Nonlinear Mode Decomposition

Iatsenko, Dmytro

ISBN 10: 3319200151 / ISBN 13: 9783319200156

Editore: Springer International Publishing AG, 2015

Series: Springer Theses. Num Pages: 158 pages, 20 black & white illustrations, 13 colour illustrations, biography. BIC Classification: PBWR; PHS; PHU; PSA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 11. Weight in Grams: 409. . 2015. Hardback. . . . . Books ship from the US and Ireland. Codice articolo V9783319200156

Riassunto:

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.

Dalla quarta di copertina: 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.

Dati bibliografici

Titolo: Nonlinear Mode Decomposition
Casa editrice: Springer International Publishing AG
Data di pubblicazione: 2015
Legatura: Rilegato
Condizione: New