Compressive sensing (CS) has received much attention in several fields such as digital
image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) …

Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Channel acquisition is one of the main challenges for the deployment of reconfigurable
intelligent surface (RIS) aided communication systems. This is because an RIS has a large …

In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …

This paper presents a new approach to single-image superresolution, based upon sparse
signal representation. Research on image statistics suggests that image patches can be well …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

Abstract Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction
information of several electromagnetic waves/sources from the outputs of a number of …

We examine the recovery of block sparse signals and extend the recovery framework in two
important directions; one by exploiting the signals' intra-block correlation and the other by …

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI)
data from under-sampled kt space data. In contrast to classical model based cine MRI …

The problem of minimizing the rank of a matrix subject to affine constraints has applications
in several areas including machine learning, and is known to be NP-hard. A tractable …