invited talk

A Non-Convex Blind Calibration Method for Randomised Sensing Strategies

Joint work with with Valerio Cambareri.

Quantized random projections of low-complexity sets

2016 Invited speaker, talk on “”.

Quasi-isometric embedding of (convex) vector sets in quantized compressed sensing

When Buffon's needle problem helps in quantizing the Johnson-Lindenstrauss Lemma

Abstract: In 1733, Georges-Louis Leclerc, Comte de Buffon in France, set the ground of geometric probability theory by defining an enlightening problem: What is the probability that a needle thrown randomly on a ground made of equispaced parallel strips lies on two of them?

Compressive Acquisition of Sparse Deflectometric Maps

Abstract: Schlieren deflectometry aims at measuring deflections of light rays from transparent objects, which is subsequently used to characterize the objects. With each location on a smooth object surface a sparse deflection map (or spectrum) is associated.

Quantized Iterative Hard Thresholding: Bridging 1-bit and High Resolution Quantized Compressed Sensing

Abstract: In this work, we show that reconstructing a sparse signal from quantized compressive measurement can be achieved in an unified formalism whatever the (scalar) quantization resolution, i.e., from 1-bit to high resolution assumption.

Robust 1-Bit Compressive Sensing: How the Sign of Random Projections Distinguishes Sparse Vectors

Joint work with J. Laska, P. Boufounos, and R. Baraniuk.