In this paper, we propose a novel neurodynamic network to deal with l 1-minimization
problem. In the framework of the fixed-time converging neurodynamic network (FxNN), time …

This paper considers the deep-learning-aided compressed video sensing problem. To this
end, a deep generative adversarial network has been proposed to provide an approximation …

Moving object detection which has attracted wide attention is the critical issue of computer
vision. Consequently, the low-rank and sparse decomposition (LRSD) has been a powerful …

In this paper, we present a novel approach for the reconstruction of sparse graph signals
using a distributed adaptive thresholding recursive least squares algorithm. Our proposed …

We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-
rank and sparse decomposition (LSD) to dramatically reduce the number of queries and …

With wide-spread real-world applications, low-rank and sparse matrix recovery, where the
concerned matrix with incomplete data is divided into a low-rank part and a sparse part …

This paper introduces a special nonlinear generalized coprime array, namely circular partial
coprime array (CPCA). The antennas of CPCA are distributed on a circle, but after the …

The present doctoral dissertation focuses on representative-based processing proper for a
big set of high-dimensional data. Compression and subset selection are considered as two …

Deep compressed sensing assumes the data has sparse representation in a latent space,
ie, it is intrinsically of low-dimension. The original data is assumed to be mapped from a low …

In order to exploit the advantages of the massive MIMO systems, it is vital to apply the
channel estimation task. The huge number of antennas at the base station of a massive …