Thomas Feuillen

Research & Interests

CASI meeting : “One Bit to Rule them All”, slides and video

I had the opportunity to present my recent work on quantized backprojection to the CASI meeting from the University of Limoges in France.

IRACON2021: “One-Bit to Rule them All”, recorded talk

I had the opportunity to take part in the second remote edition of the IRACON workshop where I presented my work on the Quantized Backprojection of Quantized data.

ITWIST2020: “One Bit to Rule Them All”

I had the opportunity to present my recent work on Quantized BackProjection at ITWIST2020. Here are the slides of this paper called : One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing

How to use OBS for presentation

In this small blogpost, I wrote a short tutorial on how I use OBS for academic presentations.

2019 IEEE RFID-TA : An Ultra-wideband Battery-less Positioning System for Space Applications

Here is the Arxiv link to the paper that we presented to RFID-TA which is a project that we worked on for ESA. We developed in collaboration with the University of Bologna an UWB position system that worked in real time and was powered remotely.

IEEE RadarConf 2019: Quantity over Quality, slides

Here are the slides of my talk at the IEEE 2019 Radar Conference in Boston during the special session “Fully Digital Radar with Hardware Constraints”.

ITWIST 2018: “An extreme bit-rate reduction scheme for 2D radar localization”

This the poster that I presented at ITWIST 2018 (arxiv link soon).

Quantity over Quality: Dithered Quantization for Compressive Radar Systems

In this second blog-post we present a work submitted in RadarConf2019, where the range recovery of targets using highly quantized data is studied. Compared to the previous post (presented in COSERA2018), we study more deeply the trade-off between the number of measurements and their resolutions for a fixed bit-rate or bit-budget, i.e., a comparison between quantity and quality.

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

In this article we investigate how to still achieve 2-D localization using FMCW radar and by reducing the bit-rate of the sampled data by \(94\%\) using 1-bit quantized dithered compressive sensing.

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