Fourier-domain coherence is a well-established technique for measuring the linear correlation between two stationary processes as a function of frequency on a scale from 0 to 1. Because
Use the continuous wavelet transform (CWT) to analyze signals jointly in time and frequency.
Use the continuous wavelet transform (CWT) to analyze signals jointly in time and frequency. The example discusses the localization of transients where the CWT outperforms the short-time
Use wavelet coherence and the wavelet cross-spectrum to identify time-localized common oscillatory behavior in two time series. The example also compares the wavelet coherence and
Use wavelet synchrosqueezing to obtain a higher resolution time-frequency analysis. The example also shows how to extract and reconstruct oscillatory modes in a signal.
Detect a pattern in a noisy image using the 2-D continuous wavelet transform (CWT). The example uses both isotropic (non-directional) and anisotropic (directional) wavelets. The
Perform continuous wavelet analysis of a cusp signal. You can use cwt for analysis using an analytic wavelet and wtmm to isolate and characterized singularities.
How applying the order biorthogonal wavelet filters can affect image reconstruction.
The difference between the discrete wavelet transform ( DWT ) and the continuous wavelet transform ( CWT ).
In this example you demonstrate an instance of discontinuities in noisy data being represented more sparsely using a Haar wavelet than when using a wavelet with larger support. This example
Reconstruct a frequency-localized approximation of Kobe earthquake data. Extract information from the CWT for frequencies in the range of [0.030, 0.070] Hz.
Create a signal consisting of exponentially weighted sine waves. The signal has two 25-Hz components -- one centered at 0.2 seconds and one centered at 0.5 seconds. It also has two 70-Hz