The construction industry is currently going through an intelligent revolution. The profound
transformation of the Industry 4.0 era is made possible by contemporary technologies such …

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …

In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …

Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

Abstract Deep Neural Networks have achieved extraordinary results on image classification
tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of …

Many real-world problems deal with collections of high-dimensional data, such as images,
videos, text, and web documents, DNA microarray data, and more. Often, such high …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

We describe methods for learning dictionaries that are appropriate for the representation of
given classes of signals and multisensor data. We further show that dimensionality reduction …