Wavelet analysis of experimental data: Some methods and the underlying physics

Jacques Lewalle

Research output: Contribution to conferencePaperpeer-review

33 Scopus citations


As a generalization of time-frequency analysis, wavelet methods combine sequential and spectral discrimination with pattern recognition. In conjunction with conventional statistical tools of time series analysis, these features are at the basis of new ways to characterize experimental data. The methods presented in this paper can be formulated as sequences of modular algorithms that characterize and/or enhance specific features of dynamical interest, and can be compared with specialized post-processing instrumentation. The investigator plays an important role in matching the wavelet-based algorithms to specific data, much in the same way as he/she selects instrumentation. The feedback between the physical insight and the implementation of new algorithms characterizes this phase of the research. In this paper, a toolbox is presented with illustrative examples.

Original languageEnglish (US)
StatePublished - 1994
EventAIAA Fluid Dynamics Conference, 1994 - Colorado Springs, United States
Duration: Jun 20 1994Jun 23 1994


OtherAIAA Fluid Dynamics Conference, 1994
Country/TerritoryUnited States
CityColorado Springs

ASJC Scopus subject areas

  • Aerospace Engineering
  • Engineering (miscellaneous)


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