Version 4.6 (R2010b) Wavelet Toolbox Software

This table summarizes what is new in Version 4.6 (R2010b):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems
Yes	No	Bug Reports Includes fixes

New features introduced in this version are:

• Cone of Influence for Continuous Wavelet Transform

• Wavelet Cross Spectrum and Coherence

• Wavelet Packet Spectrum

• Natural and Frequency Ordering of Wavelet Packet Terminal Nodes

• Image and Signal Approximation Quality Metrics

Cone of Influence for Continuous Wavelet Transform

In R2010b, you can compute the cone of influence (COI) for the continuous wavelet transform (CWT) of a signal. At each scale, the COI determines the set of CWT coefficients influenced by the value of the signal at a specified position. The COI provides an important visual aid in interpreting the CWT. By overlaying the cone of influence on the CWT image, you can determine which CWT coefficients each value of the signal affects at every scale. See conofinf for details.

Wavelet Cross Spectrum and Coherence

In R2010b, you can estimate the wavelet cross spectrum and wavelet coherence of two time series. The wavelet cross spectrum and coherence provide wavelet-based alternatives for the Fourier-based cross spectrum and coherence. These wavelet estimators are suitable for nonstationary signals. Using a complex-valued analyzing wavelet, you can also examine intervals in the time-scale plane where the two time series exhibit common phase behavior. See wcoher and the new demo Wavelet Coherence for details.

Wavelet Packet Spectrum

In R2010b, you can compute the wavelet packet spectrum with wpspectrum. The wavelet packet spectrum provides a time-frequency analysis of a time series. The wavelet packet spectrum is useful as wavelet-based counterpart of the short-time Fourier transform.

Natural and Frequency Ordering of Wavelet Packet Terminal Nodes

In R2010b, you can order the wavelet packet transform terminal nodes by natural (Payley) or frequency (sequency) order. See otnodes for details.

Image and Signal Approximation Quality Metrics

In R2010b, you can measure the quality of your signal or image approximation using a number of widely-used quality metrics. These metrics include: the peak signal-to-noise ratio (PSNR), the mean square error (MSE), the maximum absolute error, and the energy ratio of the approximation to the original. See measerr for details.

Version 4.7 (R2011a) Wavelet Toolbox Software

Version 4.5 (R2010a) Wavelet Toolbox Software

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