In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …

This article provides a new strategy for the heterogeneous change detection (HCD) problem:
solving HCD from the perspective of graph signal processing (GSP). We construct a graph to …

We propose a proximal approach to deal with a class of convex variational problems
involving nonlinear constraints. A large family of constraints, proven to be effective in the …

Many underwater acoustic (UWA) channels exhibit impulsive noise, thereby severely
degrading the performance of traditional channel estimation algorithms. This paper presents …

This work deals with the block-sparse system identification problem on the basis of the
maximum correntropy criterion (MCC). The MCC is known for its robustness against non …

Group sparsity is one of the important signal priors for regularization of inverse problems.
Sparsity with group structure is encountered in numerous applications. However, despite the …

Modeling relations between local optimum parameter vectors to estimate in multitask
networks has attracted much attention over the last years. This work considers a distributed …

Diagnosing acute leukemia is the necessary prerequisite to treating it. Multi-classification on
the gene expression data of acute leukemia is help for diagnosing it which contains B-cell …

Background: Cancer threatens human health seriously. Diagnosing cancer via gene
expression analysis is a hot topic in cancer research. Objective: The study aimed to …

This paper provides a new strategy for the Heterogeneous Change Detection (HCD)
problem: solving HCD from the perspective of Graph Signal Processing (GSP). We construct …