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Nonlinear Mode Decomposition:Theory and Applications

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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. Mat Lab 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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Detail Information

Series Title

Springer Theses

Call Number

-

Publisher

Switzerland : Springer Cham., 2015

Collation

XXIII, 135

Language

English

ISBN/ISSN

978-3-319-20015-6

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Signal
Speech and Image Processing
Theoretical
Mathematical and Computational Physics
Complex Systems
Dynamical Systems
Mathematical Software

Specific Detail Info

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Statement of Responsibility

Dmytro Iatsenko

Other Information

Cataloger

Fandi

Source

-

Validator

Taufik

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