Compressed sensing aims to undersample certain high-dimensional signals yet accurately
reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible …

Compressed sensing is a signal processing method that acquires data directly in a
compressed form. This allows one to make fewer measurements than were considered …

We present a mathematical connection between channel coding and compressed sensing.
In particular, we link, on the one hand, channel coding linear programming decoding (CC …

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

We investigate the problem of reconstructing a high-dimensional nonnegative sparse vector
from lower-dimensional linear measurements. While much work has focused on dense …

Recently, it was observed that spatially-coupled LDPC code ensembles approach the
Shannon capacity for a class of binary-input memoryless symmetric (BMS) channels. The …

We consider the problem of computing a binary linear transformation when all circuit
components are unreliable. Two models of unreliable components are considered …

This paper considers the performance of (j, k)-regular low-density parity-check (LDPC)
codes with message-passing (MP) decoding algorithms in the high-rate regime. In particular …

We study the support recovery problem for compressed sensing, where the goal is to
reconstruct the sparsity pattern of a high-dimensional K-sparse signal x∈ ℝ N, as well as the …

This is a tale of two linear programming decoders, namely channel coding linear
programming decoding (CC-LPD) and compressed sensing linear programming decoding …