Signal Processing And Networking For Big Data Applications 2017 Edition at Meripustak

Signal Processing And Networking For Big Data Applications 2017 Edition

Books from same Author: Zhu Han, Mingyi Hong, Dan Wang

Books from same Publisher: CAMBRIDGE UNIVERSITY PRESS

Related Category: Author List / Publisher List


  • Retail Price: ₹ 14471/- [ 0.00% off ]

    Seller Price: ₹ 14471

Sold By: T K Pandey      Click for Bulk Order

Offer 1: Get ₹ 111 extra discount on minimum ₹ 500 [Use Code: Bharat]

Offer 2: Get 0.00 % + Flat ₹ 100 discount on shopping of ₹ 1500 [Use Code: IND100]

Offer 3: Get 0.00 % + Flat ₹ 300 discount on shopping of ₹ 5000 [Use Code: MPSTK300]

Free Shipping (for orders above ₹ 499) *T&C apply.

In Stock

Free Shipping Available



Click for International Orders
  • Provide Fastest Delivery

  • 100% Original Guaranteed
  • General Information  
    Author(s)Zhu Han, Mingyi Hong, Dan Wang
    PublisherCAMBRIDGE UNIVERSITY PRESS
    ISBN9781107124387
    Pages474
    BindingHardbound
    LanguageEnglish
    Publish YearMay 2017

    Description

    CAMBRIDGE UNIVERSITY PRESS Signal Processing And Networking For Big Data Applications 2017 Edition by Zhu Han, Mingyi Hong, Dan Wang

    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.