To date, frames have established themselves as a standard notion in applied mathematics,
computer science, and engineering as a means to derive redundant, yet stable …

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in
electrical engineering, applied mathematics, statistics and computer science. This book …

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 …

Given a dictionary that consists of multiple blocks and a signal that lives in the range space
of only a few blocks, we study the problem of finding a block-sparse representation of the …

Compressed sensing is a novel research area, which was introduced in 2006, and since
then has already become a key concept in various areas of applied mathematics, computer …

Sparse modeling is a powerful framework for data analysis and processing. Traditionally,
encoding in this framework is performed by solving an l 1-regularized linear regression …

This paper investigates the theoretical guarantees of ℓ^1-analysis regularization when
solving linear inverse problems. Most of previous works in the literature have mainly focused …

We present a sparse sensing framework based on dynamic mode decomposition (DMD) to
identify flow regimes and bifurcations in large-scale thermofluid systems. Motivated by real …

Since 2004, the field of compressed sensing has grown quickly and seen tremendous
interest because it provides a theoretically sound and computationally tractable method to …

The theory and techniques of compressed sensing (CS) have shown their potential as a
breakthrough in accelerating k-space data acquisition for parallel magnetic resonance …