Task-cognizant sparse sensing for inference (ASPIRE)

Themes: Signal processing for communication

In the era of big data, it is of crucial importance to gather only the data that is informative for a specific inference task in order to limit the required sensing cost, as well as the related costs of storing or communicating the data.

The main goal of this project is therefore to transform classical sensing methods, often based on Nyquist-rate sampling, to low-cost sparse sensing mechanisms designed for specific inference tasks, such as estimation and detection.

The overarching objective of this project is to achieve the lowest sensing cost with a guaranteed performance for the task at hand. The results of this project will lead to major sensing energy savings and will have a huge impact on a variety of signal processing applications such as radar, radio astronomy, seismic data acquisition and ultrasound imaging.