Signal processing and networking for big data applications / Zhu Han, University of Houston, Mingyi Hong, Iowa State University, Dan Wang, the Hong Kong Polytechnic University.
By: Han, Zhu [author.]
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Contributor(s): Hong, Mingyi [author.] | Wang, Dan (Professor of computing) [author.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2017Description: 1 online resource (xii, 362 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781316408032 (ebook).Subject(s): Big data | Wireless communication systems -- Mathematics | Signal processing -- MathematicsAdditional physical formats: Print version: : No titleDDC classification: 005.7 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 25 May 2017).
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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