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000778433 019__ $$a974651307$$a981098473
000778433 020__ $$a9789811025402$$q(electronic book)
000778433 020__ $$a9811025401$$q(electronic book)
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000778433 0247_ $$a10.1007/978-981-10-2540-2$$2doi
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000778433 050_4 $$aTK5102.9
000778433 08204 $$a621.382/2$$223
000778433 1001_ $$aGiron-Sierra, Jose Maria,$$eauthor.
000778433 24510 $$aDigital signal processing with Matlab examples.$$nVolume 3,$$pModel-based actions and sparse representation /$$cJose Maria Giron-Sierra.
000778433 24630 $$aModel-based actions and sparse representation
000778433 264_1 $$aSingapore :$$bSpringer,$$c[2017]
000778433 300__ $$a1 online resource (xvi, 431 pages) :$$billustrations.
000778433 336__ $$atext$$btxt$$2rdacontent
000778433 337__ $$acomputer$$bc$$2rdamedia
000778433 338__ $$aonline resource$$bcr$$2rdacarrier
000778433 347__ $$atext file$$bPDF$$2rda
000778433 4901_ $$aSignals and communication technology,$$x1860-4862
000778433 504__ $$aIncludes bibliographical references and index.
000778433 5050_ $$aPart VI- Model-based Actions: Filtering, Prediction, Smoothing -- Kalman Filter, Particle Filter and other Bayesian Filters -- Part VII Sparse Representation. Compressed Sensing -- Sparse Representations -- Appendices -- Selected Topics of Mathematical Optimization -- Long Programs.
000778433 506__ $$aAccess limited to authorized users.
000778433 520__ $$aThis is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.
000778433 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 14, 2016).
000778433 63000 $$aMATLAB.
000778433 650_0 $$aSignal processing$$xDigital techniques$$xData processing.
000778433 77608 $$iPrint version:$$z9789811025396
000778433 830_0 $$aSignals and communication technology.
000778433 852__ $$bebk
000778433 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-2540-2$$zOnline Access$$91397441.1
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