Continuous wavelet transform: Difference between revisions

Revision as of 20:29, 14 March 2022

Latest revision as of 12:07, 24 June 2025

(29 intermediate revisions by 20 users not shown)

Line 1:

{{Short description|Integral transform}}

{{refimprovemore citations needed|date=June 2012}}

{{Use dmy dates|date=JulyJune 20132023}}

[[File:Continuous wavelet transform.svg|thumb|320px|right|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 [[wavelet]]s vary continuously.

==Definition==

The continuous wavelet transform of a function <math>x(t)</math> at a scale (a>0) <math>a\in\mathbb{R^{+*}}</math> and translational value <math>b\in\mathbb{R}</math> is expressed by the following integral

:<math display="block">X_w(a,b)=\frac{1}{|a|^{1/2}} \int_{-\infty}^\infty x(t)\overline\psi\left(\frac{t-b}{a}\right)\, dt\mathrm{d}t</math>

where <math>\psi(t)</math> 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 <math>x(t)</math>, the first inverse continuous wavelet transform can be exploited.

:<math>x(t)=C_\psi^{-1}\int_{-\infty0}^{\infty}\int_{-\infty}^{\infty} X_w(a,b)\frac{1}{|a|^{1/2}}\tilde\psi\left(\frac{t-b}{a}\right)\, db\mathrm{d}b\ \frac{da\mathrm{d}a}{a^2}</math>

<math>\tilde\psi(t)</math> is the [[Dual wavelet|dual function]] of <math>\psi(t)</math> and

:<math>C_\psi=\int_{-\infty}^{\infty}\frac{\overline\hat{\psi}(\omega)\hat{\tilde\psi}(\omega)}{|\omega|}\, \mathrm{d}\omega</math>

is admissible constant, where hat means Fourier transform operator. Sometimes, <math>\tilde\psi(t)=\psi(t)</math>, then the admissible constant becomes

:<math>C_\psi = \int_{-\infty}^{+\infty}

\frac{\left| \hat{\psi}(\omega) \right|^2}{\left| \omega \right|} \, \mathrm{d}\omega

</math>

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

:<math>0<C_\psi <\infty</math>

is called an admissible wavelet. An admissible wavelet implies that <math>\hat{\psi}(0) = 0</math>, so that an admissible wavelet must integrate to zero. To recover the original signal <math>x(t)</math>, the second inverse continuous wavelet transform can be exploited.

:<math>x(t)=\frac{1}{2\pi\overline\hat{\psi}(1)}\int_{-\infty0}^{\infty}\int_{-\infty}^{\infty} \frac{1}{a^2}X_w(a,b)\exp\left(i\frac{t-b}{a}\right)\, db\mathrm{d}b\ da\mathrm{d}a</math>

This inverse transform suggests that a wavelet should be defined as

:<math>\psi(t)=w(t)\exp(it) </math>

Line 29 ⟶ 30:

==Scale factor==

[[File:Continuous wavelet transform.gif|thumb|300px|right]]

The scale factor <math>a</math> 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.

[[File:Wavelet scale sweep for FM signal.gif|thumb|300px|Visualizing the effect of changing a wavelet's scale factor. Here, a [[Frequency modulation|frequency-modulated]] tone (plus noise) is analyzed with a [[Morlet wavelet]]; the scale is adjusted from <math>a=1</math> to <math>a=200</math> in steps of unity.]]

==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 <math>X_w(a,b)</math> 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 <math>\frac{1}{a}\cdot|X_w(a,b)|^2</math>.<ref>{{cite journal |last1=Torrence |first1=Christopher |last2=Compo |first2=Gilbert |title=A Practical Guide to Wavelet Analysis |journal=Bulletin of the American Meteorological Society |date=1998 |volume=79 |issue=1 |pages=61–78|doi=10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 |bibcode=1998BAMS...79...61T |s2cid=14928780 |doi-access=free }}</ref><ref>{{cite journal |last1=Liu |first1=Yonggang |title=Rectification of the Bias in the Wavelet Power Spectrum |journal=Journal of Atmospheric and Oceanic Technology |date=December 2007 |volume=24 |issue=12 |pages=2093–2102|doi=10.1175/2007JTECHO511.1 |bibcode=2007JAtOT..24.2093L |doi-access=free }}</ref>

[[File:Wavelet scale sweep for FM signal.gif|thumb|300px|Visualizing the effect of changing a [[Morlet wavelet|Morlet wavelet's]] scale<math>\sigma</math> factorparameter, which interpolates between the original time-series and a [[Fourier transform]]. Here, a [[Frequency modulation|frequency-modulated]] tone (plus noise) is analyzed with a [[Morlet wavelet]]; the scale is adjusted from <math>a=1/\sigma</math> is adjusted from 1 to <math>a=200</math>, 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.<ref>{{Cite journal|last1=Sejdic|first1=E.|last2=Djurovic|first2=I.|last3=Stankovic|first3=L.|date=August 2008|title=Quantitative Performance Analysis of Scalogram as Instantaneous Frequency Estimator|journal=IEEE Transactions on Signal Processing|volume=56|issue=8|pages=3837–3845|doi=10.1109/TSP.2008.924856|bibcode=2008ITSP...56.3837S|s2cid=16396084|issn=1053-587X}}</ref> 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.<ref>[https://www.youtube.com/watch?v=DTpEVQSEBBk "Novel method for stride length estimation with body area network accelerometers"], ''IEEE BioWireless 2011'', pp. 79-8279–82</ref> Wavelet transforms can also be used in [[Electroencephalography]] (EEG) data analysis to identify epileptic spikes resulting from [[epilepsy]].<ref>{{Cite journal|last1=Iranmanesh|first1=Saam|last2=Rodriguez-Villegas|first2=Esther|author-link2=Esther Rodriguez-Villegas|year=2017|title=A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy|journal=IEEE Journal of Solid-State Circuits|volume=52|issue=9|pages=2362–2373|doi=10.1109/JSSC.2017.2720636|bibcode=2017IJSSC..52.2362I|hdl-access=free|hdl=10044/1/48764|s2cid=24852887}}</ref> Wavelet transform has been also successfully used for the interpretation of time series of landslides.<ref>{{Cite journal|last1=Tomás|first1=R.|last2=Li|first2=Z.|last3=Lopez-Sanchez|first3=J. M.|last4=Liu|first4=P.|last5=Singleton|first5=A.|date=2016-06-01|title=Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide|journal=Landslides|language=en|volume=13|issue=3|pages=437–450|doi=10.1007/s10346-015-0589-y|bibcode=2016Lands..13..437T |issn=1612-510X|hdl=10045/62160|s2cid=129736286|url=http://rua.ua.es/dspace/bitstream/10045/62160/5/2016_Tomas_etal_Landslides_rev.pdf|hdl-access=free}}</ref> and land subsidence,<ref>{{Cite journal |last1=Tomás |first1=Roberto |last2=Pastor |first2=José Luis |last3=Béjar-Pizarro |first3=Marta |last4=Bonì |first4=Roberta |last5=Ezquerro |first5=Pablo |last6=Fernández-Merodo |first6=José Antonio |last7=Guardiola-Albert |first7=Carolina |last8=Herrera |first8=Gerardo |last9=Meisina |first9=Claudia |last10=Teatini |first10=Pietro |last11=Zucca |first11=Francesco |last12=Zoccarato |first12=Claudia |last13=Franceschini |first13=Andrea |date=2020-04-22 |title=Wavelet analysis of land subsidence time-series: Madrid Tertiary aquifer case study |url=https://piahs.copernicus.org/articles/382/353/2020/ |journal=Proceedings of the International Association of Hydrological Sciences |language=en |volume=382 |pages=353–359 |doi=10.5194/piahs-382-353-2020 |doi-access=free |bibcode=2020PIAHS.382..353T |issn=2199-899X|hdl=11577/3338112 |hdl-access=free }}</ref> and for calculating the changing periodicities of epidemics.<ref>{{Citation |last=von Csefalvay |first=Chris |title=Temporal dynamics of epidemics |date=2023 |url=https://linkinghub.elsevier.com/retrieve/pii/B9780323953894000165 |work=Computational Modeling of Infectious Disease |pages=217–255 |publisher=Elsevier |language=en |doi=10.1016/b978-0-32-395389-4.00016-5 |isbn=978-0-323-95389-4 |access-date=2023-02-27|url-access=subscription }}</ref>

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.<ref>Slavic, J and Simonovski, I and M. Boltezar, [http://lab.fs.uni-lj.si/ladisk/?what=abstract&ID=11 Damping identification using a continuous wavelet transform: application to real data]</ref>

Line 45:

* [[S transform]]

* [[Time-frequency analysis]]

* [[Cauchy wavelet]]

==References==

{{Reflist}}

=== Further reading===

* <ref>{{Cite journal|last1=Prasad|first1=Akhilesh|last2=Maan|first2=Jeetendrasingh|last3=Verma|first3=Sandeep Kumar|date=2021|title=Wavelet transforms associated with the index Whittaker transform|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/mma.7440|journal=Mathematical Methods in the Applied Sciences|volume=44|issue=13|pages=10734–10752|language=en|doi=10.1002/mma.7440|bibcode=2021MMAS...4410734P|s2cid=235556542|issn=1099-1476}}</ref>

*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, 723-736723–736.

* Lintao Liu and Houtse Hsu (2012) "Inversion and normalization of time-frequency transform" AMIS 6 No. 1S pp. &nbsp;67S-74S.

* [[Stéphane Mallat]], "A wavelet tour of signal processing" 2nd Edition, Academic Press, 1999, {{ISBN|0-12-466606-X}}

*Ding, Jian-Jiun (2008), [http://djj.ee.ntu.edu.tw/TFW.htm Time-Frequency Analysis and Wavelet Transform], viewed 19 January 2008

Line 57 ⟶ 59:

*Valens, Clemens (2004), [http://www.polyvalens.com/blog/wavelets/ A Really Friendly Guide to Wavelets], viewed 18 September 2018]

*[http://reference.wolfram.com/mathematica/ref/ContinuousWaveletTransform.html Mathematica Continuous Wavelet Transform]

*Lewalle, Jacques: [http://lcs3.syr.edu/faculty/lewalle/wavelets/cwt_general.pdf Continuous wavelet transform]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }}, viewed 6 February 2010

== External links ==

{{Reflist}}

* {{YouTube|jnxqHcObNK4|Wavelets: a mathematical microscope}}

{{DEFAULTSORT:Continuous Wavelet Transform}}

[[Category:ContinuousTheory mappingsof continuous functions]]

[[Category:Integral transforms]]