Toolbox™ software enables you to perform a continuous
wavelet analysis of your univariate or bivariate 1-D input signals.
You can perform continuous wavelet analyses at the command line or
with the app which you access by typing
the command line.
Key features include:
Continuous wavelet transform (CWT) of a 1-D input
signal using real-valued and complex-valued wavelets. The Wavelet
features a CWT algorithm,
which is based on the correlation of the signal with an analyzing
analytic wavelet, .
Inverse CWT of 1–D input signal. For select
analyzing wavelets, you can invert the CWT to reconstruct a time and
scale-localized approximation to your input signal. See
icwt for details.
Wavelet cross spectrum and coherence. You can use
wcoherence to compute the wavelet cross
spectrum and coherence between two time series. The wavelet cross
spectrum and coherence can reveal localized similarities between two
time series in time and scale. See Wavelet Coherence for
Pattern-adapted wavelets for signal analysis. A strength
of wavelet analysis is the ability to design wavelets that mimic the
structures you wish to detect. Using
wavemngr you can add custom wavelets
optimized to detect specified patterns in your data. See Pattern Adapted Wavelets for
Signal Detection for examples.
In this section, you'll learn how to
Load a signal
Perform a continuous wavelet transform of a signal
Produce a plot of the coefficients
Produce a plot of coefficients at a given scale
Produce a plot of local maxima of coefficients across scales
Select the displayed plots
Switch from scale to pseudo-frequency information
Zoom in on detail
Display coefficients in normal or absolute mode
Choose the scales at which analysis is performed
Since you can perform analyses either from the command line or using the Wavelet Analyzer app, this section has subsections covering each method.
The final subsection discusses how to exchange signal and coefficient information between the disk and the graphical tools.