Spectrum of the Meyer wavelet (numerically computed). 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 ν {\displaystyle \nu } as
Ψ ( ω ) := { 1 2 π sin ( π 2 ν ( 3 | ω | 2 π − 1 ) ) e j ω / 2 if 2 π / 3 < | ω | < 4 π / 3 , 1 2 π cos ( π 2 ν ( 3 | ω | 4 π − 1 ) ) e j ω / 2 if 4 π / 3 < | ω | < 8 π / 3 , 0 otherwise , {\displaystyle \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
ν ( x ) := { 0 if x < 0 , x if 0 < x < 1 , 1 if x > 1. {\displaystyle \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
ν ( x ) := { x 4 ( 35 − 84 x + 70 x 2 − 20 x 3 ) if 0 < x < 1 , 0 otherwise . {\displaystyle \nu (x):={\begin{cases}x^{4}(35-84x+70x^{2}-20x^{3})&{\text{if }}0<x<1,\\0&{\text{otherwise}}.\end{cases}}} Meyer scale function (numerically computed) The Meyer scaling function is given by
Φ ( ω ) := { 1 2 π if | ω | < 2 π / 3 , 1 2 π cos ( π 2 ν ( 3 | ω | 2 π − 1 ) ) if 2 π / 3 < | ω | < 4 π / 3 , 0 otherwise . {\displaystyle \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:
waveform of the Meyer wavelet (numerically computed) Valenzuela and de Oliveira [ 5] give the explicit expressions of Meyer wavelet and scale functions:
ϕ ( t ) = { 2 3 + 4 3 π t = 0 , sin ( 2 π 3 t ) + 4 3 t cos ( 4 π 3 t ) π t − 16 π 9 t 3 otherwise , {\displaystyle \phi (t)={\begin{cases}{\frac {2}{3}}+{\frac {4}{3\pi }}&t=0,\\{\frac {\sin({\frac {2\pi }{3}}t)+{\frac {4}{3}}t\cos({\frac {4\pi }{3}}t)}{\pi t-{\frac {16\pi }{9}}t^{3}}}&{\text{otherwise}},\end{cases}}} and
ψ ( t ) = ψ 1 ( t ) + ψ 2 ( t ) , {\displaystyle \psi (t)=\psi _{1}(t)+\psi _{2}(t),} where
ψ 1 ( t ) = 4 3 π ( t − 1 2 ) cos [ 2 π 3 ( t − 1 2 ) ] − 1 π sin [ 4 π 3 ( t − 1 2 ) ] ( t − 1 2 ) − 16 9 ( t − 1 2 ) 3 , {\displaystyle \psi _{1}(t)={\frac {{\frac {4}{3\pi }}(t-{\frac {1}{2}})\cos[{\frac {2\pi }{3}}(t-{\frac {1}{2}})]-{\frac {1}{\pi }}\sin[{\frac {4\pi }{3}}(t-{\frac {1}{2}})]}{(t-{\frac {1}{2}})-{\frac {16}{9}}(t-{\frac {1}{2}})^{3}}},} ψ 2 ( t ) = 8 3 π ( t − 1 2 ) cos [ 8 π 3 ( t − 1 2 ) ] + 1 π sin [ 4 π 3 ( t − 1 2 ) ] ( t − 1 2 ) − 64 9 ( t − 1 2 ) 3 . {\displaystyle \psi _{2}(t)={\frac {{\frac {8}{3\pi }}(t-{\frac {1}{2}})\cos[{\frac {8\pi }{3}}(t-{\frac {1}{2}})]+{\frac {1}{\pi }}\sin[{\frac {4\pi }{3}}(t-{\frac {1}{2}})]}{(t-{\frac {1}{2}})-{\frac {64}{9}}(t-{\frac {1}{2}})^{3}}}.} ^ 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 . ^ Valenzuela, Victor Vermehren; de Oliveira, H. M. (2015). "Close expressions for Meyer Wavelet and Scale Function". Anais de XXXIII Simpósio Brasileiro de Telecomunicações . p. 4. arXiv :1502.00161 . doi :10.14209/SBRT.2015.2 . S2CID 88513986 . Look up
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