Wavelet transform analysis offers a new approach to signal processing throu
gh its ability to decompose signals in both time and frequency. As such, it
is more suited to nonstationary and intermittent signals than traditional
Fourier analysis. The first part of this paper provides an introduction to
the theory and signal processing properties of both continuous and discrete
wavelet transform analysis. An account is then given of the application of
wavelet transform analysis to a variety of experimental open channel wake
flows. Feature location is undertaken using a continuous wavelet transform,
and both turbulent statistical analysis and thresholding of the turbulent
signal components are undertaken using a discrete wavelet transform.