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010   $a3-319-20016-X
024 7 $a10.1007/978-3-319-20016-3
035   $a(CKB)3710000000434391
035   $a(EBL)2120660
035   $a(OCoLC)911386372
035   $a(SSID)ssj0001525244
035   $a(PQKBManifestationID)11820644
035   $a(PQKBTitleCode)TC0001525244
035   $a(PQKBWorkID)11485621
035   $a(PQKB)11502620
035   $a(DE-He213)978-3-319-20016-3
035   $a(MiAaPQ)EBC2120660
035   $a(PPN)186401574
035   $a(EXLCZ)993710000000434391
100   $a20150619d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aNonlinear Mode Decomposition $eTheory and Applications /$fby Dmytro Iatsenko
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (152 p.)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $aDescription based upon print version of record.
311   $a3-319-20015-1 
320   $aIncludes bibliographical references.
327   $aIntroduction.- Linear Time-Frequency Analysis.- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues.- Conclusion.
330   $aThis work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. It justifies NMD mathematically, demonstrates it in several applications, and explains in detail how to use it in practice. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random fluctuations or noise. Furthermore, the basic oscillation frequencies of the modes may vary in time; for example, human blood flow manifests at least six characteristic frequencies, all of which wander in time. NMD allows us to separate these components from each other and from the noise, with immediate potential applications in diagnosis and prognosis. MatLab codes for rapid implementation are available from the author. NMD will most likely come to be used in a broad range of applications.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aPhysics
606   $aDynamics
606   $aErgodic theory
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aComputer software
606   $aStatistical physics
606   $aDynamics
606   $aNumerical and Computational Physics, Simulation$3https://scigraph.springernature.com/ontologies/product-market-codes/P19021
606   $aDynamical Systems and Ergodic Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M1204X
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aMathematical Software$3https://scigraph.springernature.com/ontologies/product-market-codes/M14042
606   $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P33000
606   $aStatistical Physics and Dynamical Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P19090
615  0$aPhysics.
615  0$aDynamics.
615  0$aErgodic theory.
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aComputer software.
615  0$aStatistical physics.
615  0$aDynamics.
615 14$aNumerical and Computational Physics, Simulation.
615 24$aDynamical Systems and Ergodic Theory.
615 24$aSignal, Image and Speech Processing.
615 24$aMathematical Software.
615 24$aComplex Systems.
615 24$aStatistical Physics and Dynamical Systems.
676   $a004
676   $a515.39
676   $a515.48
676   $a530
700   $aIatsenko$b Dmytro$4aut$4http://id.loc.gov/vocabulary/relators/aut$0792325
906   $aBOOK
912   $a9910300421103321
996   $aNonlinear Mode Decomposition$91771644
997   $aUNINA