GF12LP+ University Program awarded to Chang Gao

The GF12LP+ University Program was awarded to Chang Gao in ELCA for his research on Low-Power AI Hardware Accelerators using cutting-edge 12 nm technology to enable energy-efficient signal processing in transmitters for future wireless communication technologies. Over the next two years, the program offers four MPW tape-out opportunities in GF12LP+ technology. We look forward to revolutionizing signal processing in Wi-Fi and cellular wireless systems to significantly improve energy efficiency.

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Best Paper Award at EuCAP 2024

Caspar Coco Martin, Weiya Hu and Daniele Cavallo were awarded with the Best Paper Award at the European Conference on Antennas and Propagation 2024 for Best Antenna Theory Paper. The winning paper is

“Analysis and Design of mmWave Wideband Artificial Dielectric Flat Lens Antenna”, Caspar M. Coco Martin, Weiya Hu and Daniele Cavallo (Delft University of Technology, The Netherlands). 

The conference hosted 1500 attendees and included more than 1100 papers. This award adds to the many awards won by the TS group at international conferences, emphasizing the outstanding contributions of the group to the field of antennas.

NWO M2 grant awarded to Francesco Fioranelli

A NWO M2 grant has been awarded to Federico Corradi (TU/e) and Francesco Fioranelli (TUD). The project is entitled NERDNeuromorphic pErception fRamework for event-baseD radars and is all about neuromorphic radar-based perception. 

Emerging applications of mm-wave radar sensing, such as intelligent robotics and the internet-of-things, require more and more computing power in tiny low-energy chips processing increasingly more data. Using traditional performance scaling methods and smaller transistors, as always done by the microelectronic industry, we can still improve, but we need more. In the long term, we need to research disruptive solutions together with new models of computations. In this project, we propose to imitate from nature the extraordinary properties at the basis of active perception, echolocation, and sound-source-localization in animals, to realize innovative neuromorphic radar technologies edge applications.

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STOI incorporated in Matlab

STOI is a widely used measure for "short-time objective intelligibility" in (compressed) speech. It was developed by Cees Taal, Richard Hendriks (SPS), Richard Heusdens, and Jesper Jensen in 2010.

It allows to assess the performance of speech compression algorithms, without using listening panels. It is currently popular as a target feature in machine learning algorithms.

STOI already won best paper awards, and has now been incorporated in the 2024 edition of Matlab.

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