We study the problem of reconstructing a sparse signal from a limited number of its linear
projections when a part of its support is known, although the known part may contain some …

We consider the problem of recursively and causally reconstructing time sequences of
sparse signals (with unknown and time-varying sparsity patterns) from a limited number of …

Crossbar arrays of memristive elements are investigated for the implementation of dictionary
learning and sparse coding of natural images. A winner-take-all training algorithm, in …

It has been shown that the problem of $\ell_1 $-penalized least-square regression
commonly referred to as the Lasso or Basis Pursuit DeNoising leads to solutions that are …

While deep learning continues to permeate through all fields of signal processing and
machine learning, a critical exploit in these frameworks exists and remains unsolved. These …

For decades, advances in electronics were directly driven by the scaling of CMOS transistors
according to Moore's law. However, both the CMOS scaling and the classical computer …

Novel uses of graphical processing units for accelerated computation revolutionized the field
of high-performance scientific computing by providing specialized workflows tailored to …

In this work, we propose algorithms to recursively and causally reconstruct a sequence of
natural images from a reduced number of linear projection measurements taken in a domain …

In recent work, Kalman Filtered Compressed Sensing (KF-CS) was proposed to causally
reconstruct time sequences of sparse signals, from a limited number of …

Extraction of latent sources of complex stimuli is critical for making sense of the world. While
the brain solves this blind source separation (BSS) problem continuously, its algorithms …