In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

The recently introduced theory of compressed sensing enables the reconstruction of sparse
or compressible signals from a small set of nonadaptive, linear measurements. If properly …

Compressed sensing refers to a growing body of techniques that'undersample'high-
dimensional signals and yet recover them accurately. Such techniques make fewer …

Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some
basis) using only a small number of random measurements. Existing results in compressed …

In one-bit compressed sensing, previous results state that sparse signals may be robustly
recovered when the measurements are taken using Gaussian random vectors. In contrast to …

Compressed sensing has a wide range of applications that include error correction, imaging,
radar and many more. Given a sparse signal in a high dimensional space, one wishes to …

The fundamental principle underlying compressed sensing is that a signal, which is sparse
under some basis representation, can be recovered from a small number of linear …

Compressive Sampling Page 1 Compressive Sampling [from the GUEST EDITORS] Richard G.
Baraniuk, Emmanuel Candès, Robert Nowak, and Martin Vetterli IEEE SIGNAL PROCESSING …

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 …

The rapid developing area of compressed sensing suggests that a sparse vector lying in a
high dimensional space can be accurately and efficiently recovered from only a small set of …