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Nonlinear mode decompositiontheory a...

Iatsenko, Dmytro.

Nonlinear mode decompositiontheory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear mode decompositionby Dmytro Iatsenko.
Reminder of title:	theory and applications /
Author:	Iatsenko, Dmytro.
Published:	Cham :Springer International Publishing :2015.
Description:	xxiii, 135 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysisMathematical models.
Online resource:	http://dx.doi.org/10.1007/978-3-319-20016-3
ISBN:	9783319200163 (electronic bk.)

Nonlinear mode decompositiontheory and applications /
Iatsenko, Dmytro.

Nonlinear mode decompositiontheory and applications /[electronic resource] :by Dmytro Iatsenko. - Cham :Springer International Publishing :2015. - xxiii, 135 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Linear Time-Frequency Analysis -- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues -- Conclusion.

This 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.

ISBN: 9783319200163 (electronic bk.)

Standard No.: 10.1007/978-3-319-20016-3doiSubjects--Topical Terms:

233807
Time-series analysis
--Mathematical models.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Nonlinear mode decompositiontheory and applications /
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505 0 $a Introduction -- Linear Time-Frequency Analysis -- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues -- Conclusion.
520 $a This 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.
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