Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …

Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

Recent work has found that sparse autoencoders (SAEs) are an effective technique for
unsupervised discovery of interpretable features in language models'(LMs) activations, by …

A power grid is a complex system connecting electric power generators to consumers
through power transmission and distribution networks across a large geographical area …

Many problems in signal processing and statistical inference involve finding sparse
solutions to under-determined, or ill-conditioned, linear systems of equations. A standard …

Finding sparse approximate solutions to large underdetermined linear systems of equations
is a common problem in signal/image processing and statistics. Basis pursuit, the least …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

In this paper, we model the components of the compressive sensing (CS) problem, ie, the
signal acquisition process, the unknown signal coefficients and the model parameters for the …

Sparse representation has been demonstrated to be a powerful tool in classification of
hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining …