© 2015

Nonlinear Mode Decomposition

Theory and Applications


Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Dmytro Iatsenko
    Pages 1-6
  3. Dmytro Iatsenko
    Pages 7-42
  4. Dmytro Iatsenko
    Pages 43-57
  5. Dmytro Iatsenko
    Pages 59-81
  6. Dmytro Iatsenko
    Pages 83-111
  7. Dmytro Iatsenko
    Pages 113-116
  8. Dmytro Iatsenko
    Pages 117-135

About this book


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.


Ensemble empirical mode decomposition Nonlinear mode decomposition Signal analysis by decomposition Signal contamination by random fluctuations Signal contamination by random noise Time-frequency analysis Wavelet ridges Wavelet transform Windowed Fourier transform

Authors and affiliations

  1. 1.Department of PhysicsLancaster UniversityLancasterUnited Kingdom

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