This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear m...
This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We demonstrate that a single convex program gives an accurate estim...
—Beyond signal processing applications, frames are also powerful tools for modeling the sensing and information processing of many biological and man-made systems that exhibit in...
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals that enables sampling rates significantly below the classical Nyquist rate. Based on...
Luisa F. Polania, Rafael E. Carrillo, Manuel Blanc...