Compressed Sensing for Distributed Systems
Compressed Sensing for Distributed Systems

Author: Giulio Coluccia

Publisher: Springer

Published: 2015-05-29

Total Pages: 104

ISBN-13: 9812873902

DOWNLOAD EBOOK

This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to the theoretical bounds. It presents and discusses various distributed reconstruction algorithms, summarizing the theoretical reconstruction guarantees and providing a comparative analysis of their performance and complexity. In summary, this book will allow the reader to get started in the field of distributed compressed sensing from theory to practice. We believe that this book can find a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in mathematical optimization, network systems, graph theoretical methods, linear systems, stochastic systems, and randomized algorithms. To help the reader become familiar with the theory and algorithms presented, accompanying software is made available on the authors’ web site, implementing several of the algorithms described in the book. The only background required of the reader is a good knowledge of advanced calculus and linear algebra.

Compressed Sensing with Applications in Wireless Networks
Compressed Sensing with Applications in Wireless Networks

Author: Markus Leinonen

Publisher:

Published: 2019-11-29

Total Pages: 310

ISBN-13: 9781680836462

DOWNLOAD EBOOK

This monograph reviews several recent compressed sensing advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources.

A Mathematical Introduction to Compressive Sensing
A Mathematical Introduction to Compressive Sensing

Author: Simon Foucart

Publisher: Springer Science & Business Media

Published: 2013-08-13

Total Pages: 634

ISBN-13: 0817649484

DOWNLOAD EBOOK

At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.

Distributed Computing in Sensor Systems
Distributed Computing in Sensor Systems

Author: Bhaskar Krishnamachari

Publisher: Springer Science & Business Media

Published: 2009-05-25

Total Pages: 385

ISBN-13: 3642020844

DOWNLOAD EBOOK

The book constitutes the refereed proceedings of the Fifth International Conference on Distributed Computing in Sensor Systems, DCOSS 2009, held in Marina del Rey, CA, USA, in June 2009. The 26 revised full papers presented were carefully reviewed and selected from 116 submissions. The research contributions in this proceedings span many aspects of sensor systems, including energy efficient mechanisms, tracking and surveillance, activity recognition, simulation, query optimization, network coding, localization, application development, data and code dissemination.

Handbook of Mathematical Methods in Imaging
Handbook of Mathematical Methods in Imaging

Author: Otmar Scherzer

Publisher: Springer Science & Business Media

Published: 2010-11-23

Total Pages: 1626

ISBN-13: 0387929193

DOWNLOAD EBOOK

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

Data-Driven Science and Engineering
Data-Driven Science and Engineering

Author: Steven L. Brunton

Publisher: Cambridge University Press

Published: 2022-05-05

Total Pages: 615

ISBN-13: 1009098489

DOWNLOAD EBOOK

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Distributed Computing
Distributed Computing

Author: Yehuda Afek

Publisher: Springer

Published: 2013-10-04

Total Pages: 609

ISBN-13: 364241527X

DOWNLOAD EBOOK

This book constitutes the proceedings of the 27th International Symposium on Distributed Computing, DISC 2013, held in Jerusalem, Israel, in October 2013. The 27 full papers presented in this volume were carefully reviewed and selected from 142 submissions; 16 brief announcements are also included. The papers are organized in topical sections named: graph distributed algorithms; topology, leader election, and spanning trees; software transactional memory; shared memory executions; shared memory and storage; gossip and rumor; shared memory tasks and data structures; routing; radio networks and the SINR model; crypto, trust, and influence; and networking.

Compressed Sensing
Compressed Sensing

Author: Yonina C. Eldar

Publisher: Cambridge University Press

Published: 2012-05-17

Total Pages: 557

ISBN-13: 1107394392

DOWNLOAD EBOOK

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

Compressed Sensing & Sparse Filtering
Compressed Sensing & Sparse Filtering

Author: Avishy Y. Carmi

Publisher: Springer Science & Business Media

Published: 2013-09-13

Total Pages: 505

ISBN-13: 364238398X

DOWNLOAD EBOOK

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.

Automatic Speech Recognition
Automatic Speech Recognition

Author: Dong Yu

Publisher: Springer

Published: 2014-11-11

Total Pages: 329

ISBN-13: 1447157796

DOWNLOAD EBOOK

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.