rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Nonlinear Mode Decomposition
    sub title: Theory and Applications
  name:
    name part: Iatsenko, Dmytro.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIII, 135 p. 33 illus., 13 illus. in color. online resource.
  abstract: 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.
  table of contents: Introduction.- Linear Time-Frequency Analysis.- Extraction of Components from the TFR -- Nonlinear Mode Decomposition -- Examples, Applications and Related Issues.- Conclusion.
  note: by Dmytro Iatsenko.
  subject:
    topic: Physics
  subject:
    topic: Dynamics
  subject:
    topic: Ergodic theory
  subject:
    topic: Computer software
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Physics
  subject:
    topic: Numerical and Computational Physics
  subject:
    topic: Dynamical Systems and Ergodic Theory
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Mathematical Software
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  classification: QC1-999
  classification: 530.1
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Theses, Recognizing Outstanding Ph.D. Research
  identifier: 9783319200163
  identifier: http://dx.doi.org/10.1007/978-3-319-20016-3
  record info:
    record creation date: 150619
    record change date: 20151204164542.0
    record identifier: 978-3-319-20016-3