Nonlinear Mode Decomposition Theory and Applications /

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 includ...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Iatsenko, Dmytro (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Springer Theses, Recognizing Outstanding Ph.D. Research,
Temas:
Mathematical physics.
Dynamical systems.
Signal processing.
Computer software.
System theory.
Theoretical, Mathematical and Computational Physics.
Dynamical Systems.
Signal, Speech and Image Processing .
Mathematical Software.
Complex Systems.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Nonlinear Mode Decomposition  |h [electronic resource] :  |b Theory and Applications /  |c by Dmytro Iatsenko. 
250 |a 1st ed. 2015. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2015. 
300 |a XXIII, 135 p. 33 illus., 13 illus. in color.  |b online resource. 
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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. 
650 0 |a Mathematical physics. 
650 0 |a Dynamical systems. 
650 0 |a Signal processing. 
650 0 |a Computer software. 
650 0 |a System theory. 
650 1 4 |a Theoretical, Mathematical and Computational Physics. 
650 2 4 |a Dynamical Systems. 
650 2 4 |a Signal, Speech and Image Processing . 
650 2 4 |a Mathematical Software. 
650 2 4 |a Complex Systems. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319200170 
776 0 8 |i Printed edition:  |z 9783319200156 
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830 0 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5061 
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950 |a Physics and Astronomy (R0) (SpringerNature-43715) 

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