Nonlinear Mode Decomposition

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.

Nominated as an outstanding PhD thesis by Lancaster University, UK Free MatLab codes available for all methods used in the book Details many important aspects of time-frequency analysis that are often touched upon only briefly in the literature Includes numerous examples and applications Includes supplementary material: sn.pub/extras

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