Most existing convolutional dictionary learning (CDL) algorithms are bas...
Beamforming is a signal processing technique to steer, shape, and focus ...
Simultaneous sparse approximation (SSA) seeks to represent a set of depe...
Graph convolutional networks (GCNs) can successfully learn the graph sig...
Convolutional sparse coding improves on the standard sparse approximatio...
Multimodal image fusion aims to combine relevant information from images...
We address the problem of tensor decomposition in application to
directi...
In graph signal processing (GSP), prior information on the dependencies ...
We develop a new tensor model for slow-time multiple-input multiple outp...
Minimum achievable complexity (MAC) for a maximum likelihood (ML)
perfor...
Joint communication and radar (JCR) waveforms with fully digital baseban...
The first step for any graph signal processing (GSP) procedure is to lea...
Source localization and spectral estimation are among the most fundament...
We propose a novel pilot structure for covariance matrix estimation in
m...
We address the multi-focus image fusion problem, where multiple images
c...