This work extends compressed sensing theory to settings where the entries of the test vectors exhibit structured statistical dependencies, enabling the recovery of high-dimensional sparse signals from relatively few linear observations in the form of projections onto a collection of test vectors.Expand

This paper proposes sparse channel estimation methods based on convex/linear programming, based on recent advances from the theory of compressed sensing.Expand

The problem of recovering a sparse signal x Rn from a relatively small number of its observations of the form y = Ax Rk, where A is a known matrix and k « n, has recently received a lot of attention… Expand

A diagonal coordinate representation for Volterra filters is developed and exploited to derive efficient Voltera filter implementations for processing carrier based input signals.Expand

The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles in haystacks), provided the measurements are noiseless.Expand

We develop a polyphase NonLinear EQualizer (pNLEQ) which is capable of simultaneously mitigating distortion generated by both the on-chip circuitry and mismatches due to time interleaving.Expand

We report on a standoff chemical detection system using widely tunable external-cavity quantum cascade lasers (ECQCLs) to illuminate target surfaces in the mid infrared (λ = 7.4 – 10.5 μm).… Expand

We provide a comparative performance and complexity analysis of several classes of algorithms as a function of noise levels, error distribution, scene complexity, and spatial degrees of freedom.Expand

The purpose of this work is to introduce two variations of an approach to adaptive beam-forming for wide-band sensor array systems using a subband decomposition.Expand