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008			161214s2017 si a ob 001 0 eng d
020			_a9789811025396
020			_a9789811025402 _q(electronic bk.)
020			_a9811025398
020			_a9811025401 _q(electronic bk.)
020			_z9789811025396 _q(print)
035			_a(OCoLC)965888987 _z(OCoLC)974651307 _z(OCoLC)981098473 _z(OCoLC)1005832527
040			_aGW5XE _cGW5XE _dOCLCF _dYDX _dUAB _dCOO _dUPM _dIOG _dVT2 _dUWO _dMERER _dESU _dOCLCQ _dOCLCO _dJBG _dIAD _dICN _dOTZ _dOCLCQ _dOCLCO _dU3W _dES-MaUEC _bspa
050		4	_aTK5102.9 _bG576 2017 EB
100	1		_aGiron-Sierra, Jose Maria _eautor _9677304
245	1	0	_aDigital signal processing with Matlab examples _nVolume 3, _pModel-based actions and sparse representation _cJose Maria Giron-Sierra
246	3	0	_aModel-based actions and sparse representation
264		1	_aSingapore _bSpringer _c2017
300			_a1 recurso en línea (XVI, 431 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSignals and communication technology _x1860-4862
500			_aSpringerLink
504			_aIncluye referencias bibliográficas e índice
505	0		_aPart VI- Model-based Actions: Filtering, Prediction, Smoothing -- Kalman Filter, Particle Filter and other Bayesian Filters -- Part VII Sparse Representation. Compressed Sensing -- Sparse Representations -- Appendices -- Selected Topics of Mathematical Optimization -- Long Programs.
520	3		_aThis is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed December 14, 2016).
988			_aEBOOK, EBSPRINGER_2017B
650		7	_aProceso digital de señales _2embne _9150609
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-981-10-2540-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI