Meyer wavelet information

The Meyer wavelet is an orthogonal wavelet proposed by Yves Meyer.^[1] As a type of a continuous wavelet, it has been applied in a number of cases, such as in adaptive filters,^[2] fractal random fields,^[3] and multi-fault classification.^[4]

The Meyer wavelet is infinitely differentiable with infinite support and defined in frequency domain in terms of function $\nu$ as

\Psi (\omega ):={\begin{cases}{\frac {1}{\sqrt {2\pi }}}\sin \left({\frac {\pi }{2}}\nu \left({\frac {3|\omega |}{2\pi }}-1\right)\right)e^{j\omega /2}&{\text{if }}2\pi /3<|\omega |<4\pi /3,\\{\frac {1}{\sqrt {2\pi }}}\cos \left({\frac {\pi }{2}}\nu \left({\frac {3|\omega |}{4\pi }}-1\right)\right)e^{j\omega /2}&{\text{if }}4\pi /3<|\omega |<8\pi /3,\\0&{\text{otherwise}},\end{cases}}

where

\nu (x):={\begin{cases}0&{\text{if }}x<0,\\x&{\text{if }}0<x<1,\\1&{\text{if }}x>1.\end{cases}}

There are many different ways for defining this auxiliary function, which yields variants of the Meyer wavelet. For instance, another standard implementation adopts

\nu (x):={\begin{cases}x^{4}(35-84x+70x^{2}-20x^{3})&{\text{if }}0<x<1,\\0&{\text{otherwise}}.\end{cases}}

The Meyer scale function is given by

\Phi (\omega ):={\begin{cases}{\frac {1}{\sqrt {2\pi }}}&{\text{if }}|\omega |<2\pi /3,\\{\frac {1}{\sqrt {2\pi }}}\cos \left({\frac {\pi }{2}}\nu \left({\frac {3|\omega |}{2\pi }}-1\right)\right)&{\text{if }}2\pi /3<|\omega |<4\pi /3,\\0&{\text{otherwise}}.\end{cases}}

In the time domain, the waveform of the Meyer mother-wavelet has the shape as shown in the following figure:

^ Meyer, Yves (1990). Ondelettes et opérateurs: Ondelettes. Hermann. ISBN 9782705661250.
^ Xu, L.; Zhang, D.; Wang, K. (2005). "Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms". IEEE Transactions on Biomedical Engineering. 52 (11): 1973–1975. doi:10.1109/tbme.2005.856296. hdl:10397/193. PMID 16285403. S2CID 6897442.
^ Elliott, Jr., F. W.; Horntrop, D. J.; Majda, A. J. (1997). "A Fourier-Wavelet Monte Carlo method for fractal random fields". Journal of Computational Physics. 132 (2): 384–408. Bibcode:1997JCoPh.132..384E. doi:10.1006/jcph.1996.5647.
^ Abbasion, S.; et al. (2007). "Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine". Mechanical Systems and Signal Processing. 21 (7): 2933–2945. Bibcode:2007MSSP...21.2933A. doi:10.1016/j.ymssp.2007.02.003.

[1] Meyer, Yves (1990). Ondelettes et opérateurs: Ondelettes. Hermann. ISBN 9782705661250.

[2] Xu, L.; Zhang, D.; Wang, K. (2005). "Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms". IEEE Transactions on Biomedical Engineering. 52 (11): 1973–1975. doi:10.1109/tbme.2005.856296. hdl:10397/193. PMID 16285403. S2CID 6897442.

[3] Elliott, Jr., F. W.; Horntrop, D. J.; Majda, A. J. (1997). "A Fourier-Wavelet Monte Carlo method for fractal random fields". Journal of Computational Physics. 132 (2): 384–408. Bibcode:1997JCoPh.132..384E. doi:10.1006/jcph.1996.5647.

[4] Abbasion, S.; et al. (2007). "Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine". Mechanical Systems and Signal Processing. 21 (7): 2933–2945. Bibcode:2007MSSP...21.2933A. doi:10.1016/j.ymssp.2007.02.003.

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