# dithering

### Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.

### Dithered quantized compressive sensing with arbitrary RIP matrices

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

### Time for dithering! Quantized random embeddings with RIP random matrices

(invited by H. Tyagi and M. Cucuringu) Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

### Taking the edge off quantization: projected back projection in dithered compressive sensing

Joint work with C. Xu and V. Schellekens.

### Time for dithering! Quantized random embeddings with RIP random matrices

Invited by Simon Foucart.

### Small Width, Low Distortions: Quantized Random Embeddings of Low-complexity Sets

Abstract: Under which conditions and with which distortions can we preserve the pairwise-distances of low-complexity vectors, e.g., for structured sets such as the set of sparse vectors or the one of low-rank matrices, when these are mapped in a finite set of vectors?

### The Rare Eclipse Problem on Tiles: Quantised Embeddings of Disjoint Convex Sets

Abstract: Quantised random embeddings are an efficient dimensionality reduction technique which preserves the distances of low-complexity signals up to some controllable additive and multiplicative distortions. In this work, we instead focus on verifying when this technique preserves the separability of two disjoint closed convex sets, i.