Award was won by Tadashi Ebihara for the paper "Doppler-Resilient Orthogonal Signal-Division Multiplexing for Underwater Acoustic Communication", T. Ebihara; G. Leus; IEEE Journal of Oceanic Engineering, Volume 41, Issue 2, pp. 408-427, 2016.

Tahashi was a 3-month visitor at CAS in 2013 when he worked on this paper with Geert.

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TU Delft "Female Fellowship" Tenure Track Academic Positions

All academic levels; apply before Jan 8, 2018.

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Geert Leus appointed IEEE SPS Distinguished Lecturer

Geert Leus has been selected to serve as an IEEE Signal Processing Society Distinguished Lecturer for the term 1 January 2018 through 31 December 2019. This is considered a prestigious sign of recognition.

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Best Student Presentation Award for Jamal Amini

At the 2017 Symposium on Information Theory and Signal Processing (Delft, 11-12 May), organized by the IEEE Benelux Chapter, Jamal Amini received a best student presentation award. Congratulations!

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Dielectric Shimming

Introductory article in ETV "Maxwell"

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Rob Remis appointed Associate Professor

Per 1 January, Rob Remis has been promoted by the Dean to the rank of UHD (Associate Professor). Congratulations!

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Two IEEE SPS Best Paper Awards

Selected for the 2016 IEEE Signal Processing Society Best Paper Award:
Cees H. Taal, Richard C. Hendriks, Richard Heusdens, and Jesper Jensen
“An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech”
IEEE Transactions on Audio, Speech, and Language Processing, Volume 19, No. 7, September 2011

Selected for the 2016 IEEE Signal Processing Society Young Author Best Paper Award:
Ahmed Alkhateeb, Omar El Ayach, Geert Leus and Robert W. Heath, Jr.
“Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems”
IEEE Journal of Selected Topics in Signal Processing, Volume 8, No. 5, October 2014.

The awards will be presented at the Awards Ceremony at ICASSP 2017 in New Orleans, LA


Rob Remis wins STW Open Mind 2016 award

At their annual congres, STW awarded 5 grants (each 50 kE) to research teams to enable them to explore 'risky research' ideas. Martin van Gijzen, Andrew Webb and Rob Remis presented one of the winning proposals: an affordable MRI instrument based on permanent magnets (as opposed to superconducting magnets) for detecting hydrocephalus.

Short movie presenting the idea.

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Vacancy: Team manager for Electrical Engineering Education (EEE)

The Faculty of EEMCS is creating a special team to fully focus on teaching using our unique and innovative ‘Delft method’. This method integrates practical and theoretical electrical engineering education and trains students to be hands-on, theoretically versed electrical engineers ready for a future career in science or industry.

We are looking for a team manager specialising in Electrical Engineering Education (EEE) who will be both a group leader and a teacher in his/her capacity as the role model of EE Education.

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Geert Leus named 2016 EURASIP Fellow

To recognize outstanding achievements in the broad field of Signal Processing, each year the European Association for Signal Processing (EURASIP) elevates a select group of up to maximum four signal processing researchers to "EURASIP Fellow", the Association's most prestigious honor.

The EURASIP Board of Directors (BoD) has awarded prof. Geert Leus as one of the 2016 Fellows, "for contributions to signal processing for communications".

The award consists of a certificate presented during the Opening and Awards Ceremony at EUSIPCO 2016, held in Budapest (Hungary) on August 30, 2016.

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Best paper award for Geert Leus

IEEE Sensor Array and Multichannel Signal Processing Workshop, Brazil ("Stationary Graph Processes: Nonparametric Spectral Estimation" with Antonio Marques en Alejandro Ribeiro)


7 July 2016: Opening of CryoLab for Extremely Sensitive Electronic Measurements

The CryoLab of TU Delft's Faculty of EEMCS has been opened on Thursday 7 July by the dean Rob Fastenau. TU Delft scientists from the Tera-Hertz Sensing Group, Jochem Baselmans and Akira Endo, will be leading a team of young scientists and engineers working in the lab on astronomical instrumentation. The first instrument, DESHIMA (Delft SRON High-redshift Mapper), is being developed to be operated on the ASTE telescope in the Atacama Desert in Chile. The goal of the research is to create 3D charts of so-called submillimetre galaxies that, in contrast to 2D charts, also show distance and time.

The large number of superconducting detectors, and the advanced electronics developed at SRON, allows DESHIMA to map a very large volume of space at once. While Endo leads the development of DESHIMA, Baselmans will soon install the next cryostat for testing novel THz array antennas, that will enable his upcoming instrument MOSAIC to target multiple galaxies at once. In the future, the CryoLab is envisioned to also host new coolers from QuTech. Superconducting electronics used for astronomical instrumentation and quantum electronics have much in common, because they both push the limits of what can be observed.


Rob Remis elected best teacher at EWI

By student election (1700 votes), Rob Remis was elected as best teacher for Fac. EWI in 2016. A decade ago, Rob won already once the title 'Best teacher in EE'. This has now been extended to comprise the full faculty (EE, Mathematics, Computer Science). Later this year, Rob will compete for the title of 'Best teacher of TU Delft'.

The annual election is organised by the student associations of the Faculty (ETV, Christiaan Huygens), based on voting and written motivations.


New project "tASk-cognizant sParse sensing for InfeREnce" approved

STW project by Geert Leus

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New project "Earlier recognition of cardiovascular diseases" approved

Atrial fibrillation (AF) is a progressive disease and associated with severe complications such as stroke. Early treatment of AF is of paramount importance as it inhibits disease progression from the treatable (recurrent intermittent) to the untreatable (permanent) stage of AF. However, early treatment is seriously hampered by lack of accurate diagnostic instruments to recognize patients who will develop new onset AF or progress to a severer form of the disease.

The goal of this project is to develop age and gender based, bio-electrical diagnostic tests, the invasive and non-invasive AF Fingerprint, which consists of electrical atrial signal profiles and levels of atrial specific tissue/blood biomarkers. In daily clinical practice, this novel diagnostic instrument can be used for early recognition or progression of AF by determination of stage of the electropathology. As such, AF Fingerprinting enables optimal AF treatment, thereby improving patient?s outcome.

The project is a collaboration between Erasmus University (Dept. Cardiology), VU Medical Center (Dept. Physiology), and TU Delft (Sections CAS and Bioelectronics), and will fund 4 PhD students.


New book by Amir Zjajo: Brain-Machine Interface

low-power analog front-end circuits for brain signal conditioning and quantization and digital back-end circuits for signal detection

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Michel Antolovic granted PicoQuant Young Investor Award

On February 14, 2016, Michel Antolovic was granted the prestigious PicoQuant Young Investigator Award at Photonics West in San Francisco for his paper titled 'Analyzing blinking effects in super resolution localization microscopy with single-photon SPAD imagers�. The paper shows the first localization super resolution images obtained with a SPAD camera. The analysis includes specific timing properties of fluorescing molecules in vitro with unprecedented accuracy thanks to one of the world�s single-photon fastest cameras that was created in the AQUA laboratory. The timing properties are aimed to be used for optimizing fluorophore blinking or separation of fluorophores, enabling multichannel super resolved imaging.


Sundeep's PhD Defense

On 25 January, Sundeep Chepuri defended his PhD thesis on "sparse sensing". Here are some pictures

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Happy 2016!

Here are some pictures of the New Year Reception of the Microelectronics department

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TU Delft Female Fellowship Tenure Track Openings

Academic openings at all professor levels

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QuTech enters in collaboration with Intel

Intel and QuTech, the quantum institute of TU Delft and TNO, have finalised plans for a ten-year intensive collaboration, along with financial support for QuTech totalling approximately $50 million.

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New STW project: "Good vibrations"

Today STW announced that Rob's proposal "Good Vibrations" in the Open Technology Program will receive funding. The project will utilize the power of so-called Krylov subspace reduction techniques and develop solution methodologies for wave field problems in complex media.

The project will fund 1 PhD student: Jorn Zimmerling.

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CAS Outing a big success.

The CAS outing this year focused on socializing, team building and having a great time and it was a big success. The group went to Outdoor Valley in Bergschenhoek for a bit of serious fun. Thanks all for participating and a big hug for the organizers.

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Sundeep Chepuri wins ICASSP Best Student Paper Award

The ICASSP paper "SPARSE SENSING FOR DISTRIBUTED GAUSSIAN DETECTION" by Sundeep Chepuri and Geert Leus won the best student paper award. This is quite a prestegious achievement. Congratulations!

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Else Kooi Award ceremony at ICT Open

Professor Charbon, dr Daniele Raiteri and professor Nauta

The 2015 Else Kooi Award has been granted to Dr Daniele Raiteri for his scientific research on Technology-Aware Circuit Design for Smart Sensors on Plastic Foils. The Else Kooi Award is an annual award for young researchers in the field of applied semiconductor research conducted in the Netherlands. The award comes with a prize of 5,000 euros.

Raiteri has received the award during a special ceremony at the ICT.OPEN symposium on March 25th. The award was presented by the board of the Else Kooi Award foundation professor Nauta, chair of the foundation (TU Twente) and professor Edoardo Charbon. Edoardo Charbon from the microelectronics department of the EEMCS faculty holds the position of secretary of the Else Kooi Award Foundation.

Dr Raiteri?s research is focused on organic semiconductors. This emerging technology has specific features which severely complicate the design of circuits and systems, such as low transconductance, gain and speed, as well as high component variability. Dr Raiteri has devised several new solutions that have shown to be extremely robust to variability, achieving significantly better gain-bandwidth products in amplifiers and exceptional signal-to-noise ratios in voltage-controlled oscillators.

Photos by: Thijs ter Hart

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Dony's PhD defense

Some pictures appear here!

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New STW project: "SuperGPS"

Gerard Janssen acquired, with his colleagues Jeroen Koelemeij (VU Amsterdam, PI) and Christian Tiberius (CiTG), a new STW project called SuperGPS.

The project addresses the problem that currently, GPS is not sufficiently accurate and reliable to enable autonomous driving. The central question is: "?How do we realize highly accurate and reliable positioning using extremely accurate time-frequency reference signals, distributed through hybrid optical-wireless networks??.

The project aims at a hybrid optical-wireless system for accurate positioning, navigation, and network synchronization, to complement or even replace satellite navigation technology. This system is accomplished through a terrestrial grid of radio antenna ?pseudolites?, synchronized with extreme accuracy through the fiber-optic telecommunications network. The key deliverable of the project is a pilot demonstration of SuperGPS technology under real-life circumstances.

The technology will be developed with support and feedback from potential users in telecommunications (Royal KPN N.V.), mobility (TNO and Volvo), and Dutch high-tech manufacturers, as well as stakeholders from the scientific and R&D community, including the Dutch metrology institute VSL, the Dutch ?keepers of atomic time? UTC, and physicists and astronomers in need of better time and frequency signals.

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Best paper award for Amir, Carlo and Rene

Amir Zjajo, Carlo Galuzzi and Rene van Leuken won the Best Paper Award for the paper "Noise Analysis of Programmable Gain Analog to Digital Converter for Integrated Neural Implant Front End" at the International Conference on Biomedical Electronics and Devices (Biodevices 2015; Rome, Italy).

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New STW project "From coil to antenna"

The STW project "From coil to antenna: development of innovative transmit array elements for MRI of the body at 7 Tesla" has been granted funding. Rob Remis is one of the applicants. The PI is Prof.dr. A.G. Webb (Leids Universitair Medisch Centrum), also Univ. Utrecht is involved.

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L3SPAD honored

The STW HTSM "L3SPAD: A Single-Photon, Time-Resolved Image Sensor for Low-Light-Level Vision" program has received funding. The program is led by Edoardo Charbon.

Description

Low-light-level (LLL) image sensors have been receiving great attention because they have various applications ranging from fluorescence microscopy to automotive sensing, from safety monitoring to 3D vision for robots. Traditionally, however, LLL image sensors have been used for military purposes because of their prohibitive costs. The appearance of monolithic solid-state complementary metal-oxide-semiconductor (CMOS) processes for the design and fabrication of photon counting image sensors has paved the way to enable low-cost and high-performance LLL image sensors. In this project, we will realize a gated 1.3Mpixel photon-counting image sensor in a standard CMOS process. The target sensor, with high timing resolution, low noise, and high photon detection efficiency, is the perfect candidate to meet all these technical and cost specifications.?

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Best MSc student of TU Delft

This afternoon, Jorn Zimmerling won the competition for best MSc student of TU Delft of this year. Jorn was an MSc student of Rob Remis and Paul Urbach, and is now a PhD student with Rob at CAS.

TU Delft news article (in dutch)

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QuTech appointed as 'national icon'

The Ministery of Economic Affairs has named 4 innovations as 'national icon'; QuTech is one of them. "National icons are innovations which generate future welfare and help to solve mondial problems." The icons will receive a national support podium, including a minister or secretary of state as ambassador.

In the Department of Microelectronics, prof. Edoardo Charbon and dr. Ryochi Isihara are 2 of the 5 EWI faculty members directly involved in QuTech.

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MP Jan Vos visits PARSAX

On Friday 7 November, Jan Vos, MP for the PvdA, visited the TU Delft Climate Institute. The theme of the visit was climate change, TU Delft's research and the usefulness of and need for climate monitoring. The programme included a demonstration of cloud simulations in the Virtual Lab and a visit to the PARSAX radar. Thanks to the rain, it was possible to obtain good live measurements.


Board of Directors of EURASIP

On Sep 1, Alle-Jan was elected as incoming member of the Board of Directors of EURASIP, with a 4-year term starting 1 January 2015. EURASIP is the European Signal Processing association.

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Stefan Wijnholds finalist for Christiaan Huygensprijs 2014

Yesterday, 25 June 2014, Stefan Wijnholds received an "honorable mention" as finalist for the Christiaan Huygensprijs 2014, rewarding the best PhD thesis work in ICT over the past 4 years. The awards were handed by the Minister of Education (dr. Jet Bussemaker).

After a tough preselection by each university, a list of 32 candidates at a national level were judged by the jury. Out of these, 4 finalists were nominated who received a certificate in a ceremony in Voorburg.

Stefan received the honor for his PhD thesis on calibration and imaging for the LOFAR radio telescope. He was a PhD student with Alle-Jan van der Veen in 2006-2010, while being employed by ASTRON in Dwingeloo.

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New STW project for Rob Remis

Rob Remis was granted an STW project "Dielectric enhanced MRI". The main applicant of this project is Andrew Webb (Leiden Univ.), coapplicants are Rob Remis (CAS) and Bert-Jan Kooij (MS3). This will fund 2 PhD students in Delft.

The project aims to improve MRI imaging by inserting "bags" with dielectric materials between the magnets and the body. This should provide better illumination, in particular when using high-tesla fields. This has already been applied in practice but the effect is theoretically poorly understood. The project should provide the EM theory related to this case.

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Millad's SAM'14 paper in final selection for award

Millad's paper "Application of Krylov based methods in Calibration for Radio Astronomy" has made it to the final round of the IEEE Sensors and Multichannel (SAM) 2014 student paper competition. This selection has been made based on ranking by the TPC members. The final poster competition is Sunday, June 22, 2014 in A Coru?a.

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New project: DRIFT

The future SKA radio telescope will produce large amounts of correlation data that cannot be stored and needs to be processed quasi real-time. Image formation is the main bottleneck and requires order 350 peta-flops using current algorithms. Another bottleneck is the transportation of station data (samples) to the central location where they are correlated.

The project aims to reduce the transportation bottleneck by time-domain compressive sampling techniques, allowing the recovery of full correlation data from significantly subsampled antenna signals, and to introduce advanced algebraic techniques to speed up the image formation. Ideally, we would even skip the intermediate covariance reconstruction.

The project is funded by NWO in the "Big Bang, Big Data" program and is carried out in context of the ASTRON-IBM DOME project.

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Prof. Bastiaan Kleijn in Delft for 2 months

Professor Bastiaan Kleijn is a part-time professor in the CAS group. He will be physically present in the period 1 May-1 July 2014.

His expertise is speech and audio signal processing. He will be collaborating with Richard Heusdens and Richard Hendriks.

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New project on improved hearing aids

Richard Hendriks has acquired a new STW project aimed at improving the intelligibility of speech for users of hearing aids.

With a prevalence of about 11 %, severe hearing loss has become a serious problem in our society. While the current generation of hearing aids can be of a great help in certain situations, they generally are not able to provide the hearing-aid user a natural impression of the acoustical scene. An often-reported problem for hearing impaired people is the inability to understand speech in complex acoustical environments as well as the inability to localize sound.

Due to the development of wireless technology, it is possible to equip hearing aids with more powerful noise reduction algorithms to further increase the intelligibility. However, these more powerful multichannel noise reduction algorithms sacrifice naturalness of the sound environment, also when state-of-the-art binaural noise reduction algorithms are used.

This project aims at developing signal processing algorithms to help hearing aid users in these situations, by providing them a natural impression of the acoustical scene.

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European Conf Antennas Propagation

TU Delft is platinum sponser and exhibitor at the EuCAP 2014 - The 8th European Conference on Antennas and Propagation, to be held at the World Forum in The Hague, The Netherlands, on 7 to 11 April 2014.

The Microelectronics (ME) department from the faculty of Electrical Engineering, Mathematics and Computer Science, includes research groups actively engaged on teaching and research in the field of antennas and propagation.

Located within the microelectronics department, the mission of the THz Sensing Group is to introduce breakthrough antenna technology that will revolutionize THz Sensing for Space based and Earth based applications. In the long term the research will enable multi Tera-bit wireless communications.


Alle-Jan van der Veen appointed EURASIP Fellow

The award is for contributions to array signal processing applied to communications and radio astronomy. In 2014, four researchers have been recognized as Fellow.

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New CAS website

Today, CAS switched to a new website. Please enjoy, and let us know if something is not working right (contact: Alle-Jan van der Veen). CAS members can apply for a user account to maintain their own bio-page.

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Georg Kail new postdoc at CAS

Georg Kail is a new postdoc at CAS, working with Geert Leus on distributed localization

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Przemek Pawelczak new Assistant Professor

In July 2012, Przemek Pawelczak was awarded a VENI research grant from NWO. This grant (EUR 250k) allows the researcher to fund his own research for up to 3 years. The topic of the research is "Intelligent spectrum use in emergency networks", and it will explore statistical methods to guarantee quality of communication in Cognitive Radio Emergency Networks.

Following this, Przemek was appointed as Assistant Professor in the Embedded Software group and started in January 2013.

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A new professor

On 26 Sept 2012, the Board of Directors of TU Delft has decided to appoint Geert Leus as Antoni van Leeuwenhoek Professor in the CAS group. This is a `personal' full professorship aimed to promote young, excellent academics to Professor at an early age so that they can develop their academic careers to the fullest possible extent

Agenda

MSc SPS Thesis presentation

Multiplicative Contrast Source Inversion method in Electrical Properties Tomography (CSI-EPT) based on Jacobi matrix inversion

Florens Helfferich

The biological tissue electrical properties of conductivity and permittivity affect the interactions of electromagnetic fields in the body. These properties vary throughout the different tissues as the tissue structure and composition varies. In this thesis, medical imaging and diagnosis is used as primary example to motivate exploration of a novel regularization approach to an MRI-based electrical properties tomography (EPT) method. Total variation (TV) regularization has been shown to perform noise reduction in the iterative Contrast Source Inversion EPT (CSI-EPT) method. The Jacobi matrix inversion regularization, an alternative to the known conjugate gradient formulation, is elaborated and applied to an E-polarized MRI fields scenario such that this thesis presents the Jacobi step regularized CSI-EPT. The alternative regularization method outperforms the known regularization method in the reconstruction qualities of noise-suppression and edge-preservation in the simulated MRI experiments using a virtual body model. Further advancements are also described, such as multiple inner-iterations Jacobi regularization and an anatomical prior initialization of the contrast function. Important future research topics are the incorporation and evaluation of the Jacobi step regularization into more advanced CSI-EPT versions, which are the three-dimensional and transceive phase based algorithms to correct realistic MRI data.


Signal Processing Seminar

Underwater Acoustic Communications: TNO-TUDelft Collaboration Opportunities

Costas Pelekanakis
TNO (Netherlands Organisation for Applied Scientific Research)

This talk/initiative aims to offer students the chance to collaborate with TNO, contributing to innovative research that shapes the field of underwater acoustic communications.

About Costas Pelekanakis

Costas received his Diploma from the Department of Electronic and Computer Engineering, Technical University of Crete, Greece, in 2001 and his M.Sc. and Ph.D. degrees in Ocean Engineering from MIT, USA, in 2004 and 2009, respectively. From 2009 to 2015, he worked with the Acoustic Research Laboratory at the National University of Singapore as a Research Fellow. In Singapore, he also worked as Lecturer for the Master of Defence Technology and Systems Program at the Temasek Defence Systems Institute from 2011 to 2014.  From 2015 to 2023, he was a Senior Scientist at the NATO Centre for Maritime Research and Experimentation (CMRE) in La Spezia, Italy. He is currently a Senior Scientist at the Netherlands Organization for Applied Scientific Research (TNO) in the Hague, the Netherlands. In 2001, Costas was awarded the MIT Presidential Fellowship and in 2018, he was the co-recipient of the NATO Scientific Achievement Award. In 2019, he was the co-recipient of the IET Premium Award for Best Paper in Radar, Sonar & Navigation. His broad research area is underwater acoustic communications. Costas also serves as an Associate Editor for the IEEE Journal of Oceanic Engineering and is currently a member of the IEEE Oceanic Engineering Society (OES) Administrative Committee. He is a Senior member of the IEEE.

Additional information ...


MSc SPS Thesis presentation

Sparse Non-uniform Optical Phased Array Design

Kunlei Yu

This thesis addresses the design and optimization of sparse non-uniform optical phased arrays (OPAs) for advanced automotive LiDAR systems. As autonomous driving technologies advance, the demand for high-resolution, reliable, and compact LiDAR systems has become increasingly critical. Traditional uniform OPAs, while effective, face limitations regarding power consumption. This work introduces an innovative approach to designing sparse non-uniform OPAs that achieve desired performance metrics essential for automotive applications, including beamwidth, field of view, and sidelobe levels, while minimizing element count and, consequently, energy consumption.

Through mathematical modelling and simulation, we formulate the problem of sparse OPA design as an optimization problem, leveraging techniques from compressive sensing to identify the most efficient element arrangements. We propose using the sparse array synthesis method to formulate the sparse OPA design problem, utilizing algorithms such as LASSO, thresholding, and iterative reweighted l1-norm minimization to achieve optimal sparse configurations. Our results demonstrate substantial improvements in effectiveness, offering a practical solution to the constraints posed by current LiDAR systems. This thesis contributes to the field by providing a comprehensive framework for the design of sparse non-uniform OPAs, highlighting the trade-offs and benefits of various design strategies. The findings advance our understanding of OPA design principles.


MSc SPS Thesis presentation

Sparse Non-uniform Optical Phased Array Design

Ankush Roy

Extreme precipitation, like floods and landslides, poses major risks to safety and the economy, underscoring the need for sophisticated weather forecasting to predict these events accurately, enhancing readiness and resilience. Nowcasting, which uses real-time atmospheric data to predict short-term weather, is key in addressing this challenge. Traditional nowcasting systems, reliant on extrapolation from rainfall radar observations and constrained by simplistic physical assumptions, often struggle to detect complex, nonlinear weather patterns. This gap has opened the door for deep learning models, which have shown significant promise in improving the accuracy and reliability of short-term weather predictions, making them a focal point of recent research and the basis of this thesis's approach.

 

This thesis introduces a deep generative model designed for the nowcasting of extreme precipitation events up to 3 hours ahead, utilizing a Vector-Quantized Variational Autoencoder (VQ-VAE) to compress radar data into a low-dimensional latent representation, and an Autoregressive Transformer for predicting future radar images. Additionally, a binary classifier works in conjunction with the Autoregressive Transformer to identify extreme versus non-extreme weather events, using these classifications to inform an Extreme Value Loss (EVL) function. This loss function aims to improve the accuracy of predicting extreme weather events by addressing the data imbalance between normal and extreme precipitation occurrences. The proposed model displays comparable performance with the state-of-the-art conventional methods and other deep learning nowcasting models in predicting extreme events.


MSc SPS Thesis presentation

Towards Robust Object Detection in Unseen Catheterization Laboratories

Zipeng Wang

Deep-learning-based object detectors, while offering exceptional performance, are data-dependent and can suffer from generalization issues. In this thesis, we investigated deep neural networks for detecting people and medical instruments in the vision-based workflow analysis system inside Catheterization Laboratories (Cath Labs). The central problem explored in this thesis is the fact that the performance of the detector can degrade drastically if it is trained and tested on data from different Cath Labs. 

 

Our research aimed to investigate the underlying causes of this specific performance degradation and find solutions to mitigate this issue. We employed the YOLOv8 object detector and created datasets from clinical procedures recorded at Reinier de Graaf Hospital (RdGG) and Philips Best Campus, supplemented with publicly accessible images. An aggregated version of object detection metrics was created for multi-camera system evaluation. Through a series of experiments complemented by data visualization, we discovered that the performance degradation primarily stems from data distribution shifts in the feature space. Notably, the object detector trained on non-sensitive online images can generalize to unseen Cath Labs, outperforming the model trained on a procedure recording from a different Cath Lab. The detector trained on the online images achieved an mAP@0.5 of 0.517 on the RdGG dataset. Furthermore, by switching to the most suitable camera for each object, the multi-camera system can further improve detection performance significantly. An aggregated 1-camera mAP@0.5 of 0.679 is achieved for single-object classes on the RdGG dataset.

 


THz Symposium

Dutch Symposium on Terahertz Science and Technology - 3rd Edition


You are cordially invited to join us for the third edition of the Dutch Terahertz Symposium on January 25, 2024. It is a co-organized event between the Delft and Eindhoven University of Technology. With these series of symposiums, we aim to create an environment to exchange information and allow the Dutch Terahertz community to find collaborations and funding opportunities.

 

The 2024 Dutch Terahertz Symposium aims to bring together the Dutch research community and industry interested in the field of terahertz science and technology. There will be six featured talks from both academic and industrial institutions about the latest developments in the field. You can find the list of speakers and topics attached to this email. There will be plenty of opportunities for interaction with each other and inspired further community building.

 

Save the date to join a full day of interesting presentations, Q&As, and panel discussions!

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Signal Processing Seminar

The Virtual Array: A Possible Use Case in Array Design

Costas Kokke

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Physics-assisted multi-robot exploration of spatio-temporal dispersal phenomena

Physics-assisted multi-robot exploration of spatio-temporal dispersal phenomena

Dmitry Shutin

The problem of exploring a dispersal of a potentially hazardous or toxic material in air using robots has a number of applications for e.g., environmental monitoring, infrastructure inspection, or civil protection, to name only a few. Especially in situations when explored substances pose a health risk to human operators, autonomous solutions are of a great interest. However, the key challenge that arises on a path towards autonomy in this context is a rather complicated  dynamics of the dispersed material, coupled with specifics of spatial aperture and low temporal resolution of olfactory (chemical) sensors used for perception. While the former precludes tele-operation (or makes it rather challenging), the latter requires perception and autonomy schemes that are able to cope with very low information rate acquired through olfactory sensing. To address these challenges the proposed solution incorporates two elements that will be discussed in this talk. First, a mobile swarm of robotic sensor carriers is used to increase spatial sampling, and thus capture spatial dynamics more efficiently. Second, a prior information about the dispersal process in terms of domain-specific knowledge is used to support data processing and autonomy. Specifically, the dispersal process is modeled with an advection-diffusion partial differential equation (PDE). The advection, or plainly speaking, the wind – a  dominant transport mechanism in a majority of practically relevant applications – is modeled using Navier-Stockes equations and explored with a robotic swarm. Furthermore, using a probabilistic (Bayesian) formulation of the PDE models, the resulting representation can be relaxed to additionally allow for more control over model mismatches.  Using data samples collected by multiple robots, the multi-robot exploration then includes two steps: (i) a cooperative solution to an inverse problem of identifying parameters of the PDEs given measurements, and (ii) exploration – the design of an optimal sampling scheme for multiple robotic platforms. This work will describe the used models, discuss the developed probabilistic inference schemes, their advantages and limitations, as well as demonstrate their performance in simulations and  in experiments.


Multi-feature-based Automatic Targetless Camera-LiDAR Extrinsic Calibration

Xi Chen

In autonomous driving, environmental perception, crucial for navigation and decision-making, depends on integrating data from multiple sensors like cameras and LiDAR. Camera-LiDAR fusion combines detailed imagery with precise depth, improving environmental awareness. Effective data fusion requires accurate extrinsic calibration to align camera and LiDAR data under one coordinate system. We aim to calibrate the camera and LiDAR extrinsic automatically and without specific targets. Targetless, non-automated calibration methods are time-consuming and labor-intensive. Existing advanced methods have proven that automatic calibration methods based on edge features are effective, and most focus on the extraction and matching of single features. The proposed method matches 2D edges from LiDAR's multi-attribute density map with image-derived intensity gradient and semantic edges, facilitating 2D-2D edge registration. We innovate by incorporating semantic feature and addressing random initial setting through the PnP problem of centroid pairs, enhancing the convergence of the objective function. We introduce a weighted multi-frame averaging technique, considering frame correlation and semantic importance, for smoother calibration. Tested on the KITTI dataset, it surpasses four current methods in single-frame tests and shows more robustness in multi-frame tests than MulFEAT.

Our algorithm leverages semantic information for extrinsic calibration, striking a balance between network complexity and robustness. Future enhancements may include using machine learning to convert sparse matrices to dense formats for improved optimization efficiency.


Signal Processing Seminar

Error-correcting codes and cryptography: from theory to practice

Xinmiao Zhang
Ohio State University

Error-correcting codes (ECCs) and cryptography schemes are indispensable to the reliability and security of numerous classic and emerging systems. Advanced ECCs play essential roles in the performance of 5G/6G wireless communications, hyper-scale distributed storage, in/near-memory computing, and quantum computing. They are also key enablers of high-density next-generation memories and wafer-scale integration, which are important pillars of the CHIPS Act. With the development of new technologies, such as quantum computers and cloud computing, traditional cryptography schemes are no longer secure and/or pose privacy concerns. There are imminent needs for post-quantum cryptography and homomorphic encryption for privacy-preserving cloud computing. Protecting the circuit chips implementing advanced functions from counterfeiting is also necessary to preserve the semiconductor supremacy achieved by the CHIPS Act.

This talk presents our recent contributions on related topics. Efficient and high-speed generalized integrated interleaved ECC decoders are developed to meet the tight latency and excellent error-correcting capability requirements of next-generation hyper-speed memories. Code construction and decoder designs are jointly optimized to enable low-latency failure recovery and continued scaling of distributed storage. Our generalized logic locking includes many previous designs as special cases and achieves better resilience towards various attacks. Logic locking that can effectively protect chips implementing fault-tolerant functions, such as machine learning, are also developed by exploiting algorithmic specifics. Last by not least, high-speed and low-complexity hardware accelerators are designed for homomorphic encryption and post-quantum cryptography.

Biography

Xinmiao Zhang received her Ph.D. degree in Electrical Engineering from the University of Minnesota. She is currently a Professor at the Ohio State University. She was a Senior Technologist at Western Digital/SanDisk 2013-2017. Prior to that, she was a Timothy E. and Allison L. Schroeder Associate Professor at Case Western Reserve University. Prof. Zhang’s research spans the areas of VLSI architecture design, digital storage and communications, cryptography, security, and signal processing.

 

Prof. Zhang is a recipient of the NSF CAREER Award 2009, the College of Engineering Lumley Research Award at The Ohio State University 2022, the Best Paper Award at ACM Great Lakes Symposium on VLSI 2004, and Best Paper Award at International SanDisk Technology Conference 2016. She authored “VLSI Architectures for Modern Error-Correcting Codes” (CRC Press, 2015), and co-edited “Wireless Security and Cryptography: Specifications and Implementations” (CRC Press, 2007). Prof. Zhang was elected the Vice President-Technical Activities of the IEEE Circuits and Systems Society (CASS) 2022-2023 and served on the Board of Governors of CASS 2019-2021. She was also the Chair (2021-2022) and a Vice-Chair (2017-2020) of the Data Storage Technical Committee (DSTC) of the IEEE Communications Society.  She served on the technical program and organization committees of many conferences, including ISCAS, ICC, GLOBECOM, SiPS, GlobalSIP, MWSCAS, and GLSVLSI. She has been an Associate Editor for the IEEE Transactions on Circuits and Systems-I (TCAS-I) 2010-2019 and IEEE Open Journal of Circuits and Systems since 2019. She will be the Associate Editor-in-Chief of TCAS-I 2024-2025.


Signal Processing Seminar

Future of Automotive Radar and Signal Processing Challanges

Tarik Kazaz, Juan Osorio, Karan Jayachandra
NXP Semiconductors, Eindhoven

The last decade has seen a rapid adoption of automotive radars to improve the safety of vehicles and to enable a higher level of driving autonomy. Even though major progress has been achieved in this effort, high-level automation has remained partially evasive due to the insufficient resolution of automotive radars.

In this presentation, we will be discussing several of the proposals to tackle those challenges, specifically the ones related to digitally modulated radars. The presentation will start with a brief discussion of the evolution of automotive radar and the alternatives to increase angular resolution, we will be focusing on the opportunities and challenges we envision for the adoption of imaging radars and specifically digitally modulated from the perspective of signal processing. Lastly, the presentation will finish with a live demonstration of one of the NXP corner radar sensors.

Presenters: Tarik Kazaz, Juan OsorioKaran Jayachandra


MSc SPS Thesis presentation

Compressed Sensing in Low-Field MRI: Using Multiplicative Regularization

Marien Verseput
LUMC

Magnetic resonance imaging (MRI) is a non-invasive tool to image the body’s anatomy and physiology, but suffers from long scan times. Compressed Sensing (CS) is used to accelerate MRI scans by incoherently taking fewer measurements and using a nonlinear optimization algorithm to image the undersampled data. Convex optimization techniques are generally used for image reconstruction. Minimizing a data fidelity term along with two regularization terms, which are a total variation (TV) based and a wavelet transform based function, is a standard procedure in CS-MRI. Regularization parameters are needed to balance the different terms, but it is impossible to know upfront what the optimal regularization parameters are to get the desired output. A consequence is that the algorithm of choice needs to be executed many times for many different values of the regularization parameters, which is a time-consuming process requiring knowledge of the algorithm.

In this work we rewrite and implement the regularization functions in a multiplicative manner by multiplying the data fidelity term with the regularization terms, thereby eliminating the need to tune the regularization parameters. Moreover, we include a region of support (ROS) mask to further accelerate reconstruction. The performance of different combinations of regularization functions and reconstruction algorithms are validated on a simulation study and various experiments on a low-field MRI scanner. This also shows the capability of CS applied to low-field MRI, which has lower signal-to-noise ratio compared to conventional MRI. Of all proposed methods, a nonlinear conjugate gradient method applied to the fully multiplicatively regularized objective function shows the most robust performance.


MSc SPS Thesis presentation

Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements

Weijia Yi

“This project develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different.

We show that standard compressed sensing algorithms that treat phase noise as a constant fail when channel measurements are acquired over multiple beam refinement protocol packets. Most of the methods that have addressed this problem treat phase noise as purely random, missing the inherent structure within the measurement packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. The proposed approach leverages this partially coherent structure in the phase errors across multiple packets. Our algorithm iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors.

We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks, and derive a preliminary support detection bound as a performance guarantee.”


MSc SPS Thesis presentation

Subgraph Matching via Fused Gromov-Wasserstein Distance

Wenxin Pan

Subgraph matching is a fundamental problem in various fields such as machine learning, computer vision, image processing, and bioinformatics, where detecting specific substructures within an object is often crucial. In these domains, not only structure plays an essential role, but also the feature information on nodes should be incorporated, thus highlighting the necessity for comprehensive analytical approaches.

In this thesis, we propose two novel subgraph matching frameworks using the Fused Gromov-Wasserstein (FGW) distance, namely the Subgraph Optimal Transport (SOT) and the Sliding Subgraph Optimal Transport (SSOT). Both frameworks integrate a dummy node strategy to handle the discrepancy between two graphs of different sizes. The SSOT extends upon the SOT by incorporating a sliding window framework and Wasserstein pruning to enhance the performance, especially for sparse large graphs. Our frameworks can be easily implemented and are adaptable for problems of exact matching, top-k approximate matching, and inexact matching.

We further propose a normalized FGW distance to cater to the practical interests and enhance the performance evaluation. We adopt the Frank-Wolfe algorithm for optimization and develop computation-reducing techniques by isolating the dummy node.

By conducting experiments on both synthetic and real-world datasets, we demonstrate that the SOT method achieves excellent performance on small graphs, and the SSOT method improves the accuracy over the SOT on large graphs. Both these two methods show the ability to outperform the state-of-the-art methods in noisy environments in terms of accuracy and efficiency.


Microelectronics Research Day 2023

Microelectronics Research Day 2023


TU Delft Microelectronics Research Day
Fully booked!
Registering 2023 not for possible anymore

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EE-NL Day

EE-NL Day


EE-NL Day

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MSc SPS Thesis presentation

Malleable Kernel Interpolation for Scalable Structured Gaussian Process

Hanyuan Ban

Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor impacting accuracy and efficiency. In this thesis, we tackle this challenge.

We explore existing low-rank approximations that facilitates the computation, dissecting their strengths and limitations, particularly SKI-based methods. Subsequently, we introduce a novel approxima- tion framework, MKISSGP, which dynamically adjusts grid points us- ing a new hyperparameter of the model: density, according to changes in the kernel hyperparameters in each training iteration.

MKISSGP exhibited consistent error levels in the reconstruction of the kernel matrix, irrespective of changes in hyperparameters. This robust performance forms the bedrock for achieving accurate approx- imations of kernel matrix-related terms. When employing our rec- ommended density value (i.e., 2.7), MKISSGP achieved a comparable level of precision to that of precise GPR, while requiring only 52% of the time compared to the current state-of-the-art method.

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MSc SPS Thesis presentation

ADS-B Based Trajectory Prediction for Aerial Vehicles

Haobo Wang

The evolution of aerial vehicle technology necessitates robust trajectory prediction models. These models are crucial for maintaining safe airspace and enabling autonomous operations. Automatic dependent surveillance–broadcast (ADS-B) is a surveillance system that enables aircraft to receive data from navigation satellites and periodically broadcasts it, enabling it to be tracked. Moreover, using ADS-B data for more general aerial vehicles has become a popular trend because it can provide real-time high-resolution aircraft state information and share this information with other vehicles in real-time for the aviation safety ecosystem.

 

In this project, we delve into ADS-B-based trajectory prediction for both aircraft and drone motion trajectories with the overarching goal of improving prediction accuracy. We initially implement several model-based

Kalman filters—including interactive multiple models (IMM)—to assess the accuracy of aircraft trajectory predictions across different model structures. The results reveal that the IMM filter outperforms the single model predictions in terms of root mean square error (RMSE).

 

Furthermore, we implement the Gaussian process (GP) with a sliding window scheme to predict online drone trajectories. Recognizing the high computational complexity of the GP, we also introduce a low-rank

approximation method, structured kernel interpolation (SKI) GP, aiming to conserve computational resources. Finally, we compare the prediction performances of the IMM filter, classical GP, and SKI GP on real drone

trajectories. The results highlight that the classical GP method enhanced prediction accuracy, achieving an RMSE of less than 1.7m, which is 50% lower compared to the model-based IMM filter. Additionally, the SKI GP

realizes a 25% reduction in computation time compared to the classical GP, despite a slight compromise in prediction accuracy.


ME colloquium

Generative AI

Justin Dauwels

Generative AI

Generative AI refers to a category of artificial intelligence models that are designed to generate new content, such as text, images, audio, or other types of data. Probably the best known example of generative AI is ChatGPT, the fastest consumer application to hit 100 million monthly active users. Generative AI models use machine learning algorithms to learn patterns and structures from existing data and then produce new data that is similar in style or content to what they have been trained on.

In this presentation, I will talk about projects in our group on deep generative models. I will briefly present novel kinds of deep generative models that we are developing in our team. Next I will explain how we are designing such models for rainfall nowcasting, where we integrate physical laws into the deep generative models.  At last, I will talk recent AI related initiatives that my team is involved in.

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MSc SS Thesis Presentation

Enhancing Fiber Direction Estimation from Electrograms

Elena van Breukelen

For the heart to pump blood throughout the body, electrical impulses that trigger the cellular contraction must be generated and spread through the myocardial tissue. These signals propagate faster along the longitudinal cardiac fiber direction than the transverse direction, conferring the heart with anisotropic conduction properties. Therefore, the arrangement of the fibers within the tissue governs the impulse propagation. Given the variability of the fiber direction across the heart and between patients, incorporating it into electrophysiological models would enhance our understanding of the mechanisms and progression of different heart conditions, such as atrial fibrillation (AF). The study of this common cardiac arrhythmia relies on analyzing electrical recordings of the heart, known as electrograms (EGMs), which, if integrated with the patient’s fiber architecture into cardiac models, can enable effective personalized treatment. Over the years, researchers have proposed different approaches to estimate the fiber direction from EGMs. However, these methods have been evaluated in different, usually simplistic, cardiac tissue models, making their comparison, and therefore selection of the most accurate approach for clinical and research applications, challenging.

The current study aims to identify the best fiber direction estimation method under consistent and realistic conditions. To achieve this goal, synthetic EGMs and local activation time (LAT) maps were generated from 2D and 3D monodomain models that mimicked the muscle bundle, atrial bilayer, and ventricular transmural fiber rotation structures. A comparison analysis of existing fiber direction estimation methods, first as described by their authors and then standardized to have the same spatial resolution, showed the superior performance of the techniques based on fitting an ellipse to local conduction velocity or conduction slowness vectors from a whole LAT map. The estimation accuracy of these methods can be further improved by increasing the number of vectors to which the ellipse is fitted. Nonetheless, given the influence of underlying layers in the epicardial recordings, the estimation error increases in the tissue models where fibers in the epicardial and endocardial layers run perpendicularly. The effect on the estimate of such architecture, characteristic of the inferior side of the right atria and the ventricles, can be accounted for by combining epicardial electrical recordings obtained after pacing either in the endocardium or the epicardium. Although a preliminary assessment of the estimation methods was carried out with human EGMs, future studies should focus on validating the methods in a controlled experimental framework and refining them for more localized fiber direction estimation. All in all, the automation of the techniques and their integration into electrophysiological models brings us a step closer to creating valuable clinical tools for diagnosing and treating electropathologies.

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PhD Thesis Defence

Pitch-Matched Integrated Transceiver Circuits for High-Resolution 3-D Neonatal Brain Monitoring

Peng Guo

Peng Guo will defend his PhD thesis entitled

Pitch-Matched Integrated Transceiver Circuits for High-Resolution 3-D Neonatal Brain Monitoring

on Wednesday, Sept. 27th. The layman's talk will be at 12:00, the defence starts at 12:30.

The thesis can be downloaded from the TU Delft repository. Peng's work was part of the MIFFY project.

Livestream of the defence: link

Promotors: Michiel Pertijs and Nico de Jong

Abstract: This thesis presents the design and implementation of integrated ultrasound transceivers for use in transfontanelle ultrasonography (TFUS). Two generations of ultrasound transceiver ASICs integrated with PZT transducer arrays intended for TFUS are presented. In the first generation, a novel AFE design that combines an LNA with the continuous TGC function is realized in a bid to mitigate the gain-switching and T/R switching artifacts. Besides, a new current-mode micro-beamforming design based on boxcar integration (BI) is also implemented to reduce the channel count within a compact layout. In the second generation, the AFE is derived from the first version, while the design focuses on RX backend circuitry and channel-count reduction, including a passive BI-based µBF merged with a charge-sharing SAR ADC, which digitizes the delayed-and-summed signals, and a subsequent multi-level data link, which concatenates outputs of four ADCs. In total, a 128-fold reduction in channel count is finally achieved. The techniques we developed have established the groundwork and removed the initial barriers for an electronics architecture suitable for a wearable 3D TFUS device.


MSc SPS Thesis presentation

Friction Identification on Gantry Stage

Lan Jia

In an era marked by the demand for unprecedented levels of precision in engineering applications, the profound impact of friction forces on motion control systems cannot be underestimated. This thesis extensively investigates the frictional behavior of the Proton Motion Stage, an advanced high-precision motion control system developed by Prodrive Technologies. This research conducts both experimental investigations and computational simulations, offering valuable insights into its friction behavior across diverse conditions and scenarios. 

The research begins with an analysis of existing models used to describe friction behavior in precision engineering systems. A critical evaluation of empirical models highlighting strengths and limitations is presented, and the LuGre friction model is selected for further research. Subsequently, the chosen model is used to simulate the behavior of the Proton Motion Stage. The simulation setup is described, including the incorporation of the LuGre friction model and the identification of system parameters. The accuracy of the identification is above 99%. The sensitivity analysis of the parameters is also conducted to enable a comprehensive exploration of friction dynamics. Finally, the research delves into static and dynamic parameter experiments, where cable slab forces' position-dependent impacts and velocity-friction maps that capture the intricate Stribeck effect are presented, and closed-loop and open-loop setups to dissect friction behavior during rapid motion changes are employed. Residual analysis of histogram and 90% confidence autocorrelation and cross-correlation is also presented to study the quality of identification. Overall, this thesis combines theory and practice to enhance our understanding of friction in precision engineering systems.


MSc SPS Thesis presentation

Small end-to-end OCR model

Jingwen Dun

Optical Character Recognition (OCR) is a pivotal technology used to extract text information from images, finding wide-ranging applications in document digitization and medical records management. The integration of machine learning has ushered in an era of swift and precise OCR models. Broadly, OCR comprises two key components: detecting the bounding boxes around text instances and recognizing the characters within them. Presently, prevailing OCR models are primarily intricate two-stage systems necessitating real-time operation on remote servers. Nevertheless, end-to-end models exhibit superior performance from a data utilization perspective. There exist scenarios where offline models prove indispensable, such as in environments with restricted internet access or locales with stringent data privacy and security requirements.

This project delves into various end-to-end models, leveraging the PaddleOCR end-to-end model as a foundational reference to devise a compact OCR model tailored for edge devices. Through meticulous optimization of the backbone architecture and the introduction of diverse Feature Pyramid Network (FPN) structures within the stem network, we achieved a remarkable reduction in model size, down to 19MB. This represents a substantial advancement, constituting merely one-tenth of the original PaddleOCR end-to-end model's footprint.

By leveraging an extensive database and conducting a series of fine-tuning experiments specifically tailored for end-to-end OCR tasks involving curved text images, the model exhibits an impressive precision rate of 47.3% and an f-score of 45.3%. This achievement highlights the effectiveness of the customized loss function relative to the original model, despite its reduced size. Notably, this performance is comparable to certain end-to-end models with larger backbones. Furthermore, an Android demo has been carefully developed to demonstrate the model's capabilities on mobile devices, achieving an average processing time of 433 milliseconds per image.


MSc SPS Thesis presentation

Efficient Content-Based Image Retrieval from Videos Using Compact Deep Learning Networks with Re-ranking

Doruk Barokas Profeta

The rise of streaming and video technologies has underscored the significance of efficient access and navigation of digital content, particularly for scholars in fields like history and art. Scholars actively seek streamlined approaches to index, retrieve, and explore digital content, with a focus on locating specific instances. The process of searching for specific instances in video search is complex that requires the analysis of video sequences and the identification of relevant video segments. Advanced techniques and algorithms are necessary to ensure effective content-based retrieval of the required information.

In response to the escalating demand for accurate and swift access to relevant visual data within the vast spectrum of video resources, our research has been dedicated to the development of novel, efficient content-based image retrieval methods tailored for videos by integrating deep learning methodologies. Our comprehensive system contains two crucial components: keyframe extraction and content-based image retrieval. Keyframe extraction involves identifying significant frames within videos, while content-based image retrieval enables the retrieval of similar frames to a query image through feature extraction and ranking.

A unique aspect of our research lies in the exploration and analysis of a diverse range of feature extraction techniques derived from compact deep learning networks. We have compared our proposed method with state-of-the-art retrieval systems, evaluating performance metrics in terms of both accuracy and speed. Our method harnesses the power of compact deep learning network features in the initial ranking stage, effectively sublisting frames, and subsequently introduces re-ranking using a larger network. This innovative approach promises to deliver the best of both worlds: exceptional efficiency without compromising retrieval accuracy.

Repository link:  http://resolver.tudelft.nl/uuid:751092b8-1b3d-4335-98bd-cc26e69d374c

 


Signal Processing Seminar

Automotive Radar for Autonomous Driving: Signal Processing Meets Deep Learning

Sunqiao Sun
Univ. Alabama, USA

Millimeter-wave automotive radar emerges as one of key sensing modalities for autonomous driving, providing high resolution in four dimensions (4D), i.e., range, Doppler, and azimuth and elevation angles, yet remain a low cost for feasible mass production. In this talk, we will address the challenges in automotive radar for autonomous driving, examine how signal processing and deep learning can be combined to optimize the performance of automotive radar systems, and outline future research directions. Our focus will be on the generation of high-resolution radar imaging using multi-input multi-output (MIMO) radar and frequency-modulated continuous-wave (FMCW) technology. We will examine the challenges of waveform orthogonality, mutual interference, and sparse antenna array design and present our recent innovations in the field, including sparse array interpolation via forward-backward Hankel matrix completion, fast direction-of-arrival estimation via unrolling iterative adaptive approach, and adaptive beamforming via deep reinforcement learning, leading to the generation of high-resolution low-level automotive radar imaging, represented in bird's-eye view (BEV) format, providing rich shape information for object detection and recognition with deep neural networks. However, the radar BEVs are in general hardly shift-invariant over both angle and range since not every pixel is generated equally. The talk will highlight the importance of physics-aware machine learning in perception task on high-resolution radar imaging. We will show how incorporating radar domain knowledge and signal structure into deep neural network design can lead to more accurate and reliable object detection and recognition. Finally, we will discuss future research directions, including integrated sensing and communication, and collaborative radar imaging via an automotive radar network.

Bio:

Shunqiao Sun received the Ph.D. degree in Electrical and Computer Engineering from Rutgers, The State University of New Jersey under supervision of Prof. Athina Petropulu in Jan. 2016. He is currently an assistant professor at The University of Alabama, Tuscaloosa, AL, USA. From 2016-2019, he was with the radar core team of Aptiv, Technical Center Malibu, California, where he has worked on advanced radar signal processing and machine learning algorithms for self-driving vehicles and lead the development of DOA estimation techniques for next-generation short-range radar sensor which has been used in over 120-million automotive radar units. His research interests lie at the interface of statistical and sparse signal processing with mathematical optimizations, automotive radar, MIMO radar, machine learning, and smart sensing for autonomous vehicles. Dr. Sun has been awarded 2016 IEEE Aerospace and Electronic Systems Society Robert T. Hill Best Dissertation Award for his thesis “MIMO radar with Sparse Sensing”. He authored a paper that won the Best Student Paper Award at 2020 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). He is Vice Chair of IEEE Signal Processing Society Autonomous Systems Initiative (ASI) (2023-2024). He is an associate editor of IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing. He is a Senior Member of IEEE.


MSc SPS Thesis presentation

Indoor in-network asset localization using Crownstone network

Vishakha K. Marathe

Indoor localization is the process of determining the location of an asset within an indoor environment. Crownstone, a subsidiary company of Almende, wants to fulfill specific tasks within the Crownstone network like developing indoor localization algorithms to get the room-level location of an asset for smart building, home automation and healthcare applications. Crownstones act as sensors that receive the Bluetooth messages transmitted by the asset and measure the strength of the received signal(RSSI).
There are two most widely used and researched asset localization methods, namely, model-based(MB) and data-driven(DD) methods have their drawbacks, especially because of the presence of obstacles, signal interference, reflections, noise that influence the RSSI signals and the dependency of the methods on the knowledge of sensor positions. In this thesis, a centralized multilateration(MB-C) algorithm as well as a simple averaging consensus based distributed(MB-D) algorithm is implemented. Algorithms are tested on real data collected at the Almende office(test environment) divided into a finite number of rooms.
To deal with the challenges posed by the MB algorithms like selecting a model, learning parameters and an additional step of mapping the position output to a room location, an Ensemble based centralized machine learning(DD-C) data driven algorithm is proposed that classifies the asset in one of the rooms with an accuracy of 65%. Algorithm is further improved by distributed data handling leading to a classification accuracy of 77%. To perform in-network localization, a consensus based distributed ML algorithm (DD-D) is proposed that performs local predictions within the Crownstone network using the same globally trained model giving a classification accuracy of 73%. The results show that the proposed DD algorithms perform better than the MB algorithms in terms of accuracy and are comparable in terms of prediction time. They also indicate that the proposed DD algorithms are more scalable, robust against noise but are computationally expensive.
Thesis link: http://resolver.tudelft.nl/uuid:7b0eb6bb-323f-44e8-b994-28dd8c9c9467

 


MSc SPS Thesis presentation

Direction Finding and Localization with Bluetooth 5.2

Xuchang Zhang

The potential of indoor localization using the Bluetooth Low Energy approach increases with the introduction of the Constant Tone Extension (CTE) feature in BLE 5.1. These small and energy-efficient beacons transmit signals that Bluetooth-enabled devices can detect, allowing for proximity and positioning calculations. This technology supports novel applications such as indoor wayfinding. By applying the new feature with an appropriate antenna array, it is possible to estimate both the Angle of Arrival (AoA).

Nevertheless, estimating AoA poses significant challenges, particularly in indoor scenarios. Throughout the span of this project, an in-depth analysis is being conducted on several elements, including multipath propagation and frequency offset. We first look into the I/Q data processing and then dive into the effect of frequency offset. Following that, several AoA estimation algorithms and multipath-mitigation strategies are discussed. Finally, we model the overall AOA estimation problem, followed by a positioning algorithm based on the estimated angles.

In this project, we propose an end-to-end indoor BLE positioning solution. Matlab simulation is performed to assess its performance. The simulation reveals that, with appropriate settings, the Toeplitz Reconstruction (TR) method is the best. In the setting of a 4-by-4 uniform rectangular array (URA), over 90% of the results show a position inaccuracy that is smaller than 0.14 meters.

After the simulation, we conduct a real-world experiment to assess the practicality and effectiveness of the solutions.  The TR approach demonstrates a position error of less than 0.4 m, which is lower than previous BLE positioning research.

Finally, we suggest a few future research directions. This involves optimizing parameters, considering other antenna-affecting elements, etc.

Repository link:  http://resolver.tudelft.nl/uuid:ff13dbcd-da99-4599-ad7f-f3b7282681bb


MSc SPS Thesis presentation

Using Tensor Decompositions To Obtain Biomarkers From Auditory Event-Related Potentials

Kenneth Stunnenberg

Brain disorders in children pose significant challenges to their development, impacting cognition, speech, movement, and behavior. The uncertainty surrounding prognostic information at the time of diagnosis leaves families with numerous questions about the future. The Child Brain Lab at Erasmus MC Sophia Children's Hospital conducts IQ, electroencephalogram (EEG), speech, and movement tests in playful environments, enhancing scientific research and healthcare practices for a better understanding of disease progression.

The Otolaryngology department at the Child Brain Lab focuses on auditory-related potentials (ERPs) obtained from EEG measurements to predict the future development of children with brain disorders. Analyzing ERP data from experiments like Mismatch Negativity (MMN) and Acoustic Change Complex (ACC) yields insights into developmental trajectories and connections between hearing, language, and brain development.

This thesis aims to explore alternative methodologies for extracting comprehensive information from ERPs, overcoming limitations of the commonly used peak amplitude and latency analysis. Tensor decompositions are employed to exploit structural information present in the data, using data fusion methods to combine multiple datasets for improved classification and deeper insights into group differences.

Simulations on artificial ERP data demonstrate that data fusion methods perform better on two ERP tensors compared to single tensor decomposition when group differences are shared between datasets. On a real dataset, tensor decompositions show promise for classifying subjects based on auditory event-related potentials while giving more insights into the neurological sources.

This report proposes an alternative method for analyzing ERP data, highlighting the potential of tensor decompositions and data fusion techniques. 

 

 


MSc SPS Thesis presentation

Path planning for Lunar rovers An Artificial Potential Field-based algorithm for the path planning of a walking Lunar rove

Thomas Manteaux
EPFL

Abstract: 

Abstract E↵ective path planning is a key challenge for Lunar rovers. This allows for safe autonomous navigation over complex and unknown areas. Lunar Zebro (LZ), a project of the Delft Univer[1]sity of Technology, is developing a robot to be the first European rover to walk on the Moon. This tiny rover, no bigger than an A4 sheet of paper, aims to explore a wide area to monitor solar radiations.

The thesis derives a path plannning algorithm for the local navigation of LZ. State-of-the[1]art path planning methods for terrestrial and non-terrestrial robots are studied. Metrics based on the needs of LZ are defined to compare the algorithms. Artificial Potential Field (APF)- based methods are identified as the most promising for LZ. APF methods are a category of path planning approaches in which robot motion is influenced by virtual forces generated by the destination point and obstacles. A Monte-Carlo simulation in a lunar environment is run to compare APF-based algorithms. The most relevant algorithm is picked up and improved (Bacteria-Aritificial Obstacle (B-AO) algorithm) with respect to success rate, path length and computing time. Finally it is implemented on a LZ prototype and tested in a challenging envi[1]ronment. Testing is conducted on a real lunar testbed made of sand, rocks and craters. A rock and crater abundance model is established considering rock and crater coverage of 2% and 15% respectively to represent the lunar surface as close as possible.

The B-AO algorithm shows 200% higher success rate and 50% lower computing time than the conventional APF algorithm, for only 5% longer path length than the optimal algorithm A*. It also outperforms state-of-the-art APF-based algorithms by more than 15% in reachabil[1]ity and 10% in path length for a similar or shorter planning time. Field testing results exhibit the robustness of the B-AO algorithm to real-world uncertainties in di↵erent scenarios. They also show that near-optimal paths are computed in real-time with limited available processing power. The bacterial approach of the B-AO algorithm makes it faster to execute and smaller to store than path planning algorithms used on existing or past non-terrestrial rovers.

Keywords: Path planning, Moon rover, Artificial Potential Field methods, Lunar Zebro
 


MSc SPS Thesis presentation

Coded Excitation for Doppler Ultrasound Imaging of The Brain

Lexi Zhu

Doppler ultrasound imaging of cerebral blood flow faces challenges arising from a low signal-to-noise ratio (SNR) and a wide dynamic range. Echo signals received from blood cells are significantly weaker compared to surrounding tissues, such as the skull or brain soft tissue, resulting in inhibited visualization of small blood vessels and deep brain areas. To address this issue, this thesis explored the feasibility of employing and improving coded excitation techniques to enhance the SNR of Doppler ultrasound images. Furthermore, an optimized code for Doppler ultrasound imaging is designed, represented by a generalized encoding matrix. 

The research begins with the definition of a linear signal model that incorporates the encoding matrix. Subsequently, a trace-constraint optimization problem is formulated based on maximizing the Fisher information matrix to find the optimized encoding matrix. The feasibility and performance of the optimized encoding matrix are assessed through simulations on both small and large array settings, which operate above Nyquist sampling frequency and under Nyquist sampling frequency respectively. The imaging results indicate that the optimized code exhibits higher SNR in deep image regions compared to existing coded excitation methods like Barker code while using the same number of transmissions, bit length, and same average transmit energy, albeit with a trade-off of decreased axial resolution. Nonetheless, this resolution degradation can be mitigated through the application of the iterative imaging technique LSQR. Finally, the optimized code is tested in a clinical transducer setting, and a blood flow simulation is conducted. The outcomes showcase the capacity of the proposed optimized code to enable higher SNR in Doppler ultrasound imaging and more accurate and informative clinical assessments. 


MSc SPS Thesis presentation

Ultrasound Imaging through Aberrating Layers using a Virtual Array

Francesca De Carlo

Ultrasound images are typically generated using the Delay-And-Sum (DAS) method, which assumes a homogeneous propagation medium. When an aberrating layer is situated between the sensor array and the imaging target, this assumption does not hold, and DAS is replaced with model-based methods. These methods are computationally expensive and require to accurately model the aberrations caused by the layer. This thesis investigates novel methods for image formation and aberration estimation. The effect of the layer is described using a set of transfer functions from the sensor array to a virtual array placed after the layer. In the first part, we assume the transfer functions are known, and we propose a new method for image formation. The transfer functions allow to map the signal from the sensor array to the virtual array, and the DAS method is used on the virtual array signal. This technique is equivalent to model-based matched filtering in terms of image quality, without requiring expensive matrix computations. In the second part, the transfer functions are unknown, and a novel technique is introduced for their estimation. Using pulse-echo data, a focus-quality metric is computed to quantify the accuracy of the transfer function estimate. The transfer functions are modeled using a dictionary and the dictionary coefficients are iteratively updated to increase the defined metric. The optimization leads to improved focus quality and sharper images. In the case the layer model requires a limited dictionary, the proposed algorithm generates an accurate estimate of the transfer functions.


MSc SPS Thesis presentation

Prediction of Post-induction Hypotension Using Machine Learning

Shuoyan Zhao

Abstract: 

Anesthesia-related hypotension is a significant concern during surgery, occurring shortly after induction and potentially leading to severe complications. Since the anesthetic drug is believed to have an important role in the occurrence of post-induction hypotension (PIH), anesthesiologists now advocate for the appropriate selection of anesthetics dosage to avoid PIH.To facilitate such decision-making, an accurate prediction of PIH associated with a certain dosage of anesthetics is necessary.

This thesis presents a high-accuracy prediction model for PIH that supports anesthesia decision-making. The model is trained on data from the VitalDB database of 320 patients undergoing general anesthesia. The target output of this classification model is the occurrence of PIH, as defined through comprehensive analysis that incorporates clinical operations. Besides demographic data and vital signs, our model incorporates the dosage of propofol administered during the induction period as an input variable, mimicking real-world anesthetic plans. By employing the model in the target control infusion system of anesthesia, the anesthetics dosage can be varied as input, providing outcome predictions as security suggestions. An ensemble algorithm is employed to balance the prediction performance and the ability to elucidate the positive relationship between propofol and PIH risk, forming an anesthetics advice model. Compared to previous PIH prediction studies, our prediction model is validated in a more reliable nested cross-validation approach and achieves a higher performance (precision of 0.83 and recall of 0.84). We believe utilizing demographic and dynamic vital signs to predict HIP can be useful in determining the appropriate anesthetic dosage plan, offering potential improvements in patient care and safety.


MSc SPS Thesis presentation

Deep learning-empowered Content-based Video Image Retrieval (CBVIR)

Sinian Li

The advent of streaming and video has sparked a revolutionary shift in the presentation of materials across various fields, such as history, art, and media copyright protection. In this context, scholars and rights holders are seeking efficient solutions to index, retrieve, and browse through digital content searching for a specific instance. Unlike searching a specific instance in an image, searching in a video requires more than analyzing the visual features of an image and then comparing these features to a database, for it includes processing video sequences and retrieving video segments.

Motivated by the urgent need and promising applications across diverse disciplines, we present a novel deep-learning-empowered content-based video image retrieval (CBVIR) system with a strong emphasis on real-world applications. This system offers high efficiency and considerable accuracy, addressing the challenges associated with accessing and utilizing video materials effectively. Our initial approach revolves around the extraction of informative keyframes that effectively capture essential objects within the video. This process, known as Key Frame Extraction (KFE), enables us to distill the most crucial visual representations for further analysis. After the extraction of keyframes, the relatively smaller dataset allows for content-based image retrieval (CBIR) to be conducted, retrieving similar images from a database solely based on the content of the query image. In this project, a wide range of methods is investigated and analyzed, including traditional representation, handcrafted image feature extraction, and up-to-date machine learning-based image representations. Our contribution is striking a balance between high-level and low-level image representation for this task; targeting efficiency improvement, enhanced color-based KFE module is proposed and implemented, achieving high efficiency ratio and satisfactory accuracy and targeting accuracy, a traditional and deep learning-based hybrid feature is proposed, achieving valid efficiency ratio and highest accuracy. Overall, an automatic retrieving system requiring much less user engagement is provided together with a system GUI prototype.


MSc SPS Thesis presentation

LiDAR and Radar-Based Occupancy Grid Mapping for Autonomous Driving Exploiting Clustered Sparsity

Çağan Önen

Occupancy grid maps are fundamental to autonomous driving algorithms, offering insights into obstacle distribution and free space within an environment. These maps are used for safe navigation and decision-making in self-driving applications, forming a crucial component of the automotive perception framework. An occupancy map is a discretized representation of a chosen environment that is constructed using point cloud information obtained from sensor modalities like LiDAR and radar. In this project, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. We utilize the inherent sparsity of occupancy grid maps commonly encountered in automotive scenarios. Besides, the spatial dependencies between the grid cells are exploited to provide a better reconstruction of the boundaries of the objects inside the range of the map and to suppress the false alarms emerging from the reflections coming from the road. To address sparsity and spatial correlation jointly, we propose an occupancy grid estimation method that is based on pattern-coupled sparse Bayesian learning. The proposed method shows enhanced detection capabilities compared to two benchmark methods, based on qualitative and quantitative performance evaluation with scenes from the automotive datasets nuScenes and RADIal. 


MSc SPS Thesis presentation

Machine learning algorithm to estimate cardiac output based on arterial blood pressure measurements.

Alan Hamo

Cardiac output (CO), a vital hemodynamic parameter that reflects the blood volume pumped by the heart per minute, is crucial for determining tissue oxygen delivery and the heart's ability to meet the body's demands. Researchers developed various methods to measure cardiac output, including thermodilution using pulmonary artery catheters (PAC), also called Swan-Ganz catheters, the gold standard for cardiac output measurements. Such an approach involves an invasive procedure associated with complications, and it requires specialized equipment and expertise, limiting its use to critically ill patients undergoing operations in intensive care units (ICUs). An alternative, less invasive way to estimate CO is by analyzing arterial blood pressure (ABP) waveform. However, the relationship between cardiac output and blood pressure is unknown. This study uses machine learning and feature engineering techniques to discover the relationship between CO and ABP. We used the sparse identification non-linear dynamics (SINDy) algorithm to discover features that significantly contribute to the relationship between CO and ABP. Additionally, we investigated the optimum number of cardiac cycles needed to achieve the best performance providing insights into the temporal dynamics of CO estimation. The proposed approach achieved clinically acceptable performance regarding radial limits of agreement and bias. Further, the proposed approach was validated on an external dataset and achieved comparable performance. Finally, the learned model was interpreted as a differential equation describing the blood flow where CO acts as an external force to the system. All materials used in this study, including code, model, raw data, processed data, and extracted features, are available on GitHub to facilitate further development.


MSc SPS Thesis presentation

Finding Representative Sampling Subsets on Graphs: leveraging submodularity

Tianyi Li

In this work, we deal with the problem of reconstructing a complete bandlimited graph signal from partially sampled noisy measurements. For a known graph structure, some efficient centralized algorithms are proposed to partition the graph nodes into disjoint subsets such that sampling the graph signal from any subsets leads to a sufficiently accurate reconstruction on average. Furthermore, we consider the situation when the graph is massive, where processing the data centrally is no longer impractical. To overcome this issue, a distributed framework is proposed that allows us to implement centralized algorithms in a parallelized fashion. Finally, we provide numerical simulation results on synthetic and real-world data to show that our proposals outperform state-of-the-art node partitioning techniques.


MSc SPS Thesis presentation

Privacy Analysis of Decentralized Federated Learning

Wenrui Yu

Privacy concerns in federated learning have attracted considerable attention recently. In centralized networks, it has been observed that even without directly exchanging raw training data, the exchange of other so-called intermediate parameters such as weights/gradients can still potentially reveal private information. However, there has been relatively less research conducted on privacy concerns in decentralized networks.

In this report, we analyze privacy leakage in optimization-based decentralized federated learning, which adopts generally distributed optimization schemes such as ADMM or PDMM in federated learning. By combining local updates with global aggregations, it was proved that optimization-based approaches are more advantageous compared to the traditional average consensus-based approaches, especially in scenarios where the data at the nodes are not independent and identically distributed (non-IID).

We further extend the privacy bound in distributed optimization to the  decentralized learning framework. Different from the fact in the centralized learning framework the leaked information is the local gradients of each individual participant at all rounds, we find that in decentralized cases the leaked information is the difference of the local gradients within a certain time interval.  Motivated by the gradient inversion in centralized networks, we then design a homogeneous attack to iteratively optimize dummy data whose gradient differences are close to the true revealed gradient differences. Though the gradient difference information still brings privacy concerns,  we show that it is  more challenging for adversaries to reconstruct private data using the difference of gradients than using the gradients themselves in the centralized case.

To deal with the privacy attack, we propose several potential defense strategies such as early stopping, inexact update and quantization etc. The main advantage of these approaches is that they introduce error/noise/distortion into decentralized federated learning for protecting private information from being revealed to others without affecting the training accuracy. In addition, we also show that the larger the batchsize is, the more difficult for the adversary to reconstruct the private information.

 


MSc SPS Thesis presentation

Simplicial Unrolling ElasticNet for Edge Flow Signal Reconstruction

Chengen Liu

The edge flow reconstruction task improves the integrity and accuracy of edge flow data by recovering corrupted or incomplete signals. This can be solved by a regularized optimization problem, and the corresponding regularizers are chosen based on prior knowledge. However, obtaining prior information is challenging in some fields. Thus, we consider exploiting the learning ability of neural networks to acquire prior knowledge. In this thesis, we propose a new optimization problem for the simplicial edge flow reconstruction task, the simplicial ElasticNet, which is a regularized optimization problem that combines the advantages of the L1 and L2 norm. It is solved iteratively by the multi-block ADMM algorithm, and the convergence conditions are illustrated. By unrolling the simplicial ElasticNet's iterative steps, we propose a neural network with high interpretability and low requirement for the number of training data for the reconstruction task of simplicial edge flows. The unrolling network replaces the fixed parameters in the iterative algorithm with the learnable weights in the neural networks, thus exploiting the neural network's learning capability while preserving the iterative algorithm's interpretability. The core component of this unrolling network is simplicial convolutional filters with learnable weights to aggregate information from the edge flow neighbors, thus enhancing the learning and expressive ability of the network. We conduct numerical experiments on real-world and artificial datasets to validate the proposed approach. It is demonstrated that the simplicial unrolling network is significantly more advantageous than the traditional iterative algorithms and standard non-model-based neural networks in the case of limited training data.


MSc SPS Thesis presentation

Estimating Transmembrane Currents and Local Activation Times from Atrial Epicardial Electrograms

Teodor Licurici

Estimating the transmembrane currents travelling through the epicardium and local activation times based on atrial epicardial electrograms can greatly help in the study of cardiac arrhythmias such as atrial fibrillation. This work focuses on the accurate estimation of the aforementioned signals and features. To do this, two least squares-based regression methods were used to estimate transmembrane currents from electrograms and then find their local activation times by searching for the maximum negative slope. The first least squares optimization method consists of using standard least squares, while the second consists of regularized least squares, by combining both lasso and ridge regression, to deal with signal sparsity and multicollinearity, respectively. Furthermore, to improve estimation results, multiresolution analyses based on wavelet decompositions and principal components analysis were used to filter out parasitic components that were present in the estimated transmembrane currents by separating them from the main activation complex of the decomposed signals.

Using these algorithms on simulated data, it was shown that promising results can be achieved for both transmembrane current estimations and LAT estimations. Several wavelet support sizes were tested on the simulated data to observe performance changes. These were compared to an already existing LAT estimation algorithm. The results mainly confirm the efficiency of the proposed methods on severely diseased tissue corrupted by conduction blocks and noise.


MSc SPS Thesis presentation

Comparative analysis of clutter filtering techniques on freehand µDoppler ultrasound imaging

Xuan Gao
Erasmus MC

Micro-Doppler (µDoppler) ultrasound imaging is a high frame rate ultrasound imaging modality that provides high spatiotemporal resolution ultrasound images of blood flow. It is sensitive to slow blood flow and particularly suitable for capturing fast-changing phenomena like rapid blood flow. Clutter filtering is an essential step in µDoppler data processing to reject tissue clutter signals and keep blood flow information as much as possible. 3D freehand µDoppler imaging is an emerging ultrasound technique that can construct full spatial vasculature images with a panoramic view that conventional 2D ultrasound is not able to provide. As freehand implies the continuous and nonuniform movement of the probe, it becomes more challenging for clutter filtering to acquire high-quality images.
This thesis explores and compares different state-of-art clutter filtering techniques on freehand in-vivo µDoppler imaging of the human brain. Specifically, Singular Value Decomposition (SVD), Robust Principle Component Analysis (Robust PCA), and Independent Component Analysis (ICA) clutter filtering techniques have been investigated. The aim is to test and compare their performance on in-vivo µDoppler ultrasound data with freehand probe movement and understand how freehand motion affects the threshold selection criteria. Besides that, a newly proposed method that combines ICA clutter filtering and clustering is included in this thesis to bring another perspective for sorting independent components corresponding to blood flow and rejecting unwanted ones consisting mostly of tissue clutter signals. 


MSc SS Thesis Presentation

Rank Detection Based on Generalized Eigenvalue Threshold in Arbitrary Noise

Bingxiang Zhong

Rank detection is crucial in array processing applications, as many algorithms rely on accurately estimating the rank of the data matrix to ensure optimal performance. Under Gaussian white noise, rank can be detected through eigenvalue analysis. However, in arbitrary noise, prewhitening the data matrix with the noise covariance matrix is necessary, and rank detection is achieved by examining the generalized eigenvalues. Existing methods often assume the noise covariance structure or require a large number of noise samples. This thesis focuses on addressing the rank detection problem in scenarios with limited noise samples and arbitrary noise environments.

Firstly, we investigate the largest generalized eigenvalue threshold for the prewhitened data sample covariance matrix according to the random matrix theory. We develop a rank detection algorithm based on the threshold via a sequential test, and provide the performance analysis. A series of simulations demonstrate its superiority over conventional methods such as Minimum Description Length (MDL) and Akaike’s Information Criterion (AIC). Secondly, since the Short-time Fourier Transform (STFT) is commonly used for non-stationary signal analysis, we extend our rank detection method to the STFT domain. The correlations introduced by the STFT have a significant impact on the distribution of the noise. Therefore, we develop a technique to remove correlations among time-frequency bins based on exact expressions of these correlations.

After successfully eliminating these correlations, our proposed rank detection method achieves enhanced reliability and performance in the STFT domain. Lastly, we evaluate the effectiveness of our rank detection method in speech enhancement applications. Simulations confirm that utilizing the estimated rank improves speech quality compared to using the known number of sources.

Additional information ...


MSc SS Thesis Presentation

Self-calibrated plant counting in early crop stand scenarios using deep clustering

Jonathan Dijkstra

In recent years, the agricultural sector has seen significant techno- logical improvements under the flag of precision agriculture, assisting farmers in the manageability that coincides with large-scale farming. Moreover, precision agriculture aims to enable plant-specific farming on the macro scale that is demanded by the current global population growth. By more closely matching the individual needs of the plants, farmers are able to increase crop yield while reducing the environmen- tal footprint as well as the economic cost of farming due to savings in fertilizers and pesticides.

Visual inspection of arable land is a key factor in maintaining trace- ability of plant growth and health in precision farming. More specif- ically, plant counting, size measurement and plant localisation are of great use for farmers in yield prediction, growth tracking, and obtain- ing insight in the emergence ratio of the crop.

Most of the state-of-the-art plant counters, or object counters in gen- eral, rely on human annotations (labour intensive and error prone) as exemplars for the counting model. Self-supervised object counting, however, is a machine learning paradigm independent of human la- belled data, enabling an object counter to learn solely from raw photo- graphic data. Furthermore, the generative character of self-supervised learning models implies the potential to generalize well on unseen data to the model, such as new plant species or variations in plant size in the case of plant counting.

In this master’s thesis in cooperation with Tective Robotics, a study is performed towards the design of self-calibration based self-supervised object counting and localisation model for the scenario of early crop stand scenarios. More specifically, a novel self-calibrator has been developed to estimate the planting distance in between crops and a threshold for small object noise filtering (weeds, loose leaves ect). Im- plications of the self-calibrator are robustness to variations in plant size and allignement, accurate segmentation of occluded plant clusters and small-sized weed suppression.

Model testing on UAV orthomosaic arable land imagery collected by Tective Robotics B.V. has shown outstanding performance (R2 = 0.94) of the newly developed plant counting model, without the need of any labelled training data. The plant counter is comparable in performance to some of the commercially available plant counters. The plant counter, embedded in the entire data processing pipeline for Geotiff orthomosaics, has been made available on GitHub.


MSc SS Thesis Presentation

Coherent integration for imaging and detection using active sonar

Kaan Demir

Existing sonar systems typically rely on a minimum signal strength of a single echo, which limits their performance in low signal-to-noise conditions. This thesis explores the concept of coherent integration for active sonar, with the aim of improving imaging and detection capabilities under low signal-to-noise conditions. The goal is to provide signal processing methods that achieve long-time coherent integration of the received echoes, thereby maximising the processing gain.  Additionally, this research explores waveform design by comparing the performance of pseudo-random noise with chirps.

Two applications are seen in this thesis: moving target detection, which involves static sonar sensors, and synthetic aperture imaging, where the sensors move while the imaging scene remains static. For moving target detection, a processing methods is proposed which achieves coherent integration for constant velocity targets in a computationally efficient manner, and improves the detection performance by implementing a clutter filtering stage. For the second application, a processing method for imaging from a moving sensor pair is proposed. The resulting point-spread function for a circular sensor trajectory is investigated, from which a set of design rules are established. Additionally, a least squares algorithm is applied, which shows that the resulting image can be improved in terms of resolution and sidelobe interference.

Finally, the imaging and detection methods are tested and verified using an in-air demonstrator.


IEEE SPS Webinar

Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar

Dr. Preeti Kumari, Dr. Nitin Jonathan Myers, Dr. Robert Heath W. Jr.

Millimeter-wave (mmWave) joint communication-radar (JCR) will enable high data rate communication and high-resolution radar sensing for applications such as autonomous driving. Existing mmWave JCR systems, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the use of directional communication beams. In this presentation, we propose an adaptive beamforming design for mmWave JCR with a phased-array architecture that permits a trade-off between communication and radar performances.

To enable fast estimation of the mmWave radar channel in the Doppler-angle domain, we use a convolutional compressed sensing framework and optimize the radar waveforms within this framework. Our optimization accounts for the space-time sampling constraints that are specific to phased-array radars.

We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate-distortion theory. Numerical results demonstrate that our proposed JCR design enables the estimation of short- and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.  

To attend, use the registration link provided below.

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Symposium

Heterogeneous system integration - Driving the EU Chip Act ambitions

The Netherlands have a strong national ecosystem for quantum, photohics and semiconductor technologies, well connected to international key players. This symposium aims to build on this strength by intensifying collaboration among these domains.

Heterogeneous integration plays a crucial role in enabling future quantum, photonics and semiconductor technologies by creating new functionalities and business opportunities through the integration of different chips, technologies and materials into a single system.

This symposium will discuss the importance of heterogeneous integration and its potential for creating more industry and business value. It also aims to cultivate human resources for heterogeneous integration, further strengthening the Dutch ecosystem.

Join us to explore the exciting opportunities that heterogenous system integration can offer for the Dutch ecosystem and beyond, and to be part of the conversation on driving the EU Chip Act ambitions.


Array Processing in Atrial Fibrillation: Application of different signal models and LAT estimation techniques

Thesis link : http://resolver.tudelft.nl/uuid:82dd26ec-d49b-4780-8b14-7ce2960c1b1b


Signal Processing Seminar

Robust Pareto-Optimal Radar Receive Filter Design for Noise and Sidelobe Suppression

Costas Kokke

Additional information ...


Signal Processing Systems Seminar

Community Detection in Multilayer Networks: Algorithms and Applications

Prof. Dr. Selin Aviyente
Dept of ECE, Michigan State University

Abstract:
Modern data analysis and processing tasks typically involve large sets of structured data, where the structure carries critical information about the nature of the data.  Typically, graphs are used as mathematical tools to describe the structure of such data. Traditional network models employ simple graphs where the nodes are connected to each other by a single, static edge.  However, in many contemporary applications, this relatively simple structure cannot capture the diverse nature of the networks, e.g., multiple types of entities and interactions between them. Multilayer networks (MLNs) allow one to represent the interactions between a pair of nodes through multiple types of links. MLNs can further be categorized based on the homegeneity of the nodes and complexity of topological structure as: i) multiplex networks where each layer has the same set of entities of the same type and inter-layer edges are not shown as they are implicit; ii) heterogenous multilayer networks where the set and types of entities may be different for each layer and the relationships of entities across layers are shown using explicit inter-layer edges. A core task in the complexity reduction of these high-dimensional networks is community detection. In this talk, a joint nonnegative matrix factorization approach is proposed to detect the community structure in both multiplex and multilayer networks. The proposed approach considers the heterogeneity of layers and formulates community detection as a regularized optimization problem. The proposed approach is evaluated for both social networks and a fully connected multi-frequency brain network model.
 

Bio:
Selin Aviyente received her B.S. degree with high honors in Electrical and Electronics engineering from Bogazici University, Istanbul. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor. She joined the Department of Electrical and Computer Engineering at Michigan State University in 2002, where she is currently a Professor and Associate Chair for Undergraduate Studies. Her research focuses on statistical and nonstationary signal processing, higher-order data representations and network science with applications to neuronal signals. She has authored more than 150 peer-reviewed journal and conference papers. She is the recipient of a 2005 Withrow Teaching Excellence Award, a 2008 NSF CAREER Award and 2021 Withrow Excellence in Diversity Award. She is currently serving as the chair of IEEE Signal Processing Society Bioimaging and Signal Processing Technical Committee, on the Steering Committees of IEEE SPS Data Science Initiative and IEEE BRAIN. She has served as an Associate Editor and Senior Area Editor for IEEE Transactions on Signal Processing, IEEE Transactions on Signal and Information Processing over Networks, IEEE Open Journal of Signal Processing and Digital Signal Processing.


SPS Seminar

Understanding the importance of AI quality management

Dr. Martin Saerbeck
CTO Digital Service at TÜV SÜD (Singapore)

Organizations that want to benefit from AI while managing its risks need to overcome three challenges: adopting AI at scale, complying with regulations, and demonstrating the responsible use of AI. All these challenges can be addressed with appropriate AI quality management. Hence, organizations are looking for talent who understand both: AI technology and quality management. This lecture will introduce you to the core concepts of AI quality management. Awareness is the first step to update your skills and boost your AI careers. As a notified body, TÜV SÜD is a leading several initiatives around AI standardization and regulation. Join and gain an insight view in some of the latest developments and ongoing discussions.

Bio: In his role as CTO Digital Service at TÜV SÜD, Dr. Saerbeck oversees the technology roadmap and key implementation projects of digital testing services, including novel continuous testing services, targeting the demands of a connected smart industry. He has a long track record in academia and industry in the domains of smart sensor networks, robotics, and AI. After completing his PhD with Philips Research, Dr. Saerbeck started an interdisciplinary research team on human-machine interaction within the Institute of High Performance Computing, which developed novel technologies for several industries, including aerospace, manufacturing and retail. He is an awardee of the prestigious A*STAR Independent Investigatorship given by Agency for Science, Technology and Research, Singapore. Dr. Saerbeck has a passion for applied research, promoting translation of academic results in formal verification and artificial intelligence to make today’s connected smart systems safe, secure, and reliable.


PhD Thesis Defence

Ultrasound Imaging through Aberrating Layers

Pim van der Meulen

Whereas aberrating layers are typically viewed as forming an impediment to medical ultrasound imaging, they can surprisingly also be used to our benefit. As long as we can model the effect of an aberrating layer,we can utilize ‘model-based imaging’, the imaging technique explored throughout this thesis, to reconstruct ultrasound images where traditional beamforming methodswould fail, employing the ever increasing computational power available to us nowadays. Not only does this allow us to image through layers, but it also leads to interesting applications, such as 3D ultrasound imaging with spatially undersampled data, using an aberrating ‘coding mask’. The formulation of a measurement model, a fundamental part of model-based imaging, also gives insight into the imaging problem mathematically, and allows us to investigate methods for estimating the effect of an aberrating layer ‘blindly’, i.e., without explicitly measuring it.

In this thesis, we thus investigate (a), imaging through a layer when the layer’s aberration effect is known, and how it can be applied to imaging with spatially undersampled data, and (b), methods and algorithms for estimating the effect of the aberrating layer without knowing it a priori.

In the first part of this thesis, we illustrate how using model-based imaging can be utilized for 3D ultrasound imaging using a single ultrasound transducer, and equipping it with a plastic coding mask. The plastic mask acts as an analog coder, that scrambles the transmitted and received waves in a manner that is location dependent. As a result, the temporal shape of an ultrasound echo can be used instead of the traditional method of using phase differences between sensors in a sensor array. Imaging is instead accomplished using model-based imaging. By measuring the pulse-echo response of each pixel, we can form an image by solving a regularized linear least squares problem, which takes into account the measured pixel-specific pulse-echo signals. The proposed device and imaging method is then verified experimentally.

In the following chapter, a coding mask design method is proposed for the aforementioned imaging device. A measurement model is formulatedwhere themask geometry is an explicit parameter to be optimized. After forming this model, a numerical optimization method is proposed and numerically tested. Our numerical experiments show that optimized mask geometries exhibit an energy focusing effect on the region-of-interest, whilst simultaneously decorrelating echo signals between pixels.

In the second part of this thesis, in contrast, we consider methods for calibrating propagation models when the pulse-echo response per pixel is not known. The most important calibration challenge we consider is that of imaging through an aberrating layer in front of an ultrasound array. This could be subcutaneous fat or the human skull, for example. In this thesiswe formulate ameasurement model consisting of a partwhere wave propagation is known (i.e., the assumed homogeneous region behind the aberrating layer, where the contrast image of interest is located), and an unknown propagation part, consisting of the Green’s functions from an array sensor to any point on the the interface of the aberrating layer and the imaging medium. We then investigate methods for finding this set of Green’s functions without explicitly measuring them (so called ‘blind’ calibration).

The first proposed method exploits the singular value decomposition of the measurement data in combination with the assumed Toeplitz structure of the matrices representing the aberrating layer’s Green’s functions. However, the method is lacking in practicality since an additional set ofmeasurements is required with a phase screen mounted on the interface of the aberration layer and the imaging medium. The second method resolves these practical issues by utilizing a covariance matching technique. A sufficiently large set of measurements is obtained where each measurement is different due to e.g. moving particles such as blood flow or micro-bubbles. Using the covariance of the data, algorithms are then defined that can estimate the transfer functions of the aberrating layer from the measurement covariance data.

Finally, we propose a method for estimating the electro-mechanical impulse response of an ultrasound sensor, by simply measuring its pulse-echo response from a flat plate reflector in front of the sensor. Estimating the one-way (electro-mechanical) impulse response then becomes a de-autoconvolution problem, for which we propose a method by solving a semi-definite relaxation of the de-autoconvolution problem.

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Signal Processing Seminar

A framework for augmented reality visualization of simulated and experimental ultrasound images

Adrian Basarab
University of Lyon

In a number of clinical applications, such as breast cancer surgery, ultrasound medical images are acquired and analyzed prior to or during the intervention. However, these images are no longer available during the intervention, or are only visible on a computer screen, thus imposing important constraints to practitioners. Moreover, 2D ultrasound images are still the clinical standard, while the tissues of interest are naturally 3D.

The main idea of this work is to construct a framework whose objective is to fill the gap between 2D multimodal acquisitions and real-time visualization in augmented reality of 3D reconstructed volumes, at the time of intervention. In particular, the talk will focus on: i) an experimental platform able to visualize ultrasound images in augmented reality in real time, and ii) an efficient ultrasound image simulation method mimicking freehand scanning of a virtual 3D volume, allowing real-time visualization of the simulated images in augmented reality.


MSc SPS Thesis presentation

Digital self-timed neuron design for Spiking Neuron Networks

Tianyu Du
Innatera

Spiking Neural Networks(SNN) have been widely leveraged by neuromorphic systems due to their ability to closely mimic biological neural behavior, where information is exchanged and received between neurons in the form of sparse events(spikes). Such neuromorphic systems are highly energy-efficient because the use of a global clock can be avoided by asynchronous event-driven operations. Neurons, as the basic processing units of neuromorphic systems, are required to be lowpower and high-speed for the implementation of complex networks. In this work, two fully event-driven digital Integrate-and-Fire(IF) neuron design is presented. Both design exploits the hierarchical structure, which allows the synaptic weights can be accumulated by local compute units in parallel. Instead of using handshake protocols, the proposed design generates on-demand event pulses to drive the weight accumulation, so we call it self-timed. Both neurons are designed by SystemVerilog and synthesized in TSMC 28nm technology. According to the synthesis results, both designs can finish the accumulation of 1024 6-bit weights within 100ns, with a power consumption of 0.055pJ per spike and 0.23pJ per spike respectively.


MSc SPS Thesis presentation

On-chip Self Timed SNN Custom Digital Interconnect System

Jiongyu Huang
Innatera

A Spiking neural network (SNN) is a type of artificial neural network which encodes information using spike timing, network structure, and synaptic weights to emulate the information processing function of the human brain. Within an SNN, it is always required to support the spike transmission that travels between neurons(array). This thesis aims to design a customized high-speed interconnect system which supports multi-point communication in a neuromorphic computing system. The burst-mode two-wire protocol in point-to-point communication is applied in this interconnect system, which is designed in high-level modelling with SystemC. In order to improve the utilization of hardware resources, a virtual channel system is involved.

Furthermore, this system could be extended to a variable number of neuron arrays to support different types of spiking neural networks. Also, optimization methods are adopted to increase the transmission rate of the system and save unnecessary energy consumption. The interconnect system could achieve a throughput of 3.802 Gbits/s with the given MNIST use case, based on the evaluation of simulation results.


MSc ME Thesis presentation

Physics-Informed Data Augmentation for Human Radar Signatures

Edoardo Focante
TNO

In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to the high cost of acquiring measurements together with privacy and security concerns.
To overcome the lack of data, researchers have resorted to data augmentation (DA), a technique that aims to solve the problem at the root by generating new training samples by leveraging the available ones. In computer vision, image transformations and generative networks are used to perform DA. These techniques, however, may lead to the production of physically unfeasible samples that may hinder the generalization capabilities of classifiers in domains where the data has an underlying physical meaning.
Physics-informed machine learning aims to incorporate physical prior knowledge and governing equations of the target domain into the machine learning pipeline to improve the performance of NNs in fields with limited available data but with well-defined physical models. In this thesis, physics-informed DA in the radar domain is addressed to improve the task of classifying armed and unarmed walking individuals through micro-Doppler spectrograms. 
To begin with, the usage of model-driven micro-Doppler radar simulations to improve the existing generative augmentations is investigated. After introducing several generative NN architectures, the quality and diversity of the produced synthetic spectrograms are evaluated together with their effect on the downstream classification task.
Next, the impact of visual transformations on micro-Doppler spectrograms is studied. Their effect on the underlying physics of the micro-Doppler spectrograms and their impact on feature extraction is assessed. Based on the results, a new DA technique is devised to improve the feature extraction process by informed segmentation of the input spectrogram, halving the size of the NN model and reducing the risk of overfitting.

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IEEE Autonomous Systems Initiative Webinar

Active inference in cognitive neuroscience

Prof. Karl Friston
University College London

In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however, the approaches appear very different: current formulations of perceptual synthesis call on theories like predictive coding and Bayesian brain hypothesis. Conversely, formulations of decision-making and choice behaviour often appeal to reinforcement learning and the Bellman optimality principle. On the one hand, the brain seems to be in the game of optimising beliefs about how its sensations are caused; while, on the other hand, our choices and decisions appear to be governed by value functions and reward. Are these formulations irreconcilable, or is there some underlying imperative that renders perceptual inference and decision-making two sides of the same coin.

Speaker biography

Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference).

Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.

Zoom link

Please register for the webinar, here.


IEEE SPS BISP-TC Webinar

Tensor Decompositions in Functional Neuroimaging

Borbála Hunyadi

Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever-increasing spatio-temporal resolution. Multidimensional, as time series data are recorded at multiple locations (electrodes, voxels), from multiple subjects, under various conditions. Finally, the data are noisy: the recorded observations are a mixture of ongoing brain activity, physiological, and non-physiological noise sources. Tensors (higher order arrays) are the natural representations of such multidimensional data. Tensor decompositions, in general, aim to write a large and high-order tensor in terms of the product and summation of several smaller and low-rank tensors (including vectors and matrices). A tensor decomposition with a well-chosen number of terms and ranks can approximate the original data tensor using much fewer entries; even to capture the underlying sources separately in its individual components. This talk will first give an introduction to multilinear algebra and tensor decompositions, discuss current challenges in large-scale brain data analysis, and finally highlight some successful applications of tensor decompositions in EEG and functional ultrasound (fUS) data processing.

Registration

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IEEE SPS Autonomous Systems Initiative

Computational Self-awareness and Self-organization: A Paradigm for Building Adaptive, Resilient Computing Platforms

Dr. Nikil Dutt
University of California, Irvine

Self-awareness and self-organization have a long history in biology, psychology, medicine, engineering and (more recently) computing. In the past decade this has inspired new self-aware/self-organizing strategies for building resilient computing platforms that can adapt to the (often conflicting) challenges of resiliency, energy, heat, cost, performance, security, etc. in the face of highly dynamic operational behaviors and environmental conditions. I will begin by outlining a computational self-awareness paradigm that enables adaptivity and which supports system resilience. Computational self-awareness is achieved through introspection (i.e., modeling and observing its own internal and external behaviors) combined with both reflexive and reflective adaptations via cross-layer physical and virtual sensing and actuations applied across multiple layers of the hardware/software system stack.

 

Next I will outline strategies for combining computational self-awareness with self-organization for life-cycle management of dependable distributed computing platforms.   Our ongoing NSF/DFG Information Processing Factory (IPF) project applies principles inspired by factory management that combine self-awareness and self-organization for continuous operation and optimization of highly-integrated-but-distributed embedded computing platforms.  While each IPF computational component exhibits autonomy through self-awareness, collections of IPF entities can self-organize; the resulting emergent behavior must be controlled to ensure guaranteed service even under strict safety and availability requirements. I will outline two use cases: i) End-to-end computational pipelines for a single autonomous IPF component, and ii) Truck platooning as an exemplar for distributed-but-coupled IPF autonomous systems. The talk will conclude with the opportunities and challenges arising from adopting computational self-awareness and self-organization for making complex computational systems more resilient and self-adaptive.

Zoom link: see below.

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MSc ME Thesis presentation

Self-timed interconnect for SNN - From Point to Point Communication to Multi-array Segmented-bus solution

Jinyao Zhang

Spiking Neural Networks use Address Event Representation to communicate among different Neuron Arrays. To mimic the behavior of the human neural system and meets the requirement for large Neuron Array communication, the AER interconnect should be area-saving, have low power, and operates at high speed.

This thesis aims to build self-timed interconnects for point-to point and multi-array communication. The whole system is designed at the RTL level using SystemVerilog. For point-to-point communication, two transmitters are implemented and compared according to their synthesis results. In the multi-array communication structure, we develop a generalized segmented-bus topology and the element fence, to control its segments. Different timing problems in the design are analyzed and corresponding solutions are proposed. The whole system can operate at around 1Gbps in a self-timed manner without any timing problems.


MSc ME Thesis Presentation

A New Logarithmic Quantization Technique and Corresponding Processing Element Design for CNN Accelerators

Longxing Jiang

Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. However, due to the high data volumes and intensive computation involved in CNNs, deploying CNNs on low-power hardware systems is still challenging. The power consumption of CNNs can be prohibitive in the most common implementation platforms: CPUs and GPUs. Therefore, hardware accelerators that can exploit CNN parallelism and methods to reduce the computation burden or memory requirements are still hot research topics. Quantization is one of these methods.

One suitable quantization strategy for low-power deployments is logarithmic quantization.

Logarithmic quantization for Convolutional Neural Networks (CNN): a) fits well typical weights and activation distributions, and b) allows the replacement of the multiplication operation by a shift operation that can be implemented with fewer hardware resources. In this thesis, a new quantization method named Jumping Log Quantization (JLQ) is proposed. The key idea of JLQ is to extend the quantization range, by adding a coefficient parameter ”s” in the power of two exponents (2sx+i ).

This quantization strategy skips some values from the standard logarithmic quantization. In addition, a small hardware-friendly optimization called weight de-zeroing is proposed in this work. Zero-valued weights that cannot be performed by a single shift operation are all replaced with logarithmic weights to reduce hardware resources with little accuracy loss.

To implement the Multiply-And-Accumulate (MAC) operation (needed to compute convolutions) when the weights are JLQ-ed and dezeroed, a new Processing Element (PE) have been developed. This new PE uses a modified barrel shifter that can efficiently avoid the skipped values.

Resource utilization, area, and power consumption of the new PE standing alone and in a systolic array prototype are reported. The results show that JLQ performs better than other state-of-the-art logarithmic quantization methods when the bit width of the operands becomes very small.


MSc ME Thesis Presentation

Off-chip Self Timed SNN Custom Digital Interconnect System

Yichen Yang

To support the spike propagates between neurons, neuromorphic computing systems always require a high-speed communication link.

Meanwhile, spiking neural networks are event-driven so that the communication links normally exclude the clock signal and related blocks.

This thesis aims to develop a self-timed off-chip interconnect system with ring topology that supports multi-point communication in neuromorphic computing systems. This interconnect system is implemented in high-level modeling with SystemC and involves the burstmode two-wire protocol in point-to-point communication. In order to ensure the flexibility of the system, the distributed control system is involved. Further, the system can be configured with different numbers of chiplet to fulfill various spiking neural network structures.

We also explore optimization methods, which is a bi-directional ring topology achieving the growth of throughput. Based on evaluation and simulation results, the interconnect system can achieve 4.57Gbps with the specific application scenario.

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MSc CE Thesis Presentation

Mapping of Spiking Neural Network Architecture using VPR

Jinyun Long

As the new generation of neural networks, Spiking Neural Network architectures executes on specialized Neuromorphic devices. The mapping of Spiking Neural Network architectures affects the power consumption and performance of the system. The target platform of the thesis is a hardware platform with Neuromorphic Arrays with columns for neural signal processing.

The explorations for the mapping methods are based on VPR, an open-source academic CAD tool for FPGA architecture exploration.

The packing of VPR is used for mapping neurons to Neuromorphic Arrays. VPR includes two levels of mapping: pins and neurons.

An evaluation of the mapping methods is established. Based on the evaluation, the optimized mapping solution is generated. Modifications are made in VPR to adapt to SNN architectures. An ActivityCriticality input file is added to the VPR flow for the optimized mapping solution.

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MSc ME Thesis Presentation

A Low-Noise Transimpedance Amplifier for Ultrasound Imaging with 40dB Continuous-Time Gain Compensation

Qian Wang

This work presents a low-noise amplifier (LNA) for miniature 3D ultrasound probes. Time gain compensation (TGC) is required to provide continuously variable gain and compensate for the attenuated echo signal, resulting in decreased output dynamic range (DR). As TGC is embedded in the LNA, a power-hungry LNA is no longer needed to handle the full dynamic range of attenuated echo signal. Compared to prior art where TGC is applied after the LNA, this structure reduce die area and power consumption greatly.

The LNA with built-in TGC functionality is comprised of a transimpedance amplifier (TIA) with exponentially increasing feedback resistive network. Since a transducer with a relatively high impedance is targeted, a TIA is utilized to interface with the tranducer and sense the signal current. TGC is implemented in a continuous fashion by tunable resistors so as to alleviate imaging artifacts associated with gain switching moments. The resistive feedback network is achieved by triode transistors with exponentially decreasing gate voltages. Three parallel branches of triode transistors are varied simultaneously to obtain 40dB gain range. Each branch consists of two back-to-back triodes to mitigate non-linearity related to the body effect.

The variable-gain loop amplifier employing a current-reuse topology enables constant closed-loop bandwidth in an energy-efficient way. The first stage is a fixed-gain stage with dynamic biasing to save power at the lowest gain setting. The next two stages are variable-gain stages with variable resistive loads. The load resistor is implemented in the same fashion as the TIA’s feedback resistor to achieve intrinsic gain matching. The last stage is a buffer to provide low output impedance for stability.

The LNA has been designed in 0.18 μm CMOS technology and occupies an estimated die area of 0.0339 mm2. The effective gain range is 40 dB with ±1 dB gain error. The LNA’s noise floor at the highest gain is below 1.15 pA/rt-Hz and its harmonic distortion is better than -40 dB. During 100 μs receive period, the total power consumption is 6mW from a ±0.9 V supply. The LNA featuring small area and high power efficiency is a promising circuit for miniature 3D ultrasound probes.


MSc ME Thesis presentation

Autonomous Landing of an Unmanned Aerial Vehicle

Siddhy Ganesh Shetty

Abstract: 

With the advancement in technology, Unmanned Aerial Vehicles (UAVs) have been able to safely maneuver in risky environments. During landing, the UAV should slow down while not affecting its physical design. Currently, multiple sensors are being used to increase the accuracy while landing which might weigh down some of the smaller UAVs. The usage of a single sensor to guarantee safe landing is yet on its early stages of development. It was observed that insects use Optical Flow to maneuver, and this has been used as a method to design UAV's journey and land safely. By dividing each frame, an Optical Flow Difference is calculated which is used as a metric for the UAV to move towards the landing platform.

Once the UAV has positioned itself on top of the elevated landing platform, image dilation method is used to safely land the UAV. One of the methods tested used Image Dilation Method using IMU. Using the calculated image dilation, the control input to the UAV is generated and the UAV lands. This method failed in providing safe landing and it also did not take the vision of the UAV into consideration. Then, Image Dilation Method using Features from Accelerated Segment Test (IDMF-AST) was tested. This method tracks features observed by the UAV and calculated an estimate of the image dilation. The estimate of image landing is used to control the UAV. This showed dependency on the landing design platform and the hyperparameters that are used for implementing IDMF for the type of landing.

Different landing designs were tested for different elevated landing platforms. It was observed that concentric circles on a textured landing marker provided the highest probability of safe landing. To tackle the dependency of the hyperparameters, a Classification Model was proposed to find an optimal set of hyperparameters for each possible height of the platform. This trained model was incorporated with the IDMF, thereby designing the proposed Adaptive IDMF algorithm. The Adaptive IDMF was tested against the original IDMF on the metrics of safe landing probability, time taken to safely land and simulation time. Adaptive IDMF performed better compared to IDMF providing an 190% increase in probability of safe landing, faster safe landing and lesser simulation and compilation time.

 

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Microelectronics Colloquium

Advances in Low-Field MRI Hardware Design and Data Processing

Rob Remis

In this talk we discuss several recent advances in low-field Magnetic Resonance Imaging (MRI). We focus on magnet and gradient coil design for a low-field MR scanner in which the strong background field is generated by permanent magnets (Halbach systems). These design problems are treated as inverse source problems, which are severely ill-posed in general. How to obtain approximate (regularized) solutions to these problems is discussed and the practical implementation of these solutions is addressed as well. Several processing algorithms that can handle compressed noisy MR input data are also presented and we illustrate the performance of these algorithms on simulated and measured low-field MR data.

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MSc ME Thesis presentation

Energy-efficient seizure detection for wearable EEG

Xiaoning Shi

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MSc ME Thesis presentation

A trainable activation scheme for applying Neural Radiance Fields in novel view synthesis tasks

Chuhan Wang

3D scene reconstruction is a common computer vision task with many applications. The synthesized virtual environments are beneficial for many downstream applications such as 3D modeling, building inspection, virtual reality, etc. As conventional scene reconstruction methods often require expensive data collections and prior information on the geometry, a learning-based method named Neural Radiance Fields (NeRF) has gained a surge of interest within the computer vision community recently for its capacity to achieve state-of-the-art view synthesis performance and achieve photorealistic rendering from only a sequence of RGB images as the input. However, NeRF has a strict requirement for input images with accurate camera poses, which is often not available in real-life applications. To this end, we provide an end-to-end guideline for 3D scene reconstruction using NeRF under a real-world scenario the objective is to reconstruct the HDB facade in Singapore. This guideline requires only RGB images as the input and can achieve photorealistic rendering results which are competitive with the results from the conventional point cloud-based method. Besides, we build our own models upon NeRF and also improve its performance in representing fine details. We first research that the ability to represent high-frequency contents in the signal is limited by its ReLU activations in the Multi-layer Perceptron (MLP) network, and demonstrate that mapping the input to the MLPs from low dimensional space to high dimensional space significantly improves the reconstruction and view synthesis quality. Afterward, We make several attempts to perform input mapping. We first use Gaussian-distributed Fourier features to replace the original positional encoding used in NeRF. Then, we research the activations in coordinate MLP and propose an embedding-less NeRF model equipped with parameterized sine activations called SIRENeRF. Next, we extend the use of parameterized activations from sine activations to a class of non-periodic activations and propose a trainable activation scheme that not only achieves higher scene reconstruction results but also enjoys better flexibility to different datasets. Experiments show that all of the above attempts outperform the positional encoding in terms of scene reconstruction and view synthesis results.


MSc ME Thesis presentation

Nowcasting of extreme precipitation using deep generative model

Haoran Bi

Extreme precipitation can often cause serious hazards such as flooding and landslide. Both pose a threat to human lives and lead to substantial economic loss. It is crucial to develop a reliable weather forecasting system that can predict such extreme events to mitigate the effect of heavy precipitation and increase resilience to these hazards.

Numerical Weather Prediction (NWP) models play the dominant role in the field of weather forecasting. However, due to their long computational time, these models had limited utility in predicting weather conditions in the following several hours. This gap is filled by nowcasting, an observation-based method that uses the current state of the atmosphere to forecast future weather conditions for several hours. Operational nowcasting systems typically apply extrapolation algorithms to rainfall radar observations based on simple physics assumptions. However, the physics constraints also limit the performance, and the methods can hardly capture non-linear patterns in the radar observations. Besides the conventional methods, deep learning models have started to play an essential role in this field. Recent works have shown the promising potential of using deep learning models to tackle the nowcasting task, which is also this thesis's focus.

The thesis work mainly studied in two directions: the development of novel deep generative models for precipitation nowcasting and the application of statistical approaches for better modeling and prediction of extreme events. For the first direction, our proposed model is inspired by recently developed deep learning models from the field of visual synthesis. The model makes use of a two-stage structure: the first stage is a Vector Quantization Variational Autoencoder (VQ-VAE) which compresses the original high-resolution radar observations into a low-dimensional latent space. The second stage works in this latent space. It contains an autoregressive Transformer that models the probabilistic distribution of latent space data. The trained Transformer can predict the latent space representation of future frames. ForT better modeling and prediction of extreme events, Extreme Value Loss (EVL) is proposed and incorporated with the autoregressive Transformer. The loss function aims at penalizing predicting extreme cases as non-extreme and predicting non-extreme cases as extreme in order to solve the high imbalance between extreme and normal precipitation data. Our results show that the proposed model shows comparable performance with the state-of-the-art conventional method and other deep learning nowcasting models. The proposed EVL has also been shown to improve the overall performance and accuracy in predicting extreme events.

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MSc ME Thesis presentation

IMU-Based Adaptive Filtering for Movement Artifact Removal from ECG Recorded With a Single Lead Wearable Device

Cesar Eduardo Cornejo Ramirez

Background and Objectives: Wearable devices (WDs) capable of recording electrocardiograms (ECGs) for prolonged periods in ambulatory settings offer the possibility of detecting non-predictable events such as epileptic seizures and atrial fibrillation. Nevertheless, these systems suffer from additional noise sources, such as movement artifacts (MA). 

Several adaptive (AF) algorithms have been proposed in the literature to suppress movement artifacts from ECG without consistent results. Adaptive algorithms for signal enhancement require a reference signal correlated with the noise and not correlated with the sign of interest. This correlation can change significantly depending on the absolute and relative location of the electrodes and the location of the reference sensor (i.e., accelerometer, gyroscope); objectively measuring the correlation is not a trivial problem.

For this reason, first, we used an algorithm to obtain a rough estimate of the movement artifacts from the recorded ECG to calculate the correlation between them and the available reference signals (three-axis accelerometer and three-axis gyroscope) and selected the one with the highest correlation. Then, we compare three adaptive filter algorithms using the signal-to-noise ratio (SNR) coefficient as the evaluation parameter.     

Methods: For testing the implemented adaptive filters, first, we used a simulated signal, then data from an open-online database, and last, a single lead ECG wearable device called AFi® (Praxa Sense™, The Netherlands) with an embedded IMU. To induce the movement artifacts in a controlled setting, participants performed a set of predefined movements within three intensities; high (running, jumping), moderate (torso rotations, pushups), and low (walking). Then we analyzed the recorded data offline as follows:

  1. Test the correlation between noise and the IMU components, and select the component with the highest correlation to be used as a reference input for the adaptive filters.
  2. Compare three adaptive filters in terms of SNR improvement; the Least means squares (LMS), the Normalized least means squares (NLMS), and the Recursive least squares RLS.
  3. Filter the selected reference input with wavelet decomposition, and test if there is a filter performance improvement in SNR.

Results: The implemented adaptive filters performed as expected with the simulated signals, but they showed very poor results once we used them on real data.

The RLS filter showed superior performance than the least mean squares-based filters in terms of convergence speed and the root mean squared error minimization. Nevertheless, it requires a high correlation (ρ) above ρ>0.8 between the reference input and the undesired signal or noise to provide a proper signal enhancement and morphology recovery.

The low correlation between the movement artifacts and the components of the IMU used as a reference input for the adaptive filters affected the filter performance heavily. Filtering the reference input with the wavelet decomposition did not improve the correlation or the filter performance.

Conclusions The correlation between ECG motion artifacts and movement recorded with inertial sensors appeared to be low and inconsistent. Given this, adaptive filters using inertial sensors as reference input are unsuitable for removing ECG movement artifacts. 

 


MSc ME Thesis presentation

Predicting noise attenuation level in the earplugs using Gaussian Process Regression

Stephy Annie Curie Rakesh Arya

The earplug development followed by ALPINE, a hearing protection company is a trial and error method. This method leads to high material wastage. It is also a time-consuming and expensive development process. So, the objective of this thesis is to ease the error earplug development process by implementing a prediction model. The research was steered towards understanding the factors that influence the sound dampening properties in an earplug, building the dataset, and analysing the data and regression models. The regression model must be able to predict the noise attenuation provided by an earplug based on the material and design specifications for which Gaussian Process Regression (GPR) model was used. With RBF kernel function and through hyperparameter optimization, the GPR model was trained and tested on datasets.

The main finding of this thesis is that noise attenuation provided by an earplug is highly subjective. Sound perception is crucial in earplug design. Even though the most ideal sound attenuation prediction model for earplugs can be developed, in the end, everything relies on individual sound perception. So, the prediction of noise attenuation in earplugs has a higher level of uncertainty. However, with accurate age, gender, and ethnicity information, the earplugs can be modelled and designed for each group and the sound attenuation prediction model can be developed to make predictions with higher certainty.


MSc ME Thesis presentation

Detecting Medical Equipment in the Catheterization Laboratory using Computer Vision

Renjie Dai

Workflow analysis aims to improve the efficiency and safety in operating rooms by analyzing surgical processes and providing feedback or support, where observations can be made and evaluated by algorithms rather than human experts. For our study, we mount five calibrated cameras from different angles in a Catheterization Laboratory (Cath Lab) to observe and analyze Cardiac Angiogram procedures. To automate the classification of workflow and personnel activities, we propose an object detection algorithm based on Scaled-YOLOv4 with a filter to improve bounding box prediction. Scaled-YOLOv4, as a state-of-the-art technique, is featured with extremely fast processing speed and decent precision. However, we find that Scaled-YOLOv4 still suffers when detecting objects with flexible appearance due to fixed anchors. This can result in the prediction of bounding boxes missing parts of the object or containing a blank environment. In this work, we design a filter following Scaled-YOLOv4 to improve the prediction of the bounding box by matching the features detected from different cameras. With the keypoints detected by SuperPoint and matched by SuperGLue, the filter adjusts the boundaries of the bounding box to include all the matched keypoints. The proposed algorithm achieves 95.1% mAP in detecting medical equipment in the Catheterization Laboratory and a real-time speed of 58 FPS on RTX 3090.


MSc ME Thesis presentation

Reconstruction and Rendering of Buildings as Radiance Fields for View Synthesis

Enpu Chen

In inspection and display scenarios, reconstructing and rendering the entire surface of a building is a critical step in presenting the overall condition of the building. In building reconstruction, most works are based on point clouds because of their enhanced availability. In recent years, neural radiance fields (NeRF) have become a common function for implementing novel view synthesis. Compared to other traditional 3D graphic methods, NeRF-based models have a solid ability to produce photorealistic images with rich details that point clouds based methods cannot offer. As a result, we decided to investigate the performance of this technique in architectural scenes and look for ways to improve it for more significant scenes.

This thesis explores the ability to reconstruct large-field scenes with NeRF-based models. NeRF introduced a fully-connected network to predict the volume density and view-dependent emitted radiance at the special location, which will be projected into an image through classic volume rendering techniques. Due to the limitation of near-field ambiguity and parameterization of unbounded scenes, the original NeRF does not perform well on 360° input view, especially when the inputs are sparse. An inverted sphere parameterization that facilitates free view synthesis is introduced to address this limitation so that the foreground and background views can be trained separately. Besides that, we also compare the performance of tracing different light geometries, ray and cone, respectively. Meanwhile, to generate the reconstructed scene precisely, raw RGB images should be pre-processed to estimate the corresponding camera parameters. Finally, customized camera paths should be prepared to generate the final rendered video.

According to our experiments, training foreground and background separately is a promising method to solve practical large-scale scene reconstruction problems. A complete wrap-around view of the target building can be obtained using adjusted camera path parameters. Furthermore, introducing conical frustum casting into the original model also provides an alternative method to implement reconstruction. We named this method mip-NeRF++, which can contribute to the final results to some extent.

 


MSc ME Thesis presentation

Automatic Camera Extrinsics Estimation in the Catherization Laboratory

Jinchen Zeng

Surgical workflow analysis has gained more importance in operating rooms, which could take responsibility for the working condition, the safety of both patients and surgical personnel, as well as the working efficiency. Focusing on the optimization of the workflow, a set of cameras is installed in the Catherization Laboratory in Reinier de Graaf Gasthuis for multiple computer vision related researches. However, our cameras are calibrated only once after installation. The orientation and position of the cameras could be changed after days or months, which could lead to a wrong localization. 

Compared with the traditional calibration method (calibration patterns or markers), we propose a new image-based camera pose estimation pipeline tested in the Catheterization Laboratory. Our proposed pipeline exploits object detection model (Scaled-YOLOv4) to detect fixed objects. The mean average precision with 50% IoU threshold (mAP@.5) achieves more than 0.99 for all detected objects. Then use a self-supervised interest point detector and descriptor (SuperPoint). With the detected feature points, a feature matching technique based on graph neural networks (SuperGlue) is adopted to match the interest points detected in the target image with reference points annotated in the image databases (image DBs). The point-correspondences between the image coordinates and the 3D coordinates are applied to solve Perspective-n-Point (PnP) problem to compute the orientation and position of each camera (Camera pose). The final camera pose estimation achieves a 5.79-pixel reprojection error with a 4.97-cm Euclidean distance error. Compared with other image-based camera pose estimation techniques, our pipeline requires no 3D reconstruction or 3D point cloud in the scene model. Using the video from real procedures, we show that the pipeline is able to estimate the camera pose with high accuracy.


Microelectronics Colloquium

Sparsity-constrained Linear Dynamical Systems

Geethu Joseph

Abstract: At the intersection of control engineering and signal processing sits the upcoming field of sparse control and state estimation of linear dynamical systems. It deals with linear dynamical systems with control inputs having a few nonzero entries compared to their dimensions. Constraining the inputs to be sparse is often necessary to select a small subset of the available sensors or actuators at each time instant due to energy, bandwidth, or physical network constraints. Bringing together research from classical control theory and compressed sensing, the talk presents a comprehensive overview and critical insights into the conceptual foundations of sparsity-constrained systems, including the formulation, theory, and algorithms. We look at the concrete example of a budget-constrained external agent controlling the opinion of a social network.

Additional information ...


MSc ME Thesis presentation

Frequency Domain Joint Estimation of HRF and Stimulus from fUS Data

Yitong Tao

To better understand how brain signals are processed and even how the human mind works, analyzing the hemodynamic signal model is one of the most essential steps. In the CUBE group of Erasmus MC, functional ultrasound (fUS) data of a mouse’s brain is recorded. By using this fUS dataset, this thesis will solve the problem regarding the joint estimation of hemodynamic response function (HRF) and the underlying stimulus. Usually, hemodynamic responses are investigated in the time domain, while this thesis provides another perspective

from frequency domain signal processing.

We consider the hemodynamic response as a convolutive signal mixture, then try to transform it into an instantaneous mixing model by converting the context into the frequency domain. By applying independent vector analysis (IVA), this estimation problem can be solved without facing permutation ambiguity which is a well-unknown problem regarding independent component analysis (ICA). Additional steps before and after IVA are also discussed so that a whole estimation road map is formed.

Both simulation and experimental analysis are provided to validate this estimation algorithm. Results show that by using this method, both stimulus and HRF estimation can be achieved satisfyingly in a suitable experimental setting. This thesis provides insights and future potentials for IVA to be further investigated in neural signal processing problems.

 

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PhD Thesis Defence

Advanced Measurement Techniques and Circuits for Array-Based Transit-Time Ultrasonic Flow Meters

Douwe van Willigen

This thesis describes the design, prototyping and evaluation of matrix-based clamp-on ultrasonic flow meters. Several new measurement techniques are presented as well as an Application-Specific Integrated Circuit (ASIC) designed for accurate measurement of flow velocity with matrix transducers.

The influence of circuit topologies on the zero-flow performance of ultrasonic flow meters has been analyzed and an algorithm is presented to reduce the offset. With a linear transducer array, flow measurements have been performed via two different acoustic paths, demonstrating the ability to accurately measure flow with array transducers through a stainless-steel pipe wall. In order to improve signal quality, an ASIC has been designed that is able to drive and read-out 96 piezo transducer elements. The ASIC has been characterized electrically and flow measurements have been performed in combination with the linear transducer arrays.

Several new techniques, enabled using transducer arrays, have also been explored. By tapering the amplitude of the transmit signals, spurious waves can be suppressed. An auto-calibration technique has been developed that uses additional acoustic measurements to estimate the diameter of the pipe and the speed of sound in the pipe wall and liquid. Finally, a simulation study has been performed to explore the possibility of exploiting the beam-steering capabilities of transducer arrays to measure flow velocity profiles by using measurements obtained via multiple acoustic paths.

Thesis:
https://doi.org/10.4233/uuid:e2f4b411-3d8e-4b93-b037-096009c59f61

Collegerama (live stream of the defence):
https://collegerama.tudelft.nl/mediasite/play/571eb84f3e724d47b46df7e8d0eb3a7a1d

Additional information ...


PhD Thesis Defence

Integrated Transceiver Circuits for Catheter-based Ultrasound Probes and Wearable Ultrasound Patches

Mingliang Tan

Promotors: Michiel Pertijs and Ronald Dekker

Thesis: link

Collegerama link (live stream of the defence): link

Abstract: This thesis describes the design, prototyping, and experimental evaluation of transceiver ASICs (application-specific integrated circuits) for catheter-based ultrasound probes and wearable ultrasound patches. Various circuit techniques are proposed to address requirements and implementation bottlenecks in these applications. Prototype chips are presented to demonstrate the effectiveness of these techniques. To reduce the loading effect of micro-coaxial cables in an ICE probe based on capacitive micro-machined ultrasound transducers (CMUTs), an ASIC prototype including element-level high-voltage pulses and low-noise trans-impedance amplifiers has been implemented. Besides reducing the loading effect from micro-coaxial cables, ASICs play an important role in achieving cable-count reduction, which is crucial for 3-D imaging catheters, such as forward-looking IVUS probes. Circuit techniques are proposed to implement a prototype ASIC which only requires 4 cables to interface with a 2D piezoelectric transducer array. Additionally, to address the challenges in interface electronics for wearable ultrasound patches, a prototype ASIC is presented that contains 64 reconfigurable transceiver channels that can interface with different transducer elements by employing channel-parallelizing techniques.

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Special EI Colloquium

Piero Tortoli, Michael Kraft

Profs. Piero Tortoli and Michael Kraft

Real-time High-Frame Rate imaging: Novel Methods and Applications

Prof. Piero Tortoli
Microelectronics Systems Design Laboratory
University of Florence, Italy

Medical imaging is increasingly based on High-Frame-Rate (HFR) methods, which are in principle capable of producing one frame (or even one data volume) per transmission event. However, achieving such a goal in real-time implicitly involves the transfer and processing of huge amount of data at high rates, and this can be done only through an appropriate experimental setup.

In this talk, the main characteristics of the hardware-based open scanner ULA-OP 256 are briefly reviewed, and its recent advancements, such as the data transfer acceleration obtained through an architectural change, and the possible expansion toward the control of an unlimited number of probe elements, are reported in detail. The “virtual real-time” modality will also be described as ideal to obtain the best performance from specific HFR imaging modalities. Finally, the combination of ULA-OP 256 with properly designed sparse 2-D arrays will be shown suitable for the investigation of full volumes. The talk will be concluded with the presentation of experimental results in a few sample applications, including multi-plane imaging, HFR CFM and HFR vector Doppler.

 

Micro- and Nanosystems at ESAT, KU Leuven

Prof. Michael Kraft
ESAT, Micro- and Nano-Systems
KU Leuven, Belgium

This seminar will give an brief overview of the activities in micro- and nanosystems at the Electrical Engineering Department (ESAT) of KU Leuven. It will describe the available infrastructure and give a short overview of current research activities in the division Micro- and Nanosystems (MNS), which currently comprises 24 PhD students, 4 postdoctoral researchers and 2 technicians.

A selection of current active projects and recent highlights will be presented, including work on:

  • Coupled resonators for mass sensing applications
  • Piezoelectric ultrasound technology arrays for medical imaging and underwater communication
  • Micromachined probes for neuro recording and stimulation
  • Multi-parameter sensing chip for bioreactor condition monitoring
  • Genetic Algorithm for the design of MEMS devices (accelerometers and microgrippers)

Finally, the newly founded Leuven Institute for Micro- and Nano Integration (LIMNI) will be briefly introduced.

Note: This Colloquium precedes the PhD defence of Mingliang Tan, which will take place in the Aula on the same day at 12:00 (layman’s talk), 12:30-13:30 (defence). More information can be found here.


MSc SS Thesis Presentation

Improving the Estimation of Epicardial Activation Times Using Spatial Information

William Hunter

Atrial fibrillation is a common cardiovascular disease, affecting the regular beating of the heart through chaotic contraction of the heart's upper chambers. On its own, the condition—increasingly prevalent among the elderly—is not life threatening, but it leads to an increased risk of stroke and heart failure. As of yet, there is no consensus on the physiological mechanisms responsible for initiating and sustaining atrial fibrillation. A more detailed view of cardiac activity would improve understanding of the disease, making earlier diagnosis possible and improving options for treatment. 

The contraction of the cardiac muscles is governed by electrical signals propagating through the tissue. Cardiac activity can be monitored with a high spatial resolution by measuring the electrical potential directly on the epicardium of the heart during open-heart surgery, using an array of closely spaced electrodes. From these electrograms, estimating the time of local activation of the cardiac tissue underneath each electrode provides a quantitative way of mapping the mechanisms of atrial fibrillation. Various methods exist to estimate the activation times, but the complex signals that are typical of atrial fibrillation make it difficult to obtain accurate results. This thesis proposes combining two existing methods for estimating the local activation times. Based on a model of the electrogram as a spatial convolution of local transmembrane currents, an inverse problem is formulated and solved, resulting in a less opaque view of the cardiac activity at the electrode locations by attenuating distant disturbances and emphasizing local activity. The deconvolution output is fed to the second step, where cross-correlating certain pairs of signals gives an estimate for the mutual time delay in local activation. A graph representation of the electrode array is used to define neighbor order and decide which signal pairs are correlated. The set of pairwise time delays this produces is then converted to an estimate for the local activation times, using a least-squares estimator. 

The proposed method is evaluated using different simulated cardiac settings. In a setting with one stimulation source, earlier results of the deconvolution and cross-correlation methods are confirmed, and the proposed method is seen to produce a slightly lower mean error than reference methods. In the higher-complexity triple-source setting, the latter effect is again visible. Reinforced by the performance of the different methods in increasingly noisy settings, the main merits of the proposed method for the estimation of local activation times can be said to be found in the form of increased consistency, not significantly improving on the accuracy of existing methods.

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MSc SS Thesis Presentation

Cooperative Localization of Unmanned Aerial Vehicles using ADS-B

Xuzhou YANG

As unmanned aerial systems (UAS) turn into a full-fledged industry, the sky will be much more crowded in the future. Large-scale UAV applications make reliable UAV navigation a pressing need. Traditionally, global navigation satellite system (GNSS) is extensively used as the primary positioning, navigation, and timing (PNT) service. However, GNSS is vulnerable to intentional radio interference such as spoofing, jamming, and repeating. Hence, alternative PNT (APNT) attracted many researchers' attention. 

In this thesis, instead of GNSS signals, ADS-B signals from piloted aircraft are leveraged for UAV navigation. We propose a cooperative navigation strategy for multiple UAVs in GNSS-denied environments. It consists of: 1) a system-level, leader-follower cooperative strategy; 2) a sensor fusion algorithm for individual UAV navigation based on the extended Kalman filter. Furthermore, the effects of asynchronous clocks are studied and a joint relative positioning and synchronization algorithm is applied to tackle this problem. 

Finally, Monte Carlo experiments in a multi-UAV scene are performed to verify the proposed algorithms. The results show that the proposed algorithms achieve a performance comparable to civilian GNSS on the selected data set and under the system assumptions we made. Moreover, the proposed cooperative navigation framework only needs one ground station of limited service capacity as external aid. Compared with large-scale, specialized terrestrial APNT service networks, our proposed framework is more flexible and the system can be deployed in areas without infrastructure.

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MSc SS Thesis Presentation

Robust Formation Control against Observation Losses

Zhonggang Li

Distributed formation control has received increasing attention in multiagent systems. Maintaining certain geometry in space is advantageous in many applications such as space interferometry and underwater sensing. At present, there is a variety of distributed solutions for agents to converge to desired formations and track a series of prescribed maneuvers. They typically rely on the relative kinematics e.g., relative positions of the neighboring agents as state observations for the local controller. In harsh working environments, the acquisition of the relative kinematics is challenged and observation losses might occur, which can be detrimental to the optimality of formation.   

In this work, observation losses in noisy environments are addressed under a distributed formation control framework. Three types of solutions are proposed to enhance the robustness which is evaluated through the improvements of tracking error, convergence speed, and smoothness of trajectories in both random and permanent loss settings. Firstly, a relative localization technique is proposed using formation itself as a spatial constraint. Secondly, a dynamic model is established for the agents entailed by a Kalman filter-based solution. Finally, a fusion of the previous two types is inspired and it exhibits superior performance than both aforementioned types individually. 

  This work not only provides means of relative localization without additional sensor data but also shares insights into coping with random or permanent graph changes for stress-based formation control systems. This could potentially lead to the exploration of formation control with subgraphs or energy-efficient sensing as future directions.

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MSc SS Thesis Presentation

Distributed Gaussian Process for Multi-agent Systems

Peiyuan Zhai

This work is focused on environmental monitoring and learning of unknown field by Gaussian Process (GP) in Multi-agent Systems (MAS). The two main problems are how to develop fully-distributed and robust algorithm to (1). optimize GP hyperparameters, and (2). aggregate GP predictions from agents.  

The state-of-the-art distributed GP hyperparameter optimization algorithm is proximal alternated direction method of multipliers (pxADMM), which requires a center station in MAS. Based on pxADMM, two fully-distributed algorithms are proposed so that the center station is no longer needed. Asynchronous behavior is also introduced into the proposed algorithms to deal with heterogeneous processing time of agents.

  Current aggregation methods are classified based on whether datasets are independent. Under independence assumption, PoE and BCM families of methods are distributed by applying discrete time consensus filter (DTCF), which is proposed to be replaced by primal-dual method of multiplier (PDMM) for faster convergence. Without independence assumption, the Nested Pointwise Aggregation of Experts (NPAE) can be distributed by NPAE-JOR in complete graph with high flooding overhead. We propose fully-distributed CON-NPAE in connected graph to eliminate flooding overhead. 

Simulation results show that the proposed hyperparameter optimization algorithms are fully-distributed at a cost of 2 to 4.5 times more iterations compared to pxADMM. The fully-dsitributed PoE and BCM based methods are accelerated, and the fully-distributed CON-NPAE makes comparable aggregations as NPAE without flooding overhead. Future work will be focused on the theoretical convergence analysis of fully-distributed pxADMM, the effect of network structure on CON-NPAE and new type of distributed NPAE based on inducing points. 

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MSc SS Thesis Presentation

Detect and Avoid for Autonomous Agents in Cluttered Environments

Mosab Diab

Autonomous agents are the future of many services and industries such as delivery systems, surveillance and monitoring, and search and rescue missions. An important aspect in an autonomous agent is the navigation system it uses to traverse the environment. Not much emphasis has been paid in the past on autonomous agent navigation in cluttered environments. Cluttered and unknown environments such as forests and subaquatic environments need to have autonomous navigation systems developed just for them due to their uncertain and changing nature.

  Path planning algorithms are used for the navigation of an autonomous agent in an environment. The agent needs to reach a target location while avoiding the obstacles it detects along the path. Such a system is called a Detect and Avoid (DAA) system and there are different implementations for it of which some are explored in this thesis.  

The Artificial Potential Fields method or APF for short is a method for mobile agent navigation which is based on generating an attractive force on the agent from the target and a repulsive force from the obstacles. This leads to the agent reaching the target while avoiding the obstacles along the way. The Classical APF (CAPF) method works for structured environments well but not for cluttered environments. The CAPF method can be replaced with a modified version where the agent is surrounded by a set of points (called bacteria points) around its current location and the agent moves by selecting a bacteria point as a future location. This method is named the Bacteria APF (BAPF) method. This selection happens through combinatorial optimization based on the potential value of each bacteria point.  

In this thesis, we propose two distinct contributions to the BAPF method. The first one being the use of an adaptive parameter in the repulsive cost function which is determined through a brute-force search. The second addition is a branching cost function that changes the value of the repulsive potential based on predefined perimeters around each obstacle. We show through simulations on densely and lightly cluttered environments that this Improved BAPF (IBAPF) method significantly improves the performance of the system in terms of the convergence to the target by almost 200% and reduced the time it takes to converge by around 25% as well as maintain the safety of the navigation route by keeping the average distance from obstacles around the same value.

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MSc SS Thesis Presentation

Search by Image: Deep Learning Based Image Visual Feature Extraction

Yanan Hu

In recent years, the expansion of the Internet has brought an explosion of visual information, including social media, medical photographs, and digital history. This massive amount of visual content generation and sharing presents new challenges, especially when searching for similar information in databases —— Content-Based Image Retrieval (CBIR). Feature extraction is the foundation of image retrieval, making research into obtaining concrete features and representations of image content a vital concern. In the feature extraction module, We first pre-process the target image and input it into a CNN to obtain feature maps for different channels. These feature maps can be aggregated into compact and global uniform descriptors by pooling. Then these global descriptors are further dimensionalised and normalized by whitening methods to obtain image feature vectors that are easy to compute and compare. In this process, the accuracy of the retrieval depends on how accurately the final feature vectors represent the meaning expressed by the target image. Therefore, various CNN network structures, pooling and whitening methods are proposed to get more concrete feature vectors.

In this thesis, our study (1) fine tunes the pre-trained CNNs, (2) optimizes the application of second-order attention information in feature map, (3) applies and compares popular feature enhancement methods in both aggregating and whitening, (4) explores how to combine all strengths, and (5) propose a new model \textit{ResNet-SOI}, which achieves 53.4(M) and 59.2(M) mAP on the challenging benchmark \textit{ROxford5k+1M} and\textit{ RParis6k+1M}, and outperforms the state-of-art methods.


MSc SS Thesis Presentation

Image-Based Query Search Engine via Deep Learning

Yuanyuan Yao

Typically, people search images by text: users enter keywords and a search engine returns relevant results. However, this pattern has limitations. An obvious drawback is that when searching in one language, users may miss results labelled in other languages. Moreover, sometimes people know little about the object in the image and thus would not know what keywords could be used to search for more information. Driven by this use case with many applications, content-based image retrieval (CBIR) has recently been put under the spotlight, which aims to retrieve similar images in the database solely by the content of the query image without relying on textual information.

To achieve this objective, an essential part is that the search engine should be able to interpret images at a higher level instead of treating them simply as arrays of pixel values. In practice, this is done by extracting distinguishable features. Many effective algorithms have been proposed, from traditional handcrafted features to more recent deep learning methods. Good features may lead to good retrieval performance, but the problem is still not fully solved. To make the engine useful in real-world applications, retrieval efficiency is also an important factor to consider while has not received as much attention as feature extraction.

In this work, we focus on retrieval efficiency and provide a solution for real-time CBIR in million-scale databases. The feature vectors of database images are extracted and stored offline. During the online procedure, such feature vectors of query images are also extracted and then compared with database vectors, finding the nearest neighbours and returning the corresponding images as results. Since feature extraction only performs once for each query, the main limiting factor of retrieval efficiency in large-scale database is the time of finding nearest neighbours. Exact search has been shown to be far from adequate, and thus approximate nearest neighbour (ANN) search methods have been proposed, which mainly fall into two categories: compression-based and tree/graph-based. However, these two types of approaches are usually not discussed and compared together. Also, the possibility of combining them has not been fully studied. Our study (1) applies and compares methods in both categories, (2) reveals the gap betweentoy examples and real applications, and (3) explores how to get the best of both worlds. Moreover, a prototype of our image search engine with GUI is available on https://github.com/YYao-42/ImgSearch

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MSc SS Thesis Presentation

Gaussian Process enhanced Distributed Particle filtering

Rui Tang

In many applications of multi-agent networks, the physical systems consist of massive nonlinear and non-Gaussian elements. Hence, in the first decade of this century, intensive research on distributed particle filters (DPFs) has been conducted to address the distributed estimation problems. For distributed algorithms, communication overhead is an important metric in terms of engineering feasibility. In previous work, the approach to distributed particle filtering relies on a parameterization of the posterior proba-bility or likelihood function, to reduce communication requirements. However, as more and more effective resampling algorithms are proposed, the dependence of particle filter performance on particle set size is greatly reduced, so this thesis attempts to explore the possi-bility of DPFs based on direct particle exchange. In this thesis, the Gaussian process enhanced resampling algorithm is used. Meanwhile, several metaheuristic optimization algorithms (i.e., genetic algorithm and firefly algorithm) are further adapted to seek the global optimal particle set to improve the estimation performance. Furthermore, all algorithms are simulated in target tracking scenarios and are evaluated from three aspects: time, space, and communication complexity.

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MSc SS Thesis Presentation

Sensor-to-Cell Height Estimation for Conductivity Estimation in Cardiac Cells

Cees Kos

The heart is one of our vital organs. It functions by periodically contracting in a rhythmic way. Sometimes, this rhythmic behaviour is affected by abnormalities in the tissue. These conditions are referred to as cardiac arrhythmias. One kind is of specific interest, called atrial fibrillation (AF). To further study AF, methods have been developed to estimate the conductivity of cardiac cells based on the measured electrical signals. In addition, other parameters of interest besides the conductivity are involved. The goal of this project was to consider one of the parameters in the electrogram (EGM) model: the sensor-to-cell height.

First, we studied the effect of the height as a parameter in the model when used for conductivity estimation. To that end, a detector was built with which we can explain the effect of all involved parameters on the ability to accurately estimate any parameters of interest.

In addition, we considered the case where the height is unknown and is estimated, thus possibly including estimation errors. The focus was on the consequences of making errors in the estimation of the height with respect to conduction block detection and conductivity estimation.

Lastly, the effort was made to estimate the height. Here, the optimisation problem of height estimation was formalised and derived as its implementable form. At first, a simplified EGM model was assumed in order to mimic and estimate cell-specific effects, i.e. the cell conductivities. Then, the height was estimated in various situations to study its behaviour and performance under different conditions. Then, also the standard EGM model was used in the same way, after which we also tested the performance of the designed algorithm in combination with existing conductivity estimation methods.


MSc SS Thesis Presentation

Analyzing dynamic functional connectivity using state-space models on mice fUS data

Ruben Wijnands

In recent years, the increase in brain research led to the development of large-scale brain imaging techniques. With large-scale brain imaging techniques, such as functional magnetic resonance imaging (fMRI), functional connectivity analyses have shown altered connectivity patterns in humans and mice with neurobiological disorders, such as autism spectrum disorder (ASD). To further investigate different mutations that contribute to ASD, a behavioral neuroscientific experiment has been performed at the neuroscientific department of Erasmus MC. During the trial, brain activity in groups of wild-type and homozygous mice is measured using functional ultrasound (fUS) while allowing for spontaneous behaviors of mice. The homozygous mice lack the SHANK2 protein, resulting in hyperactivity and autistic-like behavioral alterations associated with ASD in humans. Understanding the origin of ASD is key to providing effective treatment. However, with the introduction of novel large-scale brain imaging techniques such as fUS, new methods have to be developed that enable functional connectivity analyses. Furthermore, new insights might also be acquired from dynamic functional connectivity analyses, in which changes in functional connectivity over time are evaluated. Subsequently, the question remains if it is possible to unravel differences in brain dynamics between wild-type and homozygous mice using a dynamic functional connectivity analysis.

            First, an fUS data model is developed to model how fUS signals arise from a generative perspective. This model comprises a combination of a convolutive and a state-space model. Subsequently, inference of functional networks and their temporal dynamics can be performed. Also, a pre-processing pipeline for experimental fUS data is designed to reduce problem complexity and data cleaning. The performance of the developed methods is evaluated on the experimental data set, where a difference in brain dynamics between wild-type and homozygous mice is investigated.

            It is found that a deconvolution procedure using the non-negative least absolute shrinkage and selection operator (NNLASSO) is necessary to reconstruct the underlying activity of neural populations. After that, using the hidden Markov model (HMM) as a state-space model, it is found that functional networks and their temporal dynamics can be learned from fUS data using expectation maximization (EM). It has been discovered that the developed methods consistently decompose reconstructed neural activity into biologically plausible functional networks from experimental fUS data. Also, with 96% certainty, a difference in brain dynamics between wild-type and homozygous mice is found using this method.

            In summary, in this thesis, novel methods are developed to perform a dynamic functional connectivity analysis on experimental fUS data. Also, by performing such dynamic functional connectivity analysis for the first time on fUS data, a consistent decomposition of reconstructed neural activity into biologically plausible functional networks and a possible difference in brain dynamics between wild-type and homozygous mice are found. This research highlights the potential of fUS as a large-scale brain imaging technique in the quest to understand the origin of ASD and other neurobiological disorders.


IEEE SPS IFS-TC Webinar Series

Communication Efficient Privacy-Preserving Distributed Optimization

Richard Heusdens

Privacy issues and communication costs are both major concerns in distributed optimization in networks. There is often a tradeoff between them because encryption methods used for privacy-preservation often introduce significant communication overhead. In this talk, we discuss a quantization-based approach to achieve both communication efficiency and privacy-preserving in the context of distributed optimization. By deploying an adaptive differential quantization scheme, we allow each node in the network to achieve the optimum solution with low communication costs while keeping its private data unrevealed. The proposed approach is general and can be applied in various distributed optimization methods, such as dual ascent and methods based on operator splitting (PDMM and ADMM). We consider two widely used adversary models, passive and eavesdropping, and investigate the properties of the proposed approach using different applications and demonstrate its superior performance compared to existing privacy-preserving approaches in terms of privacy, accuracy, and communication cost.

This webinar is organized by the IEEE Signal Processing Society Information Forensics and Security Technical Committee. See under "additional information" for a registration link.

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MSc SS Thesis Presentation

Estimation of Atrial Fibre Directions Based on Epicardial Electrograms

Jordi de Vries

Being able to estimate atrial tissue conductivity parameters from epicardial electrograms is an important tool in diagnosing and treating heart rhythm disorders such as atrial fibrillation. One of these parameters is the atrial fibre direction, which is often assumed to be known in conductivity estimation methods.

In this thesis, a novel method to estimate the fibre direction from epicardial electrograms is presented. This method is based on local conduction slowness vectors of a propagating activation wave, which can be calculated from a corresponding activation map of the atrial tissue. These conduction slowness vectors follow an elliptical pattern that strongly depends on the underlying conductivity parameters. The fibre direction and conductivity anisotropy ratio can therefore be estimated by fitting an ellipse to the conduction slowness vectors.

Applying the presented method on simulated data shows that it can accurately estimate the fibre direction, and that the performance of the method depends mostly on the range of wavefront directions present in the measurement area. The main advantage of the presented method over existing methods is that it still functions in the presence of conduction blocks, as long as the surrounding tissue is approximately homogeneous.

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TU Delft Women+ In Engineering

End of year event

W+IE (Women+ in Engineering) invites you to this end of year meeting where we can enjoy drinks and snacks together and learn from our guest speakers from Qorvo WiT and TU Delft Integrity Office. We'll also learn more about gender issues by playing the Dilemma game.

 

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PhD Thesis Defence

Multiband channel estimation for precise localization in wireless networks

Tarik Kazaz

Can we reach decimeter accuracy on wireless localization?

Additional information ...


MSc SS Thesis Presentation

Link adaptation and equalization for underwater acoustic communication using machine learning

Mauries van Heteren

The underwater acoustic environment is amongst the most challenging mediums for wireless communications. The three distinct challenges of underwater acoustic communication are the low and nonuniform propagation speed, frequency-dependent attenuation and time-varying multipath propagation. To cope with these challenges, physical layer communication systems allow the selection of communication parameters based on environmental conditions and constraints. This is also known as link adaptation.

In this thesis, the frequency-repetition spread-spectrum (FRSS) physical layer is studied. Various channel parameters are used to classify the optimal FRSS format. Furthermore, different machine learning classifiers are implemented to solve the classification problem. It is determined that the output signal-to-noise ratio provides enough information to switch effectively between transmission formats. Among the implemented machine learning classifiers, the decision tree strikes a good balance between performance and computational complexity. It is shown that a small performance gain can be achieved when custom channel parameters are extracted from the estimated impulse response and the equalizer error sequence using a deep neural network.

Optimizing the equalization process is another method to better cope with difficult environmental conditions. Various adaptive filter algorithms are implemented for the decision feedback equalizer used in the FRSS receiver. The optimal algorithm parameters are found by means of algorithm unrolling. It is shown that the standard least mean squares algorithm cannot be outperformed by various other optimization algorithms that use linear or non-linear filters.

The Watermark channel simulator is used to study the performance of the link adaptation and equalization optimization solutions for a wide range of underwater channels.

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CAS MSc Midterm Presentations

IMU-based Adaptive Motion Artifact Reduction in Wearable ECG

Cesar Eduardo Cornejo Ramirez


CAS MSc Midterm Presentations

Novel View Synthesis of HDB Façade in Singapore using Neural Radiance Fields

Chuhan WANG


CAS MSc Midterm Presentations

Extreme-value Neural Networks for Weather Forcasting

Zhiyi WANG

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PhD Thesis Defence

Modelling and analysis of atrial epicardial electrograms

Miao Sun

Atrial fibrillation (AF) is a frequently encountered cardiac arrhythmia characterized by rapid and irregular atrial activity, which increases the risk of strokes, heart failure and other heart-related complications. The mechanisms of AF are complicated. Although various mechanisms were proposed in previous research, the precise mechanisms of AF are not clear yet and the optimal therapy for AF patients are still under debated. A higher success rate of AF treatments requires a deeper understanding of the problemof AF and potentially a better screening of the patients.

In order to study AF, instead of using human body surface ECGs, we use the epicardial electrograms (EGMs) obtained directly from the epicardial sites of the human atria during open heart surgery. This data is measured using a high-resolution mapping array and exhibits irregular properties during AF. Although different studies have analyzed electrograms in time and frequency domain, there remain many open questions that require alternative and novel tools to investigate AF.

Experience in signal processing suggests that incorporating the spatial dimension into the time-frequency analysis on the multi-electrode electrograms may provide improved insights on the atrial activity. However, the electrophysiologcial models for describing spatial propagation are relatively complex and non-linear such that conventional signal processing methods are less suitable for a joint space, time, and frequency domain analysis. It is also difficult to use very detailed electrophysiologcial models to extract tissue parameters related to AF fromthe high-dimensional data.

In this dissertation, we propose a radically different approach to study and analyze the EGMs from a higher abstraction level and from different perspectives to get more understanding of the characteristics of AF. We also develop a simplified electrophysiological model that can capture the spatial structure of the data and propose an efficient method to estimate the tissue parameters, which are helpful to analyze the electropathology of the tissue, e.g., cell activation time or conductivity. 

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CAS MSc Midterm Presentations

Reconstruction and Rendering of Buildings as Radiance Fields for View Synthesis

Enpu CHEN

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Signal Processing Seminar

Data Processing on Expanding Graphs with Graph Filters

Bishwadeep Das

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PhD Thesis Defence

Direction of Arrival estimation and Self-Calibration techniques using an array of Acoustic Vector Sensors

Krishnaprasad Nambur Ramamohan

The localization and characterization of sound have played a vital role in various ap­plications, ranging from noise control of machinery to battlefield awareness. Micro­phone arrays are commonly used to find sound sources, which implicitly inherits a se­ries of limitations. Alternatively, acoustic vector sensors (AVSs) have shown promising results in overcoming most of those limitations, specifically having a larger operation frequency while requiring a smaller number of sensor nodes. However, literature about this topic is still evolving and mainly focused on the theoretical aspects, disregarding most real-world limitations. This thesis extends the AVS arrays' theoretical framework for direction-of-arrival (DOA) estimation of far-field sources while considering practical constraints. Specifically, the study considers the DOA estimation problem using AVS ar­rays in three main scenarios: spatially under-sampled configurations, the presence of calibration errors, and sensors with a reduced number of channels.

The idea of spatial sampling by AVS arrays has a different interpretation compared to the equivalent acoustic pressure sensor (APS) arrays. Notably, it is possible to carry out unambiguous DOA estimation using a spatially under-sampled AVS array, which is the main topic of interest in the first part of this work. Here we study the effects of the grating lobes or spatial aliasing on the performance of DOA estimation. We will observe that this idea can also be extended to beamforming applications.

Subsequently, in the second part of this work, we consider the DOA estimation prob­lem using AVS arrays in the presence of calibration errors. First, identifiability conditions are derived for the solution to exist. Then two main classes of self-calibration approaches are proposed. The first calibration approach is array geometry independent and is based on sparse recovery techniques that lead to a one-step solver to estimate both the source DOAs and the calibration parameters jointly. Further, the extension of the proposed self­calibration approach in the presence of wide-band sources is also presented. The sec­ond calibration approach applies only to a uniform linear array (ULA) of AVSs, where the Toeplitz block structure of its covariance matrix is exploited to estimate the calibration errors followed by the estimation of the source DOAs.

In the last part of the thesis, an alternate configuration of an AVS is considered for DOA estimation with a reduced channel count. We refer to such an AVS as a uniaxial AVS (U-AVS). The DOA estimation performance using a U-AVS array is analyzed, and specifically, the impact of the extra degree-of-freedom originating from the fact that each U-AVS in the array can have arbitrary orientation is studied comprehensively. Further­more, all the analyses and proposed algorithms in this thesis are supported by real ex­perimental results performed with AVS arrays in an anechoic chamber.

To conclude, this research on AVS arrays paves the way to achieve an increased sit­uational awareness across our society; this could be either by detecting and localizing problems or threats occurring in an urban environment or assisting soldiers on the bat­tlefield to make a timely decision to achieve peace.


CAS MSc Midterm Presentations

Link Adaptation and Equalization for Underwater Acoustic Communication Using Machine Learning

Mauries van Heteren

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CAS MSc Midterm Presentations

IMU-based Adaptive Motion Artefact Reduction in Wearable ECG

Cesar Eduardo Cornejo Ramirez


CAS MSc Midterm Presentations

Finding Camera Position and Orientation using Marker-Less Computer Vision in the Catheterization Laboratory

Jinchen ZENG


MSc SS Thesis Presentation

Multiple Subbands Ranging Signals Design and Investigation on Frequency Dependence of the Subband Channel Impulse Responses within an Ultra-wideband Channel

Xiaoyao Luo

This thesis covers two topics. The first one is signal design for accurate Time-of-Arrival estimation using a number of frequency separated signals. Rather than use a full UWB band, we will using sparse subband signals spanning the full band to construct a new virtual UWB signal. To evaluate the performance of the constructed signal, Cramerrao lower bound and auto-correlation are used. And given a given fixed bandwidth the number of subbands within a 1 GHz UWB channel, optimal subbands’ allocation will be found based on the evaluation results. Our results show that when three 50 MHz subbands are used to construct a virtual 1GHz UWB signal, a lower CRLB and better auto-correlation performance can be reached when subband are close to the edges of the virtual band. However, the autocorrelation still has multiple peaks, which poses a serious challenge for accurate time estimation.

The second topic is to investigate the frequency dependence of the channel impulse response of subbands with different frequency separations. We propose a covariance calculation method to determine the frequency dependence which changes with frequency separation. To validate the method, different artificial UWB channels with distinct paths are given. The results show that covariances between the subband CIRs stay at high level when measured at the direct path and the majority interference caused by other paths can be eliminated by wider bandwidth subband. Given UWB channels measured from 5 to 10 GHz with a link-budget of 120dB, the frequency dependence of the direct path and reflections are determined, different bandwidths and frequency separations are used, the results show that the channel impulse response of the subbands will become different when measured at different center frequencies, where the difference increases with increased frequency separation of the subbands. The correlation of the direct path is maintained over larger frequency distances than that of reflected paths.

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CAS MSc Midterm Presentations

Re-ranking for Improved Image Query-Based Search

Qi Zhang

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CAS MSc Midterm Presentations

Detecting Medical Equipment in the Catheterization Laboratory using Computer Vision

Renjie Dai


CAS MSc Midterm Presentations

Distributed Gaussian Process on Multi-Agents Network for Environmental Monitoring

Peiyuan Zhai

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PhD Thesis Defence

Image Reconstruction for Low-Field MRI

Merel de~Leeuw den Bouter

This thesis presents imaging algorithms for a prototype low-field MRI system, in particular regularization, handling missing data, and deep learning approaches.

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CAS MSc Midterm Presentations

Frequency Domain Joint Estimation of HRF and Stimulus from Functional Ultrasound Data

Yitong Tao


CAS MSc Midterm Presentations

Cooperative Localization using ADS-B for Unmanned Aerial Vehicles

Xuzhou Yang


CAS MSc Midterm Presentations

Development of a Detect and Avoid Control System for Autonomous Agents in Cluttered Environments using the Artificial Potential Field Method

Mosab Diab

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MSc SS Thesis Presentation

Estimating atrial activity in epicardial electrograms: a beamforming perspective

Tijs Moree

The most common serious heart rhythm disease is atrial fibrillation. It is not fatal on its own but does increase the risk of heart failures and strokes. There is little understanding about the mechanisms behind the disease, so more insight is desired. Using an array of electrodes, measurements are being performed of the electrical atrial activity directly on the heart tissue. These signals are, however, not clean and suffer from far-field interference coming from the ventricles.

During normal sinus rhythm the atrial and ventricular activities are separated in time and easy to distinguish. In case of atrial fibrillation this is not always the case. Luckily, there is a major difference between both signals: the ventricular signal comes from far away and arrives therefore approximately simultaneously at all electrodes. A simple, but effective way to remove this ventricular activity is to use a bipolar electrode. It produces the difference between two normal unipolar electrodes, thus removing the common ventricular signal component.  

The bipolar electrode, however, distorts the atrial signal component, which in some orientations can even lead to removing it altogether. This bipolar electrode is known as a differential beamformer from the field of array signal processing. There are more complex beamformers that can keep the atrial component undistorted and therefore produce better results than the bipolar electrode.  

This thesis proposes a Fourier-domain signal model for all available electrodes relying on an atrial and ventricular transfer function. It is possible to estimate these transfer functions from the data blindly. Three beamformers are derived utilizing the signal model and the transfer functions. The bipolar electrode is extended to multiple electrodes like the other beamformers as well.

 

This thesis proposes a Fourier-domain signal model for all available electrodes relying on an atrial and ventricular transfer function. It is possible to estimate these transfer functions from the data blindly. Three beamformers are derived utilizing the signal model and the transfer functions. The bipolar electrode is extended to multiple electrodes like the other beamformers as well.  

Experiments with simulated data show that the complex beamformers indeed keep the atrial activity undistorted and are still able to remove the ventricular activity effectively when using multiple electrodes, except for very complex data where the signal model is not valid. For low numbers of electrodes the beamformers are not useful, they hardly remove the ventricular activity while keeping the atrial component undistorted, where the bipolar electrode does the opposite.  

The electrograms are also used to estimate local activation times of the cells underneath the electrodes which says something about the health of the cardiac tissue. Besides the mentioned filtering, this thesis proposes a method to estimate those moments in time by looking at the time-domain version of the atrial transfer function, called the atrial impulse response. For simple data, it performs well compared to state-of-the-art methods, but for more complicated data, it does not.


MSc SS Thesis Presentation

Towards Gridless Sound Field Reconstruction

Ids van der Werf

Sound pressure varies over space and time. Knowledge about this exact behavior has many applications, e.g., room compensation, dereverberation and sound field reconstruction. Inside enclosures, the sound field is influenced by the surroundings, such as the geometry of the enclosure and the materials used. Reconstructing a satisfying sound field in the whole enclosure by extrapolating from few measurements is thus not an obvious task.

The sound field in a room can be represented by a weighted sum of room modes. Thus, we can estimate the room modes and compute the sound field from it. To estimate the room modes, compressive sensing literature uses on-the-grid, sparse reconstruction methods. However, these on-the-grid methods are known to suffer from basis mismatch.

In this work, we investigate the use of a gridless framework for estimating room modes using atomic norm minimization, a gridless method. The advantage of this approach is that it does not suffer from this basis mismatch problem. We derive a bound for the sound field reconstruction problem in a one-dimensional room with rigid walls and relate this to the frequency separation that is required by the atomic norm. We conclude that for perfect reconstruction of the room modes based on the investigated gridless approach, additional prior knowledge about the signal model is required. For example, knowledge about the shape of the room modes can be used. We show how recovery is possible in a one-dimensional setting by exploiting both the structure of the sound field and the acquisition method.

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CAS MSc Midterm Presentations

Personnel Activities Observation in the Catheterization Laboratory

Yingfeng Jiang


CAS MSc Midterm Presentations

Autonomous Landing of an UAV

Siddhy Ganesh Shetty

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CAS MSc Kick-off Presentations

Machine Learning of Ultrasound Data: Cardiovascular Parameters Detection Using Carotid Artery Ultrasound Measurements

Zhuangzhuang Yu


CAS MSc Midterm Presentations

The Impact of Jamming and Spoofing on GNSS Signals

Pim Jansen


CAS MSc Midterm Presentations

Energy-Efficient Seizure Detection for Wearable EEG

Xiaoning Shi

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CAS MSc Midterm Presentations

Distributed Particle Filtering

Rui TANG

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CAS MSc Midterm Presentations

Modular Inductive Biases: To What Extent Can They Improve Neural Reasoning and Generalization?

Nan Lin

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CAS MSc Midterm Presentations

Distributed System Design for Space-based Correlators

Brenda Hernandez Perez


Signal Processing Seminar

Single-Pulse Estimation of Target Velocity on Planar Arrays

Costas Kokke

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CAS MSc Midterm Presentations

Relative Affine Localization for Robust Formation Control

Zhonggang LI


CAS MSc Midterm Presentations

Search by Image: A Digital History Search Engine in Online Image Repositories

Yanan HU


MSc SS Thesis Presentation

Few-shot emotion recognition using intelligent voice assistants and wearables

Mihir Kapadia

Emotion Recognition is one of the vastly studied areas of affective computing. Attempts have been made to design emotion recognition systems for everyday settings. The ubiquitous nature of Intelligent voice assistants (IVAs) in households, make them a great anchor for the introduction of emotion recognition technology to consumers. The existing systems lack such pipelines and rely on dictionary-based architectures in their design. Further, these systems lack conversational properties and are merely an extension of information retrieval engines.

In this setting, we propose to introduce and develop emotion recognition pipelines that are suited to the interactions, common with these IVAs. To augment the existing emotion recognition pipelines which rely on audio information, we look at physiological information derived from wearables. Our proposed model uses multimodal embeddings with a Siamese Network to achieve the task of emotion recognition from a few samples. Physiological signals of blood volume pulse (BVP) and electrodermal activity (EDA) are used as additional input embeddings to two audio embeddings arising from the speech samples. We employ the state-of-the-art training schedules for Siamese Networks, which use a very limited amount of training on support datasets via sample pair comparisons. The performance of the model is evaluated using weighted binary accuracy and f1 scores.

The proposed model is applied on two datasets that denote two unique experimental settings - the K-EmoCon dataset and RECOLA dataset. We demonstrate an improvement in the state-of-the-art accuracy with the K-EmoCon dataset with accuracies of 63.97% and 66.91% on arousal and valence dimensions respectively. Further, on the RECOLA dataset, the model performs moderately well with 53.81% and 53.87% respectively for arousal and valence dimensions. In addition to this, we present a study of the effects of variation of available support set for training from the dataset. We make some salient observations for these experiments across individual participants and also identify how the label distributions affect the performance of the model. Further, we investigate the impact of real-world noise samples from the DEMAND dataset on the two datasets. We observe that the proposed model is robust and performs sustainingly well even in the presence of imputed noise.

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CAS MSc Midterm Presentations

Distributed Optimization using Stochastic PDMM: Convergence, Transmission Losses and Privacy

Sebastian Jordan


CAS MSc Kick-off Presentations

IMU-based Adaptive Motion Artifact Reduction in Wearable ECG

Cesar Eduardo Cornejo Ramirez


Signal Processing Seminar

High Performance Control System Architecture with an Output Regulation Theory-based Controller and Two-Stage Optimal Observer for the Fine Pointing of Large Scientific Satellites

Valerio Fogliano

This study describes the functional architecture and the control law of an attitude control system designed for large scientific satellites with high accuracy and stability pointing requirements, such as those necessary during the fine pointing operations. The satellite dynamical motion has been reproduced by an accurate software simulator implementing the error models of the sensors and actuators currently being part of the on-board system configuration of state-of-the-art space missions. Moreover the main disturbances affecting the satellite motion during its mission are considered. The control system architecture includes an optimal estimator, the well-known Multiplicative Extended Kalman Filter, modified for the time delay correction of the star tracker measurements by means of a forward state propagation for a correct implementation of measurement update. This estimator is cascaded with a second one specifically used for disturbance acceleration estimation. The estimation task is then coupled with a linear optimal control algorithm combined into an error feedback output regulator, based on a space system design involving the exosystems of the disturbances and reference profiles. The reported control system has analytically been proved to be globally stable and the reported simulations highlight the high performances of the algorithm, capable of fully satisfying a stringent pointing error requirement. Lastly, the proposed attitude control system is shown to be capable of tracking not only step reference trajectories but also sinusoidal time varying signals within a disturbed environment and promises robust performances also in case of possible actuators faults.


CAS MSc Information Market

The CAS group

If you are a first year MSc EE student (Signals & Systems, WiCos), then on Thu 24 March, come visit the CAS group at the 17th floor on the EWI tower, meet the professors, and learn about graduation topics for next year.


Signal Processing Seminar

RL-based Path Planning and Coverage for Autonomous UAVs in Unknown Environments

Gianpietro Battocletti

Unmanned Aerial Systems (UASs) have become a relevant sector in the aerospace industry. In the last decade, the increase in the capabilities of Unmanned Aerial Vehicles (UAVs), paired with a drop in their price, has led them to be used in many different applications where they are employed for their versatility and efficiency. A challenge that is being addressed in this field is that of autonomous UAVs fleets, i.e., the coordinated use of multiple UAVs to perform a common task. A particularly interesting application of UAV fleets is their use in the exploration and mapping of unknown or critical environments. This topic brings with it a significant number of challenges, from the design of the policy used to coordinate the fleet to the path planning algorithm that each UAV uses to move in the environment while exploring it. In my research, I studied and implemented a Reinforcement Learning (RL)-based approach for the cooperative exploration of unknown environments by a fleet of UAVs is presented. Different approaches have been considered and compared. A Reinforcement Learning-based approach has been developed taking inspiration from some of the other methods studied. Two RL agents are trained to address the exploration problem: the first has the task of coordinating the coverage task, optimizing the way the UAVs spread in the unknown area by assigning some waypoints to them. The waypoints are placed in order to optimize the distribution of the fleet and to maximize the exploration process efficiency. The second RL agent is a path planning algorithm and is used by each UAV to move in the environment to reach the region pointed by the first agent. The combined use of the two agents allows the fleet to coordinate in the execution of the exploration task.


CAS MSc Midterm Presentations

Analyzing Dynamic Functional Connectivity using State-Space Models on Mice fUS Data

Ruben Wijnands


CAS MSc Midterm Presentations

Link Adaptation for Underwater Acoustic Communication using Machine Learning

Mauries van Heteren

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CAS MSc Midterm Presentations

Image Search Engine Based on Deep Learning for Digital History

Yuanyuan Yao

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CAS MSc Kick-off Presentations

ICA Through Diagonalization of the Implicitly Formed Higher Order Cumulant Tensor

Pierre-Antoine Denarié

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CAS MSc Kick-off Presentations

Detect and Avoid Control System for Autonomous Drones in Cluttered Environments

Mosab Diab

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CAS MSc Midterm Presentations

Improving the Estimation of Atrial Activation Times Using Spatial Information

William Hunter

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Signal Processing Seminar

Sampling and Reconstruction in IoT Networks on Graphs

Josefine Holm


Microelectronics Colloquium

An inclusive EEMCS faculty: An emphatic approach.

Jorge Martinez

Our faculty consists of a vibrant and diverse community. Diversity is a catalyst that allows us to achieve broad knowledge, and a base upon we can drive scientific innovation and improve education.

Moreover, diversity is one of the core values of TUDelft and our faculty and comes with great responsibility. Without equality and inclusion diversity becomes an empty gesture. But realising a safe, equal and inclusive environment requires the participation of everyone in our community. It starts by having a dialog, stablish communication channels at different levels, and debunking taboos with respect to the visible and invisible differences among each other and our students. An empathic approach for this process can play a key role in realising this ambition.

In this colloquium Jorge talks about his experience within EDIT: EEMCS Diversity & Inclusion Team. Join us to know more about EDIT, and for an informal discussion on the current advancements on addressing issues like harassment, discrimination, and gender (in)equality. Or if you want to know what are the channels and means within our faculty and our University to reach for advice or help in case you encounter any issues related to these important topics.

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Signal Processing Seminar

Sparsity-Based Vascular Ultrasound Imaging Through Compressive Spatial Coding

Didem Dogan Baskaya


CAS MSc Kick-off Presentations

Extreme Weather Forecasting Using Deep Generative Model

Haoran Bi

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CAS MSc Kick-off Presentations

Truncated Array Sound Field Synthesis

Michael Kraaijeveld


CAS MSc Midterm Presentations

Estimation of Atrial Activity using Beamforming in Epicardial Electrograms

Tijs Moree


CAS MSc Kick-off Presentations

Energy-Efficient Seizure Detection for Wearable EEG

Xiaoning Shi

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CAS MSc Kick-off Presentations

Cooperative Localization of Drones using ADS-B Data

Xuzhou Yang


CAS MSc Kick-off Presentations

Distributed Gaussian Process for Multi-Agents System

Peiyuan Zhai

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CAS MSc Kick-off Presentations

Detecting Medical Instruments in Surgical Nets with Deep Learning

Renjie Dai


CAS MSc Kick-off Presentations

Extreme-Value Deep Generative Models for Weather Forecasting

Maksym Kyryliuk


CAS MSc Kick-off Presentations

Link Adaptation for Acoustic Underwater Communication Using Machine Learning

Mauries van Heteren

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Signal Processing Seminar

Introduction

Yanbo Wang

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CAS MSc Kick-off Presentations

Extreme-Value Neural Networks for Weather Forecasting

Zhiyi WANG

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CAS MSc Kick-off Presentations

Distributed Particle Filtering

Rui TANG

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CAS MSc Midterm Presentations

Towards Sustainable Satellite Swarms

Calum Turner


CAS MSc Midterm Presentations

Towards Gridless Sound Field Reconstruction

Ids van der Werf

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CAS MSc Kick-off Presentations

3D Person Tracking in the Catheterization Laboratory

Yingfeng Jiang


CAS MSc Kick-off Presentations

Autonomous Landing of an UAV

Siddhy Ganesh Shetty

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CAS MSc Kick-off Presentations

Frequency Domain Joint Estimation of HRF and Stimulus from Functional Ultrasound Data

Yitong Tao


MSc SS Thesis Presentation

A Decentralized Key Management System for the European Railway Signalling System

David Kester


MSc Biomedical Engineering Thesis Presentation

An Expanded IPFM Model for Heart Rhythm Analysis

Arthur Kordes

Atrial Fibrillation affects millions of people worldwide. It is associated with an impaired quality of life and an increased risk of stroke, cardiac failure and mortality. Treatments exist, but early detection and treatment is crucial, due to the progressive nature of the disease. Algorithms can help with early detection.

Machine learning algorithms are commonly trained to diagnose based on ECG data, but the interpretability is low. A physiological model that simulates the heart gives more insight into the situation of the patient. Current approaches, like the IPFM model, simulate only the SA node and generate RR intervals as output, while completely neglecting the interaction between the AV and SA node. By using an IPFM model and including the AV node as well, an extended and more accurate physiological model was built to more accurately detect Atrial Fibrillation. The AV node model is able to estimate PR intervals when the P waves are annotated. This result shows that the model extension is able to capture information about the signal conduction.

When the SA node model and the AV node model are cascaded and only the R peaks are considered, the classification accuracy does not improve compared to the SA node model alone. The R peaks alone do not contain sufficient information for accurate parameter estimation. The parameters governing the behavior of the AV node seem different for NSR compared to AF, but more data is needed to confirm this. The ability of the model to predict PR intervals gives hope that the inclusion of P wave data should improve the performance of the classification with the extended physiological model.


MSc CE Thesis Presentation

Neuromorphic computing application of emerging memory technologies for spiking neural networks

Jan Maarten Buis

Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the spiking neural network (SNN). In addition to being used for memory, memristive memory can be read out passively and thus also replaces digital-to-analog circuitry.

In this thesis, two solutions are proposed: one is based on a generalized memristor, the other is based on ferroelectric memory. Both solutions are implemented and simulated in SystemC AMS and tested with a spiking neural network (SNN). As a final test, both memory solutions are integrated into a full-sized SNN and simulated against the MNIST dataset. The simulation results validate the capabilities of memristive and ferroelectric memory when it comes to providing a sensible weight storage solution for neuromorphic systems.

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CAS MSc Kick-off Presentations

Finding Camera Position and Orientation using Marker-Less Computer Vision in the Catheterization Laboratory

Jinchen ZENG


Signal Processing Seminar

Nonlinear and Time-Varying Aspects of the Functional Ultrasound Response

Aybüke Erol

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CAS MSc Kick-off Presentations

The Impact of Jamming and Spoofing on GNSS Signals

Pim Jansen


CAS MSc Kick-off Presentations

An image processing solution to building inspection

Chuhan WANG


MSc SS Thesis Presentation

Analyzing Functional Ultrasound Images of the Brain Using Tensor Decompositions

Arda Kaygan

Functional ultrasound (fUS) is a neuroimaging modality that offers high spatial and temporal resolution while also providing portability. In this thesis, neuroimaging data acquired with fUS at Center for Ultrasound and Brain imaging at Erasmus MC (CUBE) is processed. Due to the fact that fUS data is inherently multidimensional, we propose using tensor decompositions, tensors here referring to generalizations of matrices, for processing of fUS data.

We define two main research questions regarding fUS data analysis. First, for compressing the large-scale raw beamformed fUS data, we apply sequentially truncated multilinear singular value decomposition. This compression method is compared against ensemble averaging used in the conventional pipeline, and shown to provide a higher compression rate while preserving more temporal resolution for specific ranks. Furthermore, it is observed to denoise the data, resulting in a more precise extraction of the active region of Superior Colliculus using correlation maps.

Secondly, in order to investigate the advantage of multi-slice processing that incorporates 3-D information, blind-source separation methods are applied to single slice and two-slice fUS recordings. After applying independent component analysis (ICA) to the matricized data as a benchmark method, block termdecomposition (BTD) is used as a way of processing the data as it is, in its natural 3-D structure without vectorization. Through a simulation study, it is shown that the method is able to separate two images even when using a rank that is lower than the true rank, as well as in noisy conditions. Subsequently, BTD is applied to real 4-D fUS data formed by concatenation of slices in a new dimension. However, this method is seen to perform worse than single slice ICA in terms of extracting the active regions. In order to amplify common information between slices, a new 3-D data structure is then formed by summing the fUS data of two slices. For extraction of this common information, a BTD is then applied to the aggregate 3-D data. The findings of this decomposition reveal that both taking a longer portion of single slice data and incorporating the second slice helps to achieve better results.

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CAS MSc Kick-off Presentations

Developing a Data-Driven Model to Predict to the Noise Attenuation Level in Earplugs

Stephy Annie Curie Rakesh Arya


Signal Processing Seminar

Faster-Than-Nyquist Signaling Short-Packet Communication

Mostafa Mohammadkarimi

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CAS MSc Kick-off Presentations

Sparse Recurrent Independent Mechanisms for Vision Tasks

Nan Lin

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MSc CE Thesis Presentation

Spiking CA-CFAR Implementation for Radar Target Detection

Bastiaan van Otterloo

Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a rangedoppler image that distinguishes each detected object by range and velocity to process radar data. A target detection algorithm is used to filter noise and unwanted reflections. Each target can be in a region with different noise levels; a simple threshold will yield false positives or miss detections depending on its value. To solve this problem, a Constant False Alarm Rate or CFAR is desirable.

A CFAR detector estimates the noise surrounding each target and has a dynamic threshold based on this. Spiking Neural Networks are the third generation of Artificial Neural Networks where, instead of continuous signals, the input is encoded into trains of spikes over time. These networks have a potentially efficient hardware implementation instead of the older generation Artificial Neural Networks and could run directly at the sensor edge, lowering latency and power consumption.

This thesis will explore a Spiking Cell Averaging CFAR implementation and attempt to use its desirable properties like a temporal average over multiple radar frames, mimicking the non-coherent integration sometimes done in radar processing. It is shown that some configurations will behave similarly in a simulated environment with additive white Gaussian noise.


MSc SS Thesis Presentation

Investigation of focal epilepsy using graph signal processing

Gaia Zin

Epilepsy is one of the most common neurological disorders worldwide. Its manifestations, the seizures, are due to a group of neurons’ abnormal and synchronous activity. The unpredictable nature of these events hinders the quality of life of those affected. In particular, focal seizures show a localized onset of abnormal activity and are the most common ones. Correct detection of the episodes can help clinicians to give the best medical treatments. This research project arises from the need to have automatic algorithms for seizure detection with a high number of correctly detected seizures for low false alarm rates.

Recent studies have shown disorganization in how brain areas interact with each other before and during a seizure. We decided to model this change in connectivity patterns by inferring graphs from EEG recordings of epileptic patients. We work with seventeen subjects suffering from focal epilepsy, and we build, for each of them, a graph of the activity preceding (preictal) and during (ictal) a seizure. After that, we exploit techniques from graph signal processing to build a detector for seizures. Last, we analyze the density of connections of the inferred graphs to indicate the seizure onsets. 

The obtained results are unsuitable for real-life applications, but they are a starting point for further research. Furthermore, we find that most the proposed ictal or preictal graphs show fewer connections in the nodes involved with the seizure onset.

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MSc CE Thesis Presentation

Hardware Spiking Neural Network based Sbox AES

Hanyu Ma

Hardware cryptographic algorithm implementation is easy to attack by side-channel attacks. The power-based side-channel attacks are powerful among several side-channel attacks. This attack methods use the relationship between the leakage model and power traces to reveal the secret key. Some existing countermeasures like mask and hide can protect the algorithms from attacking. However, they can not break the relationship between power traces and the leakage model. Based on the property of the neural network, the linear relationship can be easily broken. Furthermore, the spiking neural network is more hardware-friendly than a conventional neural network. The design replaces the sbox in AES with a pipeline spiking neural network-based sbox and implements it in hardware. The help of the FPGA attack platform demonstrates that the proposed design can resist DPA, CPA, Template Attacks, and Deep Learning-based attacks.


CAS MSc Kick-off Presentations

Robust Formation Maneuvering of Multi-agent Systems

Zhonggang LI


CAS MSc Kick-off Presentations

Search by Image: A Digital History Search Engine in Online Image Repositories

Yanan HU


CAS MSc Kick-off Presentations

Crack Detection for Building Inspection Using UAV with Image Alignment

Enpu CHEN

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CAS MSc Kick-off Presentations

Analyzing Dynamic Functional Connectivity Using State-Space Models

Ruben Wijnands


Signal Processing Seminar

Low Complex Accurate Multi-Source RTF Estimation

Changheng Li

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CAS MSc Midterm Presentations

Frequency Dependence of the Impulse response in an Ultra-wideband Channel

Xiaoyao Luo

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CAS MSc Kick-off Presentations

Sound Field Recovery by Estimating Room Modes Off-the-Grid

Ids van der Werf

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CAS MSc Kick-off Presentations

Image Search Engine Based on Deep Learning for Digital History

Yuanyuan Yao

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Microelectronics Colloquium

On my personal journey into artificial intelligence

Justin Dauwels

In this presentation, I will start with a brief introduction to artificial intelligence (AI). I will then elaborate on two types of AI approaches that our research team is investigating: graphical models and neural networks. Next I will summarize some of the main research results of our group. I will review some of the applications of AI that we have been working on over the years, and will present some of our future research plans. I will also say a few words about the spin-off companies that have emerged from our research group. At last, I will conclude with a few thoughts on the potential impact of AI on society and will formulate a few important open research questions in the field of AI.

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PhD Thesis Defence

Atrial Fibrillation Fingerprinting

Bahareh Abdi

Atrial fibrillation (AF) is a common age-related cardiac arrhythmia. AF is characterized by rapid and irregular electrical activity of the heart leading to a higher risk of stroke and heart failure. During AF, the upper chambers of the heart, called atria, experience chaotic electrical wave propagation. However, despite the various mechanisms introduced in the literature, there is still an ongoing debate on a precise and consistent mechanism underlying the initiation and perpetuation of AF. Some studies show that AF is rooted in impaired electrical conduction and structural damage of atrial tissue, known as electropathology. Atrial electrograms (EGMs) recorded directly from heart’s surface, provide an important diagnostic tool to localize and quantify the degree of electropathology in the tissue. However, the analysis of the electrograms is currently constrained by the lack of suitable methods that can reveal the hidden electrophysiological parameters of the tissue. These parameters can be used as local indication of electropathology in the tissue. We believe that understanding AF and improving AF therapy starts with developing a proper forward model that is accurate enough (from a physiological point of view) and simultaneously simple enough to allow for subsequent parameter estimation. Therefore, the main focus of this thesis is on developing a simplified forward model that can efficiently explain the observed EGM based on AF relevant tissue parameters. An initial step before performing any analysis on the data is to remove noise and artefacts. All atrial electrogram recordings suffer from strong far-field ventricular activities (VA). Therefore, as the first step, we propose a new framework for removal of VA from atrial electrograms, which is based on interpolation and subtraction followed by low-rank and sparse matrix decomposition. The proposed framework is of low complexity, does not require high resolution multi-channel recordings, or a calibration step for each individual patient. In the next step, we develop a simplified electrogram model. We represent the model in a compact matrix form and show its linear dependence on the conductivity vector, enabling the estimation of this parameter from the recorded electrograms. The results show that despite the low resolution and all simplifying assumptions, the model can efficiently estimate the conductivity map and regenerate realistic electrograms, especially during sinus rhythm. In the next contribution of this dissertation, we propose a new approach for a better estimation of local activation times for atrial mapping by reducing the spatial blurring effect that is inherent to electrogram recordings using deconvolution. Employing sparsity based regularization and first-order time derivatives in formulating the deconvolution problem, improved performance of transmembrane current estimation is obtained. In the final part, we focus on translating our findings from research to clinical application. Therefore, we studied the effect of electrode size on electrogram properties including the length of the block line observed on the resulting activation map, percentage of observed low voltage areas, percentage of electrograms with low maximum steepness, and the number of deflections in the recorded electrograms.

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MSc SS Thesis Presentation

Temporal Synchronization of Sensors

Tanmay Manjunath

Advanced automotive vehicles are based on the real-time fusion of an increasing number of automotive sensors. For precise fusion of different sensors, measurements need to be synchronized both temporally and spatially. This thesis aims to design a hardware temporal synchronization block as part of the PRISTINE systolic array accelerator project for multi-sensor data fusion. In this process, we study and address several temporal sensor synchronization issues that are characteristic of the considered system as well as any other typical sensor fusion system. First and foremost, we handle the problem of estimating the actual time of sensor measurement by exploring well-known filtering techniques such as Kalman, mean and median filters. A suitable filter is selected for implementation based on the statistical characteristics of the observed sensor cycle times, the complexity of the filters and the quality of obtained estimates. Next, we address the issue of reconstructing incoming sensor data streams according to the estimated sensor measurement times while maintaining minimal latency and synchronization error by employing an adaptive stream buffering technique utilized in distributed multimedia systems. An analysis of the effects of the stream synchronization algorithm’s parameters on buffering latency and synchronization error was presented. Finally, the above synchronization solution was efficiently implemented on hardware by making certain modifications and design decisions to the algorithm. A method to evaluate the whole temporal synchronization process is proposed and the obtained results on real sensor data are presented.

https://tudelft.zoom.us/j/99844909465?pwd=OEtwbCtuS2xzdTg2Sk5lQzNOdVp2Zz09 Meeting ID: 998 4490 9465 Passcode: 375074


MSc SS Thesis Presentation

Adaptive Graph Partition Methods for Structured Graphs

Yanbin He

Graphs can be models for many real-world systems, where nodes indicate the entities and edges indicate the pairwise connections in between. In various cases, it is important to detect informative subsets of nodes such that the nodes within the subsets are 'closer' to each other. For example, in a cellular network, determining appropriate node subsets can reduce the operation costs. A subset is usually called a cluster. This leads to the graph clustering problem. Furthermore, plenty of systems in the real world are changing over time, and consequently, graphs as models vary with time as well. It is thus also important to update the clusters when the graph changes.

In this thesis work, we studied two problems from the cellular network background. We needed to partition graphs that have certain structures and cluster their nodes to minimize certain cost functions. In the first problem, we partitioned a bipartite graph by minimizing the so-called MinMaxCut cost function, while in the second problem, we partitioned a structured graph by minimizing the so-called Modified-MinMaxCut cost function. The structural property of the graph is incorporated in defining this new cost function. The solutions we proposed are under the framework of spectral clustering, where one relies on the eigenvectors of the graph matrices, e.g., the Laplacian matrix or the adjacency matrix, and any clustering algorithm, e.g., K-means, to partition nodes into disjoint clusters.

Furthermore, for the time-variant graph, we decomposed the problem into two steps. First, we transformed the variations in the graph topology into perturbations to the graph matrices. Then we transformed the update of the clusters into an update of the (generalized) eigenvectors of these graph matrices. We utilized matrix perturbation theory to update the generalized eigenvectors and then update the clusters. Our simulations showed that on synthetic data, the proposed method can efficiently track the eigenvectors and the clusters generated by the updated eigenvectors have almost the same cost function value as that of exact computation.

https://tudelft.zoom.us/j/95884057579?pwd=YXkzY0lCRVZuRmsvTzl3NW9xRzZ3QT09

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CAS MSc Midterm Presentations

Few-Shot Learning Using Speech and Physiological Signals for Emotion Recognition for Intelligent Voice Assistants

Mihir Kapadia

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CAS MSc Kick-off Presentations

Improving the Accuracy of the Image Search Engine for Digital History

Qi Zhang


Signal Processing Seminar

Finite Impulse Response Simplicial Filters

Maosheng Yang


CAS MSc Kick-off Presentations

Improving the Estimation of Local Activation Time in Atrial Electrograms

William Hunter


CAS MSc Kick-off Presentations

Removing Far-Field Artifacts from Atrial Electrograms Using Temporal and Spatial Information

Tijs Moree


MSc SS Thesis Presentation

Targetless Camera-LiDAR Calibration for Autonomous Systems

Bichi Zhang

In recent decades, the field of autonomous driving has witnessed rapid development, benefiting from the development of artificial intelligence-related technologies such as machine learning. Autonomous perception in driving is a key challenge, in which multi-sensor fusion is a common feature. Due to the high resolution and rich information, the camera is one of the core perceptual sensor in autonomous systems. However, the camera provides no knowledge on distance (or depth), which is insufficient for the requirements of autonomous driving. On the other hand, LiDAR provides accurate distance measurements, however the information is sparse. The complementary characteristics of cameras and LiDAR have been exploited over the past decade for autonomous navigation. In order to be able to fuse the camera and LiDAR sensor system jointly, an efficient and accurate calibration process between sensors is essential. Conventional methods for calibrating the camera and LIDAR rely on deploying artificial objects, e.g., checkerboard, on the field. Given the impracticality of such solutions, targetless calibration solutions have been proposed over the past years, which require no human intervention and are readily applicable for various autonomous systems, e.g., automotive, drones, rovers, and robots.
In this thesis, we review and analyze several classic targetless calibration schemes. Based on some of their shortcomings, a new multi-feature workflow called MulFEA (Multi-Feature Edge Alignment) is proposed. MulFEA uses the cylindrical projection method to transform the 3D-2D calibration problem into a 2D-2D calibration problem and exploits a variety of LiDAR feature information to supplement the scarce LiDAR point cloud boundaries to achieve higher features similarity compared to camera images. In addition, a feature matching function with a precision factor is designed to improve the smoothness of the objective function solution space and reduce local optima. Our results are validated using the open-source KITTI dataset, and we compare our results with several existing targetless calibration methods. In many different types of roadway environments, our algorithm provides more reliable results regarding the shape of the objective function in the 6-DOF space, which is more conducive for the optimization algorithms to solve. In the end, we also analyze the shortcomings of our proposed solutions and put forward a prospect for future research in the field of joint camera-Lidar calibration algorithms.

This work is part of the EU ADACORSA project co-hosted by the TU Delft CAS group (link: https://cas.tudelft.nl/Research/project.php?id=160&pid=59)

The zoom link for the defense is https://tudelft.zoom.us/j/96202579105

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CAS MSc Midterm Presentations

Loudspeakers as Recording Devices in Public Address Systems

Tobias Roest

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Signal Processing Seminar

Information Theoretic Rank Estimation in Dynamic Contrast Enhanced Ultrasound Sequences

Metin Çalış

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CAS MSc Thesis Presentation

Hybrid Posit and Fixed Point Hardware for Quantized DNN Inference

Zep Kleijweg

The recently introduced posit number system was designed as a replacement for IEEE 754 floating point, to alleviate some of its shortcomings. As the number distribution of posits is similar to the data distributions in deep neural networks (DNNs), posits offer a good alternative to fixed point numbers in DNNs: using posits can result in high inference accuracy while using low precision numbers. The number accuracy is most important for the first and last network layers to achieve good performance. For this reason, these are often computed using larger precision fixed point numbers compared to the hidden network layers. Instead, these can be computed using low precision posit, to reduce the memory access energy consumption and the required memory bandwidth. The hidden layer computation can still be performed using cheaper fixed point numbers.
An inference accuracy analysis is performed to quantify what the effect of this approach is on the VGG16 network for the ImageNet image classification task. Using 8 bit posit for the first and last network layer instead of 16 bit fixed point is shown to result in a top-5 accuracy degradation of only 0.24%. The hidden layers are computed using 8 bit fixed point in both cases.
The design of a parameterized systolic array accelerator performing exact accumulation is proposed that can be used in a scale-out system along with fixed point systolic array tiles. To increase hardware utilization, a hybrid posit decoder is designed to enable fixed point computation on the posit hardware. Using this hardware, the entire network can be computed using 8 bit data, instead of using 16 bits for some layers. This reduces energy consumption and the complexity of the memory hierarchy


CAS MSc Midterm Presentations

Forecasting Multidimensional Graph Signals Using Graph Filters

Jelmer van der Hoeven


CAS MSc Midterm Presentations

Analyzing Functional Ultrasound Images of the Brain Using Tensor Decompositions

Arda Kaygan

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CAS MSc Midterm Presentations

A Physiological Model for Heart Rate Analysis

Arthur Kordes


CAS MSc Midterm Presentations

The Area of a Unipolar Electrogram to Identify the Arrhythmogenic Substrate

Eris van Twist

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CAS MSc Thesis Presentation

Sound Zones with a Cost Function based on Human Hearing

Niels de Koeijer

With the aid of an array of loudspeakers, sound zone algorithms seek to reproduce multiple distinct zones of audio inside an enclosure. Typical approaches determine the loudspeaker inputs by optimizing over a cost function that models the sound pressure inside the enclosure. However, recent methods propose cost functions that include a perceptual model of the human auditory system, which further models the perception of sound. This thesis investigates such an approach by proposing a framework within which sound zones are constructed through optimization over a perceptual model. The framework is used to propose two perceptual sound zone algorithms: unconstrained and constrained perceptual pressure matching. Simulations of the proposed algorithms and a reference algorithm are presented to determine the benefits of including auditory-perceptual information in sound zone algorithms. From this, it is found that the unconstrained perceptual approach outperforms the reference in terms of various perceptual measures. In addition, it is found that adding perceptual constraints to the optimization problem allows for control of sound zones which correlates well with other perceptual quality measures.


PhD Thesis Defence

In-pixel temperature sensors for dark current compensation of a CMOS image sensor

Accel Abarca Prouza

This thesis describes the integration of temperature sensors into a CMOS image sensor (CIS). The temperature sensors provide the in-situ temperature of the pixels as well as the thermal distribution of the pixel array. The temperature and the thermal distribution are intended to be used to compensate for dark current affecting the CIS. Two different types of in-pixel temperature sensors have been explored. The first type of temperature sensor is based on a substrate parasitic bipolar junction transistor (BJT). The second type of temperature sensor that has been explored is based on the nMOS source follower (SF) transistor of the same pixel. The readout system that is used for the temperature sensors and for the image pixels is based on low noise column amplifiers. Both types of in-pixel temperature sensors (IPTS) have been designed implementing different techniques to improve their accuracy. The use of the IPTSs has been proved by measuring three prototypes chips. Also, a novel technique to compensate for the dark current of a CIS by using the IPTS has been proposed.

For those who cannot attend, you can follow it by using this link:
https://collegerama.tudelft.nl/mediasite/play/84dc9775142d44db81aa38e5532c67ca1d


MSc SS Thesis Presentation

Time Synchronization for Anchorless Satellite Networks

Felix Abel

In this thesis, we propose a new class of pairwise frequency and multi-domain time synchronization and ranging algorithms and in a case study address network and mission level aspects of time synchronization on the example of the Orbiting Low Frequency Array for Radio astronomy (OLFAR), a proposed distributed radio interferometer.

The new class of frequency and multi-domain time synchronization and ranging algorithms proposed is applicable to anchorless mobile networks of asynchronous nodes. In a first step, the Frequency Pairwise Least Squares (FPLS) that estimates clock skew and relative velocity in a pairwise setup using only frequency measurements is formulated. In a second step, we extend this method to a motion model with constant acceleration. As frequency domain methods do not estimate clock offset and pairwise range, relying purely on frequency domain estimates is not feasible for most applications.

To harness the potential of frequency domain synchronization and ranging, the Combined Pairwise Least Squares (CPLS) has been developed. The combined method reduces the number of minimum required messages from 4 to 3 compared to current methods and decreases the computational complexity. Using a generic simulation with nodes in pairwise non-linear motion, we show that frequency domain methods can outperform time domain methods in clock skew and relative velocity estimation and that the proposed multi-domain method delivers better clock offset and pairwise range estimation in low to medium SNR conditions.

In the second part of our work, we apply the novel methods to OLFAR –— a spaceborne large aperture radio interferometric array platform. We address network level and mission level aspects, proposing network path planning for pairwise synchronization algorithms and determining the required resynchronization period. i


MSc SS Thesis Presentation

On the Integration of Acoustics and LiDAR

Ellen Riemens

Loudspeakers are placed in an environment unknown to the loudspeaker designers. The room influences the acoustic experience for the user. Having information about the room makes it possible to better reproduce the sound field as intended. Using microphone measurements, the location of acoustic reflectors can be inferred. Current state-of-the-art methods for room boundary detection focus on a two-dimensional setting. Detection of arbitrary reflectors in three dimensions increase complexity due to practical limitations, i.e. the need for a spherical array and the increase of computational complexity. The presence of horizontal reflectors cause inaccuracy for wall detection due to model mismatch. Loudspeakers may not present an omnidirectional directivity pattern, as usually assumed in the literature, thus making the detection of acoustic reflectors in some directions more challenging.

In this thesis, a LiDAR sensor is added to a smart loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the horizontal reflectors that are not present in the acoustic model are sought detected with the LiDAR sensor to enable elimination of their detrimental influence. Second, a method is proposed to compensate for the challenging regions for wall detection in highly directive loudspeakers, using the LiDAR sensor. Experimental results, evaluated in different simulated scenarios are shown for comparison of the proposed method and the state-of-the-art method, that exclusively uses acoustic information.

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MSc SS Thesis Presentation

Energy-efficient Particle Filter SLAM for Autonomous Exploration

Elke Salzmann

Autonomous robots are increasingly used in more and more applications, such as warehouse robots, search-and-rescue robots and autonomous vacuum cleaners. These applications are often in environments where the GPS signals are denied or inaccurate, which makes it difficult to localize the robot in an unknown environment. To overcome this problem the framework of Simultaneous Localization and Mapping (SLAM) is typically used. This solution constructs a map of the environment with the use of cameras or range sensors, while keeping track of the location of the robot in it. To extend the exploration time of these battery powered robots, the energy consumption of the SLAM algorithm could be reduced. It is assumed that if the computational load of an algorithm reduces, the energy consumption of the algorithm reduces as well. An existing paradigm to solve SLAM is the use of a particle filter, which tracks the trajectory of the robot and simultaneously maps the environment. The question answered in this thesis is how to make this algorithm more energy-efficient to be able to deploy this framework in more applications and make the existing robots more sustainable.

In this thesis two methods are investigated. In the first method, the information about the landmarks are incorporated in the trajectory estimation as spatial constraints, to try to achieve a higher accuracy with less particles and thus subsequently a smaller computational load. The proposed method is validated by simulations on synthetic datasets. This method shows improvements in terms of the estimation accuracy. However, it is more computational complex than the existing algorithms, so it is considered less energy-efficient. The second method in this thesis, is the implementation of a parallelized particle filter. This method processes the observation measurements in parallel for the different particles and communicates the information between the particles efficiently. It should reduce the computational time, to enable partial computation of the algorithm to reduce the computational load. This method shows improvements on the run time and thus on the computational load, especially for a larger number of particles and is therefore more energy-efficient. The two separate methods have been analyzed and compared with state of the art methods.


MSc SS Thesis Presentation

Quantifying the dynamic interactions between physiological signals to predict the exposure from chemicals

Sarthak Agarwal

Causal inference is a familiar topic in biomedical research and a key concept in the study of connectivity in various physiological systems. This work aimed to analyse the coupling between the beat to beat parameters derived from ECG and respiration. It was the first time such an analysis was carried out in the context of finding the differences caused by chemical's exposure.

We used conditional Granger causality, a popular method to evaluate direct causal relationships. We have incorporated the cardinality constraint in the optimization function of Granger causality (GC) to deal with the high dimensionality challenge. Further, we extended the original formulation GC to evaluate the coupling between two unequally sampled signals. Finally, end to end implementation of the machine learning prediction model using causal features is well illustrated.

We found a consistent decrease in the average coupling strength of breathing parameters after the exposure. But in the case of ECG interactions, no noticeable change was observed. Surprisingly we found no significant links between the ECG and breathing parameters. The support vector machine (SVM) and random forest trained on coupling values differentiate between healthy and exposure samples. The accuracy of trained SVM and random forest on the independent test set were 78 % and 75 %, respectively.

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MSc SS Thesis Presentation

Edge State Kalman Filtering for Distributed Formation Control Systems

Martijn van der Marel

Formation control problems consider a set of mobile agents with the underlying goal of attaining and maintaining a state where the relative positions of agents are stable in accordance with the desired configuration. Navigation for formation control is typically achieved through localization in a global reference frame, e.g., via GNSS. However, when a global reference frame is not shared among agents, a relative navigation approach is required.

Distributed filtering for relative localization in formation control systems is a relatively unexplored field. The absence of absolute positioning means motivates the need for a distributed filter that operates on the edges of the sensing graph of the multi-agent system. In this thesis, a data model for relative formation control problems and two edge-based Kalman filters are proposed. The first filter is designed for an individual edge. The second is a filter designed via decoupling of the optimal global filter which allows for the joint estimation of adjacent edges. It is shown that the joint filter is optimal under the decoupling constraints.

Monte Carlo results show that when random environmental disturbances are correlated among agents, the joint filter outperforms the local edge filter in a mean square error sense.

Lastly, systems are considered where inter-agent communications are unavailable, leading to biased prediction steps of the Kalman filters. We aim to minimize this effect through the proposal of a local Wiener filter which predicts the control actions of neighboring agents.


PhD Thesis Defence

Graph filter designs and implementations

Jiani Liu

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MSc SS Thesis Presentation

SPLITTER

Stefanie Brackenhoff

A data model and algorithm for detecting spectral lines and continuum emission of highredshift galaxies using DESHIMA 2.0


MSc SS Thesis Presentation

GNSS Chirp Interference Estimation and Mitigation

Daniel Kappelle

GNSS receivers can suffer severely from radio frequency interference (RFI). RFI can introduce errors in the position and time calculations or if the interference is very severe, can lead to a total loss of GNSS reception. This vulnerability of GNSS can have large implications on critical infrastructure such as power plants, telephony, aviation or search and rescue operations. RFI is a real threat to GNSS as many interfering incidents are reported every day.

A common type of RFI is chirp interference, which is a sweep over a wide range of frequencies that overlap with the frequencies used by GNSS. This is often emitted by cheap Personal Privacy Devices that can be bought online. The question in this thesis was how well such interference can be modelled and if modelling could help mitigation against it.

This thesis consists of two main parts. In the first part a novel estimator is proposed that assumes a mathematical model of a chirp and estimates its parameters from recordings of chirps. The estimator has shown to work well in simulations for chirps with an SNR of −9 dB or more. On real recordings the estimates were accurate for 66.7 % of the signals.

In the second part the estimator was used to derive a filter. The filter is based on the subtraction of a replica of the chirp interference from the received signal. It uses the proposed estimator to create the replica. In simulations, the filter is able to improve correlation strength by up to 7 dB. On real recordings the performance was worse as for only 46 % of the recordings the GNSS correlation was increased. Both the estimator and filter have many ways in which they could be improved. The estimator can be improved to allow for more complex chirps, which would in turn improve the filter. Both can also be made more computationally efficient.

Furthermore, in order to get a better understanding of Personal Privacy Devices, one such device has been tested. It was found that the signal from the device was very unstable and changed much over time, it was also highly dependent on ambient temperature. The device has also been opened up and reverse engineered to understand how it works.


MSc SS Thesis Presentation

Correction of Field Inhomogeneities in Low-Field MRI During Image Reconstruction

Bas Liesker

Magnetic resonance imaging (MRI) scanners are a crucial diagnostic tool for radiologists. They are able to render two­ and three­-dimensional images of the body without exposure to harmful radiation. MRI systems are, however, costly to build and maintain. This adversely impacts access to these scanners in developing regions. In an effort to combat this problem, a low-­field MRI scanner is being developed.

Conventional MRI scanners utilize a superconducting solenoid to generate the main magnetic field. The low­=field scanner, on the other hand, induces the main magnetic field through a Hallbach array of permanent neodymium magnets. While beneficial for production and maintenance costs, as well as portability, the Hallbach array is not able to generate a perfectly homogeneous magnetic field.

The inhomogeneities present in the main magnetic field result in distortion of the images when reconstructed using conventional fast Fourier transform (FFT) methods. To counteract this, a reconstruction method that utilizes field information needs to be employed. In this thesis, existing methods to determine and utilize the field information to correct image distortion are explored. From this analysis, it is evident that model-­based (MB) methods are most suitable for reconstruction of data from the low-­field scanner. Current MB methods are only implemented for two-­dimensional reconstruction. The goal of this thesis is to expand these methods to three­dimensional reconstruction. 

A novel MB method for three-­dimensional reconstruction is presented. This new method is able to circumvent memory constraints that arise from reconstruction of large data sets. 

Though the new method requires several hours to reconstruct a 128 × 128 × 30 data set, visual inspection indicates that an accurate result is achieved.


PhD Thesis Defence

Advances in graph signal processing - Graph filtering and network identification

Mario Coutino

To the surprise of most of us, complexity in nature spawns from simplicity. No matter how simple a basic unit is, when many of them work together, the interactions among these units lead to complexity. This complexity is present in the spreading of diseases, where slightly different policies, or conditions, might lead to very different results; or in biological systems where the interactions between elements maintain the delicate balance that keep life running. Fortunately, despite their complexity, current advances in technology have allowed us to have more than just a sneak-peak at these systems. With new views on how to observe such systems and gather data, we aim to understand the complexity within.

One of these new views results from, the field of graph signal processing, providing models and tools to understand and process data coming from such complex systems. With a principled view, coming fromits signal processing background, graph signal processing establishes the basis for addressing problems involving data defined over interconnected systems by combining knowledge fromgraph and network theory with signal processing tools. In this thesis, our goal is to advance the current state-of-the-art by studying the processing of network data using graph filters, the workhorse of graph signal processing, and by proposing methods for identifying the topology (interactions) of a network fromnetworkmeasurements.

To extend the capabilities of current graph filters, the network-domain counterparts of time-domain filters, we introduce a generalization of graph filters. This new family of filters does not only provide more flexibility in terms of processing networked data distributively but also reduces the communications in typical network applications, such as distributed consensus or beamforming. Furthermore, we theoretically characterize these generalized graph filters and also propose a practical and numerically-amenable cascaded implementation.

As all methods in graph signal processing make use of the structure of the network, we require to know the topology. Therefore, identifying the network interconnections from networked data is much needed for appropriately processing this data. In this thesis, we pose the network topology identification problem through the lens of system identification and study the effect of collecting information only from part of the elements of the network. We show that by using the state-space formalism, algebraic methods can be applied to the network identification problemsuccessfully. Further, we demonstrate that for the partially-observable case, although ambiguities arise, we can still retrieve a coherent network topology leveraging state-of-the-art optimization techniques.

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MSc SS Thesis Presentation

Boundary Element Method in Coil Design for Magnetic Resonance Imaging

Teun de Smalen

MRI is an non-invasive imaging technique used by many physicians to diagnose and treat diseases. The technique however is still very expensive and thus out of reach for developing countries. This has led to the goal to design a low-cost MRI system. The challenges that arise from this system make it necessary to design coils in a different way than conventional MRI.

In this work the inverse boundary element method is used to create a coil design method for an arbitrary surface. This method is described and the mathematical framework is analyzed. A regularization method for the inverse problem has been designed in the form of a regularization matrix. This regularization matrix is constructed such that it can handle arbitrary surfaces. The regularization matrix is applied using Tikhonov regularization.

To validate the design method a proof of concept radiofrequency coil for the low field MRI system at the LUMC has been realized. This coil is designed and has been used to image the human brain of an adult. The results from simulations beforehand are in agreement with the physically built coil showing that this method makes it possible to design and construct a physically feasible coil on an arbitrary surface.


Signal Processing Seminar

ONLINE TIME-VARYING TOPOLOGY IDENTIFICATION VIA PREDICTION-CORRECTION ALGORITHMS

Alberto Natali

Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.

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PhD Thesis Defence

Rate-constrained multi-microphone noise reduction for hearing aid devices

Jamal Amini

Many people around the world suffer fromhearing problems (In theNetherlands, around 11%of the population is considered hearing-impaired). To overcome their hearing problems, advanced technologies like hearing aid devices can be used. Hearing aids are meant to assist the hearing-impaired to improve the speech intelligibility and the quality of sounds that they intend to hear. Usually these include processors which are mainly designed to enhance the sound signals originating form the source of interest by reducing the environmental noise. Binaural hearing aids, on the other hand, can also help to preserve some spatial information from the acoustic scene, which can help the hearing aid user to hear the sounds from the correct locations. To construct the binaural hearing aid system, two hearing aids are needed to be placed in the left and the right ears, which can potentially communicate through a wireless link. In addition, one can think of additional assisting devices with microphones placed in the environment. One common way to reduce the noise is to use advanced binaural multi-microphone noise reduction algorithms, which aim at estimating some desired sources while reducing the power of the undesired sources. One typical method is to use spatial filtering, which aims at estimating the target signal by shaping the beam towards the location of the desired source while canceling/suppressing the other sources.

To performbinaural noise reduction, while assuming centralized processing, the signals recorded at remote microphones (for example from additional assisting devices or in the binaural hearing aid setup, the sound signals from the contralateral hearing aid) need to be transmitted to the central processor. Due to the power and bandwidth limitations, the data needs to be compressed before transmission. Therefore, the main question would be, at which rate the data should be compressed to have reasonably good noise reduction performance. This links the noise reduction problem to the data compression problem. Generally, the higher the data rate, the better the noise reduction performance. Therefore, there is a trade-off between the performance of the noise reduction algorithmand the data-rate at which the information is compressed. This problem is closely connected to the rate-distortion problem from an information-theoretic viewpoint. Studying the effect of data compression on the performance of noise reduction problems would be of great interest to reduce the power consumption of hearing assistive devices.

Oneway to incorporate data compression into the noise reduction problem is to perform quantization, which leads to a rate-constrained noise reduction problem. In the rate-constrained noise reduction, the goal is to estimate the desired sources based on the imperfect data. The observations from remote sensors are quantized and transmitted to the fusion center. The main challenge in the binaural rate-constrained noise reduction is to find the best quantization rates for the different sensors at different frequencies, given the physical constraints like bitrate and power constraints.

Another aspect of the rate-constrained noise reduction is to expand the network to receive more information on the acoustic scene using additional assistive devices. Target source estimation using information form such assistive devices (rather than only binaural hearing aids) is shown to result in better noise reduction performance. Now the question is how to allocate the bitrates to the assistive devices as well. These assistive devices can be thought of as the remote embedded microphones on the cell-phones (mobile) or wearablemicrophones placed at the users’ bodies. The binaural hearing aid system can thus be generalized to allow other assistive devices to contribute to noise reduction.

In this dissertation, we study and propose different rate-constrained multi- microphone noise reduction algorithms. We try to expand the notion of the binaural rateconstrained noise reduction to multi-microphone rate-constrained noise reduction for general wireless acoustic sensor networks (WASNs). The WASN in this case can include the binaural setup along with other assistive devices. We propose different algorithms to cover the main objectives of rate-constrained noise reduction problems. These objectives mainly include good target estimation (less environmental noise power) given the compressed data, good rate allocation strategies in WASNs, and preferably preserved spatial information of the sources in the acoustic scene to get the correct impression of the acoustic scene.

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Ph.D. Thesis Defense of Sining Pan

Resistor-based temperature sensors in CMOS technology

Sining Pan

Time: Monday, 12 April 2021, 12:00-12:15 (layman’s talk), 12:30-13:30 (defense)

Abstract: This thesis describes the principle and design of an emerging type of CMOS temperature sensors based on the temperature dependency of on-chip resistors. Compared to traditional BJT-based designs, resistor-based sensors have higher energy-efficiency, better scalability, and can operate under a wider supply range. Nine design examples are shown in this thesis to demonstrate how resistor-based sensors can be optimized for accuracy, energy-efficiency, or other application-driven specifications. Among all the records the designs achieved, the energy-efficiency improvement is the most impressive: 65× better than state-of-the-art before this research, or only 6× away from the theoretical value.

Please feel welcome to join the live stream: http://collegerama.tudelft.nl/mediasite/play/be505395bbf24debb7cf8fd61454a5261d

Thesis: https://doi.org/10.4233/uuid:28108302-2d9b-4560-a806-8ba6d381812e

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MSc Thesis Presentation

Mice tracking using infrared subcutaneous implants for error detection

Javi Guinea Perez

The objective of this thesis was to develop a rodent tracker using the FlashTrack implants, and allow for tracking data to be used for behavioural research. The task was split into three main problems: detection, tracking and error detection.

The detection was solved with basic image processing. The first step was background removal, using median filtering. Later, blob detection after some processing was done. Detections were differentiated into cases where mice are together in contact events and where they are alone. Bounding boxes were generated for the contours of lone mice, while the distance transformwas employed to detect the joint ones.

To track the moving targets, Kalman filtering was used on the bounding boxes of the detector. This approach was based on the Simple Online Realtime Tracking framework, adapting it to the particularities of mice. Other approaches were tried, but SORT was the chosen one.

The infrared subcutaneous implants of FlashTrack allowfor identity verification through code detections. To process the codes of each track, a GaussianMixtureModel is trained to be the classifier of the detections. A track handling modulewas built tomonitor the estimated tracks and code detections, and verify correct assignments. Detected erroneous tracks were discarded.

Synthetic data was used to evaluate the tool. Artificial datasets were developed in Blender. Common metrics for evaluation of multiple object trackers were gathered and discussed, as well as a comparison with one of the state of the art animal trackers.


The (parametric) Voice of Your Heart

Towards Parametric Cardiac Modelling for early recognition and Treatment of AF

Richard Hendriks

Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia, with a prevalence that continuously increased over the past decades. The risk of developing AF among people aged 40 years or older is currently about 25 %. Despite its high prevalence, the exact mechanisms underlying AF are unknown and available treatment strategies are not effective. Although AF in itself is not directly life-threatening, it can lead to many serious complications like heart failure and strokes. As the disease is progressive, early (non-invasive) detection is important.

To increase understanding on the mechanisms underlying AF and develop a better treatment strategy we have set up a collaboration between the Erasmus medical center (Unit Electrophysiology) and TUDelft, where we develop atrial parametric models that can aid in the understanding, detection and treatment of AF. In this presentation I will highlight some of the recent activities, opportunities and outcomes of this collaboration.

If you you would like to join this colloquium, send an email to secr-me-ewi@tudelft.nl and you will receive the TEAMS link.

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Signal Processing Seminar

Elvin Isufi

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Signal Processing Seminar

MSc Kickoff presentation


Signal Processing Seminar

Computational Array Signal Processing

Ayush Bhandari
Imperial College London, UK

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Signal Processing Seminar

Linear systems with sparsity constraints

Geethu Joseph
Syracuse University, NY, USA

Many applications in digital signal processing, machine learning, and communications feature signals that admit a sparse representation in an appropriate basis, i.e., the vector of interest (unknown) has a few nonzeros elements compared to its dimension. The talk covers new mathematical theory and algorithms for recovering sparse vectors from a reduced set of available measurements. We discuss several real-world problems like anomaly imaging, wireless channel estimation, and image denoising and compression. For each application, we describe the inherent sparsity structure and constraints in the system. The talk explores the design and analysis of algorithms that can exploit the underlying signal structures, and the results reveal the impact of these structures on the recovery problems. 

Geethu Joseph is currently a post-doctoral fellow at the Department of Electrical Engineering and Computer Science, Syracuse University, NY. Her research interests include statistical signal processing, adaptive filter theory, sparse Bayesian learning, and compressive sensing.


Signal Processing Seminar

MSc kick off presentation, Gaia Zin and Sybold Hijlkema


CAS MSc Midterm Presentations

Towards multimodal reflecting boundaries detection, Perceptual Sound Zones: Improving Sound Zones by Adding a Model of the Human Hearing

Ellen Riemens, Niels de Koeijer


CAS MSc Midterm Presentations

GPS spoofing detection

Daniel Kappelle


CAS MSc Midterm Presentations

Wideband astronomical spectroscopy in high contrast

Stefanie Brackenhoff, Elke Salzmann


Medical Delta Café

Medical Delta Café 'Zorg naar huis, en dan….? Van monitoren tot behandelen'

Wouter Serdijn, Frank Willem Jansen (Medical Delta), Gisela Terwindt (LUMC), Ries Biggelaar van den (ErasmusMC)

In het online Medical Delta Café 'Zorg naar huis, en dan….? Van monitoren tot behandelen' belichten prof. dr. Gisela Terwindt (LUMC) en drs. Ries van den Biggelaar (Erasmus MC) deze kwesties, waarna deelnemers worden uitgenodigd mee te discussiëren en kennis uit te wisselen in een paneldiscussie met onder andere Medical Delta hoogleraar prof. dr. ir. Wouter Serdijn (TU Delft).

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MSc SS Thesis Presentation

Graph-aware Anomalous Network Agent Detection

Ahmet Gercekcioglu

Networks with a large number of participants and a highly dynamic data exchange are better off using a distributed networking system due to network failures in centralized networks. However, with the increase in distributed networking, security problems arise in distributed processes. Injection of malicious data, for example, must be dealt with by using the tools provided by detection theory. The detection probability pd can be taken as the performance metric that we aim to optimize. In order to achieve this, one must first define a hypothesis testing problem and derive an optimization problem for pd that is dependent on which nodes are assumed to be compromised by these malicious agents that inject data.

For the injected data, there are three different models taken into consideration for the change in values over time and nodes. For every model, we assume that the outlier data can be injected with two different attack modes. These attack modes enforce different network topology related constraints on the set of compromised nodes due to different motivations. Furthermore, additional constraints are assumed due to the limited resources of the agents.

Additionally, with the given framework, we can also derive an optimization problem that can be solved with the help of the well-known linear regression method, i.e., Lasso. The problem that arise with this method is the difficulty of implementing the network topology related constraints into this optimization problem.

In order to solve these optimization problems, several methods are combined for the relaxation and solution of the optimization problems.

From numerical evaluation, it can be observed that our empirical performance is non-negligibly lower than the theoretical performance for all three models and both attacking modes. This can be linked to the dependence of the empirical distribution to the derived subset of compromised nodes, these subsets are chosen such that the cost function is optimized. Hence, we observe that the empirical values are much higher than it is theoretically assumed, in case that the network is 'clean'.

A second factor for performance evaluation is the number of wrongly indexed nodes, it can be observed how this factor is dependent on the distribution of the energy of the outlier data over the nodes and time.

Overall, this study shows that the provided framework shows an increasing performance for an increasing outlier-to-noise energy ratio. For an energy ratio higher than 0.5, the empirical and theoretical ROC-curves are nearly perfectly saturated for all models. The number of wrongly indexed nodes for our methods is generally speaking lower compared with the Lasso-method.


CAS MSc Kickoff Presentation

Analyzing functional ultrasound images of the brain using multilinear decompositions

Arda Kaygan

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Signal Processing Seminar

Rank detection thresholds for Hankel or Toeplitz data matrices

Alle-Jan van der Veen

To detect the number of sources in a data matrix, we look at the singular values. What is a good threshold?

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PhD Thesis Defence

ELECTROMAGNETIC FIELDS IN MRI: Analytical Methods and Applications

Patrick Fuchs

Electrical properties, the conductivity and permittivity of tissue, are quantities that describe the interaction of an object and electromagnetic fields. The properties influence electromagnetic fields and are influenced themselves by physiological phenomena such as lesions or a stroke. Therefore, they are important in identifying or diagnosing the severity of pathologies and are essential in magnetic resonance imaging (MRI) safety and efficiency by determining tissue heating or sensitivity to excitation pulses and antenna designs.

In two-dimensional electromagnetic fields, which occur in specific measurement geometries, it is possible to simplify the relationship between electromagnetic fields and electrical properties, and reconstruct these properties using essentially a forward operation, foregoing a full inversion scheme. These insights also help to find, and explain, the cause of specific artefacts, such as those caused by mismatches in incident field used in the computation of the full electromagnetic fields.

The two-dimensional field assumption necessary for the simplified relationship described above is subsequently tested, and it is shown that this assumption does not hold when the object is sufficiently translation variant in the longitudinal direction. That is, even if the fields for a translation invariant object would be two-dimensional, they become three-dimensional through the interaction of the tissue parameters with the fields, which cause out of plane current and field contributions.

Another interesting application of closed form expressions between currents and fields is the target field method, which solves the inverse source problem between electric currents and static magnetic fields in a regularised manner by constraining their relationship to a cylindrical geometry. This method is adapted for transverse oriented magnetic fields to be used with Halbach type magnet arrays, and an open source tool is developed to make the method easy to apply for various design considerations. Moving away from constraints on the field or current structure, we show the intricate relationship between electrical properties and the measured signal in an MRI scanner. This is done by deriving the electro- (and magneto-) motive force for a typical MRI scenario without any assumptions on the object or electro-magnetic fields. This model can then even be used to reconstruct electrical properties from the simplest MRI signal, namely the free induced decay (FID) signal.

To round off our investigation of electrical properties we take a small detour to the magnetic tissue property, the permeability or magnetic susceptibility. For reconstructing this tissue property a dipole deconvolution is required, where the dipole convolution loses information of the original object through the zeros of the dipole kernel. A new machine learning based approach to reconstruct the lost information is investigated in the final chapter of this thesis.

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PhD Thesis Defence

Integrated Circuits for Miniature 3-D Ultrasound Probes: Solutions for the Interconnection Bottleneck

Zhao Chen

14:30-15:00 (layman’s talk), 15:00-16:00 (defence)

Please feel welcome to join the live stream

Promotors: Michiel Pertijs and Nico de Jong

Abstract: This thesis describes low-power application-specific integrated circuit (ASIC) designs to mitigate the constraint of cable count in miniature 3-D TEE probes. Receive cable-count reduction techniques including subarray beamforming and digital time-division multiplexing (TDM) have been explored and the effectiveness of these techniques has been demonstrated by experimental prototypes. Digital TDM is a reliable technique to reduce cable count, but it requires an in-probe datalink for high-speed data communication. A quantitative study on the impact of the datalink performance on B-mode ultrasound image quality has been introduced in this thesis for data communication in future digitized ultrasound probes. Finally, a high-voltage transmitter prototype has been presented for effective cable-count reduction in transmission while achieving good power efficiency. The application of these techniques is not limited to only the design of TEE probes and can be easily extended to the design of other miniature 3-D ultrasound probes, for instance intracardiac echocardiography (ICE) probes and IVUS probes, which are facing similar interconnect challenges with an increased number of transducer elements to enhance imaging quality.

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CAS MSc Midterm Presentations

Hanyu Ma, Preetha Vijayan


CAS MSc Midterm Presentations

Privacy-Preserving Distributed Graph Filtering

Jane Li


CAS MSc Midterm Presentations

Tanmay Manjunath, Roy Arriëns


Signal Processing Seminar

Focussing Waves in Unknown Media

Jörn Zimmerling

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CAS MSc Midterm Presentations

Daniel Kappelle, Randy Prozee


Signal Processing Seminar

Signal Processing for communication

Didem Doğan Başkaya


CAS MSc Midterm Presentations

Bas Liesker


CAS MSc Midterm Presentations

Shreya Sanjeev Kshirasagar


MSc SS Thesis Presentation

Deep Learning-Based Sound Identification

Shaoqing Chen

Environmental sound identification and recognition aim to detect sound events within an audio clip. This technology is useful in many real-world applications such as security systems, smart vehicle navigation and surveillance of noise pollution, etc. Research on this topic has received increased attention in recent years. Performance is increasing rapidly as a result of deep learning methods. In this project, our goal is to realize urban sound classification using several neural network models. We select log-Mel spectrogram as the audio representation and use two types of neural networks to perform the classification task. The first is the convolutional neural network (CNN), which is the most straightforward and widely used method for a classification problem. The second type of network is autoencoder based models. This type of model includes the variational autoencoder (VAE), beta-VAE and bounded information rate variational autoencoder (BIR-VAE). The encoders of these systems extract a low dimensionality representation. The classification is then performed on this so-called latent representation. Our experiments assess the performances of different models by evaluation metrics. The results show that CNN is the most promising classifier in our case, autoencoder-based models can successfully reconstruct the log-Mel spectrogram and the latent features learned by encoders are meaningful as classification can be achieved.


Signal Processing Seminar

A Generic Framework and Algorithmic Solution for Radar Resource Management

Max Schöpe

Abstract: Recent advances in multi-function radar (MFR) systems led to an increase of their degrees of freedom. As a result, modern MFR systems are capable to adjust many parameters during run-time. An automatic adaptation of the radar system to changing situations, like weather conditions, interference or target maneuvers is usually called radar resource management (RRM). After an introduction to the topic of RRM, I will discuss our approach. We model the different sensor tasks as partially observable Markov decision processes (POMDP) and solve them by applying a combination of Lagrangian relaxation and policy rollout. The algorithm has a generic architecture and can be applied to different radar or sensor systems and cost functions. I will show this through simulations of two-dimensional tracking scenarios. Moreover, I will demonstrate how the algorithm allocates the sensor time budgets dynamically to a changing environment in a non-myopic fashion.

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Microelectronics Colloquium

Artificial Retina: A Future Cellular-Resolution Brain-Machine Interface

Dante Muratore

A healthy retina transduces incoming visual stimuli into patterns of neural activity, which are then transmitted to the brain via the optic nerve. Degenerative diseases, like macular degeneration or retinitis pigmentosa, destroy the ability of the retina to transduce light, causing profound blindness. An artificial retina is a device that replaces the function of retinal circuitry lost to disease. Present-day devices can elicit visual percepts in patients, providing a proof of concept. However, the patterns of neural activity they produce are far from natural, and the visual sensations experienced by patients are coarse and of limited use to patients.

A main hurdle is that there are many types of cells in the retina. For example, some cells respond to increases of light intensity, while other cells respond to decreases of light intensity. In order to reproduce a meaningful neural code, it is crucial to respect the specificity and selectivity of these cells. Because cells of different types are intermixed in the circuitry of the retina, cell type specific activation of this kind requires that a future artificial retina be able to stimulate at single cell resolution, over a significant area in the central retina.

To achieve this goal, we are designing an epi-retinal interface that operates in two modes: calibration and runtime. During calibration, the interface learns which cells and which cell types are available for stimulation, by recording neural activity from the retina. During runtime, the interface stimulates the available cells to best approximate the desired scene. I will present a system architecture we are developing that can accomplish the overall performance goals, and the implications of this architecture for brain-machine interfaces.

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PhD Thesis Defence

ACCURATE STRUCTURAL HEALTH MONITORING IN COMPOSITES WITH FIBRE BRAGG GRATING SENSORS

Aydin Rajabzadeh

Compared to metals, composite materials offer higher stiffness, more resilience to corrosion, have lighter weights, and their mechanical properties can be tailored by their layup configuration. Despite these features, composite materials are susceptible to a diversity of damages, including matrix cracks, delamination, and fibre breakage. If these damages are not detected and mended, they can spread and result in the failure of the whole structure. In particular, when the structure is under fatigue and vibrations during flight, this process can expedite. Moreover, if such damages occur in the internal layers of the composite material, they will be difficult to detect and to characterise. There is thus a huge demand for reliable and accurate structural health monitoring methods to identify these defects. Such methods either try to monitor the structural integrity of the composite during service, or they are used for studying a desired configuration of a composite material during fatigue and tensile tests. This thesis provides structural health monitoring solutions that can potentially be used for both these categories. The structural health monitoring applications developed in this thesis range from accurate strain and displacement measurement, to detection of cracks and the identification of damages in composites.

In this thesis, fibre Bragg grating (FBG) sensors were chosen for this purpose. The miniature size and small diameter of these sensors makes them an ideal candidate for embedding them between composite layers, without severely altering the mechanical properties of the host composite material. They can thus provide us with direct information about the current state of the laminated composite, potentially at any depth. This is especially useful for acquiring information about the internal layers of the composite material, as barely visible impact damages and micro-cracks often form beneath the surface of the material without being visible on its exterior.

In spite of their interesting physical characteristics, applications of FBG sensors are typically limited to point strain or temperature sensors. Further, it is often assumed that the strain field along the sensor length is uniform. For this reason, there is currently a gap in the field of structural health monitoring in retrieving meaningful information about the non-uniform strain field to which the FBG sensor is subjected in damaged structures. The focus of this thesis is on analysing the response of FBG sensors to highly non-uniform strain fields, which are a characteristic of the existence of damage in composites.

To tackle this problem, first a new model for the analysis of FBG responses to nonuniform strain fields will be presented. Using this model, two algorithms are presented to accurately estimate the average of such non-uniform axial strain fields, which conventional strain estimation algorithms fail to deliver. In fact, it is shown that the state-of-the-art strain estimation methods using FBG sensors can lead to errors of up to a few thousand microstrains, and the presented algorithms in this thesis can compensate for such errors. It was also shown that these methods are robust against spectral noise from the interrogation system, which can pave the way for more affordable FBG based strain estimation solutions.

Another contribution of this thesis is the demonstration of two new algorithms for the detection of matrix cracks, and for accurate monitoring of the delamination growth in composites, using conventional FBG sensors. These algorithms are in particular useful for studying the mechanical behaviour of laminated composites in laboratory setups. For instance, the matrix crack detection algorithm is capable of characterising internal transverse cracks along the FBG length during tensile tests. Along the same lines, the delamination growth monitoring algorithm can accurately localise the delamination crack tip along the FBG length in mode-I tensile and fatigue tests. These algorithms can perform in real-time, which makes them ideal for dynamic measurement of crack propagation under fatigue, and their spatial resolution and accuracy is superior to the other state-of-the-art damage detection techniques.

Finally, to enhance the precision of the damage detection schemes presented in this thesis, two different methods are proposed to accurately determine the active gauge length of the FBG sensor, and its position along the optical fibre. This information is generally not provided for commercial FBG sensors with such accuracy, which can adversely affect the precision of crack tip localisation algorithms. Following the algorithms provided in this thesis, the sensor position can be marked on the optical fibre with micrometer accuracy.

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Signal Processing Seminar

Signal processing in distributed networks; audio signal processing

Metin Çalış

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CAS MSc Midterm Presentations

Tracking of rodents from infrared video sequences for behaviour studies

Javier Guinea Perez


Signal Processing Seminar

Biomedical signal processing

Aybüke Erol

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Signal Processing Seminar

Intelligent X-ray sensing for real-time image guidance in proton therapy

Dennis Schaart
Applied Physics, TU Delft

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MSc SS Thesis Presentation

Distributed Coordination for Multi-feet Truck Platooning

Yikai Zeng

Truck platooning refers to coordinating a group of heavy-duty vehicles at a close inter-vehicle distance to reduce overall fuel consumption. This coordination between trucks is traditionally achieved by adjusting the schedule, velocity and routines to increase the platooning chances, and thus improve the overall fuel efficiency. However, the data model built for the coordination problem is typically integer-constrained, making it generally hard to solve. On the other hand, the interaction among self-interested fleets which are operated by different companies is not well-studied. This thesis aims to build a distributed framework for multi-fleet truck platooning coordination to enable the coordination without a third-party service provider.

The interaction among fleets is considered a non-cooperative finite game, for which we propose the best response search method, which essentially requires to solve a cooperative truck platooning optimization problem iteratively. We refer to the optimization problem as a best-response problem, which is formulated as a mixed-integer linear problem with relaxation skills.

 To achieve a feasible time complexity for the best-response subproblem, we propose a decentralized algorithm, distributing the computational load to connected automated vehicles within the fleet.

The proposed method is examined under a real-world featured demand set to compare the performance in optimality and time complexity with previous studies. The result suggests that the decentralized algorithm delivers the optimal objective value in this case, while the best-response search does not deliver extra benefits as a the dominating time costs in the cost functions eliminate the potential for improvement.


MSc SS Thesis Presentation

Atrial Fibrillation classification from a short single lead ECG recordin

Yuchen Yin

This thesis focuses on classifying AF and Normal rhythm ECG recordings. AF is a common arrhythmia occurring in millions of people every year, which could lead to blood clots, stroke or even heart failure. When AF is occurring, the P waves are often absent and RR intervals are often irregular.

This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.

  Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.  

The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively.

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MSc SS Thesis Presentation

Clock skew invariant beamforming

Laurens Buijs

This thesis is focused on Wireless Acoustic Sensor Networks (WASNs) used for beamforming in a speech enhancement task. Since each node in aWASN has its own clock, clock osets and clock skews between the nodes are inevitable. Clock osets and clock skew can be detrimental to the beamformer performance. In this thesis we focus on the eect of clock skew on the beamformer performance. Existing methods for clock skew compensation for the speech enhancement application do this explicitly. In this thesis we investigate the possibility to formulate the beamformer such that explicit clock skew compensation is not necessary.

Instead, we propose an algorithm for implicit clock skew compensation, which takes advantage of the Generalized Eigenvalue Decomposition (GEVD) to construct beamformers (e.g. Minimum Variance Distortionless Response (MVDR)), recently proposed in the literature. Using the GEVD, no explicit compensation has to be applied to the received data. Compared to the state-of-the-art, where clock skew estimation/compensation algorithms are used, this reduces the computational complexity for beamformer processing.

The algorithm depends on exact knowledge of the noisy correlation matrix across the microphones. In practice, this matrix is unknown and estimation will reduce the performance of the proposed algorithm. We therefore quantify the error made in the estimation of the correlation matrix using the standard Welch method and also look at a recursive smoothing based method for correlation matrix estimation. Compared to a selected state-of-the-art algorithm, the proposed algorithm shows similar or better performance using this recursive smoothing method. For future work on this subject, more study can be done on correlation matrix estimation methods, as these play a key role in clock skew invariant beamforming.

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Signal Processing Seminar

Biomedial Signal Processing

Hanie Moghaddasi

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MSc SS Thesis Presentation

Ultra fast MRI acquisition at 7 Tesla

Alejandro Monreal Madrigal

Magnetic Resonance Imaging is one of the most widely used imaging modalities nowadays and it performs especially well imaging human organs such as the brain and liver. One of its main limitations is the relatively long imaging times, to overcome this issue and speed up the data acquisition, several techniques such as Parallel Imaging or PI have been developed. These techniques require advanced hardware and software to be able to decrease the acquisition time. On the hardware side, a highly efficient insert gradient coil has been designed and built at the University Medical Center Utrecht. Specialized software has to be implemented to optimally make use of this hardware. One of the recently proposed PI methods called Wave-CAIPI has been proved to achieve a ninth fold acceleration factor without compromising image quality.  

This project aims to investigate the time gain that can be achieved when combing the insert gradient coil with a Wave-CAIPI strategy. Two main aspects are reviewed. The first one is the maximum achievable under-sampling factor that does not compromise image quality. The second one is the decrease in acquisition time that can be obtained when using the insert gradient coil compared to conventional gradient systems while maintaining image quality. To do so, the strategy has been implemented and extensive simulations have been performed to optimize the MR acquisition parameters. To prove the results from the simulations, the Wave-CAIPI sequence was implemented in a 7T scanner at the UMCU, where the acquired data was retrospectively under-sampled, obtaining the wave image to be further reconstructed.


MSc SS Thesis Presentation

Automatic Depth Matching for Petrophysical Borehole Logs

Aitor García Manso

In the oil and gas industry a crucial step for detecting and developing natural resources is to drill wells and measure miscellaneous properties along the well depth.  One important source of this disturbances is depth misalignment and in order to compare different  measurements care must be taken to ensure that all measurements (log curves) are properly positioned in depth. This process is called depth matching. In spite of multiple attempts for automating this process it is still mostly done manually.   

Based on the Parametric Time Warping (PTW), a parameterised warping function that warps one of the curves  is assumed and its parameters are determined by solving an optimization problem maximizing the cross-correlation between the two curves. The warping function is assumed to have the parametric form of a piecewise linear function in order to accommodate the linear shifts that take place during the measurement process. This method, combined with preprocessing techniques such as an offset correction and low pass filtering, gives a robust solution and can correctly align the most commonly accruing examples. Furthermore, the methodology is extended to depth match logs with severe distortion by applying the technique in an iterative fashion. Several examples are given when developed algorithm is tested on real log data supplemented with the analysis of the computational complexity this method has and the scalability to larger data sets.


MSc SS Thesis Presentation

Optimal Sensor Placement for Calibration-Involved Radio Astronomy Imaging Applications

Kaiwen Zhang

In radio astronomy (RA), one of the key tasks is the estimation of the celestial source powers, i.e. imaging. To maximize the performance, it is crucial to optimize the receiver locations before the construction of a telescope array. However, although system calibration is an integral and crucial process of imaging, it has rarely been addressed for RA sensor placement problems previously. This motivates us to investigate whether incorporating calibration can result in better array designs.

In this thesis, we focus on the calibration of the sensors’ complex-scalar gains in particular, which are treated as nuisance parameters for the image estimation. The associated Cramer-Rao bound (CRB) is derived and employed as the design criterion. The nonlinear CRB-based sensor placement problem is cast as an NP-hard combinatorial optimization problem, and we adopt two approaches to solve such by approximation: (i) greedy submodular maximization and (ii) convex optimization with semidefinite relaxation. The former is chosen for simulations due to its good performance and lower computational complexity. Extensive simulations demonstrate that compared to the calibration-excluded design, the proposed one only provides slight improvements to the imaging quality. However, the proposed array demonstrates the potential of accelerating the convergence of the gain estimation procedures. Through further investigation, we conclude that the lack of imaging quality improvident can be a consequence of the gain and image being near-orthogonal parameters.


PhD Thesis Defence

Antenna Array Synthesis and Beamforming for 5G Applications: An Interdisciplinary Approach

Yanki Aslan

Realization of the future 5G systems requires the design of novel mm-wave base station antenna systems that are capable of generating multiple beams with low mutual interference, while serving multiple users simultaneously using the same frequency band. Besides, small wavelengths and high packaging densities of front-ends lead to overheating of such systems, which prevents safe and reliable operation. Since the strict cost and energy requirements of the first phase 5G systems favor the use of low complexity beamforming architectures, computationally efficient signal processing techniques and fully passive cooling strategies, it is a major challenge for the antenna community to design multibeam antenna topologies and front-ends with enhanced spatial multiplexing, limited inter-beam interference, acceptable implementation complexity, suitable processing burden, and natural-only/radiative cooling.Traditionally, array design has been performed based on satisfying the given criteria solely on the radiation patterns (gain, side lobe level (SLL), beamwidth etc.). However, in addition to the electromagnetic aspects, multi-beam antenna synthesis and performance evaluation in 5G systems at mm-waves must combine different disciplines, including but not limited to, signal processing, front-end circuitry design, thermal management, channel & propagation, and medium access control aspects. Considering the interdisciplinary nature of the problem, the main objective of this research is to develop, evaluate and verify innovative multibeam array techniques and solutions for 5G base station antennas, not yet used nor proposed for mobile communications. The research topics include the investigation of (i) new array topologies, compatible with IC passive cooling, including sparse, space tapered arrays and optimized subarrays, meeting key requirements of 3-D multi-user coverage with frequency re-use and power-efficient side-lobe control, (ii) adaptive multiple beamforming strategies and digital signal processing algorithms, tailored to these new topologies, and (iii) lowcost/competitive and sufficiently generic implementation of the above array topologies and multi-beam generation concepts to serve multiple users with the same antenna(s) with the best spectrum and power efficiencies. This doctoral thesis consists of three parts. Part I focuses on the system-driven aspects which cover the system modeling (including the link budget and precoding), propagation in mm-wave channels and statistical assessment of the Quality of Service (QoS). Although separate comprehensive studies exist both in the field of propagation/system modeling and antennas/beamforming, the link between the two disciplines is still weak. In this part, the aim of the study is to bridge the gap between the two domains and to identify the trade-offs between the complexity of beamforming, the QoS, and the computational cost of precoding in the 5G multi-beam base station arrays for various use cases. Based on the system model developed, a novel quantitative relation between the antenna SLLs/pattern nulls and the statistical QoS is established in a line-of-sight (LoS) dominated mm-wave propagation scenario. Moreover, the potential of using smart (low in-sector side-lobe) array layouts (with simple beam steering) in obtaining sufficiently high and robust QoS, while achieving the optimally low processing costs is highlighted. For a possible pure non-line-of-sight (NLoS) scenario, the system advantages (in terms of the beamforming complexity and the interference level) of creating a single, directive beam towards the strongest multipath component of a user are explained via ray-tracing based propagation simulations. The insightful system observations from Part I lead to several fundamental research questions: Could we simplify the multiple beamforming architecture while keeping a satisfying QoS? Are there any efficient yet effective alternative interference suppression methods to further improve the QoS? How should we deal with the large heat generation at the base station? These questions, together with the research objectives, form the basis for the studies performed in the remaining parts. Part II of the thesis focuses on the electromagnetism-driven aspects which include innovative, low-complexity subarray based multibeam architectures and new array optimization strategies for effective SLL suppression. The currently proposed multi-beam 5G base stations in the literature for beamforming complexity reduction use either a hybrid array of phased subarrays, which limits the field-of-view significantly, or employ a fully-connected analog structure, which increases the hardware requirements remarkably. Therefore, in the first half of this part, the aim is to design low-complexity hybrid (or hybrid-like) multiple beamforming topologies with a wide angular coverage. For this purpose, two new subarray based multiple beamforming concepts are proposed: (i) a hybrid array of active multiport subarrays with several digitally controlled Butler Matrix beams and (ii) an array of cosecant subarrays with a fixed cosecant shaped beam in elevation and digital beamforming in azimuth. Using the active (but not phased) multiport subarrays, the angular sector coverage is widened as compared to that of a hybrid array of phased subarrays, the system complexity is decreased as compared to that of a hybrid structure with a fully-connected analog network, and the effort in digital signal processing is reduced greatly. The cosecant subarray beamforming, on the other hand, is shown to be extremely efficient in serving multiple simultaneous co-frequency users in the case of a fairness-motivated LoS communication thanks to its low complexity and power equalization capability. Another critical issue with the currently proposed 5G antennas is the large inter-user interference caused by the high average SLL of the regular, periodic arrays. Therefore, in the second half of Part II, the aim is to develop computationally and power-efficient SLL suppression techniques that are compatible with the 5G’s multibeam nature in a wide angular sector. To achieve this, two novel techniques (based on iterative parameter perturbations) are proposed: (i) a phase-only control technique and (ii) a position-only control technique. The phase-only technique provides peak SLL minimization and simultaneous pattern nulling, which is more effective than the available phase tapering methods in the literature. The position-only technique, on the other hand, yields uniform-amplitude, (fully-aperiodic and quasi-modular) irregular planar phased arrays with simultaneous multibeam optimization. The latter technique combines interference-awareness (via multibeam SLL minimization in a predefined cell sector) and thermal-awareness (via uniform amplitudes and minimum element spacing constraint) for the first time in an efficient and easy-to-solve optimization algorithm. Part III of the thesis concentrates on the thermal-driven aspects which cover the thermal system modeling of electronics, passive cooling at the base stations, and the role of antenna researchers in array cooling. The major aim here is to form a novel connection between the antenna system design and thermal management, which is not yet widely discussed in the literature. In this part, an efficient thermal system model is developed to perform the thermal simulations. To effectively address the challenge of thermal management at the base stations, fanless CPU heatsinks are exploited for the first time for fully-passive and low-cost cooling of the active integrated antennas. To reduce the size of the heatsinks and ease the thermal problem, novel planar antenna design methodologies are also proposed. In the case of having a low thermal conductivity board, using a sparse irregular antenna array with a large inter-element spacing (such as a sunflower array) is suggested. Alternatively, for the densely packed arrays, increasing the equivalent substrate conductivity by using thick ground planes and simultaneously enlarging the substrate dimensions is proven to be useful. The performed research presents the first-ever irregular/sparse and subarray based antennas with wide scan multi-beam capability, low temperature, high-efficiency power amplifiers, and low level of side lobes. The developed antenna arrays and beam generation concepts could have also an impact over a broad range of applications where they should help overcome the capacity problem by use of multiple adaptive antennas, improve reliability and reduce interference.

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MSc SS Thesis Presentation

Low-field MR Imaging Using a Nonuniform Fast Fourier Transform

Maria Macarulla Rodriguez

Low-field Magnetic Resonance Imaging (LF MRI) is a cheap and safe technique to visualise the internal structure of the human body. Unlike other imaging techniques, Magnetic Resonance Imaging does not use ionising radiation to generate the images. Instead, it uses magnetic fields and radio waves which are nonthreatening to the health. The LF MRI scanners are constructed out of inexpensive materials and their maintenance is affordable. Therefore, these scanners are a promising alternative for developing countries that present economic limitations. Nonetheless, since Magnetic Resonance scanners use a weak magnetic field, the process of image reconstruction requires complex algorithms that need time. This thesis will examine the way in which the computational time of the image reconstruction from a low-field Magnetic Resonance Imaging can be reduced, using an algorithm based on the fast Fourier transform.


SSCS WYE Webinar

To Academia, or to Industry, That is the Question.

Kofi Makinwa, Shin-Lien Lu

Abstract:

You are about to finish graduate school or perhaps a young or seasoned professional, contemplating a career transition. Which is better - a career in academia or industry? What are the pros and cons of one versus the other? How can you start exploring and build up your career accordingly? In this webinar, we will interview Dr. Linus Lu, a professor-turned-industry veteran, and Prof. Kofi Makinwa, an industry veteran-turned-professor, who will share their insights and perspectives from their personal journeys in both academia and industry careers. They will also address what triggered their transitions, how they staged their transitions, and offer their crystal ball projections on present and future career prospects in the solid-state-circuits profession.

REGISTER TODAY!

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MSc SS Thesis Presentation

Multi-target Detection and Tracking with 8 GHz FMCW Radar System

Siyan Wan

Currently, most of FMCW radar systems for target detection and localization are based on the radar system with multiple receiving antennas, but little based on the SISO system. In this project, we will show a unique signal processing pipeline based on the 8 GHz SISO FMCW radar system.  An advanced algorithm of multi-target detection and tracking will be designed to monitor the range, angle, and Doppler velocity information of targets.


Signal Processing Seminar

sensor networks, rank-constrained optimization, algebraic techniques

Matthew Morency

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Signal Processing Seminar

Second Master presentation

Maria Macarulla Rodriguez, Aitor García Manso


Signal Processing Seminar

Task-cognizant sparse sensing for inference (ASPIRE)

Pim van der Meulen

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Signal Processing Seminar

Second Master presentation

Vasudha Sathyapriyan, Maarten Enthoven


Signal Processing Seminar

Second Master presentation

Kriti Dhingra, Prernna Bhatnagar


Signal Processing Seminar

Signal Processing for Communication

Zengfu Wang

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Signal Processing Seminar

Alejandro Monreal

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Signal Processing Seminar

Second Master Presentation

Yuchen Yin, Shaoqing Chen


MSc SS Thesis Presentation

Room geometry estimation from stereo recordings using neural networks

Giovanni Bologni

Acoustic room geometry estimation is often performed in ad hoc settings, i.e. using multiple microphones and sources distributed around the room, or assuming control over the excitation signals. To facilitate practical applications, we propose a fully convolutional network (FCN) that localizes reflective surfaces under milder assumptions, such as 1. a compact array of only two microphones is available, 2. emitter and receivers are not synchronized, and 3., both the excitation signals and the impulse responses of the enclosures are unknown.

Our FCN is designed to extract spectral and temporal patterns from stereo recordings, aggregate the temporal information over time-frames, and predict the likelihood of virtual sources corresponding to reflective surfaces being at specific locations.

Numerical experiments confirm that the network is able to generalize to mismatched microphone array sizes, sensor directivity patterns, or audio signal types, while highlighting front-back ambiguity as a prominent source of uncertainty.

When a single reflective surface is present, up to 80% of the sources are detected, while this figure approaches 50% in rectangular rooms.

Further tests on real-world recordings report similar accuracy as with artificially reverberated speech signals, validating the generalization capabilities of the framework.

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Signal Processing Seminar

Graph Signal Processing

Alberto Natali

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MSc SS Thesis Presentation

Single-Pixel Thermopile Sensors for people counting

Erik Hagenaars

People counting data in offices is used in many applications like HVAC system control and space management to increase comfort, decrease energy consumption and optimise space utilisation. In contrast to past approaches using imaging modalities that tend to be either expensive or intrusive, we consider single-pixel thermopile sensors for people counting. These sensors may already be deployed as part of a smart lighting system to provide temperature data for HVAC controls.

Firstly, a statistical sensor model for thermopile temperature measurements is proposed. The proposed people counting method enhances the CUSUM RLS algorithm to estimate temperature change caused by people entering or leaving. We estimate mean temperature changes upon detection of an occupancy event, and then estimate the people count using a maximum likelihood on the estimated temperature change. Finally, PIR vacancy data is merged with the people count estimation to increase accuracy. We obtain an average counting error of 0.11 and 0.19 for 90\% of the instants respectively when considering 15 minute windows for simulated and experimental datasets.

A second aspect of the thesis considers the problem of commissioning plan detection. We leverage the two-sided CUSUM signals to address this problem. The two-sided CUSUM scores for a pair of sensors are used to calculate similarity measures; these features are used in a Random Forest Classifier to detect commissioning changes of the sensor pair. Using simulated data with the thermopile signal model, we show that the proposed method achieves a true positive rate (determining the correct layout) of 90.2\% and false positive rate of 1.3\%.

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Microelectronics colloquium

Radar; Compressive Sensing, Information Geometry and Neural Networks in Radar /Radar – Old but gold

Radmila Pribić (Thales/TUD), Francesco Fioranelli (TUD)

Abstract Radmila Pribić

Radar; Compressive Sensing, Information Geometry and Neural Networks in Radar

Compressive Sensing (CS) is a recent paradigm in sensing (since 2006) that works with fewer data because it is optimized to information in data rather than to the sensing bandwidth only. Most promising benefits of CS in radar are fewer data, high resolution and multi-target analysis.

Information geometry (IG) is an approach to stochastic signal processing (since the eighties) whose most promising benefits have been found in using information distances for resolution bounds, parameter estimation and analysis of accuracy and detection.

Neural networks (NNs) provide mighty numerical tools for learning radar-sensing models directly from data. IG and CS work naturally with NNs in the probabilistic inferences.

The mixture of CS, IG and NNs enables an elegant and straightforward framework for optimizing the demands of data acquisition and signal processing in radar.

Abstract Francesco Fioranelli

Radar – Old but gold

On the birthday cake for Radar we put this year 116 candles – it was indeed in 1904 that German engineer Christian Hülsmeyer publicly demonstrated for the first time radar technology to detect ships even in foggy conditions from a bridge in Köln (Cologne). However, research in sensing using transmitted and received electromagnetic signals is still enjoying exciting developments, as detecting and recognising objects at large distances and in all weather conditions is still a significant need today in aviation, navigation, autonomous systems, security.

One of these recent developments is the combination of modern artificial intelligence with radar based classification, to make our radar systems more capable, intelligent, autonomous. In my talk I will present some recent results and outstanding challenges from research activities in my previous post at the University of Glasgow and those planned and started here at TU Delft. Specifically, I will discuss applications of radar imaging (micro-Doppler signatures but not only) to the domain of human activities/gait identification in the context of healthcare, and classification of small Unmanned Aerial Vehicles (drones), relevant in security/defense context.

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CAS MSc Midterm Presentations

Kriti Dhingra Aitor García Manso

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Signal Processing Seminar

Statistical and Array Signal Processing, Compressed Sensing, Tensor Analysis, Wireless Communications

Feiyu Wang

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Signal Processing Seminar

PCaVision: Detection of prostate cancer using ultrasound

Mark Bloemendaal
CEO at Angiogenesis Analytics

Mark Bloemendaal represents his new startup company, Angiogenesis Analytics.

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Distributed Perception and Learning Between Robots and the Cloud

Prof Sachin Katti
Stanford University

Today’s robotic fleets are increasingly facing two coupled challenges. First, they are measuring growing volumes of high-bitrate video and LIDAR sensory streams, which, second, requires them to use increasingly compute-intensive models, such as deep neural networks (DNNs), for downstream perception or control. To cope with such challenges, compute and storage-limited robots, such as low-power drones, can offload data to central servers (or “the cloud”), for more accurate real-time perception as well as offline model learning. However, cloud processing of robotic sensory streams introduces acute systems bottlenecks ranging from network delay for real-time inference, to cloud storage, human annotation, and cloud-computing cost for offline model learning.

In this talk, I will present learning-based approaches for robots to improve model performance with cloud offloading, but with minimal systems cost. For real-time inference, I will present a deep reinforcement learning based offloader that decides when a robot should exploit low latency, on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, for continual learning, I will present an intelligent, on-robot sampler that mines real-time sensory streams for valuable training examples to send to the cloud for model re-training. Using insights from months of field data and experiments on state-of-the-art embedded deep learning hardware, I will show how simple learning algorithms allow robots to significantly transcend their on-board sensing and control performance, but with limited communication cost.

Please confirm your presence by sending an email to Ms. Marsha Ginsberg (M.Ginsberg@tudelft.nl).

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Signal Processing Seminar

Graph Signal Processing, Data Science

Alberto Natali

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CAS MSc Midterm Presentations

Yuchen Yin Shaoqing Chen

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Signal Processing Seminar

Signal processing for communications

Tarik Kazaz

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MSc SS Thesis Presentation

Two-Dimensional Blood Flow Estimation in the Brain with Ultrafast Ultrasound

Karishma Kumar

Detailed imaging of blood flow may improve the understanding of brain functions. The current state-of-the-art non-invasive flow imaging of the brain is limited to a one-dimensional Doppler setting. We propose a method to estimate the two-dimensional flow vector in the fine vascular network of the brain by using a speckle tracking technique. The framework of Orthogonal Matching Pursuit is used for speckle tracking where prior constraints are applied to guide the matching process to find the best possible displacements between two frames. The prior constraint is in the form of a directional constraint which determines the probability of vector flow in all the given directions according to the orientation of the vessels. The orientation of the vessels is computed using Power Doppler Imaging. In this work, the proposed method for two-dimensional vector estimation is compared with the standard block matching technique of Normalised Cross-Correlation. We see that the variance of the final velocity estimates is less and the direction of blood flow is found within the curvature of the vessel.


CAS MSc Midterm Presentations

Master Presentations

Jente Zandstra, Laurens Buijs, Maosheng Yang, Siyan Wan

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PhD thesis defense

ENERGY-AWARE NOISE REDUCTION FOR WIRELESS ACOUSTIC SENSOR NETWORKS

Jie Zhang

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Signal Processing Seminar

Statistical Signal Processing and Machine Learning for Speech Processing

Timo Gerkmann
Universität Hamburg

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Signal Processing Seminar

Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging

Didem Doğan Başkaya

Computational imaging is the process of indirectly forming images from measurements using image reconstruction algorithms that solve inverse problems. In many inverse problems in multidimensional imaging such as spectral and depth imaging, the measurements are in the form of superimposed convolutions related to the unknown image. In this presentation, we first provide a general formulation for these problems named as convolutional inverse problems, and then present fast image reconstruction algorithms that exploit sparse models in analysis and synthesis forms. These priors involve sparsifying transforms or data-adaptive dictionaries that are patch-based and convolutional. The numerical performance of the developed algorithms is evaluated for a three-dimensional image reconstruction problem in spectral imaging. The results demonstrate the superiority of the convolutional dictionary prior over others. The developed algorithms are also extended to the compressive setting with compressed convolutional measurements.


MSc CE Thesis Presentation

An Object Detecting Architecture using Spiking Neural Networks

Davide Spessot

Recent trends in the embedded consumer market increased the need for low-power and reliable classification engines. Spiking Neural Network (SNN) is a new technology that promises to deliver 4 orders of magnitude more performance per watt than competing solutions. Moreover, the adoption of RADAR for gesture detection provides higher reliability compared to image sensors. However, no accepted topology for a temporal SNN classifier focusing on RADAR data exists. In addition, previous research didn’t account for several design limitations necessary to export the design in analog hardware.

In this work, we explore the possible SNN topologies and propose a Liquid State Machine (LSM) with fully-supervised readout, suitable to be exported to a mixed-signal neuron array. A complete parametric model of the architecture and learning rule has been implemented in a simulation environment. Following, the design space was explored in search for the optimal operating region. By analysing the results, the effects of several parameters and the trade-off between accuracy and power consumption were highlighted and explained. In particular, our work puts emphasis on a good balance between global excitation and inhibition in the LSM.

The final design contains 87 neurons and has a peak classification performance on 6 classes of 50%. The limitations of the proposed design point to the importance of an adequate feature extraction for a stable LSM behaviour and to the unpredictable nature of the SNN backpropagation algorithm, caused by the non differentiability of the spike signals.


MSc SS Thesis Presentation

Wave Dynamics in Inverse Krylov Subspaces

Joris Belier

Recent studies have shown an increased interest in modal solutions of wave problems with resonating structures. These studies demonstrate that resonating structures with physical dimensions close to a wavelength can be accurately described by a few relevant resonating modes. The physical dimensions of the demonstrated resonating structures were close to a wavelength, which suggests that these highly-resonating modes have relatively low eigenvalues. Those resonating-modes are therefore dominantly present in Krylov subspaces generated by inverse projections of the wave-operator. Relevant wave dynamics can, therefore, be effectively computed from inverse Krylov subspaces. Furthermore, inverse Krylov subspaces are computationally stable and are therefore a powerful way to compute high-fidelity modal solutions. With interesting applications in high Q-factor wave problems.

The aim of this work is on improving the performance of inverse Krylov subspaces. Improvements to inverse Krylov subspace can be grouped into two approaches. In the first approach symmetry is exploited in the inverse wave-operator for reduced computational complexity and in the second approach the wave-operator is conditioned for desirable characteristics at the relatively low side of the spectrum. We will study several wave-operator configurations and optimize according to those approaches.

Earlier studies have shown that in the dimensions with pseudo-periodic boundary conditions, the double-curl is efficiently eigendecomposed as spatial derivatives are diagonal operators acting on frequency representations. We extend this work by providing an alternative, more compact presentation in the continuous domain of the eigendecomposition of the double-curl. This eigendecomposition is used to create a nullspace free eigenvalue problem.

Consecutively, we analyse the characteristics of the inverse wave-operator with Perfectly Matched Layers (PML). This analysis shows that in terms of inverse Krylov subspaces, the PML is not the obvious choice for the optimal absorbing boundary condition. Most notably, the PML introduces undesirable effects at the lower end of the spectrum, significantly impeding the performance of inverse Krylov subspaces, which leads to the conclusion that absorbing boundary conditions should be reassessed in terms of inverse Krylov subspaces behaviour.

Lastly, we will study the so-called Fixed-Frequency PML (FF-PML), which is a PML inspired time-independent absorbing boundary condition. Our study has shown that the FF-PML is a more suitable absorbing boundary condition candidate for inverse Krylov subspaces. It does not have the undesirable effects at the lower end of the spectrum, which the traditional PML has. Furthermore, and even more importantly, we derive analytic expressions of the inverse wave operator with FF-PML absorbing boundary conditions. This simple and novel insight is easily exploited to invert the wave-operator efficiently, which enables a new approach to the computation of modal solutions of open scattering problems. The first results of which look promising.


CAS MSc Midterm Presentations

Optimization of Radio Astronomy Sensor Placement with Calibration Taken into Account

Kaiwen Zhang


MSc BME Thesis Presentation

Investigating brain function and anatomy through ICA-based functional ultrasound imaging

Mado Ntekouli

Understanding the hidden organizational principles existing in the human brain was always one of the great challenges in Neuroscience. To uncover the way the brain functions, advancements in the fields of Medical Imaging and Computational Science have been of great importance. Powerful imaging tools, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), have already enabled scanning the whole brain volume and visualizing the brain functioning, both at rest and during task execution, to a signicant degree.

However, several limitations especially in spatiotemporal resolution led to the need for further advancements in the eld of functional imaging. An alternative technique, that overcomes most of the previously existing problems, is functional ultrasound (fUS). fUS is capable of imaging even the microvasculature blood-flow dynamics in response to brain activation with high spatiotemporal resolution. The wealth of fUS-acquired data calls for advanced data-analytic methods to uncover new information, beyond the well-applied simple univariant correlation method.

This is the main goal of this MSc thesis, to use a proper analysis technique, mainly borrowed from the same-principle fMRI technique, in order to produce powerful inferences. For this reason, a detailed literature review regarding fUS imaging and fMRI analysis methods is introduced. Then, the main analysis part is focused on the Independent Component Analysis (ICA) method, trying to segregate the brain into spatially independent components that share a similar activity response. Here, the whole processing pipeline is established, describing all the necessary preprocessing steps along with ICA parameters and approaches (single- and group-ICA) using the ICASSO software package. As a post-processing step, functional images-to-Allen brain atlas registration is also performed in order to identify the different regions represented in the ICA-derived spatial components. The effectiveness of the methods is assessed based on the collected results on different datasets, obtained from 2D visual-stimulation as well as 3D resting-state experiments conducted on mice at the Neuroscience department of the Erasmus MC. As a conclusion, ICA was able to separate different anatomical and functional sub-networks. More specically, from the visual-stimulation experiments, brain regions such as Lateral geniculate nucleus (LGN) that play a role in the visual pathway are identied, while from the resting-state the spatial continuity of different regions is confirmed.


MSc TC Thesis Presentation

Space-Time Codes for Massive MIMO Systems

Vishnu Rachuri

Ubiquitous connectivity requirements and stringent quality-of-services (QoS) in recent wireless communications demand new revolutionary wireless network technologies to support the exponentially increasing traffic growth. Massive multiple-input-multiple-output (MIMO) with the capability of high spectral efficiency achieved by large multiplexing and diversity gains grabbed a lot of attention as a promising solution for future cellular networks. Achieving ultra-reliable and low latency communication is very challenging without increasing the network infrastructure cost, or extra processing complexity. Adapting space-time-block-codes (STBC) can improve the reliability of the system, but this increases the downlink pilot overhead. Two precoders are devised to exploit full spatial diversity with the blind combining process to avoid downlink pilot overhead.


MSc CE Thesis Presentation

Neuromorphic Retina Design for LIDAR

Rahul Vyas

Autonomous vehicle (AV technology) relies heavily on vision based applications like object recognition, obstacle/collision avoidance etc. In order to achieve this, understanding and estimating the dynamics in the environment is extremely important. LIDARs are proven to detect both shape as well as the speed/movement of the objects in the scene but one of the biggest challenges faced in adapting LIDAR technology is the huge amount of data it produces and the way it is processed. Most of this data is redundant static information which results in wastage of system memory, computational resources, power and time. Inspired from biological retina, first Neuromorphic-Retina for LIDAR is proposed that is able to extract and encode movement happening at particular distance, particular angle and with particular velocity from raw LIDAR temporal pulses into unique spike sequences so that the information about the dynamic environment can be efficiently classified and processed by event based and low powered Neuromorphic processing unit.

The system is designed in such a way that it avoids consumption of large amount of computational resources and system memory. Simulation results show that the Retina is able to filter out redundant static information from the LIDAR data stream thereby reducing data throughput of around 50 - 70 % with 5 - 22 % spatial quality loss (based on scenario) as well as remove noise caused due to luminous reflections. This has tremendous impact on system latency and power consumption due to drop in memory accesses.

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MSc SS Thesis Presentation

Particle Filter based Speaker Tracking in Distributed Pairwise Microphone Networks

Lantian Kou

The particle filter (PF) algorithm is appropriate to solve the problem of speaker tracking in a reverberant and noisy environment using distributed pairwise microphone networks. First, complete the tracking task based on PF algorithm in centralized manner, a processing center is required to collect the signal from all microphones to carry out the PF processing. The computation complexity and time consumption of the particle filter algorithm are relatively high, mainly because of the large number of particles exploited in the filtering process since the effectiveness and accuracy of the particle filter particularly rely on the sample set size.

However, almost all the existing particle filtering algorithms exploit the fixed number of particles, especially in the field of acoustic source tracking. To deal with this matter, Kullback-Leibler distance (KLD) sampling method was utilized as an adaptation technique to adjust the sample size instead of setting fixed number.

Two approaches based on particle filter algorithm for tracking speaker in distributed way are proposed. Compared to the centralized scheme, each microphone pair in the distributed network executes the local PF individually and exchanges local weights or posterior parameters among neighboring nodes to efficiently achieve the global estimate of the sound source position. Finally, simulation experiments demonstrate these two methods are feasible to track the speaker in distributed microphone networks with a variable number of particles.


MSc SS Thesis Presentation

Active Semi-Supervised Learning For Diffusions on Graphs

Bishwadeep Das

In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal training set keeping the classifier in mind. Random or heuristic-driven selection does not care for the classification process or are trivially defined.

We are interested in the graph structure formed by the data, as seen in citation, social and biological networks. Accordingly, active semi-supervised learning on graphs labels nodes to enhance the performance of classification.

We propose a new methodology to perform active learning for diffusion-based semi-supervised classifiers. In particular, we focus on a classifier which diffuses probability distributions over the graph through random walks. We postulate the active learning problem as i) a linear inverse problem with a sparse starting distribution over the nodes; ii) a model output selection problem. For the former, we use sparsity-regularized inverse problems to select nodes. For the latter, we use tools from Compressed Sensing and Sparse Sensing to select the nodes with the relevant model output. We show that we can select all the relevant nodes in a single shot fashion, hence avoiding reliance on multiple training phases.

Results on simulated as well as real data-sets show the proposed methods outperform random labeling, thereby proving to be relevant for active semi-supervised learning on graphs.

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MSc SS Thesis Presentation

Magnetic Resonance Imaging Motion Correction in k-Space

Rajesh Rajwade

Magnetic Resonance Imaging is a widely used technique to obtain images of the interior of the human body for diagnosis and treatment. MRI machines capture the raw signal in spatial frequency domain i.e. k-space and the image is obtained via Fourier transform.

The Cartesian acquisition is one of the most commonly used acquisition patterns in MRI and is most susceptible to the patient's motion. Due to long scanning times, the possibility of the patient's movement is higher which introduces bulk motion artifacts reducing the quality of the image. Motion artifacts can affect the diagnosis and the necessity of re-scanning can cause significant financial costs as well as delays in diagnostics. 

Current methods for correcting motion artifacts work in image domain which need completely sampled k-space for reconstruction and hence are not useful for real-time artifacts correction.

In this thesis, machine learning methods that can detect, estimate and correct motion artifacts in k-space were investigated making it possible to correct artifacts in real-time without the necessity of reconstruction.  For each of these methods, we analyze the performance and discuss the merits and demerits.

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MSc CE Thesis Presentation

Object Detecting Architecture using Spiking Neural Networks

Joppe Lauriks

Spiking Neural Networks have opened new doors in the world of Neural Networks. This work implements and shows a viable architecture to detect and classify blob-like input data. An architecture consisting of three parts a region proposal network, weight calculations, and the classifier is discussed and implemented.

The region proposal network is build based on a blob detecting Laplacian of Gaussian function. The architecture is tested and verified on the Multi MNIST dataset that is generated based on the MNIST dataset that consists of handwritten digits. Results show that, on average, the region proposal network can locate the blobs in the input with an accuracy of within a single pixel distance from the ground truth. Two different ways of decoding the rate data coming from the region proposal network where discussed the Peak based decoder could propose regions even if these regions are situated closely together. A Center of Mass decoder is slightly more accurate than the Peak based decoder but at a higher computational cost and performance degradation when the regions are close together.

The region proposal network at worst only accounts for 3.19% of inaccuracy. The implementation shows that the architecture is a viable way of detecting and classifying multiple objects within the input. The data shows that the region proposal network itself is a feasible way of detecting blob-like objects within its input.


Signal Processing Seminar

Partial discharges recognition and (localization) in Gas Insulated Systems (GIS) using the cross wavelet transform.

Fabio Muñoz Muñoz
TU Delft

Partial Discharges (PD) detection is an essential tool for the diagnosis of high-voltage equipment because of their accuracy to detect and quantify defects and damages in the dielectric insulation, where the detection implies the measurement, acquisition, storage and processing of the PD phenomenon. Nowadays, the most widespread PD detection system is based on electrical measurements, in which the PD signals are acquired in the form of individual or series of electrical pulses.

In spite of the PD measurement has been exhaustively researched over the years, the separation of PD pulses from noise is one of the main challenges, especially in online applications. Noise contamination still one of the significant problems for PD measurements. Several studies have focused on the PD pulses separation and denoising techniques for PD measurements, in which the wavelet transform has been extensively used because is capable of locating time and frequency components allowing the analysis of aperiodic signals with irregular and transition features, such as the partial discharges. However, a major problem that most of these denoising techniques face is the ingress of external interferences having time-frequency characteristics similar to the partial discharge signals: periodic pulse-shaped interferences from power electronics, PD and corona discharges from the external power system, electrical pulses from switching operations, lightings, etc. This external noise can cause a false indication of PD activity, reducing the effectivity of the PD measurements as a diagnostic tool.

In PD measurement systems multiple signals can be simultaneously acquired for each PD event. Recording each signal through different sensors may provide extra useful information about the real nature of the waveform recorded. Tools like the correlation and trend analysis can provide the significance of relationships between the signals recorded. Nevertheless, these tools may not detect correlations if the signals are phase shifted; for instance, a phase shift of 180 ° between the signals may appear uncorrelated. The cross-correlation and the cross-spectral analysis can detect the phase shift, but only as average values and in stationary signals. For analysing aperiodic signals with irregular and transition features, the most suitable tool is the cross wavelet transform because it exposes regions with high common power and reveals the local relative phase between both signals.

In this presentation, we introduce the partial discharge measurements, the PD signals propagation in GIS, the cross wavelet as a tool to separate the PDs from the external disturbances, and some of the challenges that we are facing in the PD localization and detection in GIS.


Signal Processing Seminar

On Unlimited Sampling and Reconstruction: A New Way to Sense the Continuum

Ayush Bhandari

Almost all forms of data are captured using digital sensors or analog-to-digital converters (ADCs) which are inherently limited by dynamic range. Consequently, whenever a physical signal exceeds the maximum recordable voltage, the digital sensor saturates and results in clipped measurements. For example, a camera pointed towards the sun leads to an all-white photograph. Motivated by a variety of applications including scientific imaging, communication theory and digital sensing, a natural question that arises is: Can we capture a signal with arbitrary dynamic range?

In this work, we introduce the Unlimited Sensing framework which is a novel, non-linear sensing architecture that allows for recovery of an arbitrarily high dynamic range, continuous-time signal from its low dynamic range, digital measurements. Our work is based on a radically different ADC design, which allows for the ADC to reset rather than to saturate, thus producing modulo or folded samples.

In the first part of this talk, we discuss a recovery guarantee akin to Shannon’s sampling theorem which, remarkably, is independent of the maximum recordable ADC voltage. Our theory is complemented with a stable recovery algorithm. Moving further, we reinterpret the unlimited sensing framework as a generalized linear model and discuss the recovery of structured signals such as continuous-time sparse signals. This new sensing paradigm that is based on a co-design of hardware and algorithms leads to several interesting future research directions. On the theoretical front, a fundamental interplay of sampling theory and inverse problems raises new standalone questions. On the practical front, the benefits of a new way to sense the world (without dynamic range limitations) are clearly visible. We conclude this talk with a discussion on future directions and relevant applications.

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Signal Processing Seminar

Sparse Bayesian Learning: A Beamforming and Toeplitz Approximation Perspective

Bhaskar Rao
UCSD

Sparse Bayesian Learning (SBL) methods that employ a Gaussian scale mixture prior have been successfully applied for solving the sparse signal recovery (SSR) problem. The SBL-EM based inference algorithm will be examined and interpreted using a beamforming framework. A contrast with the classical minimum power distortionless response (MPDR) beamformer will be drawn and the benefits highlighted. An interesting finding is the ability of SBL to deal with correlated sources. For a uniform linear array (ULA), the Toeplitz approximation property of SBL will be discussed and the potential benefits for a nested array demonstrated.

Speaker Biography

Bhaskar D. Rao received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in 1979 and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1981 and 1983, respectively. Since 1983, he has been with the University of California at San Diego, La Jolla, where he is currently a Distinguished Professor in the Electrical and Computer Engineering department. He is the holder of the Ericsson endowed chair in Wireless Access Networks and was the Director of the Center for Wireless Communications (2008-2011). Prof. Rao’s interests are in the areas of digital signal processing, estimation theory, and optimization theory, with applications to digital communications, speech signal processing, and biomedical signal processing.

Prof. Rao was elected fellow of IEEE in 2000 for his contributions to the statistical analysis of subspace algorithms for harmonic retrieval. His work has received several paper awards; 2013 best paper award at the Fall 2013, IEEE Vehicular Technology Conference for the paper “Multicell Random Beamforming with CDF-based Scheduling: Exact Rate and Scaling Laws,” by Yichao Huang and Bhaskar D Rao, 2012 Signal Processing Society (SPS) best paper award for the paper “An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem,” by David P. Wipf and Bhaskar D. Rao published in IEEE Transaction on Signal Processing, Volume: 55, No. 7, July 2007, 2008 Stephen O. Rice Prize paper award in the field of communication systems for the paper “Network Duality for Multiuser MIMO Beamforming Networks and Applications,” by B. Song, R. L. Cruz and B. D. Rao that appeared in the IEEE Transactions on Communications, Vol. 55, No. 3, March 2007, pp. 618 630. (http://www.comsoc.org/ awards/rice.html), among others. Prof. Rao is also the recipient of the 2016 IEEE Signal Processing Society Technical Achievement Award.

Prof. Rao has been a member of the Statistical Signal and Array Processing technical committee, the Signal Processing Theory and Methods technical committee, the Communications technical committee of the IEEE Signal Processing Society and is currently chair of the Machine learning for Signal Processing technical committee.

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Signal Processing Seminar

Realizing the potential of precision medicine with mobile sensing and analytics

Emre Ertin
Ohio State University

Recent advances in wearable sensing and mobile computing have given researchers the ability to collect unprecedented amounts of data about everything from biology to behavior that can explain and improve people's health status. Day-to-day data from wearable sensors allows for better and more personalized decisions in regard to health care and management. Specifically, continuous monitoring of physiology and behavior can help us to assess disease risks, perform disease prevention and early detection of chronic conditions. However, there still exist a multitude of challenges to implement this vision of precision medicine. Wearable sensors provide large, noisy, complex data streams about the many facets of our life and health, but there is still a a gaping need for computational techniques that can transform sensor data into set of useful bio-markers readily interpretable by clinicians. This talk will describe our recent work in pairing rich probabilistic models with Bayesian methods to dramatically expand the scale and quality of physiological data we can obtain in the field while minimizing the burden to participants.


Signal Processing Seminar

Privacy-Preserving Distributed Optimization via subspace perturbation: A generalized convex optimization approach

Qiongxiu Li (Jane)
Audio Analysis Lab, Aalborg University


MSc ME Thesis Presentation

A Dynamic Zoom ADC for Audio Applications

Efraïm Eland

Audio ADCs used in high-fidelity portable audio and IoT are not only required to have high linearity and dynamic range (DR) but are also expected to be very energy efficient and occupy minimum silicon area. Zoom-ADCs combine a coarse asynchronous SAR with a fine Delta-Sigma Modulator (∆ΣM) to satisfy these requirements. Existing zoom ADC architectures are limited in terms of SQNR due to the need for the fine ADC to have some over-ranging. That, together with the leakage of the SAR ADC’s quantization noise, “fuzz,” into the audio band, puts a lower limit on the sampling frequency.
This thesis describes the design of a zoom-ADC for an audio bandwidth of 20kHz. Using a 4-level quantizer, instead of a conventional 1b quantizer, mitigates the adverse effects of over-ranging, making it possible to keep a very low sampling frequency. On top of that, it makes use of a simple, low power analog “fuzz” cancellation scheme to prevent the SAR quantization noise from leaking into the audio band.
The chip has been prototyped in a standard 160nm CMOS technology and consumes 339μW with 107.7dB DR and 105dB SNDR. Compared to state-of-the-art ADCs with a similar bandwidth, this work achieves a 2x lower OSR (fs = 2.5MHz), significantly improving the energy efficiency and achieving a Schreier FoM of 185.4dB.


MSc ME Thesis Presentation

Rail-to-rail input and output amplifier for ADC front-end applications.

Shubham Khandelwal

This work presents a unity-gain stable operational amplifier for an ADC front-end application. The op-amp focuses on delivering high linearity with low noise and offset while driving a switched capacitor load. To accomplish this the op-amp employs Current Spillover, Chopping and Gain-Boosting techniques. The op-amp achieves THD of -108 dB at 10kHz, offset of 2.7 µV and input noise density of 19.3 nV/√Hz while consuming 504 µW; resulting in an NEF of 12.28. The op-amp is fabricated in 0.16 µm CMOS technology and occupies 0.1 mm2 area.


MSc SS Thesis Presentation

Estimating the room impulse response

Gabriele Zacca

The response of a sound system in a room primarily varies with the room itself, the position of the loudspeakers and the listening position. The room boundaries cause reflections of the sound that can lead to undesired effects such as echoes, resonances or reverberation. Therefore the location of these large reflecting surfaces is important information for sound field estimation in a room.

This work focuses on exploiting the inherent information present in echoes measured by microphones, to infer the location of nearby reflecting surfaces. A built-in microphone array is used that is co-located with the loudspeaker. The loudspeaker probes the room by emitting a known signal. A signal model is proposed which provides a relationship between reflector locations and measured microphone signals.

The locations of reflections are estimated by fitting a sparse set of modeled reflections with measurements. We present two novelties with respect to prior art. First, the method is end-to-end where from raw microphone measurements it outputs an estimate of the location of reflectors. Where specifically for the compact uniform circular microphone array the symmetry is exploited to create an algorithm that is of reduced computational complexity. Secondly, the model is extended to include a loudspeaker model that is aware of the inherent directivity pattern of the loudspeaker.

The performance of the proposed localization method is compared in simulation to the existing state-of-the-art localization methods. Real world measurements are also used to validate the proposed loudspeaker model.


MSc SS Thesis Presentation

Atrial Fibrillation: Estimation of the local activation time in high-resolution mapping data

Bart Kölling

A common cardiac arrhythmia is atrial fibrillation, which is becoming more widespread worldwide. Currently there is some understanding about the mechanisms behind atrial fibrillation, however more insight into the conduction of the atrial tissue is desired.

Therefore, invasive mapping studies have been performed where an array of electrodes is used to record the electrical activity on the heart’s surface during open-chest surgery. The moment in time when the tissue under an electrode depolarizes, called the local activation time can be used to reconstruct the propagation pattern of the signal that triggers the tissue to contract.

In this thesis, the application of the cross-correlation for estimation of the local activation time of the atria is investigated. Specifically, the benefits of not only cross-correlating electrode pairs that are close, but also pairs that are far away are evaluated. A framework is constructed, based on a graph, that defines these higher order neighbouring pairs of electrodes.

This is compared to the golden standard of using the steepest deflection of an electrogram, as well as to other methods using the cross-correlation. Experiments are done on simulated electrograms where the true activation times are available, as well as on natural data recorded from patients. Finally some future research is proposed to investigate for which morphologies the proposed cross-correlation based methods may be most effective.

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MSc SS Thesis Presentation

Signal Modelling and Imaging of Low Field MRI

Sherine Brahma

MRI machines are devices that are used to non-invasively obtain images of the internal anatomy and physiological processes of the human body. It is safe to use as the patient is not exposed to any harmful radiation, and there are no known side effects. But such machines that are commercially available are very expensive. Due to this reason, it eludes access to a large portion of the population, particularly in developing countries.

This thesis investigates an inexpensive MRI machine that is based on a rotating inhomogeneous magnetic field map. Unlike conventional scanners, because of the rotating field, the signal model of this device has to account for it. The objective of this work is to examine the aforementioned model, and also to implement Krylov subspace-based reconstruction algorithms available in the IRTools package.

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Signal Processing Seminar

Delamination monitoring in composites with fibre Bragg grating sensors

Aydin Rajabzadeh

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MSc ES Thesis Presentation

N-Shot Training Methodology

Ninad Joshi

Traditional Artificial Neural Networks(ANNs)like CNNs have shown tremendous opportunities in various domains like autonomous cars, disease diagnosis, etc. Proven learning algorithms like backpropagation help ANNs in achieving higher accuracy. But there is a serious challenge with the increasing popularity of traditional ANNs is of energy consumption and computational complexity.

Spiking Neural Networks (SNNs) are considered to be next-generation neural networks that are capable of doing complex deep learning applications at fraction of energy that is needed in current deep learning applications because of its similarity to biological neurons. However, SNN is still not able to match the classification accuracy of ANNs which poses a big challenge for wide acceptance of SNN in various applications as traditional learning methods like backpropagation are not possible in SNN.

During training of a neural network the weight matrix is of the highest importance as it eventually decides the trajectory of learning. Currently, one existing solution is to just manually convert ANNs into SNNs to get weight matrix which doesnot focus on getting weight matrix from a small dataset and doesn’t consider spiking neuron parameters.

We aim to address this challenge by proposing a novel N-shot training methodology that is capable of providing a weight matrix for SNN and can give sufficient classification accuracy. The methodology not only provides the weight matrix but can do training with a very small dataset(up to 1 image per class) and still can give considerably higher accuracy. For a reduced MNIST dataset, the method can give an accuracy of 71.68% 10 images per class.

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MSc ME Thesis Presentation

A PLL-based eddy current displacement sensor for button applications

Matheus Ferreira Pimenta

This thesis presents an eddy current sensor (ECS) for button readout applications. The interface embeds the coil sensor in a digitally controlled oscillator (DCO) and uses a highly digital phase locked loop (PLL) to convert the displacement information into a digital output.
The sensor achieves more than 12bit effective resolution, which translates into an equivalent displacement resolution in excess of 10nm RMS. The interface consumes less than 235µA from a 1.8V supply, resulting in a very power efficient architecture.


MSc SS Thesis Presentation

Indoor localization using narrowband radios and switched antennas in indoor environment

Ye Cui

In this thesis, we explore the potential of indoor localization using Bluetooth narrowband radios. To start with, a data model according to the property of the conducted measurement data is developed. The conducted measurement data is radio channel measure- ments based on channel sounding technique. Then the data model is developed as a channel impulse response model and multipath signals are indicated by different time delays.

Delays are estimated after subspace estimation of the data covariance matrix. Smoothing techniques are employed to improve the covariance matrix estimate. To detect the rank of the subspace, two techniques are investigated, namely the MDL algorithm and the threshold method. New estimates for the thresholds are derived, valid for Hankel-structured data matrices. Experiments are conducted to investigate the performance and reliability of those two techniques, under different parameter values.

Next, we consider subspace-based super-resolution algorithm, in particular the MUSIC algorithm. The functionality of the MUSIC algorithm on narrowband radios measurements is tested and evaluated firstly by simulation experiments, which demonstrate the practicability of applying MUSIC algorithm on narrowband radios measurements. Then experiments are extended to the measurement data that conducted from real indoor environments, for the purpose of indoor localization realization using narrowband radios.


MS3 seminar

Ultra Wide Band Surveillance Radar

Dr. Mark E. Davis, IEEE Fellow, IEEE Distinguished Lecturer
IEEE

Ultra Wide Band Surveillance Radar is an emerging technology for detecting and characterizing targets and cultural features for military and geosciences applications. It is essential to have fine range and cross-range resolution to characterize objects near and under severe clutter. This lecture will provide an in-depth look into:

  • The early history of battlefield surveillance radar
  • UWB phased array antenna
  • UWB Synthetic aperture radar (SAR)
  • UWB ground moving target indication
  • New research in multi-node ultra wind band radar

Lecturer Biography: Dr Mark E Davis has over 50 years’ experience in Radar technology and systems development. He has held senior management positions in the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory, and General Electric Aerospace. At DARPA, he was the program manager on both the foliage penetration (FOPEN) radar advanced development program and the GeoSAR foliage penetration mapping radar.

His education includes a PhD in Physics from The Ohio State University, and Bachelor and Master’s Degrees in Electrical Engineering from Syracuse University. He is a Life Fellow of both the IEEE and Military Sensing Symposia, and a member of IEEE Aerospace Electronics Systems Society Board of Governors, VP Conferences, and past-Chair the Radar Systems Panel. He is the 2011 recipient of the AESS Warren D White Award for Excellence in Radar Engineering, and the 2018 IEEE Dennis J. Pickard Medal for Radar Technologies and Applications.


MSc SS Thesis Presentation

Radio astronomy image formation using Bayesian learning techniques

Yajie Tang

Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the ill-posedness, side information should be involved. Based on the sparsity assumption of the sky image, we consider L1-regularization. We formulate the image formation problem as a L1-regularized weighted least square (WLS) problem and associate each variable with one regularization parameter. We use Bayesian learning to learn the regularization parameters from data by maximizing the posterior density. With the iterative update of the regularization parameters, the solution is updated until convergence of the regularization parameters. We involve a stopping rule based on the noise level to improve the computational eachciency and control the sparsity of the solution. We compare the performance of this Bayesian learning method with other existing imaging methods by simulations. Finally, we propose some future research directions in improving the performance of this Bayesian learning method.


Signal Processing Seminar

Analytical Full-Wave Free Induction Decay Signal Model for MRI

Patrick Fuchs

The derivation of the standard signal model in Magnetic Resonance Imaging (MRI) is based on a quasi-static electromagnetic field approximation and is essentially obtained through an application of the Biot-Savart law. Such an approach works fine for relatively low MR background fields (up till 1.5 T, say), but the model may lose its validity at higher static background fields, since the oscillation frequency of the electromagnetic radio-frequency fields is linearly related to the magnitude of this background field via the well-known Larmor equation. Consequently, an increase in the strength of the static background field leads to an increased Larmor frequency and the quasi-static field approximation may no longer be applicable.

In this presentation, We derive a signal model based on the full Maxwell system and no quasi-static field approximations are applied. We show that the measured signal consists of a direct term that relates the measured signal to the time-varying magnetization within the sample and a scattering term that is due to the dielectric contrast of the sample with respect to its surroundings (assuming no contrast in the permeability). Similar to the quasi-static case, the contribution of each term to the measured signal can be expressed in terms of so-called electric and magnetic receive fields, which basically act as frequency-dependent sensitivity functions. Furthermore, the scattering term is expressed in terms of the electric field strength inside the sample but this field is unknown in general. However, if the dielectric constitution of the sample is known then this field can be determined in principle. Additionally, for low-contrast samples, the Born approximation may be applied leading to an explicit analytic full-wave signal model in terms of the magnetization of the sample, its conductivity, and its permittivity. Finally, through simulations, we illustrate the receive field sensitivity functions for different measurement scenarios and show which terms in the signal model provide the largest contribution to the measured signal as a function of receiver location, frequency, and dielectric composition and size of the sample under test.

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CAS MSc Midterm Presentations

CAS MSc Midterm Presentations

Marnix Abrahams (mid)


Signal Processing Seminar

A Data Scientific Approach to Efficient Submillimeter Astronomical Spectroscopy

Akio Taniguchi
Nagoya University, Japan

Astronomical data have become huge, as a result of recent advances in wide-field and wide-band instruments. To efficiently extract astronomical signals from observations using these instruments, data scientific approaches are essential. In the (sub)millimeter waveband, spectroscopy with ground-based single-dish telescopes is the best method for surveying interstellar molecules and atoms. However, such observations are not efficient yet, because they always suffer from the intense and time-varying atmosphere of the Earth.

In this talk, I present a statistical method to remove the atmospheric emission from a large spectroscopic dataset by using its intrinsic frequency correlation or spectral shape. As an application, I introduce a recent development of frequency modulation (FM) spectroscopy, which is three times more efficient than a conventional method [1]. As a collaboration with TU Delft, I introduce another application of spectral-cleaning for an ultra-wide-band (UWB) spectrometer DESHIMA [2]. Grasping the UWB atmospheric characteristics by using our data analysis software [3], it removes atmospheric effects on an astronomical spectrum much better than a conventional method.

[1] Akio Taniguchi, Yoichi Tamura et al., "A new off-point-less observing method for millimeter and submillimeter spectroscopy with a frequency-modulating local oscillator (FMLO)", submitted to Publications of the Astronomical Society of Japan (2019)
[2] Akira Endo, Kenichi Karatsu, Yoichi Tamura, Tai Oshima, Akio Taniguchi, ..., Jochem J. A. Baselmans, "First light demonstration of the integrated superconducting spectrometer", Nature Astronomy (2019), Advanced Online Publication https://rdcu.be/bM2FN
[3] Akio Taniguchi, Tsuyoshi Ishida, "De:code - DESHIMA code for data analysis", DOI 10.5281/zenodo.3384216

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Signal Processing Seminar

Local Activation Time estimation in Atrial Electrograms

Bahareh Abdi

The interpretation of unipolar electrograms is complicated by interference from nonlocal activities of neighbouring tissue. This happens due to the spatial blurring that is inherent to electrogram recordings. In this study, we aim to exploit the high-resolution multi-electrode recordings during atrial mapping to amplify local activities and suppress non-local activities in each of the electrograms. This will subsequently improve the annotation of local deflections and local activation times (LATs) of the electrograms.

According to electrophysiological models, electrogram array can be modelled as a spatial convolution of per cell transmembrane currents with an appropriate distance kernel, which depends on cells’ distances to the electrodes. By deconvolving the effect of the distance kernel from the electrogram array, we undo the blurring and estimate the underlying transmembrane currents as our desired local activities. However, deconvolution problems are typically highly ill-posed and result in unstable solutions.

To overcome this issue, we propose to use a regularization term that exploits the sparsity of the first-order time derivative of the electrograms. We also discuss, in summary, the required electrode array specifications including the spatial resolution and electrode diameter for an appropriate electrogram array recording and subsequent deconvolution.

We perform experiments on simulated two-dimensional tissues, as well as clinically recorded electrograms during paroxysmal atrial fibrillation. The results show that the proposed approach for deconvolution can efficiently amplify the local deflection in fractionated electrograms and attenuate nonlocal activities. This, in turn, improves the annotation of the true LAT in the fractionated electrogram.

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MSc SS Thesis Presentation

A Generative Neural Network Model for Speech Enhancement

Husain Kapadia

Listening in noise is a challenging problem that affects the hearing capability of not only normal hearing but especially hearing impaired people. Since the last four decades, enhancing the quality and intelligibility of noise corrupted speech by reducing the effect of noise has been addressed using statistical signal processing techniques as well as neural networks. However, the fundamental idea behind implementing these methods is the same, i.e., to achieve the best possible estimate of a single target speech waveform. This thesis explores a different route using generative modeling with deep neural networks where speech is artificially generated by conditioning the model on previously predicted samples and features extracted from noisy speech. The proposed system consists of the U-Net model for enhancing the noisy features and the WaveRNN synthesizer (originally proposed for text-to-speech synthesis) re-designed for synthesizing clean sounding speech from noisy features. Subjective results indicate that speech generated by the proposed system is preferred over listening to noisy speech, however, the improvement in intelligibility is limited. 

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Signal Processing Seminar

Signal Processing for reduced hardware radio astronomy imaging

Manuel Stein

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CAS MSc Midterm Presentations

Erik Hagenaars (mid)

Erik Hagenaars (mid)


MSc ME Thesis Presentation

A low-noise amplifier for ultrasound imaging with continuous time-gain compensation

Qiyou Jiang

This work presents a low-noise amplifier (LNA) for ultrasound imaging with built-in continuous time-gain compensation (TGC), which compensates for the time-dependent attenuation of the received echo signal and thus significantly reduces its dynamic range (DR).

The proposed design combines the LNA and TGC functions in a single variable-gain current-to-current amplifier. Compared to conventional ultrasound front-ends, which implement the TGC function after an LNA that needs to handle the full DR of the echo signal, this approach can highly reduce the power consumption and the size. Compared to earlier programmable gain LNAs with discrete gain steps, the continuous gain control avoids switching transients that may lead to imaging artefacts.

The TGC function is realized by a novel feedback network consisting of a double differential pair that feeds a fraction of the output current back to the input. This fraction can be changed continuously using a control voltage that is applied to the gates of the differential pairs, to realize a gain range from -20 dB to +20 dB. To achieve an approximately constant closed-loop bandwidth in the presence of the changing feedback factor, a loop amplifier has been implemented whose gain is changed along with the feedback factor by dynamically changing its bias currents. This loop amplifier employs a current-reuse architecture to achieve high power-efficiency. In addition, a variable bias current source has been designed to appropriately bias the TGC feedback network. By employing a similar double differential pair topology as in the feedback network, this current source provides the required low noise at the highest gain setting and high current at the lowest gain setting within the available headroom.

The LNA with built-in TGC function has been realized in 180nm CMOS technology. It has been optimized to interface with a 7.5 MHz capacitive micro-machined ultrasonic transducer (CMUT). Simulation results show that it achieves a 3dB bandwidth higher than 40 MHz across the full gain range. At the highest gain setting, its input current noise is 0.96 pA/rt-Hz at 7.5 MHz. This leads to an input dynamic range of 93 dB, which is compressed into an output dynamic range of 53 dB by means of the 40 dB variable gain. The amplifier consumes 10.8 mW from a 1.8V supply, and occupies an estimated 320 x 320 um2 die area.


MSc SS Thesis Presentation

Spoofing detection in a loosely coupled GNSS and INS system via Synthetic Arrays

Kostadin Biserkov

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Master thesis defence

Multiple-input Multiple-output Grating Lobe Selection Scheme for Radar Applications

Nick Cancrinus


MSc SS Thesis Presentation

Calibration of single element 3D ultrasound with a aberration mask

Bram Visser

Previous work has demonstrated the possibility of high-resolution 3D ultrasound imaging through the use of a single element and an aberration mask. This thesis will expand on the previous work by examining the proposed method for errors in the creation of the model.

The analysis is performed by examining the various aspects of the measurements setup and underlying theoretical model, after which measurements are performed to determine their contribution and correctness with regard to the model. Results demonstrated a systematic error of a non-linear frequency scaling and semi-linear phase shift. The origin of the error lies in the unwanted addition of transfer functions of some of the components. A Tikhonov regularized least squares method is proposed to estimate this transfer function and supply compensation based on all the measurements.

The results of the application of this method on the uncalibrated model are demonstrated through 1D imaging experiments. The result of which shows a significant improvement over the previous uncalibrated results. After which the possibility of calibration due to a singular measurement is explored and an adaptation of the Tikhonov regularized least squares method is proposed for a close approximation of the previously found transfer function. Further to obtain an indication of possible remaining hurdles and successes with this method, extensive simulations are performed to examine the individual impact of various sources of noise and interference.

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CAS MSc Midterm Presentations

Bart Hettema (mid), Gabriele Zacca (mid)


CAS MSc Midterm Presentations

Bas Otterloo van (mid)


MSc TC Thesis Presentation

Development of Data Processing Algorithms for UWB Radar-based Long-Term Health Monitoring

Yiting Lu

In the last two decades, a lot of attention has been focused on contactless radar-based vital signs monitoring (heartbeat and respiration rate) as an emerging and complementary value to our medical care. It is very challenging in real indoor environments to perform concurrent localization and reliable vital signs monitoring of multiple subjects within practical distance ranges. In fact, the multipath propagation results in the reflected signal dispersed in time, which not only causes false ToF (Time of Flight) estimation but also leads to inter-subject interference, jeopardizing the vital signs extraction and the localization.

Here we show a methodology based on radar techniques to automatically locate multiple subjects in indoor environments while keep monitoring their vital signs. This approach, based on the parametric models both of the propagation channel and of the radar signals, is able to cancel the undesired contributions from static clutters and multipath components, by which it is possible to accurately locate the subjects and extract their heart rates and respiration rates.


CAS MSc Midterm Presentations

Bishwadeep Das (mid), Rajwade Rajwade (mid), Sherine Brahma (mid)


Signal Processing Seminar

Biomedical Signal Processing

Miao Sun

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MSc SS Thesis Presentation

Gradient Coil Design and Construction for a Halbach Based MRI System

Bart de Vos

MRI as a medical diagnostics tool is still unavailable to the majority of the developing world. Therefore the design and development of new low-cost hardware are essential. The design of gradient coils corresponding to this hardware is necessary for conventional imaging and reconstruction methods to be used.

The target field method, which was originally developed to deal with longitudinal main magnetic fields, is applied to a transverse field, as produced by a Halbach permanent magnet array. Using this method current densities for gradient fields in the three spatial directions are derived. Subsequently, using stream functions, wire patterns for the three gradient coils are determined. These are verified using a commercial magneto-static solver. Furthermore, one of the gradients is constructed to validate the performance of the method.

The measured fields are in good agreement with the simulations and their prescribed target fields. This confirms that the proposed method provides a reliable way to design and manufacture gradient coils for various requirements. Based on the experimental review of the constructed coil three optimized gradients are proposed for the low field MRI system developed at the LUMC in cooperation with the TU Delft. The method can also be readily generalized to other geometries and requirements due to the robust fundamental physical basis and accuracy with respect to computer simulations.


Microelectronics Colloquium

Quantum Computer on a Chip

Bogdan Staszewski
University College Dublin

Quantum computing is a new paradigm that exploits fundamental principles of quantum mechanics, such as superposition and entanglement, to tackle problems in mathematics, chemistry and material science that are well beyond the reach of supercomputers. Despite the intensive worldwide race to build a useful quantum computer, it is projected to take decades before reaching the state of useful quantum supremacy. The main challenge is that qubits operate at the atomic level, thus are extremely fragile, and difficult to control and read out. The current state-of-art implements a few dozen magnetic-spin based qubits in a highly specialized technology and cools them down to a few tens of millikelvin. The high cost of cryogenic cooling prevents its widespread use. A companion classical electronic controller, needed to control and read out the qubits, is mostly realized with room-temperature laboratory instrumentation. This makes it bulky and nearly impossible to scale up to the thousands or millions of qubits needed for practical quantum algorithms.

As part of our startup company, we propose a new quantum computer paradigm that exploits the wonderful scaling achievements of mainstream integrated circuits (IC) technology which underpins personal computers and mobile phones. Just like with a small IC chip, where a single nanometer-sized CMOS transistor can be reliably replicated millions of times to build a digital processor, we propose a new structure of a qubit realized as a CMOS-compatible charge-based quantum dot that can be reliably replicated thousands of times to construct a quantum processor. Combined with an on-chip CMOS controller, it will realize a useful quantum computer which can operate at a much higher temperature of 4 kelvin.

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MS3 seminar

Diversity and nonlinear processing: trends for future radar systems

Prof. Dr. Stéphanie Bidon
Department of Electronics, Optronics and Signal at ISAE-SUPAERO, Université de Toulouse, France

Radar is an exciting field where systems are constantly evolving thanks to technical advances in several domains including RF, electronics and signal processing. Focusing on the latter, this talk illustrates two important trends that are contributing to the development of future radar systems, namely diversity and nonlinear techniques. On the one hand, diversity brings redundant information about the radar scene thereby enabling target discrimination in a given space. On the other hand, nonlinear techniques produce outputs that are not linearly related to the input signals thereby enabling relevant processing in complex scenarios. Benefits of combining both diversity and nonlinear algorithms will be presented in two radar applications: 1) to estimate range migrating targets in blind velocities with a wideband waveform 2) to estimate targets hidden in the pedestal inherent to a multicarrier waveform.

Short bio

Stéphanie Bidon received the engineer degree in aeronautics and the master degree in signal processing from ENSICA, Toulouse, in 2004 and 2005 respectively. She obtained the Ph.D. degree and the Habilitation à Diriger des Recherches in signal processing from INP, Toulouse, in 2008 and 2015 respectively.

She is currently with the Department of Electronics, Optronics and Signal at ISAE-SUPAERO, Université de Toulouse, France, as a professor.

Her research interests include digital signal processing particularly with application to radar systems (STAP, wideband radar detection, RadCom) and GNSS (robust phase tracking).


MS3 seminar

Misspecification, Robustness and Cognition in Radar Signal Processing: Some Results

Prof. Dr. Maria Sabrina Greco
Dept. of Information Engineering of the University of Pisa

After a brief survey of the activities of the Radar Signal Processing Group of the Dept. of Information Engineering, University of Pisa, the talk will focus on some of the recent and on-going research topics in which Prof. Greco is involved.

Any scientific experiment which aims to gain some knowledge about a real-word phenomenon, in radar systems as in other applications, starts with the data collection. In statistical signal processing, all the available knowledge about a physical phenomenon of interest is summarized in the probability density function (pdf) of the collected observations. In practice, the pdf or/and its characteristic parameters are partly or fully unknown, then any inference procedure starts with its estimation. The easy case is when the hypothesized statistical model and the true one are the same, so they are matched. However, a certain amount of mismatch is often inevitable in practice. The reasons for a model misspecification can be various: it may be due to an imperfect knowledge of the true data model or to the need to fulfill some operative constraints on the estimation algorithm (processing time, simple hardware implementation, and so on).

The first part of the talk aims at providing a short overview on the misspecified estimation framework with a particular focus on the Misspecified Cramér-Rao bound (MCRB). Then a possible approach to minimize the misspecification risk is presented. Specifically, a more general semiparametric characterization of the statistical behavior of the collected data is addressed and some application to the radar scenario is shown.

The talk will then continue with a short introduction to the concept of cognition applied to passive and active radars highlighting the limits and the path forward and will describe some new results regarding the application of some machine learning techniques to “cognitive” MIMO radar.

Short Bio

Maria Sabrina Greco graduated in Electronic Engineering in 1993 and received the Ph.D. degree in Telecommunication Engineering in 1998, from University of Pisa, Italy. From December 1997 to May 1998 she joined the Georgia Tech Research Institute, Atlanta, USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian background.

In 1993 she joined the Dept. of Information Engineering of the University of Pisa, where she is Full Professor since 2017. She’s IEEE fellow since Jan. 2011 and she was co-recipient of the 2001 and 2012 IEEE Aerospace and Electronic Systems Society’s Barry Carlton Awards for Best Paper and recipient of the 2008 Fred Nathanson Young Engineer of the Year award for contributions to signal processing, estimation, and detection theory. In May-June 2015 and in January-February 2018 she visited as invited Professor the Université Paris-Sud, CentraleSupélec, Paris, France.

She has been general-chair, technical program chair and organizing committee member of many international conferences over the last 10 years. She has been guest editor of the special issue on “Machine Learning for Cognition in Radio Communications and Radar” of the IEEE Journal on Special Topics of Signal Processing, lead guest editor of the special issue on "Advanced Signal Processing for Radar Applications" of the IEEE Journal on Special Topics of Signal Processing, December 2015, guest co-editor of the special issue of the Journal of the IEEE Signal Processing Society on Special Topics in Signal Processing on "Adaptive Waveform Design for Agile Sensing and Communication," published in June 2007 and lead guest editor of the special issue of International Journal of Navigation and Observation on” Modelling and Processing of Radar Signals for Earth Observation published in August 2008. She’s Associate Editor of IET Proceedings – Sonar, Radar and Navigation, member of the Editorial Board of the Springer Journal of Advances in Signal Processing (JASP), and Senior area chair of the IEEE Transactions on Signal Processing. She’s member of the IEEE AESS Board of Governors and has been member of the IEEE SPS BoG (2015-17) and Chair of the IEEE AESS Radar Panel (2015-16). She has been as well SPS Distinguished Lecturer for the years 2014-2015, and now she's AESS Distinguished Lecturer for the years 2015-2019, and AESS VP Publications.

Her general interests are in the areas of statistical signal processing, estimation and detection theory. In particular, her research interests include clutter models, coherent and incoherent detection in non-Gaussian clutter, CFAR techniques, radar waveform diversity and bistatic/mustistatic active and passive radars, cognitive radars. She co-authored many book chapters and more than 190 journal and conference papers.


Signal Processing Seminar

Low-complexity 1-bit A/D conversion

Manuel Stein

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Signal Processing Seminar

Deep learning in ultrasound imaging

Ruud van Sloun
TU Eindhoven

This talk will elaborate on deep learning strategies in ultrasound systems, from the front-end to advanced applications, thereby discussing the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, it will outline methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. Several illustrative examples will be given, covering efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learning of compressive encodings for color Doppler, and a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

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MSc CE Thesis Presentation

A Real-time Low Latency Signal Concentrator for Ship Tracking using AIS

Ramkoemar Bhoera

Global AIS coverage is not possible with terrestrial AIS as base stations are required to be build on sea, which is impractical. With the use of LEO satellites, the field of view of a single receiver is increased and is capable of communicating with many AIS cells simultaneously. As many vessels are transmitting data to the same receiver, message collisions occur which results in data loss.

In order to increase the performance of the AIS receiver, blind beamforming techniques are used. This makes it possible to separate multiple collided messages. This solution is build in a hardware receiver which returns analog signals.

The goal of this thesis is to build a low latency data acquisition system, in order to process the signals from the hardware receiver. This system requires a processing board to send the samples over to the single user receiver, which is build in software.

The Raspberry Pi is used as the processing board, but as it was unable to do the realtime work, a microcontroller is added for this specific task. Fetching data from the ADC is realized through the popular Industrial IO subsystem which allows easy integration with other IIO compliant software and is used to transfer sample data over the network to the software receiver. The software receiver decodes the AIS data and sends it to chart plotter applications which can be used to plot data on a map.

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CAS MSc Midterm Presentations

CAS MSc Midterm Presentations

Marnix Abrahams (1st)


CAS MSc Midterm Presentations

CAS MSc Midterm Presentations

Xi An (mid)


CAS MSc Midterm Presentations

CAS MSc Midterm Presentations

Joris Belier (mid)


CAS MSc Midterm Presentations

CAS MSc Midterm Presentations

Joppe Lauriks (mid)


Signal Processing Seminar

Task-cognizant sparse sensing for inference (ASPIRE)

Pim van der Meulen

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Signal Processing Seminar

Acoustic signal processing

Jamal Amini

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CAS MSc Midterm Presentations

Joppe Lauriks (mid), Joris Belier (mid)


Signal Processing Seminar

Biomedical signal processing

Bahareh Abdi

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Signal Processing Seminar

Acoustic localization

Michał Machnicki
Microflown

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Signal Processing Seminar

Adaptive classification of radar emitters

Aybuke Erol
METU, Turkey

Radar receivers collect interleaved signals from all electromagnetic sources in the environment. The ultimate goal of electronic intelligence is to separate these sources (deinterleaving) and find their types (emitter identification). Knowing the type of a source, it is possible to comment on its mission and operation. All in all, deinterleaving and emitter identification together build a system that solves an adaptive classification problem. One of the biggest challenges in this problem is that the system does not know all emitter types in the world since a great part of this information is confidential within each country. In addition, radar receivers sequentially provide radar pulses to the system. Therefore, the classifier should be able to increase its number of classes whenever an unfamiliar emitter type is encountered. What’s more, it should be able to distinguish between the unfamiliar emitter types, which enforces online learning.

The proposed system solves deinterleaving using fuzzy ARTMAP due to several reasons. First, it is supervised which makes the system able to start with a priori information or data. Secondly, it works with sequential input and enables online learning. Last but not least, it can increase its number of classes. After fuzzy ARTMAP, radar clusters are formed. Next, a representation for each cluster should be found, to be compared with the representations of already known emitter types. The challenge here is that describing an emitter type by single numeric values would not be fair as radar features are generally interval based. For example, emitters today do not operate on a single frequency, they rather have a frequency range in which they can operate. Hence, the representation and comparison of emitter types and radar clusters are considered under symbolic data analysis. Both parts, solved with fuzzy ARTMAP and symbolic data analysis, are improved in terms of classification accuracy from their baseline methods with the use of Jaccard index.


CAS MSc Midterm Presentations

Ye Cui (mid), Metin Calis (mid)


CAS MSc Midterm Presentations

Lantian Kou (mid)


MSc SS Thesis Presentation

Detecting Electrode Array Tip Fold-over in Cochlear Implantation

Juriaan van der Graaf

In cochlear implantation surgery, the appropriate placement of the electrode array into the cochlea is vital. Suboptimal placement of the electrode array may lead to reduced hearing performance and speech recognition after the surgery. Currently, there are methods to confirm the electrode position post-operatively (e.g. through a CT scan), but it is not possible to monitor the insertion intra-operatively. This, combined with the fact that there is difference in surgical precision and insertion technique between surgeons, leads to great variability in electrode placement and in some cases to electrode malpositioning issues. One of the more problematic issues that may arise is folding of the electrode tip. Folding of the tip causes the electrode array to not reach deep enough into the cochlea, and it is likely to cause trauma due to the increased pressure on cochlear walls and membranes. On top of that, you effectively have “less” contacts to work with because contacts can be positioned very close to eachother due to the folding. Folding of the electrode array also disrupts the tonotopic organization of the cochlear implant (the contacts near the end of the array no longer correspond to the lowest frequencies). The effectivity of the treatment is thus reduced in patients with tip fold-overs. However, many modern day cochlear implants possess telemetry features. These are primarily used to check the implant’s proper functioning, but may also be helpful in monitoring the insertion of the electrode array. The telemetry features of a cochlear implant make it possible to measure the intracochlear electrical potential. The measured current spread is related to the electrode array’s shape and position, and thus may provide a way to detect folding of the tip. This can be done post-operatively, but can possibly also be done intra-operatively to monitor the insertion in real time. This application could be a useful tool to aid surgeons and clinicians. When used post-operatively, it may provide a cost-free method to detect tip fold-over. When used intra-operatively, it may provide a way to detect and prevent both fold-over and trauma to the cochlea.

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Conferences

IEEE-EURASIP Summer School on Network- and Data-driven Learning: Fundamentals and Applications

Geert Leus, e.a.
IEEE-EURASIP

The 2019 IEEE-EURASIP Summer School on “*Network- and Data-driven Learning: Fundamentals and Applications*,” will take place from May 20 to May 24 in the beautiful city of Lecce, Italy.

It will bring together researchers to share exciting advances in network and data sciences theory and applications.

The event will host students interested in signal processing, offering them opportunities to network with world-renowned professors and industry researchers as well as to engage in hands-on tutorials in signal processing and machine learning. In addition to the beautiful ambiance offered by /“The Florence of the South of Italy,” /attendants will benefit from a stimulating environment to learn about the latest advances in an exciting field. Students will have the possibility to present their current research work in a poster session.

The technical focus of this summer school is on fundamentals and algorithmic advances for learning from large volumes of data, with emphasis on network (i.e., graph) data.

More information about the Technical Program, the speakers and the registration procedure, can be found at the webpage.

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Inauguration Earl McCune and Cicero Vaucher

Who's talking, who's listening?

Earl McCune, Cicero Vaucher
TU Delft

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CAS MSc Midterm Presentations

Kevin vanderMark (mid)


CAS MSc Midterm Presentations

Kostadin Biserkov (mid), Husain Kapadia (mid)


CAS MSc Midterm Presentations

Bishwadeep Das (1st), Bas Otterloo van (1st), Yiting Lu (mid), Yajie Tang (mid)


Signal Processing Seminar

sensor networks, rank-constrained optimization, algebraic techniques

Matthew Morency

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MSc ME Thesis Presentation

A Highly Concurrent, Memory-Efficient AER Architecture for Neuro-Synaptic Spike Routing

Joris Coenen

One of the challenges of neuromorphic computing is efficiently routing spikes from neurons to their connected synapses. The aim of this thesis is to design a spike-routing architecture for flexible connections on single-chip neuromorphic systems. A model for estimating area, power consumption, memory, spike latency and link utilisation for neuromorphic spike-routing architecture is described This model leads to the proposal for a new spike-routing architecture with a hybrid addressing scheme and a novel synaptic encoding scheme.

The proposed architecture is implemented in a SystemC simulation tool with a supporting tool for encoding arbitrary SNN topologies for the synapse encoding scheme.

Running the simulations with synthetic benchmarks and a handwriting recognition SNN shows that the proposed architecture is memory-efficient and provides low latency spike-routing with high synaptic activation concurrency.

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CAS MSc Midterm Presentations

Bram Visser (mid), Karishma Kumar (mid), Ninad Joshi (mid)


NEXT-GENERATION MOLECULAR IMAGING AND PARTICLE THERAPY SEMINAR

Symposium on advances in Positron Emission Tomography, in context of the PhD defense of Esteban Venialgo

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CAS MSc Midterm Presentations

Bart Vos de (mid), Pranav Prakash (mid), Pavel Rapoport (mid)


MSc CE Thesis Presentation

Area Minimization of DTB Multiplexer - A Chip Component with High Wire Density and Congestion

Reynaldi Canggaputra

DTB Multiplexer is a component within an NXP chip called the BAP3. This component provides a testing functionality for the chip. This component is purely combinational, and requires no clock, however this makes the component wiring-costly. This high wiring requirement leads to the area constraint imposed by the wiring demand rather than cell area, and this also leads to the DTB multiplexer reducing the placement area available for other modules.

In this thesis, the wiring area is going to be estimated as the amount of congestion, which would cause detour in the design which results in extra wiring. In this thesis, DTB multiplexer is placed by external method instead of using the place and route tools usually used by the design team. Instead, the placement is done on MATLAB which is later ported to the place and route tools using script. The placement algorithm implemented in MATLAB is primarily based on two algorithm, Dplace for initial preplacement, which in turn utilizes diffusion preplacement algorithm, and modified C-ECOP for the congestion reduction. More detailed congestion estimation done by using an additional routing estimation algorithm which is based on One-Steiner routing algorithm.

The result indicates that the modified C-ECOP can be used to reduce congestion, thus wiring area when paired with a good initial placement algorithm, but the initial placement algorithm and detailed congestion estimation algorithm with one-steiner could be further improved, and further work is needed to integrate the result with commercial place and route tools.

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Micro electronics colloquium

High performance data converters; Rethink analog IC design

Nan Sun, Muhammed Bolatkale

Nan Sun: Rethink Analog IC Design

I will present several unconventional data conversion architectures. First, I will talk about how we can make use of noise, which is usually deemed as an undesirable thing, to estimate the conversion residue and increase the SNR of a SAR ADC. It is an interesting example of stochastic resonance, in which the presence of noise can lead to not SNR degradation but SNR enhancement. Second, I will talk about how we can perform data conversion below the Nyquist rate by exploiting the sparsity of the input signal. I will show two example compressive sensing ADCs and how the effective ADC conversion rate can be reduced by 4 times but without losing information. Third, I will show how we can prevent the seemingly inevitable kT/C noise in a Nyquist-rate pipelined ADC by using a continuous-time SAR based 1st-stage. This can substantially reduce the requirement on the ADC input capacitance, greatly reducing the ADC driver power and reference buffer power

Biography of Nan Sun

Nan Sun is Associate Professor at the University of Texas at Austin. He received the B.S. from Tsinghua in 2006 and Ph.D. degree from Harvard in 2010. Dr. Sun received the NSF Career Award in 2013. He serves on the Technical Program Committee of the IEEE Custom Integrated Circuits Conference and the IEEE Asian Solid-State Circuit Conference. He is an Associate Editor of the IEEE Transactions on Circuits and Systems – I: Regular Papers, and a Guest Editor of the IEEE Journal of Solid-State Circuits. He also serves as IEEE Circuits-and-Systems Society Distinguished Lecturer from 2019 to 2020.

Muhammed Bolatkale: High Performance Data Converters

A next generation automotive radio receiver, an all-digital Class-D amplifier, and an advanced Bluetooth transceiver have one thing in common: they rely on high-performance data converter architectures to enable best in class performance. This talk will give an overview of GHz-sampling data converters, especially focusing on wideband delta-sigma and hybrid data converter architectures. We will touch upon state-of-the-art systems and circuit level designs fabricated in advance CMOS nodes.

Bio Muhammed Bolatkale

Muhammed Bolatkale is Senior Principle Scientist at NXP Semiconductors and part-time Associate Professor in the Electronics Instrumentation Laboratory at Delft University of Technology. He received his B. Sc. (high honors) degree from Middle East Technical University, Turkey, in 2004 and the M. Sc. (cum laude) and Ph.D. degrees in Electrical Engineering from Delft University of Technology, the Netherlands, in 2007 and 2013. Since 2007, Dr. Bolatkale has worked for NXP Semiconductors, specializing in wideband Delta-Sigma ADCs for wireless communications and automotive applications. Dr. Bolatkale received the ISSCC 2016 and 2011 Jan Van Vessem Award for Outstanding European Paper and the IEEE Journal of Solid-State Circuits 2016 and 2011 Best Paper Award.

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MSc SS Thesis Presentation

Localization using Time-of-Arrival Estimation in the LoRa Network

Ming DAI

LoRa (Long Range) is a low-power, long-range and low-cost wireless communication system that can facilitate a wide variety of infrastructures for the Internet of Things (IoT). Current algorithms to locate LoRa tags have a resolution of ca 100 m in practice, and a question is if that can be improved without changing the tags or adding too much to the gateways (basestations).

Conventional delay estimation ranging algorithms extract useful information from the channel frequency response and use this information to estimate delays. In this thesis, three localization techniques are presented: the matched filter, FBCM-MUSIC and TLS-ESPRIT algorithms. Then a multiband architecture is proposed and integrated into the matched filter. These algorithms are implemented in the LoRa system model. The simulations indicate that FBCM-MUSIC and TLS-ESPRIT have better performance than the matched filter in NLOS channels. The results also show that TLS-ESPRIT is more effective and robust compared to MUSIC. The proposed multiband architecture can improve the resolution of TOA estimation and decreases the 90th percentile error by around 40%


MS3 seminar

Radar Adaptivity: Antenna Based Signal Processing

Alfonso Farina
IEEE SPS Distinguished Industry Speaker

This lecture will provide an in-depth look into the history of radar systems and radar signal processing, from the beginning of radar to adaptive antenna arrays, including examples such as ground based radar systems, land and naval phased arrays, STAP for airborne radars, knowledge-based STAP, and Over the Horizon radar systems.

Biography

Alfonso FARINA , LFIEEE, FIET, FREng, Fellow of EURASIP, received the doctor degree in Electronic Engineering from the University of Rome (IT) in 1973. In 1974, he joined Selenia, then Selex ES, where he became Director of the Analysis of Integrated Systems Unit and subsequently Director of Engineering of the Large Business Systems Division. In 2012, he was Senior VP and Chief Technology Officer of the company, reporting directly to the President. From 2013 to 2014, he was senior advisor to the CTO. He retired in October 2014.

From 1979 to 1985, he was also professor of “Radar Techniques” at the University of Naples (IT). He is the author of more than 800 peer-reviewed technical publications and of books and monographs (published worldwide), some of them also translated in to Russian and Chinese.


CAS MSc Midterm Presentations

Improving processing of epicardial sensor array data for robust detection of anomalies

Bart Kölling

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Signal Processing Seminar

Network Data Science

Huijuan Wang

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CAS MSc Midterm Presentations

Xin An (1st), Sherine Brahma (1st), Bart Hettema (1st), Kevin Mark van der (1st), Manolis Papadakis (mid)


Microelectronics Colloquium

Introducing new CAS professors

Andrew Webb, Borbála Hunyadi

Andrew Webb:

MRI is one of the most important clinical imaging modalities for diagnosis and treatment monitoring. Recent trends have been towards ever higher magnetic fields and operating frequencies. This talk outlines some of the technical challenges faced by very high field and conversely very low field MRI, and the roles that electromagnetics and signal processing can play in improving image quality

Borbala Hunyadi

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) record a mixture of ongoing neural processes, physiological and non-physiological noise. The pattern of interest is often hidden within this noisy mixture. This talk gives an overview of signal processing and machine learning techniques to address this issue by capturing the spatiotemporal structure in the (multimodal) data. Special attention is given to tensor-based blind source separation techniques, with applications in epilepsy research.


Signal Processing Seminar

Sensor and Machine Learning at The Arizona State University

Andreas Spanias
Arizona State University - SenSIP

This seminar provides a description of the ASU Sensor Signal and Information Processing (SenSIP) center and its application-driven research projects. The center research activities include algorithm development for extracting information from sensors and IoT systems. More specifically center activities are focused on developing signal processing and machine learning methods for various applications including AI-enabled sensing for automotive, IoT solar energy system monitoring, surveillance systems, health monitoring, and sound systems. The center has several industry members that define and monitor research projects typically for Ph.D. student work. SenSIP has also affiliated faculty working on sensor circuits, flexible sensors, radar, smart cameras, motion estimation, secure sensor networks and other systems.

Biography

Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). Member companies of the NSF SenSIP center and industry consortium on sensor information processing include: Intel, National Instruments, LG, NXP, Raytheon, Sprint and several SBIR type companies. He is an IEEE Fellow and he recently received the IEEE Phoenix Section Award for Patents and Innovation. He also received the IEEE Region 6 section award (across 12 states) for education and research in signal processing. He is author of more than 300 papers,15 patents, two text books and several lecture monographs. He served as Distinguished lecturer for the IEEE Signal processing society in 2004.

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Radio-frequency engineering for space

Václav Valenta
European Space Agency

The key challenges in the design of radio-frequency instruments for space will be reviewed. Space environmental aspects will be discussed as well as the practical measures that need to be implemented to assure a high level of reliability. Selected examples will be presented, covering a wide spectrum of applications: from new satellite communication trends, such as active reconfigurable antennas to future scientific RF instruments that will be placed on other planets. Special focus will be put on high-power amplification concepts and integration solutions.

Speaker Bio: Václav Valenta was born in Czechoslovakia and received Master and Doctoral degrees in radio engineering and mathematics from the Brno University of Technology in the Czech Republic and Université Paris-Est in France, respectively. In the past, Dr. Valenta has designed and demonstrated active and passive radar systems operating up to a frequency of 140 GHz. His expertise is in the area of multi-functional RFIC design (SiGe BiCMOS and III-V) covering key functions from amplification, frequency generation/conversion, modulation/demodulation, and heterogenous RFIC integration. Dr. Valenta is currently with the European Space Agency, RF Equipment and Technology Section, running and supporting several R&D projects. Dr. Valenta is responsible for the development of the radio-science instrument "LaRa", which is a scientific payload that will be launched to Mars in the frame of the mission ExoMars 2020.


CAS MSc Midterm Presentations

Yajie Tang (1st), Joppe Lauriks (1st), Rajwade Rajwade (1st), Husain Kapadia (1st), Gabriele Zacca (1st)

Problem statement presentations by new MSc students


Signal Processing Seminar

From matrices to tensors: the power of tensor methods in signal processing

Borbála Hunyadi

In this presentation, I give an overview of the fundamental differences between linear and multilinear algebra. After generalising the basic concepts of matrix rank and matrix SVD to tensors, the richness of tensor representations and decompositions become clear. The richness of tensor algebra, in turn, provides powerful models for signal processing.

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CAS MSc Midterm Presentations

Ye Cui (1st), Karishma Kumar (1st), Metin Calis (1st), Joris Belier (1st)


Signal Processing Seminar

Signal processing for communication, sensor networks

Jac Romme

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Signal Processing Seminar

Wireless communication and networking, Digital Design

Tarik Kazaz

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PhD Thesis Defence

Graph-Time Signal Processing Filtering and Sampling Strategies

Elvin Isufi

The necessity to process signals living in non-Euclidean domains, such as signals defined on the top of a graph, has led to the extension of signal processing techniques to the graph setting. Among different approaches, graph signal processing distinguishes itself by providing a Fourier analysis of these signals. Analogously to the Fourier transform for time and image signals, the graph Fourier transform decomposes the graph signals decomposes in terms of the harmonics provided by the underlying topology. For instance, a graph signal characterized by a slow variation between adjacent nodes has a low frequency content.

Along with the graph Fourier transform, graph filters are the key tool to alter the graph frequency content of a graph signal. This thesis focuses on graph filters that are performed distributively in the node domain–that is, each node needs to exchange information only within its neighbor to perform a given filtering operation. Similarly to the classical filters, we propose ways to design and implement distributed finite impulse response and infinite impulse response graph filters.

One of the key contributions of this thesis is to bring the temporal dimension to graph signal processing and build upon a graph-time signal processing framework. This is done in different ways. First, we analyze the effects that the temporal variations on the graph signal and graph topology have on the filtering output. Second, we introduce the notion of joint graph-time filtering. Third, we presentpr a statistical analysis of the distributed graph filtering when the graph signal and the graph topology change randomly in time. Finally, we extend the sampling framework from the reconstruction of graph signals to the observation and tracking of time-varying graph processes.

We characterize the behavior of the distributed autoregressivemoving average (ARMA) graph filters when the graph signal and the graph topology are time-varying. The latter analysis is exploited in two ways: i ) to quantify the limitations of graph filters in a dynamic environment, such as a moving sensors processing a time-varying signal in a sensor network; and i i ) to provide ways for filtering with low computation and communication complexity time-varying graph signals.

We develop the notion of distributed graph-time filtering, which is an operation that jointly processes the graph frequencies of a time-varying graph signal on one hand and its temporal frequencies on the other hand. We propose distributed finite impulse response and infinite impulse response recursions to implement a two-dimensional graphtime filtering operation. Finally, we propose design strategies to find the filter coefficients that approximate a desired two-dimensional frequency response.

We extend the analysis of graph filters to a stochastic environment, i.e., when the graph topology and the graph signal change randomly over time. By characterizing the first and second order moments of the filter output, we quantify the impact of the graph signal and the graph topology randomness into the distributed filtering operation. The latter allows us to develop the notion of graph filtering in the mean, which is also used to ease the computational burden of classical graph filters.

Finally, we propose a sampling framework for time-varying graph signals. Particularly, when the graph signal changes over time following a state-space model, we extend the graph signal sampling theory to the tasks of observing and tracking the time-varying graph signal froma few relevant nodes. The latter theory considers the graph signal sampling as a particular case and shows that tools from sparse sensing and sensor selection can be used for sampling.

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Signal Processing Seminar

Graph Neural Networks and Graph Scattering Transforms

Alejandro Ribeiro
University of Pennsylvania

Convolutional Neural Networks (CNN) are layered information processing architectures in which each of the layers is itself the composition of a convolution operation with a pointwise nonlinearity. Graph Neural Networks (GNNs) replace the regular convolution operation with a graph convolution operation. We will discuss graph convolutions, their use in building GNN architectures, and explore stability properties of GNN operators. The stability results establish that a GNN is stable to graph deformations that are close to permutations. This result provides a theoretical basis to characterize classes of machine learning problems in which we expect GNNs to work well. 

Biography

Alejandro Ribeiro received the B.Sc. degree in electrical engineering from the Universidad de la Republica Oriental del Uruguay, Montevideo, in 1998 and the M.Sc. and Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, the University of Minnesota, Minneapolis in 2005 and 2007. From 1998 to 2003, he was a member of the technical staff at Bellsouth Montevideo. After his M.Sc. and Ph.D studies, in 2008 he joined the University of Pennsylvania (Penn), Philadelphia, where he is currently Professor of Electrical and Systems Engineering. His research interests are in the applications of statistical signal processing to collaborative intelligent systems. His specific interests are in wireless autonomous networks, machine learning on network data and distributed collaborative learning. Papers coauthored by Dr. Ribeiro received the 2014 O. Hugo Schuck best paper award, and paper awards at CDC 2017, SSP Workshop 2016, SAM Workshop 2016, Asilomar SSC Conference 2015, ACC 2013, ICASSP 2006, and ICASSP 2005. His teaching has been recognized with the 2017 Lindback award for distinguished teaching and the 2012 S. Reid Warren, Jr. Award presented by Penn’s undergraduate student body for outstanding teaching. Dr. Ribeiro is a Fulbright scholar class of 2003 and a Penn Fellow class of 2015.

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PhD Thesis Defence

Efficient computational methods in Magnetic Resonance Imaging

Jeroen van Gemert

This dissertation describes how to design dielectric pads that can be used to increase image quality in Magnetic Resonance Imaging, and how to accelerate image reconstruction times using a preconditioner.

Image quality is limited by the signal to noise ratio of a scan. This ratio is increased for higher static magnetic field strengths and therefore there is great interest in high-field MRI. The wavelength of the transmitted magnetic RF field decreases for higher field strengths, and it becomes comparable to the dimensions of the human body. Consequently, RF interference patterns are encountered which can severely degrade image quality because of a low transmit efficiency or because of inhomogeneities in the field distribution. Dielectric pads can be used to improve this distribution as the pads tailor the field by inducing a secondary magnetic field due to its high permittivity. Typically, the pads are placed tangential to the body and in the vicinity of the region of interest. The exact location, dimensions, and constitution of the pad need to be carefully determined, however, and depend on the application and the MR configuration. Normally, parametric design studies are carried out using electromagnetic field solvers to find a suitable pad, but this is a very time consuming process which can last hours to days. In contrast with these design studies, we present methods to efficiently model and design the dielectric pads using reduced order modeling and optimization techniques. Subsequently, we have created a design tool to bridge the gap between the advanced design methods and the practical application by the MR community. Now, pads can be designed for any 7T neuroimaging and 3T body imaging application within minutes.

In the second part of the thesis a preconditioner is designed for parallel imaging (PI) and compressed sensing (CS) reconstructions. MRI acquisition times can be strongly reduced by using PI and CS techniques by acquiring less data than prescribed by the Nyquist criterion to fully reconstruct the anatomic image; this is beneficial for patient's comfort and for minimizing the risk of patient's movement. Although acquisition times are reduced, the reconstruction times are increased significantly. The reconstruction times can be reduced when a preconditioner is used. In this thesis, we construct such a preconditioner for the frequently used iterative Split Bregman framework. We have tested the performance in a conjugate gradient framework, and show that for different coil configurations, undersampling patterns, and anatomies, a five-fold acceleration can be obtained for solving the linear system part of Split Bregman.

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MSc SS Thesis Presentation

Improving Ultrafast Doppler Imaging using Subspace Tracking

Bastian Generowicz

Ultrafast Doppler imaging provides a new way to image blood motion at thousands of frames per second. It has gained popularity due to its high spatio-temporal resolution, which is required to distinguish blood motion from clutter signals caused by slow moving tissue. By conducting functional UltraSound (fUS) experiments on the brain using this method, we are able to better understand the underlying processes during brain activity through neurovascular coupling. fUS relies on optimized signal processing techniques to acquire and process high frame-rate images in real-time.

For my thesis I have set up the backbone to allow for fUS experiments as well as created the analysis framework required to analyse and interpret the incoming data. Furthermore, I have developed a more computationally efficient method of obtaining vascular images, based on the Projection Approximation Subspace Tracking (PAST) method. The PAST algorithm is able to display accurate representations of the blood subspace, while maintaining a lower computational complexity than the state-of-the-art method, making it suitable for Doppler imaging. When applied to functional ultrasound, the exponentially weighted PASTd method achieves similar Pearson Correlation coefficients compared to the current state-of-the-art method, over multiple functional experiments. These findings highlight the potential of applying PAST to Ultrafast Doppler imaging.

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MSc SS Thesis Presentation

An Investigation of the Medical Ultrasound Image Sparse Spaces used for Model-Based Imaging

Zheheng Liu

In this thesis, we investigate a sparse basis for ultrasound images, so that we can use sparse regularization in imaging. Actually, there are few previous researches explicitly demonstrating that medical ultrasound images can be sparsified for some dictionary. We consider various orthogonal transforms such as wavelet transforms, cosine transforms and wave atom transforms. Then, we perform those transforms on various ultrasound images and analyzes their sparsity. These ultrasound images include the images of two computer ultrasound phantoms and beamformed ultrasound images with good quality from real people. We looked at sparsity of the true pre-beamformed images, as well as beamformed images. We also consider constructing a specific ultrasound image dictionary using the K-SVD algorithm. We observed that, the pre-beamformed images hardly have no sparse basis, and the sparsity of beamformed images will only increase slightly if we use different 1D-DWT in each direction. We also found that the wide overdetermined dictionary generated by K-SVD significantly increases sparsity. After this, we simulate the ultrasound image reconstruction from the ultrasound RF measurements, and we analyze the effects of the different sparse spaces on the reconstruction performance. We observed that, the l1-regularization can work for ultrasound imaging better than l2-regularization, but the orthogonal transforms as well as the dictionary do not improve the reconstruction image quality much.


Signal Processing Seminar

audio signal processing

Wangyang Yu

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Signal Processing Seminar

Introductory presentation

Seyedmahdi Izadkhast

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PhD Thesis Defence

Multi-Microphone Noise Reduction for Hearing Assistive Devices

Andreas Koutrouvelis

The paramount importance of good hearing in everyday life has driven an exploration into the improvement of hearing capabilities of (hearing impaired) people in acoustic challenging situations using hearing assistive devices (HADs). HADs are small portable devices, which primarily aim at improving the intelligibility of an acoustic source that has drawn the attention of the HAD user. One of the most important steps to achieve this is via filtering the sound recorded using the HAD microphones, such that ideally all unwanted acoustic sources in the acoustic scene are suppressed, while the target source is maintained undistorted. Modern HAD systems often consist of two collaborative (typically wirelessly connected) HADs, each placed on a different ear. These HAD systems are commonly referred to as binaural HAD systems. The noise reduction filters designed for binaural HAD systems are referred to as binaural beamformers.

Binaural beamformers typically change the magnitude and phase relations of the microphone signals by forming a beam towards the target's direction while ideally suppressing all other directions. This may alter the spatial impression of the acoustic scene, as the filtered sources now reach both ears with possibly different relative phase and magnitude differences compared to before processing. This will appear unnatural to the HAD user. Therefore, there is an increasing interest in the preservation of the spatial information (also referred to as binaural cues) of the acoustic scene after processing. The present dissertation is mainly concerned with this particular problem and proposes several alternative binaural beamformers which try to exploit the available degrees of freedom to achieve optimal performance in both noise reduction and binaural-cue preservation.

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Signal Processing Seminar

audio signal processing, localization

Jie Zhang

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PhD Thesis Defence

Image formation for future radio telescopes

Shahrzad Naghibzadeh

Fundamental scientific questions such as how the first stars were formed or how the universe came into existence and evolved to its present state drive us to observe weak radio signals impinging on the earth from the early days of the universe. During the last century, radio astronomy has been vastly advancing. Important discoveries on the formation of various celestial objects such as pulsars, neutron stars, black holes, radio galaxies and quasars are the result of radio astronomical observations. To study celestial objects and the astrophysical processes that are responsible for their radio emissions, images must be formed. This is done with the help of large radio telescope arrays.

Next generation radio telescopes such as the Low Frequency Array Radio Telescope (LOFAR) and the Square Kilometer Array (SKA), bring about increasingly more observational evidence for the study of the radio sky by generating very high resolution and high fidelity images. In this dissertation, we study radio astronomical imaging as the problem of estimating the sky spatial intensity distribution over the field of view of the radio telescope array from the incomplete and noisy array data. The increased sensitivity, resolution and sky coverage of the new instruments pose additional challenges to the current radio astronomical imaging pipeline. Namely, the large amount of data captured by the radio telescopes cannot be stored and needs to be processed quasi-realtime.

Many pixel-based imaging algorithms, such as the widely-used CLEAN [3] algorithm, are not scalable to the size of the required images and perform very slow in high resolution scenarios. Therefore, there is an urgent need for new efficient imaging algorithms. Moreover, regardless of the amount of collected data, there is an inherent loss of information in themeasurement process due to physical limitations. Therefore, to recover physically meaningful images additional information in the form of constraints and regularizing assumptions are necessary. The central objective of the current dissertation is to introduce advanced algebraic techniques together with custom-made regularization schemes to speed up the image formation pipeline of the next generation radio telescopes.

Signal processing provides powerful tools to address these issues. In the current work, following a signal processing model of the radio astronomical observation process, we first analyze the imaging system based on tools from numerical linear algebra, sampling, interpolation and filtering theory to investigate the inherent loss of information in the measurement process. Based on these results, we show that the imaging problem in radio astronomy is highly ill-posed and regularization is necessary to find a stable and physically meaningful image. We continue by deriving an adequate model for the imagingproblem in radio interferometry in the context of statistical estimation theory. Moreover, we introduce a framework to incorporate regularization assumptions into the measurement model by borrowing the concept of preconditioning from numerical linear algebra.

Radio emissions observed by radio telescopes appear either as distributed radiation from diffuse media or as compact emission from isolated point-like sources. Based on this observation, different source models need to be applied in the imaging problem formulation to obtain the best reconstruction performance. Due to the ill-posedness of the imaging problem in radio astronomy, to guarantee a reliable image reconstruction, propermodeling of the source emissions and regularizing assumptions are of utmost importance. We integrate these assumptions implementing a multi-basis dictionary based on the proposed preconditioning formalism.

In traditional radio astronomical imaging methods, the constraints and priormodels, such as positivity and sparsity, are employed for the complete image. However, large radio sky images usually manifest individual source occupancy regions in a large empty background. Based on this observation, we propose to split the field of view into multiple regions of source occupancy. Leveraging a stochastic primal dual algorithm we apply adequate regularization on each facet. We demonstrate the merits of applying facet-based regularization in terms of memory savings and computation time by realistic simulations.

The formulation of the radio astronomical imaging problem has a direct consequence on the radio sky estimation performance. We define the astronomical imaging problem in a Bayesian-inspired regularized maximum likelihood formulation. Based on this formalism, we develop a general algorithmic framework that can handle diffuse as well as compact source models. Leveraging the linearity of radio astronomical imaging problem, we propose to directly embody the regularization operator into the system by right preconditioning. We employ an iterative method based on projections onto Krylov subspaces to solve the subsequent system. The proposed algorithmis named PRIor-conditioned Fast Iterative Radio Astronomy (PRIFIRA). We motivate the use of a beamformed image as an efficient regularizing prior-conditioner for diffuse emission recovery. Different sparsity-based regularization priors are incorporated in the algorithmic framework by generalizing the core algorithm with iterative re-weighting schemes.

We evaluate the performance of PRIFIRA based on simulated measurements as well as astronomical data and compare to the state-of-the-art imaging methods. We conclude that the proposed method maintains competitive reconstruction quality with the current techniques while remaining flexible in terms of different regularization schemes. Moreover, we show that the imaging efficiency can be greatly improved by exploiting Krylov subspace methods together with an appropriate noise-based stopping criteria.

Based on the results from this thesis we can conclude that with the help of advanced techniques from signal processing and numerical linear algebra, customized algorithms can be designed to tackle some of the challenges in the next generation radio telescope imaging. We note that since radio interferometric imaging can be considered as an instance of the broad area of inverse imaging problems, the numerical techniques as well as regularization methods developed in this dissertation have a direct impact on many different imaging application areas, such as biomedical and geophysics/seismic imaging.

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Signal Processing Seminar

Image Reconstruction Using Training Images

Per Christian Hansen
Technical University of Denmark

Priors are essential for computing stable solutions to ill-posed problems, and they take many different forms.  Here we consider priors in the form of cross-section images of the object, and this information must be used in a fast, reliable, and computationally efficient manner. We describe an algorithmic framework for this: From a set of training images we use techniques from machine learning to form a dictionary that captures the desired features, and we then compute a reconstruction with a sparse representation in this dictionary. We describe how to stably compute the dictionary through a regularized non-negative matrix factorization, and we study how this dictionary affects the reconstruction quality. Simulations show that for textural images our approach is superior to other methods used for limited-data problems.

About the speaker

Professor Per Christian Hansen has worked with numerical regularization algorithms for 30 years, and he has published 4 books and 100+ papers in leading journals. He has developed a number of software packages, of which Regularization Tools (now in its 4th version) is a popular toolbox for analysis and solution of discrete inverse problems. His current research projects involve algorithms for tomographic reconstruction and iterative image deblurring algorithms. He is a SIAM fellow in recognition of his work on inverse problems and regularization.

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Signal Processing Seminar

Audio processing

Andreas Koutrouvelis

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Signal Processing Seminar

Array signal processing, Sensor networks, Optimization, Numerical Lineal Algebra

Mario Coutiño Minguez


Signal Processing Seminar

Distributed Convex Optimization: A Monotone Perspective

Thomas Sherson

Over the last few decades, methods of parallel and distributed computation have become essential tools in a wide range of applications such as machine learning, wireless sensor network processing and big data signal processing. Motivated by this point and the synergy between signal processing and convex optimization, in this work we demonstrate recent results in the area of distributed optimisation to facilitate such computation. In particular we highlight the primal dual method of multipliers (PDMM), a relatively recent algorithm proposed for distributed optimization. We demonstrate how PDMM can be derived from classic monotone operator theory which in turn provides insight into previously unknown convergence results for the algorithm. Using this insight we generalise PDMM to solve the class of separable problems with separable constraints and analyze how, in the case of strongly convex and smooth functions, the convergence rate of PDMM is influenced by the underlying network topology.

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MSc SS Thesis Presentation

Respiration monitoring based on information fusion from Impedance pneumography and Electrocardiography

Yuyang Wang

Wearable health has become a striking area in our daily life. Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable devices, which is widely used in heart rate monitoring and cardiac diagnosis. However, automatic ECG signal analysis is dicult in real application because the signals are easy to be contaminated by the noise and artifacts. Thus, the quality of ECG signals is essential for the accurate analysis.

The objective of this project is to design a reliable automated ECG signal quality indicator based on the supervised learning algorithm, which intends to estimate the quality of the signals and distinguish them. The methodology of this project is creating a classication model to indicate the quality of ECG signals based on the machine learning algorithm. The model is trained by the extracted features based on the Fourier transform, Wavelet transform, Autocorrelation function and Principal component analysis of ECG signals. Subsequently, the feature selection techniques are proposed to remove the irrelevant and redundant features and then the selected features are fed to classi- cation algorithms. The classier was then trained and tested on the expert-labeled data from the collected ECG signals. Particularly, we focus on the performance of classier and use the best training model to predict the quality of new ECG signals.


MSc BME Thesis Presentation

The effect of dopamine release on electrical neural activity in the prefrontal cortex

Jack Tchimino

How can certain oscillations be detected from the measured brain signals?

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Graph signal processing: Analyzing and filtering data over networks

Elvin Isufi

Nowadays, we are surrounded by massive data that reside on irregular structures. Examples include data generated by biological, financial, and sensor networks. Graphs indeed offer the ability to model the interactions between them. Current efforts in signal processing are being focused on providing analysis and processing tools for these data such that the underlying structure is taken into account. These include the development of a Fourier transform and filtering operations for graph data. The field that gathers these tools is called graph signal processing (GSP).

     This talk will be focused on the fundamentals of GSP spanning concepts like the signal variation over a graph, the graph Fourier transform, the graph filter, and its distributed implementation. The talk will be concluded with some illustrative examples and future work directions.

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Signal Processing Seminar

Tensor-based blind source separation in epileptic EEG and fMRI

Borbála Hunyadi

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Joint optimization of milk intake and sleep time for babies under one month old

Elena

(daughter of Jorge)


Signal Processing Seminar

Biomedical signal processing/wavefield imaging

Patrick Fuchs

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Signal Processing Seminar

Signal Processing Mini-Symposium

Hagit Messer, KVS Hari, Andrea Simonetto

Talk 1: Capitalizing on the Cellular Technology Opportunities and Challenges for Near Ground Weather Monitoring

Prof. Hagit Messer
School of Electrical Engineering, Tel Aviv University, Israel

Talk 2: Spatial Modulation Techniques in Wireless Systems

Prof. K.V.S. Hari
Dept. of ECE, Indian Institute of Science, Bangalore

Talk 3: Time-varying optimization: algorithms and engineering applications

Dr. Andrea Simonetto
IBM Research Ireland, Dublin, Ireland

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PhD Thesis Defence

Spatio-temporal environment monitoring leveraging sparsity

Venkat Roy

Linking sensor measurements to unknown field intensities, with application to rainfall monitoring from cellular networks

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Signal Processing Seminar

manufacturing defect detection

Aydin Rajabzadeh

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Signal Processing Seminar

Machine learning in physical sciences

Peter Gerstoft
UC San Diego

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

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MSc ME Thesis Presentation

Design Space Exploration of a Neuromorphic ECG Classification System using a Spiking Self-Organizing Map

Johan Mes

The Self-Organizing Map (SOM) is an unsupervised neural network topology that incorporates competitive learning for the classification of data. In this thesis we investigate the design space of a system incorporating such a topology based on Spiking Neural Networks (SNNs), and apply it to classifying Electrocardiogram (ECG) beats. We present novel insights into the characterization of the SOM and its encapsulating system by exploring configuration parameters such as learning rate, neuron models, potentiation and depression ratios, and synaptic conductivity parameters by performing high-level architectural simulations of the system whose SNN is developed with the aim of being implemented using power efficient neuromorphic hardware.

Due to the amount of manual work needed to monitor and analyze ECG signals when diagnosing cardiovascular problems, and because it is the leading cause of death in the world, an automated, realtime, and low power detection & classification system is essential. Unsupervised and in realtime, this system performs beat detection with an average TPR of 99.10% and a PPV of 99.58% and classification of 500 detected beats with an EMDS of 0.0169 and a beat recognition percentage of 100%.


Signal Processing Seminar

Signal processing algorithms for acoustic vector sensors

Krishnaprasad Nambur Ramamohan


Symposium MRI for Low-Resource Setting

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Steven Schiff, Johnes Obungoloch
Penn State University (USA) and Mbarara University (Uganda)

MRIs are expensive and require sophisticated facilities to use them, which is why many people worldwide do not have access to this diagnostic service. Luckily an interdisciplinary team of scientists and medical professionals from the Netherlands (TU Delft and LUMC), the US (Penn State) and Uganda (MUST) is working on creating an affordable and simple MRI scanner. Want to know more? Come to the symposium!

In the talk, Steven Schiff of Penn State University (USA) and Johnes Obungoloch of Mbarara University (Uganda) will explain how their Ultra-low field MRI technology is a response to the need for appropriate medical technologies in countries like those in Sub-Saharan Africa. Together with TU Delft and LUMC they intend to use simple non-cooled magnets that can do the job by combining them with algorithms and a reference set of advanced MRI images. The ultimate goal is to create an MRI scanner that is inexpensive and that can be assembled, operated and maintained in developing countries. Their initial focus is hydrocephalus – the most common condition in children worldwide that requires brain imaging and neurosurgical treatment.

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MSc SS Thesis Presentation

Phase estimation of recurring patterns in nonstationary signals

Rik van der Vlist

A phase estimation algorithm is presented to estimate the phase of a recurring pattern in a nonstationary signal. The signal is modelled by a template signal that represents one revolution of the recurring pattern, and that the frequency of this pattern can change at any time with no assumptions about local stationarity. The algorithm uses a constraint maximum likelihood estimator (MLE) to estimate the phase of the recurring pattern in the time series. Using the dynamic programming techniques from the dynamic time warping (DTW) algorithm, the solution is found in an efficient manner. The algorithm is applied to the digitization of meter readings from analog consumption meters.

As of today, analog consumption meters are still widely used to measure the consumption of gas, electricity and water. Often, smart home appliance use a simple reflective photosensor located on a rotating part of the meter to obtain information about the state of the consumption meter. The algorithm presented in this thesis accurately estimates the phase of the repeating pattern that occurs in the sensor observation when the meter rotates. Using this estimate, the signal of the photosensor can be converted to an estimate of the total resource consumption and consumption rate.

The algorithm improves in accuracy over conventional methods based on peak detection, and is shown to work in cases where the peak detection methods fails. Examples of this are signals where there is no distinctive peak in the signal or a signal where the recurring pattern is reversed. Furthermore, a template compression scheme is proposed that is used to decrease the computational complexity of the algorithm. Different time series compression methods are applied to the algorithm and evaluated on their performance.

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MSc SS Thesis Presentation

Snoring Sound Production and Modelling; Acoustic Tube Modelling

Mert Ergin

The thesis project is aimed at designing an unobtrusive method to determine from snoring sounds the obstruction location and severity for persons that have benign snoring and that are residing in their natural sleep environment.

Similar to speech generation, which is enabled by opening and closing of the vocal cords, the sound of snoring consists of a series of impulses caused by the rapid obstruction and reopening of parts of the upper airway. By exploiting this similarity, we try to explain snoring sound production using analysis and synthesis techniques that have been applied to speech. In particular, 1-D tube models and linear prediction have been studied and employed. 

From a trial on anti-snoring device efficacy, audio recordings were available. These contained snoring sounds of multiple participants over multiple nights and were used for analysis. Similar to speech analysis, the Iterative Adaptive Inverse Filtering (IAIF) method has been used to find excitation flow and airway tract transfer functions from snoring sounds during the inhalation period. It has been found that the linear prediction part in IAIF is not suitable to directly determine the cross-sectional area of the upper airway from snoring sounds. 

A model has been built using acoustic tube theory with the purpose of reflecting the physical realities of the upper airways. The tube model allows modeling flow obstructions at arbitrary positions in the upper airways. The transfer spectra from the acoustic tube having obstructions at various positions can be compared to the tube model resulting from IAIF using a gain-independent Itakura Spectral Distance Measure and the best match can be determined. This alternative approach was found to indicate obstruction positions which vary over persons. Further research is required to establish if the thus determined locations are adequately matching the true snoring cause and constitute a viable way for generating advice for anti-snoring devices and measures.

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Microelectronics Colloquium

Tenure track colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Wideband phased arrays for future wireless communication terminals, Daniele Cavallo (TS group)

Wireless data traffic will grow exponentially in the next years, due to the proliferation of user terminals and bandwidth-greedy applications. To address this demand, the next generations of mobile communication (5G and beyond) will have to shift the operation to higher frequencies, especially to millimetre-wave (mmWave) spectrum (30-300 GHz), that can provide extremely high-speed data links. To enable mm-wave wireless communication, mobile terminals such as smartphones will need phased arrays antennas, able to radiate or receive greater power in specific directions that can be dynamically steered electronically. However, to cover the different 5G mm-wave bands simultaneously (28, 39, 60 GHz, …) and to achieve total angular coverage, too many of such antennas should be on the same device: the main bottleneck is the insufficient space available to place all antenna modules. Therefore, I propose to investigate novel phased array antenna solutions with very large angular coverage and ultra-wide frequency bandwidth, to massively reduce the overall space occupation of handset antennas and overcome the current limitations of mobile terminal antenna development.

Towards smart organs-on-chip, Massimo Mastrangeli (ECTM Group)

Organs-on-chip are microfluidic systems that enable dynamic tissue co-cultures under physiologically realistic conditions. OOCs are helping innovating the drug screening process and gaining new fundamental insights in human physiology. In this talk, after a summary of my past research journey, I will describe how the ECTM group at TU Delft is envisioning the use microfabrication and materials science to embed real-time sensing and actuation in innovative and scalable OOC platforms.

Emerging electronic materials: from lab to fab, Sten Vollebregt (ECTM group)

Due to their nm-size features and often unique physical properties nanomaterials, like nanotubes and 2D materials, can potentially outperform classical materials or even provide functionality which cannot be obtained otherwise. Because of this, these nanomaterials hold many promises for the next generation of devices for sensing & communication and health & wellbeing.

Unfortunately, many promising applications of nanomaterials never reach sufficient maturity to be implemented in actual products. This is mostly because the interest in the academic community reduces once the initial properties have been demonstrated, while the risk for industrialization is still too high for most companies to start their own R&D activities. My goal is to bridge these two worlds by investigating the integration of novel nanomaterials in semiconductor technology and demonstrating the scalability of novel sensing devices. In this talk, I will give examples on how carbon nanotubes, graphene and other emerging nanomaterials can be used in the next generation of sensing devices.


Signal Processing Seminar

Tutorial on: Sum-of-squares Representation in Optimization and Applications in Signal Processing

Tuomas Aittomäki

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Signal Processing Seminar

Low-cost sparse sensing designed for specific tasks

Pim van der Meulen

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MSc TC Thesis Presentation

Bluetooth Direction Finding

Lichen Yao

This thesis project focuses on the algorithm developement and practical considerations for Indoor Direction Finding feature that will be incorporated in the next generation Bluetooth standard.


MSc SS Thesis Presentation

Vessel Layer Separation of X-ray Angiographic Images using Deep Learning Methods

Haidong Hao

Percutaneous coronary intervention is a minimally-invasive procedure to treat coronary artery disease. In such procedures, X-ray angiography, a real-time imaging technique, is commonly used for image guidance to identify lesion sites and navigate catheters and guide-wires within coronary arteries. Due to the physical nature of X-ray imaging, photon energy undergoes absorption when penetrating tissues, rendering a 2D projection image of a 3D scene, in which semi-transparent structures overlap with each other. The overlapping structures make robust information processing of X-ray images challenging. To tackle this issue, layer separation techniques for X-ray images were proposed to separate those structures into different image layers based on structure appearance or motion information. These techniques have been proven effective for vessel enhancement in X-ray angiograms. However, layer separation approaches still suffer either from non-robust separation or long processing time, which prevent their application in clinics.

The purposes of this work are to investigate whether vessel layer separation from X-ray angiography images is possible via deep learning methods and further to what extent vessel layer separation can be achieved with deep learning methods.

To this end, several deep learning based methods were developed and evaluated to extract the vessel layer. In particular, all the proposed methods utilize a fully convolutional network (FCN) with two different architectures, which was trained by two different strategies: conventional losses and an adversarial loss.

The results of all the methods show good vessel layer separation on 42 clinical sequences. Compared to the previous state-of-the-art, the proposed methods have similar performance but runs much faster, which makes it a potential real-time clinical application. In addition, the proposed methods were assessed for low-contrast / low-dose scenarios with synthetic X-ray angiography data, and the results showed robust performance.


MSc SS Thesis Presentation

Automatic Interferer Selection for Binaural Beamforming

Costas Kokke

Spatial cues allow a listener to determine the direction sound is coming from. In addition, recognising spatially separated sound sources facilitate the listener to focus on specific sound sources. Because of this, preservation of spatial cues in multi-microphone hearing assistive devices is important to the listening experience and safety of the user. A number of linearly-constrained-minimum-variance-based methods exist for this purpose. Most of these are limited in the number of interfering sources for which they can preserve the spatial cues. In this thesis, a method of selecting the most important interfering point sources using convex optimisation is proposed.

The method is presented based on two different convex relaxations, which are compared, using simulation experiments, to existing, exhaustive search and randomised methods in terms of noise suppression and localisation errors. 

Both methods are shown to improve the performance of the joint binaural linearly constrained minimum variance beamformer, an existing method for simultaneous noise reduction and spatial cue preservation, by giving it more degrees of freedom for noise reduction and allowing it to handle a larger number of (virtual) sources present in the scene.

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MSc SS Thesis Presentation

Audio-visual authentication for mobile devices

Lucas Montesinos Garcia

Authentication is becoming an increasingly important application in the connected world and is driven by the growing use of mobile and IoT devices that use an increasing number of applications that require transactions of sensitive data. Security usually relies on passwords and/or two-factor authentication which are too intrusive for daily use. Biometric solutions such as fingerprints, voice, iris and retina are a good alternative to overcome previous problems.

In this project an audio-visual identity verification is presented, where the use of multiple modes that can already be captured from most IoT devices (microphone and camera) make authentication robust to adverse conditions. End-factor analysis (i-vectors) with cosine distance is implemented as the main classification algorithm which takes into account variations within and between speakers. Mel Frequencies Cepstrum Coefficients (MFCC) are used as audio features, 2D-DCT coefficients of a single snapshot and  Motion Vectors (MV) of the lips are extracted for visual features. Improvements combining different modes are shown using VidTimit dataset where the proposed algorithm achieves 0.7% of Half Total Error (HTER) in the test set outperforming single modes audio and visual by 9.5% and 6.4%, respectively.


MSc SS Thesis Presentation

Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations

Dirk Schut

Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated or frozen to destroy the tumor tissue. To guide the catheter, Ultrasound(US) imaging is used which shows the catheter position in real time. However, some tumors are not visible on US images. To make these tumors visible, image fusion can be used between the inter-operative US image and a pre-operative contrast enhanced CT(CECT) scan, on which the tumors are visible. Several methods exist for tracking the motions of the US transducer relative to the CECT scan, but they all require a manual initialization or external tracking hardware to align the coordinate systems of both scans. In this thesis we present a technique for finding an initialization using only the image data. To achieve this, deep learning is used to segment liver vessels and the boundary of the liver in 3D US images. To find the rigid transformation parameters, the SaDE evolutionary algorithm was used to optimize the alignment between the blood vessels and the liver boundary between both scans.

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MSc TC Thesis Presentation

Phase Domain Ranging for Narrowband ISM Radio Bands

Aulia Recky Soepeno

In this thesis, we study ranging algorithms in an indoor environment using narrow-band industrial, scientific, and medical (ISM) radio bands at 2.4 GHz. Previously, a phase difference approach has been implemented for this problem. However, the distance estimation is rather inaccurate for indoor ranging, mainly due to multipath and noise. This thesis studies several direction of arrival (DOA) techniques such as matched filter (MF), minimum variance distortionless response (MVDR), and multiple signal classification (MUSIC) to reduce the impact of indoor multipath. Forward-backward smoothing as well as the Akaike information criterion (AIC) and the minimum descriptive length (MDL) are also proposed to diminish the multipath effect further and estimate the number of separable multipath in the channel. Besides, a MUSIC-like method is discussed to prevent incorrect estimation of the number of sources. We test the proposed algorithm under different channel parameter values, compensate the bias, and show the related performance improvement as the absolute bias value is reduced up an order of magnitude.


MSc SS Thesis Presentation

Multidomain Graph Signal Processing: Learning and Sampling

Guillermo Ortiz Jiménez

In this era of data deluge, we are overwhelmed with massive volumes of extremely complex datasets. Data generated today is complex because it lacks a clear geometric structure, comes in great volumes, and it often contains information from multiple domains. In this thesis, we address these issues and propose two theoretical frameworks to handle such multidomain datasets. To begin with, we extend the recently developed geometric deep learning framework to multidomain graph signals, e.g., time-varying signals, defining a new type of convolutional layer that will allow us to deal with graph signals defined on top of several domains, e.g., electroencephalograms or traffic networks. After discussing its properties and motivating its use, we show how this operation can be efficiently implemented to run on a GPU and demonstrate its generalization abilities on a synthetic dataset. Next, we consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors. To keep the framework general, we do not restrict ourselves to multidomain signals defined on irregular domains. Nonetheless, this particularization to multidomain graph signals is also presented. To do so, we leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop several low-complexity greedy algorithms based on submodular optimization methods that compute near-optimal sampling sets. To validate the developed theory, we present several numerical examples, ranging from multi-antenna communications to graph signal processing.

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MSc SS Thesis Presentation

Impedance-based bioassay for characterization of single malignant melanoma cancer cells usinG CMOS-MEA systems

Makrina Sekeri

Malignant Melanoma (MM) is the most aggressive type of skin-cancer. Current diagnostic tools for the detection of malignancies of the skin (MM cancer) include histological, optical, ultrasound, and impedance-based techniques. The inadequacies of the first three practices are overwhelmed by the Electrical Impedance Spectroscopy (EIS) method. EIS overcomes reported spatiotemporal tradeoffs as a label-free and optics-free analytical method. Yet, MM’s enhanced heterogeneity and metastatic potential still results in misdiagnosis, or late diagnosis leading to stages characterized by high mortality rates. Important biological information and processing ability on single-cell level is missing. Single-cell dynamics recorded with a high-throughput system, contain important biological information on the heterogeneous subpopulations which are responsible for the MM aggressiveness.

This project aims to investigate experimentally the possibility and capabilities of such a bioassay development, create working protocols and generate a fundamental basis for analysis and interpretation of the big-data-sets which derive from Impedance monitoring from a high-throughput transducer.

Experiments were performed, employing two diverse, human-derived, MM cancer cell-lines, and using a high-throughput HD-MEA system with a 1024-channel impedance readout unit developed at IMEC, in Belgium. The measurements were realized at 1kHz aiming to extract Rseal information. The main proposal presents an experimental protocol of mid-term and long-term experiments Temporal and spatial resolutions were enhanced (Control System Automation), allowing for implementation of an experimental set to test the assay’s capabilities and determine any necessary additions to make the assay more robust for research (i.e. Z-Map, templates and scripts for OriginLab and Matlab, statistical methods for validation of findings on the big-data sets, optimizations in the experimental process, etc).

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Symposium

Waves, model reduction and imaging

Symposium in context of the PhD defense of Jorn Zimmerling on 2 July. Click here for the program.

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PhD Thesis Defence

Model Reduction of Wave Equations

Jörn Zimmerling

How do you look inside a box without opening it? How can we know whether or not a heart valve is functioning correctly without cutting a person open?

Imaging – the art of seeing the unseeable. A CT-scan at the doctor’s office, crack detection in the wing of an airplane, or medical ultrasound are all examples of imaging techniques that allow us to inspect the interior of an object or person and enable us to observe features that are not directly visible to the naked eye. Science continuously improves upon existing imaging methods and occasionally invents new ones leading to improved image quality and faster image acquisition.

Many imaging applications rely on acoustic, electromagnetic, or elastodynamic waves for imaging. These waves illuminate a penetrable object and an image is formed of its interior from measurements of the transmitted or scattered waves. Being able to efficiently compute wavefields in complex geometries is key in such wavefield imaging problems. To keep up with the developments within the imaging industry to move to larger domains, higher resolution, and larger data sets, new mathematical methods and algorithms need to be developed, since advancements in the computer industry cannot keep up with these demands.

This thesis is about reduced-order modeling of the equations that describe the dynamics of wave propagation. In reduced-order modeling, the aim is to systematically develop a small model that describes a complex system without losing information that is valuable for a specific application. Evaluating such a model is computationally much more efficient than direct evaluation of the unreduced system and in the context of imaging it can lighten the computational burden associated with imaging algorithms. The central question is, of course: How does one construct a model that describes the wave dynamics relevant for a particular application?

Wave equations are partial differential equations that interrelate the spatial and temporal variations of some physical wavefield quantity. When we discretize such equations in space, sparse systems of equations with hundreds of thousands or even millions of unknowns are obtained. Via projection onto a small subspace such a large-scale system can be reduced to a much smaller reduced system. The solution of this small system is called a reduced-order model. A properly constructed reduced-order model can be easily evaluated and gives an accurate wavefield description over a certain time or frequency interval or parameter range of interest.

In this thesis, we discuss different choices for the subspaces that are used for projection in model-order reduction. In particular, we show what types of subspaces are effective for wavefields that are localized and highly resonant and how to efficiently generate such subspaces by exploiting certain symmetry properties of the wave equations. We illustrate the effectiveness of the resulting reduced-order models by computing optical wavefield responses in three-dimensional metallic nano-resonators.

Not all wavefields are determined by a few resonances, of course. Waves can also travel over long distances without losing information; a property that is used by mobile phones every day. The reduction methods developed for resonating fields are not efficient for these types of propagation problems and require a different approach. In this thesis, we present a so-called phase-preconditioning reduction method, in which a specific subspace is generated that explicitly takes the large travel times of the waves into account. We demonstrate the effectiveness of this reduction approach using examples from geophysics, where waves with long travel times are frequently encountered or used to probe the subsurface of the Earth.

Finally, we show how reduced-order modeling techniques can be incorporated in advanced nonlinear imaging algorithms. Here, we focus on an imaging application in geophysics, where the goal is to retrieve the conductivity tensor of a bounded anomaly located in the subsurface of the Earth, based on measured electromagnetic field data that is collected on a borehole axis. We demonstrate that the use of reduced-order models in a nonlinear optimization framework that attempts to solve this imaging problem indeed leads to significant computational savings without sacrificing the quality of the imaging results. To illustrate the wide applicability of model-order reduction techniques in imaging, an additional example from nuclear geophysical imaging is presented as well.

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5G Phased Arrays

International Summer School on 5G Phased Arrays

Understanding of phased array operation requires multi- disciplinary approach, which is based on the antenna array, microwave circuit and signal processing theories. By bringing these three areas together, the school provides integral approach to phased array front-ends for 5G communication systems.

At the school the phased array foundations will be considered from antenna, RF technology and signal processing points of view. Realization of 5G capabilities such as high data-rate communication link to moving objects will be discussed. The education will be concluded by a design project.

The summer school is open for all young specialists and researchers from both industry and academia. The attendees should have basic knowledge about EM, electrical circuits and signal processing (graduate courses on electromagnetic waves, electrical circuits including microwave (RF) circuits, and signal processing).

Topics:

  • Foundations of antenna arrays
  • Antenna array topologies for 5G applications
  • Analog and digital beamforming in antenna arrays
  • Front-end architecture and performance
  • 5G applications and system requirements

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Conferences

PRORISC 2018 Conference

Annual conference on Integrated Circuit (IC) design, organized within the three technical Dutch universities Twente, Delft and Eindhoven

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Conferences

SAFE 2018 Conference

Annual conference on Micro-systems, Materials, Technology and RF-devices, organized within the three technical Dutch universities of Twente, Delft and Eindhoven.

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MSc SS Thesis Presentation

Direction-of-Arrival Estimation using an Unsynchronized Array of Acoustic Vector Sensors

Bart Coonen

Direction-Of-Arrival (DOA) estimation of acoustic signals is of great interest in various applications including battlefield acoustic and noise localization. Acoustic sensors are employed in an array configuration to estimate DOAs based on the time differences of arrival DOAs. However, the acoustic sensors in the network have all their own Data AcQuisition (DAQ) unit with independent clocks than, it might not be possible to perfectly synchronize the network which affects the performance of the time differences of arrival reliably.  

In this thesis  we consider the issue of clock synchronization errors in a network where Acoustic Vector Sensors (AVSs) are used. AVSs are shown to be advantageous in terms of direction finding compared to conventional Acoustic Pressure Sensors (APSs) due to their directional particle velocity measurement capability. Initiallity the measurement model for AVSs is presented. After that the behavior of the clocks is incorporated in the measurement model of the full array setup. Subsequently, the effects of the clocks on the MVDR DOA estimation method is discussed.

  The model with clock errors is used in the development of three new DOA-estimation methods. The first two techniques are eigenstructure methods that are capable of finding the DOAs regardless of the accuracy of the synchronization. However, to find the DOAs with high accuracy in a real-time application these methods are not due to their high computational cost. Alternatively, the third proposed algorithm takes the DOA estimate from previous methods with low accuracy as its input. The algorithm estimates the DOA in an iterative fashion with high accuracy based on these estimates with low accuracy.  

Finally, measurements are conducted in a controlled environment in order to show that these methods are usable in practical situations.

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The possibilities of swarm robotics research

The possibilities of swarm robotics research

Matthijs Otten

During this presentation, I would like to introduce the topic of swarm robotics and the vision on the development of this technology that the Zebro Team (18th floor, under Chris Verhoeven) has. At Zebro we develop both the swarming technology, as well as the robots that eventually execute the swarming algorithms. We use an integrated approach, letting research and engineering go hand in hand. I would like to show the robotic platform (Zebro) that we have developed to test the swarm, alongside with the technology (Swarming algorithms, ad-hoc communication protocols and localization methods). At the end of my short presentation (+- 10 – 15min) I would like to have a brainstorm session to discuss the possibilities for cooperation. We would like the Zebro Swarming Platform to be useful to the TU Delft, and allow researchers to use the platform for their own research. Research topics for which swarm robotics could include (but are not limited to): • Indoor / Outdoor (Mobile) Localization • Signal processing in sensor networks • Sensor systems for self-deploying sensor networks • Distributed Algorithms • Ad-Hoc Mass Communication • Potential field sensing


Electronic Instrumentation Colloquium

Reducing Switching Artifacts in Chopper Amplifiers

Yoshinori Kusuda

Abstract

Chopping is a technique with which amplifier offset can be reduced to sub-μV levels, at the expense of reduced signal bandwidth due to chopping artifacts such as up-modulated ripple and glitches. In this talk, some circuit techniques to reduce such artifacts are proposed.These circuit techniques have been used in three commercially-available operational amplifiers, whose design and measured performance will be discussed. Lastly, some of the challenges associated in testing low-offset amplifiers in mass-production will be discussed..

Biography

Yoshinori Kusuda received the B.S. degree in electrical and electronic engineering in 2002, and M.S. degree in PhysicalElectronics in 2004, both from Tokyo Institute of Technology. Upon his graduation in 2004, he joined the Japan DesignCenter of Analog Devices (ADI) as an IC design engineer. He is currently based in San Jose, CA, U.S.A., working for the Linear and Precision Technology Group of ADI. The focus of his work is on precision CMOS analog designs, including stand-alone amplifiers and application specific mixed-signal products. This has resulted in presentations and papers at IEEE conferences and journals, as well as nine issued U.S. patents. Since August2015, he has been a guest researcher at the ElectronicInstrumentation Laboratory of the TU Delft.


Predicting the intelligibility of speech

Predicting the intelligibility of speech

Steven van Kuyk
visitor New Zealand

When designing a speech-based communication system it is important to understand how the system will affect intelligibility (i.e., the proportion of correctly identified words). Although formal listening tests can provide valid data, such tests are laborious to conduct. For this reason, algorithms that predict the intelligibility of communication systems have been proposed. This talk will describe a recently proposed algorithm called SIIB, which is based on information theoretic principles. In addition, we will analyze the results of a recent evaluation that investigates the accuracy of popular intelligibility metrics.


Active Implantable Biomedical Microsystems Course

Active Implantable Biomedical Microsystems Course

Vasiliki Giagka, Virgilio Valente, Christos Strydis, Wouter Serdijn
Delft University of Technology and Erasmus Medical Center

Course on the understanding, design and future developments of active implantable biomedical microsystems, such as cochlear implants, cardiac pacemakers, spinal cord implants, neurostimulators and bioelectronic medicine.

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Signal Processing Seminar

Compressive Covariance Sensing

Geert Leus

There are many engineering applications that rely on frequency or angular spectrum sensing, such as cognitive radio, radio astronomy, radar, seismic acquisition, and so on. Many of these applications do not require the reconstruction of the full signal, and can perfectly rely on an estimate of the power spectral density (PSD), or in other words, the second-order statistics of the signal. However, the large bandwidths of the involved signals lead to high sampling rates and thus high sampling costs, which can be prevented by a direct compression step carried out in the analog domain (e.g., by means of an analog-to-information converter, multi-coset sampling, analog beamforming, antenna selection, etc.). This leads to the problem of sensing the PSD or covariance using compressive observations, labeled as compressive covariance sensing (CCS). In this tutorial we will give an overview of the state-of-the-art in CCS and present its connections to compressive sensing (CS). We focus on the design constraints of the compression matrices, which are completely different as in classical CS, and elaborate on the estimation/detection techniques to sense the covariance using compressive measurements. In this context, both non-uniform and random sampling are discussed. We further elaborate on distributed CCS, where compressive measurements in one domain are fused in the dual domain, i.e., temporal compressive measurements are gathered at different spatial sensors or spatial compressive measurements from different time slots are combined. Finally, connections to super resolution techniques such as atomic norm minimization are discussed. We end this tutorial by sketching some open issues and presenting the concluding remarks.

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PhD Thesis Defence

Front-End ASICs for 3-D Ultrasound: From Beamforming to Digitization

Chao Chen

Program:
12:00 - 12:15 Introductory presentation
12:30 - 13:30 Public defense
13:45 - 14:00 Diploma ceremony
Address: Senaatszaal of the Aula Congress Center

SUMMARY
This thesis describes the analysis, design and evaluation of front-end application-specific integrated circuits (ASICs) for 3-D medical ultrasound imaging, with the focus on the receive electronics. They are specifically designed for next-generation miniature 3-D ultrasound devices, such as transesophageal echocardiography (TEE), intracardiac echocardiography (ICE) and intravascular ultrasound (IVUS) probes. These probes, equipped with 2-D array transducers and thus the capability of volumetric visualization, are crucial for both accurate diagnosis and therapy guidance of cardiovascular diseases. However, their stringent size constraints, as well as the limited power budget, increase the difficulty in integrating in-probe electronics. The mismatch between the increasing number of transducer elements and the limited cable count that can be accommodated, also makes it challenging to acquire data from these probes. Front-end ASICs that are optimized in both system architecture and circuit-level implementation are proposed in this thesis to tackle these problems.
The techniques described in this thesis have been applied in several prototype realizations, including one LNA test chip, one PVDF readout IC, two analog beamforming ASICs and one ASIC with on-chip digitization and datalinks. All prototypes have been evaluated both electrically and acoustically. The LNA test chip achieved a noise-efficiency factor (NEF) that is 2.5 × better than the state-of-the-art. One of the analog beamforming ASIC achieved a 0.27 mW/element power efficiency with a compact layout matched to a 150 µm element pitch. This is the highest power-efficiency and smallest pitch to date, in comparison with state-of-the-art ultrasound front-end ASICs. The ASIC with integrated beamforming ADC consumed only 0.91 mW/element within the same element area. A comparison with previous digitization solutions for 3-D ultrasound shows that this work achieved a 10 × improvement in power-efficiency, as well as a 3.3 × improvement in integration density.

The dissertation can be found in the TU Delft repository: http://doi.org/10.4233/uuid:a5002bb0-4701-4e33-aef6-3c78d0c9fd70

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Signal Processing Seminar

Time-Varying System Theory - Can it be connected to Graph Signal Processing?

Alle-Jan van der Veen

Long time ago, I was developing time-varying system theory. The rows of an "arbitrary" matrix can be viewed as impulse responses of a time-varying system. Next, there is a notion of causality, which relates to upper triangular matrices. And there is a "shift operator" which provides connections between the rows. From these ingredients, it turned out that we can develop a state-space theory where the matrix is implemented by a series of "nodes" that communicate to each other via "states". The ARMA graph filtering work of Elvin e.a. results in similar expressions, where the shift operator is the Laplacian. An open question is if this can be connected to the TV system theory? If so, we know how to do realization theory (given the responses, find a minimal realization, i.e. minimize the number of communication links) and approximation theory (given a realization, find one of lower complexity that has approximately the same response). The talk won't give the answers, but I hope it can start a discussion.

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Dutch Ultra Low Power Conference

The medicine of the future you’ll need to take only once, and it’s a bioelectronic one

Wouter Serdijn

The Dutch Ultra Low Power Conference brings together Belgian and Dutch professionals and companies involved in the development and application of devices with ultra low power technologies. It targets engineers, designers and technical managers in the advanced field of energy harvesting and ultra low power and energy-efficient designs. The keynote will be given by Wouter Serdijn, professor of bioelectronics at Delft University of Technology.

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MSc CE Thesis Presentation

Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks

Eralp Kolagasioglu

Cardiovascular diseases are the leading cause of death in the developed world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consuming process. Consequently, a wearable system that can automatically categorize beats is essential.

Neuromorphic machines have been introduced relatively recently in the science community. The aim of these machines is to emulate the brain. Their low power design makes them an optimal choice for a low power wearable ECG classifier.

As features are crucial in any machine learning system, this thesis aims at proposing an energy efficient feature extraction algorithm for ECG arrhythmia classification using neuromorphic machines. The feature extraction algorithm proposed in this thesis consists of the merger of a low power feature detection and a feature selection algorithm. Also, different network configurations have been investigated to achieve classification using an LSM architecture. The resulting system can accurately cluster seven beat types, has an overall classification rate of 95.5%, and consumes an estimate of 803.62 nW.


MEST Symposium

Mini Symposium on Hardware Security

Three talks from leading companies in the industry: Brighsight, Intrinsic ID and Riscure with the following topics:

  1.    “Past , Present and Future of Hardware Attacks on Smart Cards and SOCs” by Gerard van Battum, Sr. Security Evaluator at Brightsight;
  2.     “Removing the barriers of securing a broad range of IoT devices” by Dr. Georgios Selimis, Senior Security Engineer, Intrinsic ID;
  3.    “How to use Deep Learning for hardware security testing?” by Marc Witteman (MSc), Chief Executive Officer, Riscure.
Organized by the Micro-electronic Systems and Technology Association (MEST).

Free but required registration at the link below.

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MSc SS Thesis Presentation

The cocktail party problem: GSVD-beamformers in reverberant environments

Derk-Jan Hulsinga

Hearing aids as a form of audio preprocessing is increasingly common in everyday life. The goal of this thesis is to implement a blind approach to the cocktail party problem and challenge some of the regular assumptions made in literature. We approach the problem as wideband FD-BSS. From this field of research, the common assumption of continuous activity is dropped. Instead a number of users detection is implemented as a preprocessing step and ensure the appropriate number of demixing vectors for each time frequency bin. The validity of the standard mixing model used for STFT’s is challenged by looking at the response of a linear array.

Source separation is achieved by demixing vectors based on the GSVD, derived in a model-based approach. While most permutation solvers offer an a posteriori solution for all users, we looked at finding local solutions for a single user. Combining this with the user identification called the alignment step, we conclude that the permutation problem can be reduced to selecting a demixing vector for each discrete time-frequency instance. The correlation coefficient proves to be a sufficient metric to couple reconstructions to the original data as it selects most of the active time-frequency bins.

In simulations, our demixing vectors achieve comparable inteligibility, measured by STOI, as the compared techniques and it is more robust against smaller sample sizes than the theoretically SINR optimal MVDR.

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MSc TC Thesis Presentation

Blind Signal Identification

Dennis van der Geest

The capability to efficiently find signals of interest in a very dense electromagnetic spectrum is becoming increasingly important with the continuous increase in spectrum usage. In this research project, methods are developed to identify communication signals by estimating signal features (symbol rate, modulation scheme, etc.) in the absence of a-priori knowledge, i.e. blind. By modelling the received communication signal both as a stationary and a cyclostationary process, various feature estimation methods are evaluated based on their computational complexity, their estimation accuracy and their robustness in the presence of signal contamination, such as frequency offsets. By efficiently combining various estimation methods, a signal classification algorithm is derived which is aimed to provide an optimal tradeoff between computational complexity and classification performance.

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Signal Processing Seminar

An introduction to distributed signal processing

Richard Heusdens

Due to the explosion in size and complexity of modern data sets, it is increasingly important to be able to solve problems with a very large number of features or training examples. In industry, this trend has been referred to as ‘Big Data’, and it has had a significant impact in areas as varied as artificial intelligence, internet applications, computational biology, medicine, finance, marketing, journalism, network analysis, weather forecast, telecommunication, and logistics. As a result, both the decentralized collection or storage of these data sets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this talk, we will give an introduction to the design of distributed algorithms. We will discuss the basic requirements of these algorithms, like being simple, resource efficient, scalable, robust against changes in network topology, asynchronous, etc. We will demonstrate the design of such algorithm by considering the example of distributed averaging in a sensor network.

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Signal Processing Seminar

Synchronization for underwater communications based on dual Zadoff-Chu sequences

Yiyin Wang

Abstract: Underwater acoustic channels are not only characterized by multipath propagation but also by Doppler scaling effects. These characteristics challenge the preliminary tasks of an acoustic receiver, such as timing and frequency synchronization, and Doppler scale and channel estimation. In this talk, we propose a novel preamble design based on a dual Zadoff-Chu (ZC) sequence. With the help of the well design preamble, a cyclic feature based detector is developed to bypass the requirement of channel statistic information. The Doppler scale estimation is simplified as the frequency estimation adopting the ESPRIT type algorithm. Furthermore, the special structure of the preamble facilitates the estimation of the residual carrier frequency offset (CFO), and the good correlation properties of the preamble enable a low-cost channel estimation. Therefore, with a single preamble, multiple preliminary tasks of the receiver are accomplished. Simulation results indicate the superior performance of the proposed methods.

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Conferences

WIC Midwinter Meeting on Deep Learning

Organized by Werkgemeenschap voor Informatie- en Communicatietheorie, and IEEE Benelux Chapter on Information Theory

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PhD Thesis Defence

Efficient computational methods in magnetic resonance imaging

Jeroen van Gemert
Technical University Delft

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CAS Christmas dinner

Christmas celebration with food and drinks from all corners of the world.


MSc Thesis Presentation

Blind Graph Topology Change Detection: A Graph Signal Processing Approach

Ashvant Mahabir

Graphs are used to model irregular data structures and serve as models to represent/capture the interrelationships between data. The data in graphs are also referred as graph signals. Graph signal processing (GSP) can then be applied which basically extends classical signal processing to solve problems. Anomaly detection is an example of such a problem. Two hypothetical situations are given, and a detector has to be designed to distinguish between these. Under the null hypothesis, graph structures are considered to be untouched. Under the alternative hypothesis, (unknown) topological changes might have occurred. Now by incorporating a priori knowledge about the graphs, the decision making process should improve. In most works, a priori knowledge of the graphs under the null and alternative hypothesis was incorpo- rated. This means that detectors were designed which were able to anticipate on possible topological changes. In this thesis, the problem is considered where only a priori knowledge of the graph under the null hypothesis is exploited. This means that detectors are not able to anticipate on potential changes and this where blind detection comes into play. Blind detection is important because it considers a more realistic scenario. In this work, the blind topology change detector (BTCD) and the constrained blind topology change detector (CTCD) are derived which exploit different properties of the data re- lated to the known graph structure. For the BTCD, the bandlimitedness of graph signals was exploited and for the CTCD, the graph signal smoothness. The main question in this work, was to find out what the potentials are with the blind detection principle for graph change detection. Different test scenarios are used to evaluate the detectors on both synthetic and real data. For the BTCD, the obtained results compare well when information about the alternative graph is available. For this detector, the potential of blind detection was highly visible. For bandlimited graph signals, the BTCD as good as detectors using full information. For the CTCD, comparable results (with detectors using full information) are attained for just a few test scenarios. For small changes, the graph signal smoothness seems to be less powerful as to the graph signal bandlimitedness. This study showed that graph change detection is still possible without having full information. Some graph signal properties are more powerful w.r.t. others.


Signal Processing Seminar

When is Network Lasso Accurate: The Vector Case

Nguyen Tran

A recently proposed learning algorithm for massive network-structured data sets (big data over networks) is the network Lasso (nLasso), which extends the well- known Lasso estimator from sparse models to network-structured datasets. Efficient implementations of the nLasso have been presented using modern convex optimization methods. In this paper, we provide sufficient conditions on the network structure and available label information such that nLasso accurately learns a vector-valued graph signal (representing label information) from the information provided by the labels of a few data points.


Signal Processing Seminar

Semi-supervised learning for likelihood-based classifiers

Marco Loog
Bioinformatis/Pattern Recognition group

Bio: Marco Loog received an M.Sc. degree in mathematics from Utrecht University and in 2004 a Ph.D. degree from the Image Sciences Institute for the development and improvement of contextual statistical pattern recognition methods and their use in the processing and analysis of images. After this joyful event, he moved to Copenhagen where he acted as assistant and, eventually, associate professor next to which he worked as a research scientist at Nordic Bioscience. In 2008, after several splendid years in Denmark, Marco moved to Delft University of Technology where he now works as an assistant professor in the Pattern Recognition Laboratory. He currently is associate editor of Pattern Recognition and honorary full professor in pattern recognition at the University of Copenhagen. Marco's research interests primarily include all types of variations to supervised learning.


Signal Processing Seminar

Wangyang Yu

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Signal Processing Seminar

The Quest for Fast Learning from Few Examples

Andreas Loukas

Though the data in our disposal are numerous and diverse, deriving meaning from them is often non trivial. This talk centers on two key challenges of data analysis, relating to the sample complexity (how many examples suffice to learn something with statistical significance) and computational complexity (how long does the computation take) of learning algorithms. In particular, we are going to consider two famous unsupervised algorithms, principal component analysis and spectral clustering, and ask what can they learn when given very few examples or a fraction of the computation time.


MSc ME Thesis Presentation

FPGA based real time detection and signal, processing of electric nanosecond Partial Discharge (PD) pulses to extract parameters facilitating PD classication.

Ayush Joshi

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5G Multi-Beam Antenna Topologies

5G Multi-Beam Antenna Topologies

Yanki Aslan

Description: Using the concept of beam-division multiple access, a base station can communicate with multiple users sharing the same time and frequency resources. In this seminar, I will talk about possible ways to design low-cost 5G phased array base station antenna systems at mm-waves for multiple beam forming with enhanced spatial multiplexing, limited interference, acceptable power consumption, passive cooling and suitable processing complexity and speed.

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Signal Processing Seminar

Mario Coutiño Minguez


MSc Thesis Presentation

Semi-Controllable Compression Schemes for Ultrasound Imaging

Xuyang Li


Signal Processing Seminar

Jamal Amini

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Signal Processing Seminar

Jiani Liu

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Signal Processing Seminar

Farnaz Nassirinia

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MSc BME thesis presentation

System Building Blocks for Mathematical Operators Using Stochastic Resonance -- Application in an Action Potential Detection System

Insani Abdi Bangsa

MSc thesis presentation on Stochastic Resonance Systems for Biomedical Applications


PhD Thesis Defence

Signal Strength Based Localization and Path-Loss Exponent Self-Estimation in Wireless Networks

Yongchang Hu

In wireless communications, received signal strength (SS) measurements are easy and convenient to gather. SS-based techniques can be incorporated into any device that is equipped with a wireless chip.

This thesis studies SS-based localization and path-loss exponent (PLE) self-estimation. Although these two research lines might seem unrelated, they are actually marching towards the same goal. The former can easily enable a very simple wireless chip to infer its location. But to solve that localization problem, the PLE is required, which is one of the key parameters in wireless propagation channels that decides the SS level. This makes the PLE very crucial to SS-based localization, although it is often unknown. Therefore, we need to develop accurate and robust PLE self-estimation approaches,which will eventually contribute to the improvement of the localization performance.

We start with the first research line, where we try to cope with all possible issues that we encounter in solving the localization problem. To eliminate the unknown transmit power issue, we adopt differential received signal strength (DRSS) measurements. Colored noise, non-linearity and non-convexity are the next three major issues. To deal with the first two, we introduce a whitened linear data model for DRSSbased localization. Based on that and assuming the PLE is known, three different approaches are respectively proposed to tackle the non-convexity issue: an advanced best linear unbiased estimator (A-BLUE), a Lagrangian estimator (LE) and a robust semidefinite programming (SDP)-based estimator (RSDPE). To cope with an unknown PLE, we propose a robust SDP-based block coordinate descent estimator (RSDP-BCDE) that jointly estimates the PLE and the target location. Its performance iteratively converges to that of the RSDPE with a known PLE.

As mentioned earlier, while generating DRSS measurements, we eliminate the unknown transmit power. This is very similar to the way time-difference-of-arrival (TDOA) methods cope with an unknown transmit time. Both of them use a differencing process to cope with an unknown linear nuisance parameter. Our DRSS study shows the differencing process does not cause any information loss and hence the selection of the reference is not important. However, this apparently contradicts what is commonly known in TDOA-based localization, where selecting a good reference is very crucial. To resolve this conflict, we introduce a unified framework for linear nuisance parameters such that all our conclusions apply to any kind of problem that can be written into this form. Three methods that can cope with linear nuisance parameters are considered by investigating their best linear unbiased estimators (BLUEs): joint estimation, orthogonal subspace projection (OSP) method and differential method. The results coincide with those obtained in our DRSS study. For TDOA-based localization, it is actually the modelling process that causes a reference dependent information loss, not the differencing process. Many other interesting conclusions are also drawn here.

Next, we turn our attention to the second research line. Undoubtedly, knowledge of the PLE is decisive to SS-based localization and hence accurately estimating the PLE will lead to a better localization performance. However, estimating the PLE also has benefits for other applications. If each node can self-estimate the PLE in a distributed fashion without any external assistance or information, it might be very helpful for efficiently designing some wireless communication and networking systems, since the PLE yields a multi-faceted influence therein. Driven by this idea, we propose two closedform (weighted) total least squares (TLS) methods for self-estimating the PLE, which are merely based on the locally collected SS measurements. To solve the unknown nodal distance issue, we particularly extract information fromthe random placement of neighbours in order to facilitate the derivations. We also elaborate on many possible applications thereafter, since this kind of PLE self-estimation has never been introduced before.

Although the previous two methods estimate the PLE by minimizing some residue, we also want to introduce Bayesian methods, such as maximizing the likelihood. Some obstacles related to such approaches are the totally unknown distribution for the SS measurements and the mathematical difficulties of computing it, since the SS is subject to not only the wireless channel effects but also the geometric dynamics (the random node placement). To deal with that, we start with a simple case that only considers the geometric path-loss for wireless channels. We are the first to discover that in this case the SS measurements in random networks are Pareto distributed. Based on that, we derive the CRLB and introduce two maximum likelihood (ML) estimators for PLE selfestimation. Although we considered a simplified setting, finding the general SS distribution would still be very useful for studying wireless communications and networking.

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Distributed and MIMO radar

Marc Lesturgie
ONERA, France

MIMO (multiple-input multiple-output) radar refers to the use of multiple transmitters and receivers, for sensing the environment and the targets present in this environment. Basically MIMO radar uses multiple antennas that transmit correlated or uncorrelated waveforms. For the last ten years MIMO has led to extensive research and publications, both in communications and Radar domains. Why such interest for MIMO in radar? Beside the prolific amount of publications, how to assess the interest of MIMO to overcome the current limitations of conventional radar?

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Signal Processing Seminar

Blind calibration of radio astronomical phased arrays

Stefan Wijnholds
ASTRON

Radio astronomical phased arrays are usually calibrated under the assumption that the observed scene is known. That assumption may not hold if a new frequency window is opened (for example in the case of the currently planned antenna arrays in space observing below 10 MHz) or when there are unexpected source signals such as transients or radio frequency interference (RFI). In this talk, I present recent work on blind calibration of radio astronomical phased assuming that the observed scene is sparse. The resulting method applies sparse reconstruction techniques to the measured array covariance matrices instead of time series data. I discuss the computation speed-up provided by this shift from signal domain to power domain and explain how phase transition diagrams need to be reinterpreted in this context 

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Signal Processing Seminar

Tuomas Aittomäki

With the increasing demand for wireless communication with high data rates, more and more spectral resources are allocated for communication systems. This has lead to the risk of decreasing spectrum allocated exclusively for radars. As the use of radars is likely to increase in future, it is necesary to look at how radar and communication systems could co-exist. This talk is an overview of developments in shared use of spectral and hardware resources between these systems.

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Signal Processing Seminar

Accurate Calculation of the Mean Strain of Non-uniform Strain Fields Using a Conventional FBG Sensor

Aydin Rajabzadeh

In the past few decades fibre Bragg grating (FBG) sensors have gained a lot of attention in the field of distributed point strain measurement. One of the most interesting properties of these sensors is the presumed linear relationship between the strain and the peak wavelength shift of FBG reflection spectra. However, subjecting sensors to a non-uniform stress field will in general result in a strain estimation error when using this linear relationship, which is due to the difference between the average strain value over the length of the sensor and the point strain value based on the peak wavelength shift of the FBG reflected spectra. In this presentation, we will first introduce a new formulation for analysis of FBG reflected spectra under an arbitrary strain distribution. The presented method is an approximation of the classic transfer matrix model, and will be called the approximated transfer matrix model or ATMM. Using the properties of this new formulation, a new method will be presented that compensates for the mean strain estimation error, and it will be validated using simulations and experimental FBG measurements.

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Signal Processing Seminar

Graph Sampling for Covariance Estimation

Geert Leus

In this talk, the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers.

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MSc SS Thesis Presentation

Multiway Component Analysis for the Removal of Far Ventricular Signal in Unipolar Epicardial Electrograms of Patients with Atrial Fibrillation

Jelimo Maswan

Atrial fibrillation (AF) is one of the more common clinical arrhythmias with a high morbidity and mortality. Despite this, the electrophysiological and pathological mechanisms associated with AF largely remain a mystery, encouraging the use of ever more sophisticated techniques to extract vital information for diagnostic and therapeutic purposes. Contamination by signals of ventricular origin is considered the main artifact present in high-resolution epicardial electrograms (EGMs) that hinders the accurate and efficient analysis of AF EGM datasets. Furthermore, the complexity and dynamism of AF signals calls for robust data analysis tools that can effectively reduce or remove ventricular activity (VA) while preserving the texture and morphology of atrial activity (AA).

Multiway component analysis, specifically block term decomposition (BTD), proves useful for the decontamination of epicardial EGMs as demonstrated in this project by enabling the automatic estimation of VA on an electrode-by-electrode basis, which is thereafter temporally and/or power spectrally subtracted thus retaining AA at a relatively high accuracy.

The performance of BTD compared to average beat subtraction (ABS) and the more restrictive canonical polyadic decomposition (CPD) is visually verified and numerically confirmed based on a set of key performance indices. Additionally, the technique is entirely data-driven i.e., does not depend on any statistical properties, but if/when available, can contribute to enhanced performance via the imposition of appropriate constraints in the tensor decomposition.

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Signal Processing Seminar

Rethinking Sketching as Sampling: A Graph Signal Processing Approach

Fernando Gama

Sampling of bandlimited graph signals has well-documented merits for dimensionality reduction, affordable storage, and online processing of streaming network data. Most existing sampling methods are designed to minimize the error incurred when reconstructing the original signal from its samples. Oftentimes these parsimonious signals serve as inputs to computationally-intensive linear operator (e.g., graph filters and transforms). Hence, interest shifts from reconstructing the signal itself towards instead approximating the output of the prescribed linear operator efficiently.

In this context, we propose a novel sampling scheme that leverages the bandlimitedness of the input as well as the transformation whose output we wish to approximate. We formulate problems to jointly optimize sample selection and a sketch of the target linear transformation, so when the latter is affordably applied to the sampled input signal the result is close to the desired output. These designs are carried out off line, and several heuristic (sub)optimal solvers are proposed to accommodate high-dimensional problems, especially when computational resources are at a premium.

Similar sketching as sampling ideas are also shown effective in the context of linear inverse problems. The developed sampling plus reduced-complexity processing pipeline is particularly useful for streaming data, where the linear transform has to be applied fast and repeatedly to successive inputs or response signals.

Numerical tests show the effectiveness of the proposed algorithms in classifying handwritten digits from as few as 20 out of 784 pixels in the input images, as well as in accurately estimating the frequency components of bandlimited graph signals sampled at few nodes.

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Quantized Distributed Optimization Schemes; a monotone operator approach

Jake Jonkman

Recently, the effects of quantization on the Primal-Dual Method of Multipliers were studied. In this thesis, we have used this method as an example to further investigate the effects of quantization on distributed optimization schemes in a much broader sense. Using monotone operator theory, the effect of quantization on all distributed optimization algorithms that can be cast as a monotone operator was researched for two different problem subclasses. The averaging problem was used as an example of a quadratic problem, while the Gaussian channel capacity problem was an example of the non-linear problem subclass. A fixed bit rate quantizer was used in combination with a dynamic cell width, to analyse the robustness of distributed optimization schemes against quantization effects. In particular, we have shown that for practical implementations it is possible to incorporate fixed bit rate quantization with dynamic cell width in a distributed optimization algorithm without loss of performance for both problem classes.

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Signal Processing Seminar

Signal Processing on Kernel-based Random Graphs

Matthew Morency

We present the theory of sequences of random graphs and their convergence to limit objects. Sequences of random dense graphs are shown to converge to their limit objects in both their structural properties and their spectra. The limit objects are bounded symmetric functions on $[0,1]^2$. The kernel functions define an equivalence class and thus identify collections of large random graphs who are spectrally and structurally equivalent. As the spectrum of the graph shift operator defines the graph Fourier transform (GFT), the behavior of the spectrum of the underlying graph has a great impact on the design and implementation of graph signal processing operators such as filters. The spectra of several graph limits are derived analytically and verified with numerical examples.

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Signal Processing Seminar

Electromagnetic 3D anisotropic imaging in the model reduction framework

Jörn Zimmerling
T.U. Delft

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Msc Thesis Presentation

Multi-FPGA Interconnection Simulation

He Zhang

The scalable simulation of neuron communication needs a large amount of computing resources. The high throughput of data cause the high requirement of interconnection network. This thesis is aimed at the finding the proper multi-FPGA connection for the neuron network. First describe the characteristics of the network in terms of the topology, routing and flow control. To find the proper connection, analysis of the throughput for the different network with different traffic pattern by considering the hopcount and bandwidth are made. It shows that the multicast is a good solution. Based on the interconnection router architecture, a simulator is built to make a cycle accurate simulation in systemC and test different traffic pattern by unicast and multicast routing. To break the limitation of FPGA ports, the source synchronous serdes connection is built by using the primitive in the Xilinx FPGA. With the requirement of bandwidth, the possible solution of number of channels and the overhead are anaylsed.


Msc Thesis Presentation

Full-Custom Multi-Compartment Synaptic Circuits in Neuromorphic Structures

Xuefei You

Neuromorphic engineering, aiming at emulating neuro-biological architectures in efficient ways, has been widely studied both on component and VLSI system level. The design space of neuromorphic neuron, the basic unit to conduct signal processing and transmission in nervous system, has been widely explored while that of synapse, the specialized functional unit connecting neurons, is less investigated. In this thesis, a current-based phenomenological synapse model with power-efficient structures, consisting of efficient synaptic learning algor ithms and multi-compartment synapses, has been proposed. A vertical insight is given into the design space of spike-based learning rules in regards to design complexity and biological fidelity. Due to various biological conducting mechanisms, the receptors, namely AMPA, NMDA and GABAa, demonstrate different kinetics in response to stimulus. The designed circuit offers distinctive features of receptors as well as the joint synaptic function. A better computation ability is demonstrated through a cross-correlation detection experiment with a recurrent network of synapse clusters. The analog multi-compartment synapse structure is able to detect and amplify the temporal synchrony embedded in the synaptic noise. The maximum amplification level is 2 times larger than that of single-receptor configurations The final design implemented in UMC65nm technology consumes 1.92, 3.36, 1.11 and 35.22pJ per spike event of energy for AMPA, NMDA, GABAa receptors and the advanced learning circuit, respectively.

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Msc Thesis Presentation

A 32 x 32 Spiking Neural Network System On Chip

Ester Stienstra

In this thesis a prototyping system on chip of a 32 x 32 spiking neural network is presented. This network has been designed in UMC 65. In order to determine which neuron model to use three different analog CMOS neuron models are studied. One of these models is used in the network. The network consists of arrays of synapses and neurons, 32 synapses for each neuron. In order to be able to control all the synaptic inputs and read all the neural outputs, logic is presented that minimizes the number of pads needed, while maintaining controllability and keeping all the important information in the neural signal. Simulations are performed to determine the influence of several behaviors of the neuron and the synapse on the output of the network. Also a floorplan and place and route design for the chip are presented.


Msc Thesis Presentation

Source-Synchronous Interface with All-Digital Data Recovery

Shizhao Zhang

This thesis proposes a low-cost high-efficiency source-synchronous interface for high-speed inter-chip communication. The interface is composed of LVDS transceivers as external I/O buffers, and an alldigital data recovery, which can calibrate the received data phase to be aligned to the 90◦ phase of the received half-rate reference clock, for error free data sampling. The proposed data recovery adopts a fulldigital scheme, which uses time-to-digital converters (TDC) as phase acquisition, a digitally-controlled delay line (DCDL) to calibrate the phase, and a finite-state machine (FSM) as the control unit. Reference clock generated from phase-locked loops (PLL) or delay-locked loops (DLL) is not needed for the proposed data recovery. The interface is implemented in UMC 65 nm Low-leakage technology, with circuits designed at both transistor-level and RTL-level. The postlayout simulation shows the proposed interface works properly with data rates from 412.4Mbps to 1.25Gbps in all process corners. The total layout area is 688 ñm × 87 ñm, and the total power consumption is 16.74 mW.


CSI-EPT: Towards Practical Implementation

Jiying Dai

The information of electrical properties of biological tissues within human body can be useful information for diagnosis, tissue characterization, hyperthermia treatment planning and MR safety control. Contrast Source Inversion Electrical Properties Tomography (CSI-EPT) is an MR base imaging modality, which reconstructs the electrical properties of the object. However, this method has not been implemented in practice due to the following issues: 1) The exact transmit phase, which is a necessary input of regular CSI-EPT algorithm, is not available from MR acquisitions. 2) The incident field, another input of CSI-EPT reconstruction, cannot be measured directly. 3) The reconstruction loses sensitivity at those regions with low electrical fields. 4) The sufficiency of the two-dimensional configuration, which has been used in most published results as an approximation


Signal Processing Seminar

Thomas Sherson

Thomas is going to present his recent research.

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Network topology inference from graph stationary signals

Network topology inference from graph stationary signals

Antonio Garcia Marques (King Juan Carlos University)

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social, brain, and amino-acid networks.

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MSc Defense of Boliang Xu

Packet loss concealment for speech transmissions in real-time wireless applications

Boliang Xu

Packet communication applications cannot guarantee correct delivery of every packet. Congestions and interferences in the network lead to lost packets. However, real-time applications require timely delivery of data or information and always tolerate packet loss to achieve this aim. When some speech packets are lost, packet loss concealment (PLC) is used to replace the missing speech.

In this thesis, after investigating packet loss characteristics in realistic wireless networks and problems in existing PLC algorithms, we propose a new PLC scheme named adaptive PLC, which is composed of three algorithms: odd-even interpolation, waveform similarity matching and silence substitution. Adaptive PLC adjusts the algorithm to use depending on loss situations. Odd-even interpolation recovers the loss by interpolating odd or even samples in a packet. Waveform similarity matching estimates waveform segments from correctly received or already recovered packets. Silence substitution just fills in the missing part by zeros.

The adaptive PLC achieves improvements in speech quality relative to each single PLC algorithm and other existing PLC algorithms.


Signal Processing Seminar

Jie Zhang

Jie is going to present his recent research.

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