The applications of compressive sensing (CS) in the field of information security have
captured a great deal of researchers' attention in the past decade. To supply guidance for …

We analyze the complexity of learning $ n $-qubit quantum phase states. A degree-$ d $
phase state is defined as a superposition of all $2^ n $ basis vectors $ x $ with amplitudes …

The need of reconstructing discrete-valued sparse signals from few measurements, that is
solving an underdetermined system of linear equations, appears frequently in science and …

This paper considers the problem of compressive sensing over a finite alphabet, where the
finite alphabet may be inherent to the nature of the data or a result of quantization. We show …

Applications such as wireless communications require efficient sensing techniques of
signals with the a priori knowledge of those being lattice-valued. In this paper, we study the …

This paper establishes information-theoretic limits for estimating a finite-field low-rank matrix
given random linear measurements of it. These linear measurements are obtained by taking …

Toward the goal of high security and efficiency for data collection in wireless sensor network,
this article proposed an adaptable secure compressive sensing–based data collection …

In this paper we present F 2 OMP, a recovery algorithm for Compressed Sensing over finite
fields. Classical recovery algorithms do not exploit the fact that a signal may belong to a …

The recovery of signals with finite-valued components from few linear measurements is a
problem with widespread applications and interesting mathematical characteristics. In the …

We provide a new methodology for statistical recovery of single linear mixtures of piecewise
constant signals (sources) with unknown mixing weights and change points in a multiscale …