
	Where results make sense

Topic: Continuous wavelet transform

Related Topics

Mexican hat wavelet

Haar wavelet

Wavelet

Starfruit

Numerical analysis

Kvikkalkul

Daniel Shirley

1846 in Canada

	Continuous Wavelet Transform
		The CWT is a convolution of the data sequence with a scaled and translated version of the mother wavelet, the psi function.
		For the FFT fast convolution to be free of wraparound effects that arise as a consequence of non-periodicity in both the data and the response function (daughter wavelet), zero padding is needed equal to the half the length of the non-zero elements in the daughter wavelet's frequency response.
		The wavelet critical limit gradients are the following colors by default: 8-level grayscale from 10 to 50%, 8-level cyanscale from 50% to 90%, 8-level greenscale from 90% to 95%, 8-level yellowscale from 95% to 99%, and 8-level redscale from 99% to 99.9%.
	www.clecom.co.uk /science/autosignal/help/Continuous_Wavelet_Transfor.htm (3571 words)

	[No title]
		Continuous wavelet transform was applied to sequence of thermal images of a multilayer sample with internal defects for evaluation of defect depth and area.
		Continuous wavelet transform (especially two-dimensional) provides redundant information on analyzed signal but its representation in the terms of scale and translation is in a number of cases rather obvious and descriptive.
		The 2D continuous wavelet transform of thermal image of ceramic plate with internal air bubbles permits to retrieve the on-plane dimension (diameter) of the bubble and its center from the energetic criteria.
	www.fsb.hr /Qirt2002/abstract/47.doc (907 words)

	THE WAVELET TUTORIAL PART III by ROBI POLIKAR
		The wavelet analysis is done in a similar way to the STFT analysis, in the sense that the signal is multiplied with a function, {\it the wavelet}, similar to the window function in the STFT, and the transform is computed separately for different segments of the time-domain signal.
		However, in the definition of the wavelet transform, the scaling term is used in the denominator, and therefore, the opposite of the above statements holds, i.e., scales s > 1 dilates the signals whereas scales s < 1, compresses the signal.
		This definition of the CWT shows that the wavelet analysis is a measure of similarity between the basis functions (wavelets) and the signal itself.
	users.rowan.edu /~polikar/WAVELETS/WTpart3.html (5094 words)

	Wavelet Transform (Site not responding. Last check: 2007-10-19)
		Wavelet analysis is a technique to transform an array of N numbers from their actual numerical values to an array of N wavelet coefficients.
		Each wavelet coefficient represents the closeness of the fit (or correlation) between the wavelet function at a particular size and a particular location within the data array.
		This property makes wavelet analysis very useful for signal or image processing; the "localized" nature of the wavelet transform allows you to easily pick out features in your data such as spikes (for example, noise or discontinuities), discrete objects (in, for example, astronomical images or satellite photos), edges of objects, etc.
	idlastro.gsfc.nasa.gov /idl_html_help/examples3.html (325 words)

	Motivation and Background
		Continuous wavelet transform is powerful, but the computation amount of the continuous wavelet transform is very large.
		For practical computing and application, the wavelet transform can be discretized by sampling both the scale parameter s and the translation parameter t.
		Below is the model of the multi-frequency channel separation of the wavelet transform in the recursive filter bank.
	heliso.tripod.com /java_hls/wavelet/dwt.htm (187 words)

	Systat Software Inc. - AutoSignal - HTML Help
		The CWT is a convolution of the data sequence with a scaled and translated version of the mother wavelet, the
		The CWT spectrum is rendered using a bivariate B-spline interpolant.
		The critical limits are based on Monte Carlo trials where a large number of white noise sets were analyzed to determine variance-normalized CWT spectral maxima as a function of data set size, wavelet, wavelet adjustable parameter, and real/complex state of the wavelet.
	www.systat.com /products/AutoSignal/help?sec=1102 (3497 words)

	Systat Software Inc. - AutoSignal - Tutorials
		This is the essence of the Continuous Wavelet Transform (CWT).
		You could likely use the Morlet wavelet with a wavenumber of 8 for all time-frequency analysis work, and never miss the small refinements that would derive from selecting the wavelet whose shape is most compatible with the oscillations in the data and whose oscillation count yields the optimum time-frequency resolution.
		Unlike a conventional Fourier analysis where the spectrum is generated by the transform, the FFT is used in the CWT is strictly for performing fast convolution of the scaled and translated wavelet with the data stream.
	www.systat.com /products/AutoSignal?sec=1039 (3152 words)

	A Really Friendly Guide To Wavelets
		In (1) the wavelet transform is calculated by continuously shifting a continuously scalable function over a signal and calculating the correlation between the two.
		This is a disadvantage of discrete wavelets: the resulting wavelet transform is no longer shift invariant, which means that the wavelet transforms of a signal and of a time-shifted version of the same signal are not simply shifted versions of each other.
		The first number is the number of vanishing moments of the analyzing wavelet (the wavelet that decomposes a signal) and the second number is the number of vanishing moments of the synthesizing wavelet (the wavelet that reconstructs the signal).
	perso.wanadoo.fr /polyvalens/clemens/wavelets/wavelets.html (5926 words)

	Wavelet Transform
		Wavelet transform for log periodogram regression in long memory stochastic volat...
		Research into Wavelet Transform Analysis at the School of the Built Environment...
		Introduction to Time-Frequency and Wavelet Transforms - $53.55...
	www.scienceoxygen.com /signal/293.html (350 words)

	Wavelet Digest, Vol. 4, Nr. 3.
		The use of a wavelet transform over x to reduce this PDE to a set of ODEs for a 32-dimensional coefficient array WA(t) is explained.
		Wavelet transforms are novel techniques which can be used to analyze localized data with multiple scales efficiently.
		Wavelet transform is a generic term and we use, in particular, the continuous wavelet transform and the wavelet-packet transform.
	www.ucalgary.ca /~morrow/Wavelets/msg00051.html (3373 words)

	Continuous wavelet transform in business information analysis - BaseGroup Labs
		The idea of continuous wavelet transform consists in calculation of scalar product (the value showing the degree of 'similarity' of two patterns) of the data under analysis and different shifts of a certain wavelet on different scales.
		To do so we shall lay off the wavelet's shifts on one axis (time axis) and the scales – on the other one (scales axis), and then paint the points of the obtained chart depending on the values of the respective coefficients: the higher the coefficient value, the brighter the colour.
		Since wavelets are well localized in frequency domain, on the conversion map these oscillations look like a chain of ‘hills’ with their tops on the scale that corresponds to the oscillation frequency.
	www.basegroup.ru /filtration/wavelet_for_bussines.en.htm (1710 words)

	u41a in fm97
		Wavelet Transform decomposes the signal using basis functions called wavelets which are band and time limited.
		The two dimensional wavelet transform is a very efficient bandpass filter, which can be used to separate various scales of processes and show their relative phase/location.
		Wavelet coherence curves, describing local phase correlation, are % wavelet filtered thin plate fit to model coherence functions in order first to obtain local apparent flexural wavelength.
	www.agu.org /cgi-bin/SFgate/SFgate?&listenv=table&multiple=1&range=1&directget=1&application=fm97&database=/data/epubs/wais/indexes/fm97/fm97&maxhits=200&="U41A" (2843 words)

	Wavelet Transform (Site not responding. Last check: 2007-10-19)
		The wavelet transform replaces the Fourier transform's sinusoidal waves by a family generated by translations and dilations of a window called a wavelet.
		it is a complete, stable and redundant representation of the signal; in particular, the wavelet transform is left invertible.
		As far as the continuous wavelet transform is concerned, a wavelet is simply a finite energy function with a zero mean.
	cas.ensmp.fr /~chaplais/Wavetour_presentation/transformees/Ondelettes/Wavelet_Transform.html (212 words)

	Motivation and Background (Site not responding. Last check: 2007-10-19)
		A wavelet is finite energy and band-pass, it oscillates in time like a short wave, hence the name "Wavelet".
		The wavelet transform convolves the signal x(t) with a wavelet family which is the translation and dilation of a unique wavelet
		The Java Learning Program(Not aviable here, this is a part of the web course "Wavelet Tutorial") show how to extract the different frequency components by dilation of the mother wavelet.
	heliso.tripod.com /java_hls/wavelet/cwt.htm (117 words)

	Donald Jordan - Curriculum Vitae
		The first specific aim is to develop new signal analysis techniques based on the continuous wavelet transform, which is an analysis tool better suited for handling the nonlinear time-dependent nature of AF than are classical signal analysis techniques.
		The theorem is derived for any wavelet function that is used for the continuous wavelet transform and presents a clear description of how combinations of scaled and translated wavelet functions quadratically mix to generate localized fluctuations at new scales.
		A technique termed partial reconstruction of wavelet transforms has been developed for this purpose and current research is focused on addressing the consequences of the time-frequency uncertainty principle on measuring the characteristics of response modes having closely spaced frequencies.
	www.mae.virginia.edu /faculty/dj_cv.html (1074 words)

	Continuous Wavelet Transform (CWT) (Site not responding. Last check: 2007-10-19)
		Where b is space shift and a is scale
		We are using the "Mexican Hat" wavelet which is the second derivative of the Gaussian.
		It has fine space resolution and zero first moment.
	www.msi.umn.edu /~esevre/poster/wavelets/cwt.html (41 words)

	Continuous Wavelet Transform (Site not responding. Last check: 2007-10-19)
		The Continuous Wavelet Transform (CWT) accomplishes the above multi-resolution tiling by time-scaling and time-shifting a prototype function ψ(t), often called the mother wavelet.
		As such, it is usually said that wavelets perform a "time-scale" analysis rather than a time-frequency analysis.
		While the CWT discussed above is an interesting theoretical and pedagogical tool, the discrete wavelet transform (DWT) is much more practical.
	cnx.org /content/m10418/latest (454 words)

	The convolution from the continuous wavelet transform (Site not responding. Last check: 2007-10-19)
		We will examine here the computation of a convolution by using the continuous wavelet transform in order to get a framework for linear smoothings.
		Its efficiency depends on the number of terms in the wavelet transform associated with g(x) for a given signal f(x).
		The computing time is longer than the one obtained with the wavelet transform if we concentrate the energy on very few coefficients.
	www.eso.org /projects/esomidas/doc/user/98NOV/volb/node329.html (228 words)

	The continuous wavelet transform (Site not responding. Last check: 2007-10-19)
		is the analyzing wavelet, a (>0) is the scale parameter and b is the position parameter.
		The last property makes the wavelet transform very suitable for analyzing hierarchical structures.
		Now consider a function W(a,b) which is the wavelet transform of a given function f(x).
	www.eso.org /projects/esomidas/doc/user/98NOV/volb/node310.html (115 words)

	cwt (Wavelet Toolbox)
		Then the wavelet coefficient of s at scale a and position b is defined by:
		This example demonstrates the difference between discrete and continuous wavelet transforms.
		subplot(312), colormap(pink(64)); img = image(flipud(wcodemat(cfd,64,'row'))); set(get(img,'parent'),'YtickLabels',[]); title('Discrete Transform, absolute coefficients.') ylabel('level') % Perform continuous wavelet transform by sym2 at all integer % scales from 1 to 32.
	www.tau.ac.il /cc/pages/docs/matlab/help/toolbox/wavelet/cwt.html (260 words)

	Yawtb's homepage (Site not responding. Last check: 2007-10-19)
		The parameters of the Morlet wavelet are the default ones, that is, k_0=6 and sigma=1.
		The wavelets available for cwt2d are: the Cauchy wavelet, the Morlet (2D) wavelet, the DoG (Derivative of gaussian) wavelet, the SDoG (Scale Difference of Gausian) wavelet, the Sushil's wavelet EndStop1 and EndStop2 and the Mexican Hat with the respective keywords 'cauchy', 'dog', 'sdog', 'endstop1', 'endstop2' and 'mexican'.
		where the the absolute value of the cwt is presented by default and where the isosurface presented has the abs(cwt) central value.
	www.fyma.ucl.ac.be /projects/yawtb/screenshots.php?page=cwtsph (873 words)

	cwt (Wavelet Toolbox) (Site not responding. Last check: 2007-10-19)
		The signal S is real, the wavelet can be real or complex.
		The wavelet coefficient of s at scale a and position b is defined by:
		Since s(t) is a discrete signal, we use a piecewise constant interpolation of the s(k) values, k = 1 to
	www.rdg.ac.uk /CSC/Topic/Graphics/GrGMatl601/Matlab6/toolbox/wavelet/cwt.html (367 words)

	Fast Continuous Wavelet
		We introduce a general framework for the efficient computation of the real continuous wavelet transform (CWT) using a filter bank.
		We derive error bounds on the wavelet approximation and show how to obtain any desired level of accuracy through the use of longer filters.
		However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from Elsevier.
	bigwww.epfl.ch /publications/vrhel9702.html (264 words)

	[No title] (Site not responding. Last check: 2007-10-19)
		Properties of wavelet, continuous/discrete wavelet transform, multiresolution formulation, scaling function and wavelet function that works as filter, subsampling, energy conservation, inverse wavelet transform for reconstruction, 2D wavelet transform, drawbacks of Haar wavelet are also investigated.
		2 Wavelets 2.1 The Fourier transform vs. the wavelet transform The Fourier transform is an excellent tool for the analysis of stationary, periodic data but incapable of the detection of local signals that are heavily time dependant due to its inability to describe the signal in the different time resolution.
		Using wavelets, we represent the signal in the time-scale domain instead of the time-frequency domain because the term “frequency” is reserved for the Fourier transform not just because the frequency and scale works opposite indeed.
	pages.cpsc.ucalgary.ca /~ylee/projectreport_wavelet.doc (2006 words)

	R: Cauchy's wavelet transform (Site not responding. Last check: 2007-10-19)
		Compute the continuous wavelet transform with (complex-valued) Cauchy's wavelet.
		, display the modulus of the continuous wavelet transform on the graphic device.
		The output contains the (complex) values of the wavelet transform of the input signal.
	biomserv.univ-lyon1.fr /library/Rwave/html/cwtTh.html (88 words)

	Application of Continuous Wavelet Transform for Study of Voltage Flicker-Generated Signals (Site not responding. Last check: 2007-10-19)
		An application of continuous wavelet transform (CWT) for the analysis of voltage flicker-generated signals is proposed.
		With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be integrally presented, thereby enhancing the monitoring of voltage flicker-generated signals at different time intervals.
		The Morlet wavelet has been selected as the basis function for the CWT in the proposed method.
	www.ewh.ieee.org /soc/aes/taes/aes363/3630925.htm (183 words)

	WV_CWT (Site not responding. Last check: 2007-10-19)
		The WV_CWT function returns the one-dimensional continuous wavelet transform of the input array.
		The transform is done using a user-inputted wavelet function.
		Set this keyword to a named variable in which to return the scale values used for the continuous wavelet transform.
	idlastro.gsfc.nasa.gov /idl_html_help/ref6.html (267 words)

	The Wavelet Digest :: View topic - Question: Fast continuous wavelet transform?
		The Wavelet Digest :: View topic - Question: Fast continuous wavelet transform?
		Transform." Is there such a thing, and where do I go for more
		Can I just use the usual wavelet transform with an
	www.wavelet.org /phpBB2/viewtopic.php?t=3130 (101 words)

	Continuous Wavelet
		We introduce a general framework for the efficient computation of the continuous wavelet transform (CWT).
		We derive error bounds on the wavelet approximation and show how to obtain any desired level of accuracy through the use of higher order representations.
		However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
	bigwww.epfl.ch /publications/vrhel9501.html (257 words)

	Identification of sources of potential fields with the continuous wavelet transform: Application to self-potential ...
		We show how the continuous wavelet transform may be used to quickly localize and characterize the sources of self-potential anomalies.
		The method is applied to synthetic examples and to a self-potential profile crossing a shallow fault zone.
		Citation: Gibert, D., and M. Pessel (2001), Identification of sources of potential fields with the continuous wavelet transform: Application to self-potential profiles, Geophys.
	www.agu.org /pubs/crossref/2001/2000GL012041.shtml (133 words)

Try your search on: Qwika (all wikis)