000 02383nam a22003738i 4500
001 CR9781316408032
003 UkCbUP
005 20240730160757.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 150310s2017||||enk o ||1 0|eng|d
020 _a9781316408032 (ebook)
020 _z9781107124387 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aQA76.9.B45
_bH356 2017
082 0 0 _a005.7
_223
100 1 _aHan, Zhu,
_d1974-
_eauthor.
_974651
245 1 0 _aSignal processing and networking for big data applications /
_cZhu Han, University of Houston, Mingyi Hong, Iowa State University, Dan Wang, the Hong Kong Polytechnic University.
264 1 _aCambridge :
_bCambridge University Press,
_c2017.
300 _a1 online resource (xii, 362 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 25 May 2017).
520 _aThis 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.
650 0 _aBig data.
_94174
650 0 _aWireless communication systems
_xMathematics.
_974652
650 0 _aSignal processing
_xMathematics.
_93827
700 1 _aHong, Mingyi,
_eauthor.
_974653
700 1 _aWang, Dan
_c(Professor of computing),
_eauthor.
_974654
776 0 8 _iPrint version:
_z9781107124387
856 4 0 _uhttps://doi.org/10.1017/9781316408032
942 _cEBK
999 _c84198
_d84198