Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Neural collapse is an emergent phenomenon in deep learning that was recently discovered
by Papyan, Han and Donoho. We propose a simple unconstrained features model in which …

This paper is concerned with an important matrix condition in compressed sensing known as
the restricted isometry property (RIP). We demonstrate that testing whether a matrix satisfies …

It is often of interest to identify a given number of points in projective space such that the
minimum distance between any two points is as large as possible. Such configurations yield …

It has been observed that the performances of many high-dimensional estimation problems
are universal with respect to underlying sensing (or design) matrices. Specifically, matrices …

In compressed sensing, one takes samples of an N-dimensional vector using an matrix A,
obtaining undersampled measurements. For random matrices with independent standard …

Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing
the biomechanics of human or animal movements. However, long-term EMG signal …

Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight
frames (ETFs), their construction has been proven extremely difficult. The few known …

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 …

There are three steps for compressive sensing, such as the sparse representation of signal,
the design of observation matrix and the reconstruction of signal. The existing observation …