We address the downlink channel estimation problem for massive multiple-input multiple-
output (MIMO) systems in this paper, where the inherit burst-sparsity structure is exploited to …

Due to the nonsparse representation, the use of compressed sensing (CS) for physiological
signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We …

A promising research that has drawn considerable attentions is exploiting the inherent
structures in the sparse signal. In this work, we apply the property to the multiple …

Extracting the underlying low-dimensional space where high-dimensional signals often
reside has been at the center of numerous algorithms in the signal processing and machine …

An iterative variational Bayesian method is proposed for estimation of the statistical
properties of the composite gamma log-normal distribution, specifically, the Nakagami …

This paper addresses the problem of Bayesian Block Sparse Modeling when coefficients
within the blocks are correlated. In contrast to the current hierarchical methods which do not …

The generalized inverse Gaussian (GIG) Lévy process is a limit of compound Poisson
processes, including the stationary gamma process and the stationary inverse Gaussian …

Structured matrix estimation belongs to the family of learning tasks whose main goal is to
reveal low-dimensional embeddings of high-dimensional data. Nowadays, this task appears …