"Wavelet Analysis of Experimental Data: Some
Methods and the Underlying Physics"
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 postprocessing
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
Invited Paper AIAA-94-2281, 1994.
Jacques Lewalle, firstname.lastname@example.org