Signal Processing and Networking for Big Data Applications

Nonfiction, Science & Nature, Technology, Engineering, Computers, General Computing
Cover of the book Signal Processing and Networking for Big Data Applications by Zhu Han, Mingyi Hong, Dan Wang, Cambridge University Press
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Author: Zhu Han, Mingyi Hong, Dan Wang ISBN: 9781108155496
Publisher: Cambridge University Press Publication: April 27, 2017
Imprint: Cambridge University Press Language: English
Author: Zhu Han, Mingyi Hong, Dan Wang
ISBN: 9781108155496
Publisher: Cambridge University Press
Publication: April 27, 2017
Imprint: Cambridge University Press
Language: English

This 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This 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.

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