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 …

Convolutional neural networks (CNN) have led to many state-of-the-art results spanning
through various fields. However, a clear and profound theoretical understanding of the …

Signal processing relies heavily on data models; these are mathematical constructions
imposed on the data source that force a dimensionality reduction of some sort. The vast …

Finite frame theory has a number of real-world applications. In applications like sparse
signal processing, data transmission with robustness to erasures, and reconstruction without …

Recent years have seen the rapid development of miniaturized reconstructive spectrometers
(RSs), yet they still confront a range of technical challenges, such as bandwidth/resolution …

An equiangular tight frame (ETF) is a set of equal norm vectors in a Euclidean space whose
coherence is as small as possible, equaling the Welch bound. Also known as Welch-bound …

In this paper, we improve iterative greedy search algorithms in which atoms are selected
serially over iterations, ie, one-by-one over iterations. For serial atom selection, we devise …

An equiangular tight frame (ETF) is a type of optimal packing of lines in Euclidean space. A
regular simplex is a special type of ETF in which the number of vectors is one more than the …

We study classic streaming and sparse recovery problems using deterministic linear
sketches, including ℓ 1/ℓ 1 and ℓ∞/ℓ 1 sparse recovery problems (the latter also being …

Compressed sensing is a novel signal sampling theory based on the sparsity of signals. To
recover a sparse signal from fewer measurements, it is desired that measurement matrices …