quantization
Quasi-isometric embeddings of vector sets with quantized sub-Gaussian projections
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Last January, I was honored to be invited in RWTH Aachen University by Holger Rauhut and Sjoerd Dirksen to give a talk on the general topic of quantized compressed sensing.
Compressed sensing, quantization and quasi-isometric embedding
Abstract: The advent of Compressed Sensing (CS) ten years ago has precipitated a radical re-thinking of signal acquisition, sensing, processing and transmission system design. A significant aspect of such systems is quantization (or digitization) of the acquired data before further processing or for the purpose of transmission and compression.
A Quantized Johnson Lindenstrauss Lemma: The Finding of Buffon's Needle
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?
Quantization and Compressive Sensing
Abstract: Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems.
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.