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000727815 019__ $$a911846631
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000727815 08204 $$a519.5/5$$223
000727815 1001_ $$aIatsenko, Dmytro,$$eauthor.
000727815 24510 $$aNonlinear mode decomposition$$h[electronic resource] :$$btheory and applications /$$cDmytro Iatsenko.
000727815 264_1 $$aCham :$$bSpringer,$$c[2015]
000727815 264_4 $$c©2015
000727815 300__ $$a1 online resource :$$billustrations.
000727815 336__ $$atext$$btxt$$2rdacontent
000727815 337__ $$acomputer$$bc$$2rdamedia
000727815 338__ $$aonline resource$$bcr$$2rdacarrier
000727815 4901_ $$aSpringer theses : recognizing outstanding Ph.D. research,$$x2190-5061
000727815 500__ $$aOriginally presented as the author's thesis (doctoral)--University of Lancaster, 2015.
000727815 504__ $$aIncludes bibliographical references.
000727815 5050_ $$aIntroduction.- Linear Time-Frequency Analysis.- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues.- Conclusion.
000727815 506__ $$aAccess limited to authorized users.
000727815 520__ $$aThis work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. It justifies NMD mathematically, demonstrates it in several applications, and explains in detail how to use it in practice. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random fluctuations or noise. Furthermore, the basic oscillation frequencies of the modes may vary in time; for example, human blood flow manifests at least six characteristic frequencies, all of which wander in time. NMD allows us to separate these components from each other and from the noise, with immediate potential applications in diagnosis and prognosis. MatLab codes for rapid implementation are available from the author. NMD will most likely come to be used in a broad range of applications.
000727815 588__ $$aOnline resource; title from PDF title page (viewed June 25, 2015).
000727815 650_0 $$aTime-series analysis$$xMathematical models.
000727815 830_0 $$aSpringer theses.
000727815 852__ $$bebk
000727815 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-20016-3$$zOnline Access$$91397441.1
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000727815 983__ $$aOnline
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