Graph Signal Processing in Action (GRASPA)

Themes: Biomedical signal processing/wavefield imaging, Signal processing for communication

Graph signal processing, Graph topology identification, Graph filtering, Dynamic graphs, Graph learning, Functional ultrasound, Recommender systems

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.

Project data

Researchers: Geert Leus, Elvin Isufi, Pieter Kruizinga, Alberto Natali, Kaiwen Zhang, Ruben Wijnands
Starting date: June 2022
Closing date: June 2027
Funding: 740 kE; related to group 250 kE
Sponsor: NWO-TTW
Users: Philips, Neurasmus, NaverLabs, Realtech
Contact: Geert Leus

Publication list