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Signal processing and networking for big data applications

By: Han, Zhu.
Contributor(s): Hong, Mingyi [Co author] | Wang, Dan [Co author].
Material type: materialTypeLabelBookPublisher: Cambridge Cambridge University Press 2017Description: xii, 362p. With index.ISBN: 9781107124387.Subject(s): Big data | Wireless communication systems - Mathematics | Signal processing - MathematicsDDC classification: 005.7 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. https://www.cambridge.org/core/books/signal-processing-and-networking-for-big-data-applications/A05CAB38B34884372E3BA65D6B5136C8#fndtn-information
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Slot 81 (0 Floor, West Wing) Non-fiction 005.7 H2S4 (Browse shelf) Available 199301

Table of Contents

Part I - Overview of Big Data Applications page
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 Sets in MapReduce
14 - Massive Data Collection Using Wireless Sensor Networks

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.

https://www.cambridge.org/core/books/signal-processing-and-networking-for-big-data-applications/A05CAB38B34884372E3BA65D6B5136C8#fndtn-information

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