The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
—Data loss in wireless communications greatly affects the reconstruction quality of a signal. In the case of images, data loss results in a reduction in quality of the received i...
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., ...
We examine the use of a structured thresholding algorithm for sparse underwater channel estimation using compressed sensing. This method shows some improvements over standard algo...
In [12] the authors proved an asymptotic sampling theorem for sparse signals, showing that n random measurements permit to reconstruct an N-vector having k nonzeros provided n >...