Wavelets

MatDeck contains a series of functions for wavelet signal processing including single level discrete wavelet decomposition, discretewavet(), multilevel discrete wavelet decomposition, wavedec(), discrete wavelet packet decomposition, wavepacketd() and other corresponding reconstruction functions. MatDeck supports over 100 different wavelet filters which can be obtained by using wavefilter() and includes Daubechies, Haar wavelets, Coiflet, and Symlets.

MatDeck’s function, discretewavet(), performs single level 1-D discrete wavelet decomposition. The decomposition is done using specific wavelet decomposition filters which are defined by the user. The wavelet filter coefficients can be determined independently by using the wavefilter() function. The signal reconstruction is performed using the discretewaver() function.

The MatDeck function, wavedec(), performs multilevel discrete wavelet decomposition using wavelet filters which are specified by the user. The input arguments of the function are: input signal which can be a row vector or a column vector, integer level, value, and the string which specifies which wavelet filter is to be used. Wavelet reconstruction is performed by using the function, waverec().

MatDeck’s function, wavepacketd(), performs discrete wavelet packet decomposition using the wavelet filters specified by the user. The wavelet packet decomposition is a generalization of wavelet decomposition that offers a much richer signal analysis. Input arguments of the function are: input signal can be a row vector or a column vector, integer level value, and the string which specifies the type of wavelet filter which is to be used. Wavelet reconstruction is performed using the function, wavepacketr().

There are several examples which illustrate discrete wavelets in MatDeck, among them is wavelet packet denoising which is very useful for practical applications.

Basic examples:

Advanced example:

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