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008			150903s2006 xxk| o |||| 0|eng d
020			_a9781846282188 _99781846282188
024	7		_a10.1007/1846282187 _2doi
035			_avtls000343742
039		9	_a201509030750 _bVLOAD _c201404120957 _dVLOAD _c201404090735 _dVLOAD _y201402061203 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTK5102.9
100	1		_aChi, Chong-Yung. _eautor _9323154
245	1	0	_aBlind Equalization and System Identification : _bBatch Processing Algorithms, Performance and Applications / _cby Chong-Yung Chi, Chii-Horng Chen, Chih-Chun Feng, Ching-Yung Chen.
264		1	_aLondon : _bSpringer London, _c2006.
300			_axiii, 469 páginas 112 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aChen, Chii-Horng. _eautor _9323155
700	1		_aFeng, Chih-Chun. _eautor _9323156
700	1		_aChen, Ching-Yung. _eautor _9323157
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781846280221
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-218-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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