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Nonlinear mode decomposition[electronic resource] :theory and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.55
書名/作者:	Nonlinear mode decomposition : theory and applications // by Dmytro Iatsenko.
作者:	Iatsenko, Dmytro.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xxiii, 135 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Time-series analysis - Mathematical models.
標題:	Physics.
標題:	Numerical and Computational Physics.
標題:	Dynamical Systems and Ergodic Theory.
標題:	Signal, Image and Speech Processing.
標題:	Mathematical Software.
標題:	Statistical Physics, Dynamical Systems and Complexity.
ISBN:	9783319200163 (electronic bk.)
ISBN:	9783319200156 (paper)
內容註:	Introduction -- Linear Time-Frequency Analysis -- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues -- Conclusion.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-20016-3

Nonlinear mode decomposition[electronic resource] :theory 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:

608863
Time-series analysis
--Mathematical models.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Nonlinear mode decomposition[electronic resource] :theory and applications /
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