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001 978-1-84628-218-8
003 DE-He213
005 20141014113455.0
007 cr nn 008mamaa
008 100301s2006 xxk| s |||| 0|eng d
020 _a9781846282188
_9978-1-84628-218-8
024 7 _a10.1007/1-84628-218-7
_2doi
041 _aeng
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aChi, Chong-Yung.
_eauthor.
245 1 0 _aBlind Equalization and System Identification
_h[electronic resource] :
_bBatch Processing Algorithms, Performance and Applications /
_cby Chong-Yung Chi, Chii-Horng Chen, Chih-Chun Feng, Ching-Yung Chen.
260 1 _aLondon :
_bSpringer London,
_c2006.
264 1 _aLondon :
_bSpringer London,
_c2006.
300 _aXIII, 469 p. 112 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aMathematical Background -- Fundamentals of Statistical Signal Processing -- SISO Blind Equalization Algorithms -- MIMO Blind Equalization Algorithms -- Applications of MIMO Blind Equalization Algorithms -- Two-Dimensional Blind Deconvolution Algorithms -- Applications of Two-Dimensional Blind Deconvolution Algorithms.
520 _aDiscrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades. The rapid progress of digital and mixed-signal integrated circuits in processing speed, functionality and cost-effectiveness has led to their ubiquitous employment in signal processing and transmission in diverse milieux. The absence of training or pilot signals from many kinds of transmission – in, for example, speech analysis, seismic exploration and texture image analysis – necessitates the widespread use of blind equalization and system identification. There have been a great many algorithms developed for these purposes, working with one- or two-dimensional (2-d) signals and with single-input single-output (SISO) or multiple-input multiple-output (MIMO), real or complex systems. It is now time for a unified treatment of this subject, pointing out the common characteristics and the sometimes close relations of these algorithms as well as learning from their different perspectives. Blind Equalization and System Identification provides such a unified treatment presenting theory, performance analysis, simulation, implementation and applications. Topics covered include: • SISO, MIMO and 2-d non-blind equalization (deconvolution) algorithms; • SISO, MIMO and 2-d blind equalization (deconvolution) algorithms; • SISO, MIMO and 2-d blind system identification algorithms; • algorithm analyses and improvements; • applications of SISO, MIMO and 2-d blind equalization/identification algorithms. Each chapter is completed by exercises and computer assignments designed to further understanding and to give practical experience with the algorithms discussed. This is a textbook for graduate-level courses in discrete-time random processes, statistical signal processing, and blind equalization and system identification. It contains material which will also interest researchers and practicing engineers working in digital communications, source separation, speech processing, image processing, seismic exploration, sonar, radar and other, similar applications.
650 0 _aEngineering.
650 0 _aPhysical geography.
650 0 _aComputer software.
650 0 _aComputer vision.
650 0 _aTelecommunication.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aGeophysics/Geodesy.
700 1 _aChen, Chii-Horng.
_eauthor.
700 1 _aFeng, Chih-Chun.
_eauthor.
700 1 _aChen, Ching-Yung.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781846280221
856 4 0 _uhttp://dx.doi.org/10.1007/1-84628-218-7
912 _aZDB-2-ENG
942 _cEB
999 _c2210
_d2210