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Author: Giulio Coluccia
Publisher: Springer
ISBN: 9812873902
Category : Technology & Engineering
Languages : en
Pages : 104
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Book Description
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.
Author: Giulio Coluccia
Publisher: Springer
ISBN: 9812873902
Category : Technology & Engineering
Languages : en
Pages : 104
Get Book Here
Book Description
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.
Author: Simon Foucart
Publisher: Springer Science & Business Media
ISBN: 0817649484
Category : Computers
Languages : en
Pages : 634
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Book Description
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.
Author: Otmar Scherzer
Publisher: Springer Science & Business Media
ISBN: 0387929193
Category : Mathematics
Languages : en
Pages : 1626
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Book Description
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.
Author: Bhaskar Krishnamachari
Publisher: Springer Science & Business Media
ISBN: 3642020844
Category : Computers
Languages : en
Pages : 385
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Book Description
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.
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
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Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: Yehuda Afek
Publisher: Springer
ISBN: 364241527X
Category : Computers
Languages : en
Pages : 609
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Book Description
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.
Author: Avishy Y. Carmi
Publisher: Springer Science & Business Media
ISBN: 364238398X
Category : Technology & Engineering
Languages : en
Pages : 505
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Book Description
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.
Author: Antonio De Maio
Publisher: Cambridge University Press
ISBN: 110857694X
Category : Technology & Engineering
Languages : en
Pages : 381
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Book Description
Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.
Author: Ehsan Miandji
Publisher: Linköping University Electronic Press
ISBN: 9176851869
Category :
Languages : en
Pages : 180
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Book Description
The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications. Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time. This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.
Author: Gregorio Martinez Perez
Publisher: Springer
ISBN: 3642545254
Category : Computers
Languages : en
Pages : 583
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Book Description
This book constitutes the refereed proceedings of the Second International Conference on Security in Computer Networks and Distributed Systems, SNDS 2014, held in Trivandrum, India, in March 2014. The 32 revised full papers presented together with 9 short papers and 8 workshop papers were carefully reviewed and selected from 129 submissions. The papers are organized in topical sections on security and privacy in networked systems; multimedia security; cryptosystems, algorithms, primitives; system and network security; short papers. The workshop papers were presented at the following workshops: Second International Workshop on Security in Self-Organising Networks (Self Net 2014); Workshop on Multidisciplinary Perspectives in Cryptology and Information Security (CIS 2014); Second International Workshop on Trust and Privacy in Cyberspace (Cyber Trust 2014).
