The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

This paper provides principles and applications of the sparse microwave imaging theory and
technology. Synthetic aperture radar (SAR) is an important method of modern remote …

In this paper, we propose a unified primal-dual algorithm framework for two classes of
problems that arise from various signal and image processing applications. We also show …

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that
enables numerous innovative services related to healthcare, manufacturing, smart cities and …

Compressive Sampling reconstruction techniques require computationally intensive
algorithms, often using L 1 optimization to reconstruct a signal that was originally sampled at …

Sparse approximation is a hypothesized coding strategy where a population of sensory
neurons (eg V1) encodes a stimulus using as few active neurons as possible. We present …

Compressive Sensing (CS) signal reconstruction can be implemented using convex
relaxation, non-convex, or local optimization algorithms. Though the reconstruction using …

Compressive sensing (CS) is a novel signal processing technology to reconstruct the sparse
signal at sub-Nyquist rate. Orthogonal matching pursuit (OMP) is one of the most widely …

Compressed sensing (CS) has emerged as a promising method in the field of magnetic
resonance imaging. Taking advantage of the signal sparsity in certain domain via L 1 …

Sparse approximation is an important optimization problem in signal and image processing
applications. A Hopfield-Network-like system of integrate and fire (IF) neurons is proposed …