Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave)
hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) …

Deep learning (DL)-based compressed sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most existing …

This tutorial provides an exposition of a flexible geometric framework for high-dimensional
estimation problems with constraints. The tutorial develops geometric intuition about high …

Motivated by recent work on stochastic gradient descent methods, we develop two
stochastic variants of greedy algorithms for possibly non-convex optimization problems with …

In this paper, a new three-dimensional chaotic system is proposed for image encryption. The
core of the encryption algorithm is the combination of chaotic system and compressed …

In recent years, a large number of researchers have begun to embed convolutional neural
networks (CNNs) into traditional compressive sensing (CS) reconstruction algorithms. They …

Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be
expressed as a linear combination of only a few spectra (endmembers) in a spectral library …

Moving force identification (MFI) techniques have been widely studied in recent years.
However, the contradiction between response acquisition and energy consumption limits …

In this paper, we develop a framework to design sensing matrices for compressive sensing
applications that lead to good mean squared error (MSE) performance subject to sensing …