This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a ...
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS...
Rafael E. Carrillo, Luisa F. Polania, Kenneth E. B...
This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or ...
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...
This paper explores a novel setting for compressed sensing (CS) in which the sampling trajectory length is a critical bottleneck and must be minimized subject to constraints on th...