In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth
generation wireless communication networks, which is expected to support massive …

Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse
signals that enables sampling rates significantly below the classical Nyquist rate. Based on …

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to
lower energy consumption in wireless body area networks for electrocardiogram (ECG) …

We present a Xampling-based technology enabling interference-free operation of radar and
communication systems over a common spectrum. Our system uses a recently developed …

Despite the importance of sparsity signal models and the increasing prevalence of high-
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …

Commonly employed reconstruction algorithms in compressed sensing (CS) use the L2
norm as the metric for the residual error. However, it is well-known that least squares (LS) …

Electrocardiogram (ECG) signal compression is a vital signal processing area, especially
with the growing usage of wireless body sensor networks (WBSN). ECG signals need to be …

Weighted-minimization has been studied as a technique for the reconstruction of a sparse
signal from compressively sampled measurements when prior information about the signal …

Coded aperture compressive spectral imagers (CSI) sense a three-dimensional data cube
by using two-dimensional projections of the coded and spectrally dispersed input image …