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Signal Processing and Networking for Big Data Applications by Mingyi Hong, Zhu Han and Dan Wang (2017, Hardcover)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101107124387

ISBN-139781107124387

eBay Product ID (ePID)229544144

Product Key Features

Number of Pages474 Pages

Publication NameSignal Processing and Networking for Big Data Applications

LanguageEnglish

SubjectSignals & Signal Processing

Publication Year2017

TypeTextbook

Subject AreaTechnology & Engineering

AuthorMingyi Hong, Zhu Han, Dan Wang

FormatHardcover

Dimensions

Item Height0.9 in

Item Weight31.4 Oz

Item Length10 in

Item Width7 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2017-018291

Dewey Edition23

ReviewsAdvance praise: 'A very nice balanced treatment over two large-scale signal processing aspects: mathematical backgrounds versus big data applications, with a strong flavor of distributed optimization and computation.' Shuguang Cui, University of California, Davis

IllustratedYes

Dewey Decimal005.7

Table Of ContentPart I. Overview of Big Data Applications: 1. Introduction; 2. Data parallelism: the supporting architecture; Part II. Methodology and Mathematical Background: 3. First order methods; 4. Sparse optimization; 5. Sublinear algorithms; 6. Tensor for big data; 7. Deep learning and applications; Part III. Big Data Applications: 8. Compressive sensing based big data analysis; 9. Distributed large-scale optimization; 10. Optimization of finite sums; 11. Big data optimization for communication networks; 12. Big data optimization for smart grid systems; 13. Processing large data set in MapReduce; 14. Massive data collection using wireless sensor networks.

SynopsisThis unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics., This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. Covering fundamental signal processing theories, software implementations, and techniques for analysis, design, and optimization, it is ideal for researchers, practitioners, and students in signal processing and communications networks.

LC Classification NumberQA76.9.B45