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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.
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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