The sparse error correction is intimately related to Compressive Sensing. Exploiting this connection, the paper proposes an error correction scheme pivoted on partial Fourier matr...
According to the recent theory of compressed sensing, accurate reconstruction is possible even from data samples dramatically smaller than Nyquist sampling limit as long as the un...
A new framework of compressive sensing (CS), namely statistical compressive sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical dist...
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 ...
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...