Continuous wavelet transform

Continuous wavelet transform of frequency breakdown signal. Used symlet with 5 vanishing moments.

In mathematics, the continuous wavelet transform (CWT) is a formal (i.e., non-numerical) tool that provides an overcomplete representation of a signal by letting the translation and scale parameter of the wavelets vary continuously.

Definition

The continuous wavelet transform of a function ${\displaystyle x(t)}$ at a scale ${\displaystyle a\in \mathbb {R^{+*}} }$ and translational value ${\displaystyle b\in \mathbb {R} }$ is expressed by the following integral

$${\displaystyle X_{w}(a,b)={\frac {1}{|a|^{1/2}}}\int _{-\infty }^{\infty }x(t){\overline {\psi }}\left({\frac {t-b}{a}}\right)\,\mathrm {d} t}$$

where ${\displaystyle \psi (t)}$ is a continuous function in both the time domain and the frequency domain called the mother wavelet and the overline represents operation of complex conjugate. The main purpose of the mother wavelet is to provide a source function to generate the daughter wavelets which are simply the translated and scaled versions of the mother wavelet. To recover the original signal ${\displaystyle x(t)}$, the first inverse continuous wavelet transform can be exploited.

${\displaystyle x(t)=C_{\psi }^{-1}\int _{0}^{\infty }\int _{-\infty }^{\infty }X_{w}(a,b){\frac {1}{|a|^{1/2}}}{\tilde {\psi }}\left({\frac {t-b}{a}}\right)\,\mathrm {d} b\ {\frac {\mathrm {d} a}{a^{2}}}}$

${\displaystyle {\tilde {\psi }}(t)}$ is the dual function of ${\displaystyle \psi (t)}$ and

${\displaystyle C_{\psi }=\int _{-\infty }^{\infty }{\frac {{\overline {\hat {\psi }}}(\omega ){\hat {\tilde {\psi }}}(\omega )}{|\omega |}}\,\mathrm {d} \omega }$

is admissible constant, where hat means Fourier transform operator. Sometimes, ${\displaystyle {\tilde {\psi }}(t)=\psi (t)}$, then the admissible constant becomes

${\displaystyle C_{\psi }=\int _{-\infty }^{+\infty }{\frac {\left|{\hat {\psi }}(\omega )\right|^{2}}{\left|\omega \right|}}\,\mathrm {d} \omega }$

Traditionally, this constant is called wavelet admissible constant. A wavelet whose admissible constant satisfies

${\displaystyle 0<C_{\psi }<\infty }$

is called an admissible wavelet. To recover the original signal ${\displaystyle x(t)}$, the second inverse continuous wavelet transform can be exploited.

${\displaystyle x(t)={\frac {1}{2\pi {\overline {\hat {\psi }}}(1)}}\int _{0}^{\infty }\int _{-\infty }^{\infty }{\frac {1}{a^{2}}}X_{w}(a,b)\exp \left(i{\frac {t-b}{a}}\right)\,\mathrm {d} b\ \mathrm {d} a}$

This inverse transform suggests that a wavelet should be defined as

${\displaystyle \psi (t)=w(t)\exp(it)}$

where ${\displaystyle w(t)}$ is a window. Such defined wavelet can be called as an analyzing wavelet, because it admits to time-frequency analysis. An analyzing wavelet is unnecessary to be admissible.

Scale factor

The scale factor ${\displaystyle a}$ either dilates or compresses a signal. When the scale factor is relatively low, the signal is more contracted which in turn results in a more detailed resulting graph. However, the drawback is that low scale factor does not last for the entire duration of the signal. On the other hand, when the scale factor is high, the signal is stretched out which means that the resulting graph will be presented in less detail. Nevertheless, it usually lasts the entire duration of the signal.

Continuous wavelet transform properties

In definition, the continuous wavelet transform is a convolution of the input data sequence with a set of functions generated by the mother wavelet. The convolution can be computed by using a fast Fourier transform (FFT) algorithm. Normally, the output ${\displaystyle X_{w}(a,b)}$ is a real valued function except when the mother wavelet is complex. A complex mother wavelet will convert the continuous wavelet transform to a complex valued function. The power spectrum of the continuous wavelet transform can be represented by ${\displaystyle {\frac {1}{a}}\cdot |X_{w}(a,b)|^{2}}$. ^{[1] [2]}

Visualizing the effect of changing a Morlet wavelet's ${\displaystyle \sigma }$ parameter, which interpolates between the original time-series and a Fourier transform. Here, a frequency-modulated tone (plus noise) is analyzed; ${\displaystyle 1/\sigma }$ is adjusted from 1 to 200, in steps of unity.

Applications of the wavelet transform

One of the most popular applications of wavelet transform is image compression. The advantage of using wavelet-based coding in image compression is that it provides significant improvements in picture quality at higher compression ratios over conventional techniques. Since wavelet transform has the ability to decompose complex information and patterns into elementary forms, it is commonly used in acoustics processing and pattern recognition, but it has been also proposed as an instantaneous frequency estimator. ^[3] Moreover, wavelet transforms can be applied to the following scientific research areas: edge and corner detection, partial differential equation solving, transient detection, filter design, electrocardiogram (ECG) analysis, texture analysis, business information analysis and gait analysis. ^[4] Wavelet transforms can also be used in Electroencephalography (EEG) data analysis to identify epileptic spikes resulting from epilepsy. ^[5] Wavelet transform has been also successfully used for the interpretation of time series of landslides ^[6] and land subsidence ^[7] , and for calculating the changing periodicities of epidemics. ^[8]

Continuous Wavelet Transform (CWT) is very efficient in determining the damping ratio of oscillating signals (e.g. identification of damping in dynamic systems). CWT is also very resistant to the noise in the signal. ^[9]

See also

• Continuous wavelet
• S transform
• Time-frequency analysis
• Cauchy wavelet

References

• ^[10]
• A. Grossmann & J. Morlet, 1984, Decomposition of Hardy functions into square integrable wavelets of constant shape, Soc. Int. Am. Math. (SIAM), J. Math. Analys., 15, 723–736.
• Lintao Liu and Houtse Hsu (2012) "Inversion and normalization of time-frequency transform" AMIS 6 No. 1S pp. 67S-74S.
• Stéphane Mallat, "A wavelet tour of signal processing" 2nd Edition, Academic Press, 1999, ISBN   0-12-466606-X
• Ding, Jian-Jiun (2008), Time-Frequency Analysis and Wavelet Transform, viewed 19 January 2008
• Polikar, Robi (2001), The Wavelet Tutorial, viewed 19 January 2008
• WaveMetrics (2004), Time Frequency Analysis, viewed 18 January 2008
• Valens, Clemens (2004), A Really Friendly Guide to Wavelets, viewed 18 September 2018]
• Mathematica Continuous Wavelet Transform
• Lewalle, Jacques: Continuous wavelet transform ^{[ permanent dead link ]}, viewed 6 February 2010

1. Torrence, Christopher; Compo, Gilbert (1998). "A Practical Guide to Wavelet Analysis". Bulletin of the American Meteorological Society. 79 (1): 61–78. Bibcode:1998BAMS...79...61T. doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 . S2CID   14928780.
2. Liu, Yonggang (December 2007). "Rectification of the Bias in the Wavelet Power Spectrum". Journal of Atmospheric and Oceanic Technology. 24 (12): 2093–2102. Bibcode:2007JAtOT..24.2093L. doi: 10.1175/2007JTECHO511.1 .
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4. "Novel method for stride length estimation with body area network accelerometers", IEEE BioWireless 2011, pp. 79–82
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6. Tomás, R.; Li, Z.; Lopez-Sanchez, J. M.; Liu, P.; Singleton, A. (1 June 2016). "Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide" (PDF). Landslides. 13 (3): 437–450. Bibcode:2016Lands..13..437T. doi:10.1007/s10346-015-0589-y. hdl: 10045/62160 . ISSN   1612-510X. S2CID   129736286.
7. Tomás, Roberto; Pastor, José Luis; Béjar-Pizarro, Marta; Bonì, Roberta; Ezquerro, Pablo; Fernández-Merodo, José Antonio; Guardiola-Albert, Carolina; Herrera, Gerardo; Meisina, Claudia; Teatini, Pietro; Zucca, Francesco; Zoccarato, Claudia; Franceschini, Andrea (22 April 2020). "Wavelet analysis of land subsidence time-series: Madrid Tertiary aquifer case study". Proceedings of the International Association of Hydrological Sciences. 382: 353–359. Bibcode:2020PIAHS.382..353T. doi: 10.5194/piahs-382-353-2020 . ISSN   2199-899X.
8. von Csefalvay, Chris (2023), "Temporal dynamics of epidemics", Computational Modeling of Infectious Disease, Elsevier, pp. 217–255, doi:10.1016/b978-0-32-395389-4.00016-5, ISBN   978-0-323-95389-4 , retrieved 27 February 2023
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10. Prasad, Akhilesh; Maan, Jeetendrasingh; Verma, Sandeep Kumar (2021). "Wavelet transforms associated with the index Whittaker transform". Mathematical Methods in the Applied Sciences. 44 (13): 10734–10752. Bibcode:2021MMAS...4410734P. doi:10.1002/mma.7440. ISSN   1099-1476. S2CID   235556542.

External links

• Wavelets: a mathematical microscope on YouTube