Compressed sensing is a novel technique to acquire sparse signals with few
measurements. Normally, compressed sensing uses random projections as measurements …

The goal of this paper is to develop fast algorithms for signal reconstruction from magnitudes
of frame coefficients. This problem is important to several areas of research in signal …

This paper is concerned with the question of reconstructing a vector in a finite-dimensional
real Hilbert space when only the magnitudes of the coefficients of the vector under a …

This paper is concerned with the question of reconstructing a vector in a finite-dimensional
complex Hilbert space when only the magnitudes of the coefficients of the vector under a …

This paper investigates two parameters that measure the coherence of a frame: worst-case
and average coherence. We first use worst-case and average coherence to derive near …

In this paper, we present a new algorithm for compressive sensing that makes use of binary
measurement matrices and achieves exact recovery of ultrasparse vectors in a single pass …

This paper focuses on the error probability performance of LoRa, a long-range low-power
wireless communication technology suited for the Internet of Things (IoT). We propose …

Compressed sensing (or compressive sensing) refers to the recovery of high-dimensional
but low-complexity objects from a limited number of measurements. Two canonical …

This paper applies techniques from algebraic and differential geometry to determine how to
best pack points in real projective spaces. We present a computer-assisted proof of the …

Matrices with the restricted isometry property (RIP) are of particular interest in compressed
sensing. To date, the best known RIP matrices are constructed using random processes …