Compressive sensing (CS) has received much attention in several fields such as digital
image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) …

We consider the channel estimation problem in point-to-point reconfigurable intelligent
surface (RIS)-aided millimeter-wave (mmWave) MIMO systems. By exploiting the low-rank …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

The sparse prior has been widely adopted to establish data models for numerous
applications. In this context, most of them are based on one of three foundational paradigms …

One of the important classes of sparse signals is the non-negative signals. Many algorithms
have already been proposed to recover such non-negative representations, where greedy …

Abstract Information Pursuit (IP) is a classical active testing algorithm for predicting an output
by sequentially and greedily querying the input in order of information gain. However, IP is …

Dynamic range limitations in signal processing often lead to clip**, or saturation, in
signals. The task of audio declip** is estimating the original audio signal, given its clipped …

The paper presents a novel method for the compressed acquisition of electrocardiographic
(ECG) signals. The proposed method is intended to be applied to Internet-of-Medical-Things …

With increasingly smaller size, more powerful sensing capabilities and higher level of
autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to …

In this paper we present a generalized Deep Learning-based approach for solving ill-posed
large-scale inverse problems occuring in medical image reconstruction. Recently, Deep …