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| fixed length control field |
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| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220627b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781107124387 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.7 |
| Item number |
HAN |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Han, Zhu |
| 245 ## - TITLE STATEMENT |
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| Title |
Signal processing and networking for big data applications |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2017 |
| Place of publication, distribution, etc |
Cambridge : |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 362 p. ; |
| Other physical details |
ill., |
| Dimensions |
26 cm |
| 365 ## - TRADE PRICE |
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| Price amount |
111.00 |
| Price type code |
GBP |
| Unit of pricing |
100.50 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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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| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Big data |
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Signal processing |
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Mathematics |
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Disk access |
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Computer science |
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ADMM |
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Bregman method |
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BSUM method |
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Cloud computing |
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Convex set |
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Deep learning |
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Distributed subgradient method |
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False data injection |
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Gibbs sampling |
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Hadloop Distributed file system |
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Iterative support detection |
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Laggrangian function |
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MapReduce |
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Network virtualization |
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Orthogonal matching pursuit |
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Parametric quadratic programming |
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Restricted isometry principle |
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Sublinear algorithm |
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Tensor |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Hong, Mingyi |
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| Personal name |
Wang, Dan |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |