As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …

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

Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving high-data rate …

Based on the classic augmented Lagrangian multiplier method, we propose, analyze and
test an algorithm for solving a class of equality-constrained non-smooth optimization …

Emerging communication network applications including fifth-generation (5G) cellular and
the Internet-of-Things (IoT) will almost certainly require location information at as many …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

The theory of compressive sensing has shown that sparse signals can be reconstructed
exactly from many fewer measurements than traditionally believed necessary. In [1], it was …

First‐pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration
since its representation in the combined temporal Fourier and spatial domain is sparse and …

In this thesis, I propose and study an efficient algorithm for solving a class of compressive
sensing problems with total variation regularization. This research is motivated by the need …

Compressive sensing is a signal acquisition framework based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable …