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Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004., 2005
In this paper, we propose a new type of continuous wavelet transform. However we discretize the variables of integral a and b, any numerical integral has a high resolution and a does not appear in the denominator of the integrand. Furthermore, we give two discretization methods of the new wavelet transform.
null Yunhui Shi, null Qiuqi Ruan
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In this paper, we propose a new type of continuous wavelet transform. However we discretize the variables of integral a and b, any numerical integral has a high resolution and a does not appear in the denominator of the integrand. Furthermore, we give two discretization methods of the new wavelet transform.
null Yunhui Shi, null Qiuqi Ruan
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Fast continuous wavelet transform
1995 International Conference on Acoustics, Speech, and Signal Processing, 2002We introduce a general framework for the efficient computation of the continuous wavelet transform (CWT). The method allows arbitrary sampling along the scale axis, and achieves O(N) complexity per scale where N is the length of the signal. Our approach makes use of a compactly supported scaling function to approximate the analyzing wavelet.
M. Vrhel, null Chulhee Lee, I. Unser
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Continuous Watson wavelet transform
Integral Transforms and Special Functions, 2012We generalize the results of [2], and using the theory of Watson convolution, the continuous Watson wavelet transform is defined. Some properties related to the Watson wavelet transform are studied.
S. K. Upadhyay, Alok Tripathi
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1998
Abstract The objective of this chapter is to generalize the important subject of the continuous wavelet transform to the spherical case. The spherical continuous wavelet transform arises naturally as a result of an integral reproducing formula that is closely related to the theory of singular integral operators.
W Freeden, T Gervens, M Schreiner
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Abstract The objective of this chapter is to generalize the important subject of the continuous wavelet transform to the spherical case. The spherical continuous wavelet transform arises naturally as a result of an integral reproducing formula that is closely related to the theory of singular integral operators.
W Freeden, T Gervens, M Schreiner
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2010
Wavelet transform is a mathematical tool that converts a signal into a different form. This conversion has the goal to reveal the characteristics or “features” hidden within the original signal and represent the original signal more succinctly. A base wavelet is needed in order to realize the wavelet transform.
Robert X. Gao, Ruqiang Yan
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Wavelet transform is a mathematical tool that converts a signal into a different form. This conversion has the goal to reveal the characteristics or “features” hidden within the original signal and represent the original signal more succinctly. A base wavelet is needed in order to realize the wavelet transform.
Robert X. Gao, Ruqiang Yan
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Continuous quaternion fourier and wavelet transforms
International Journal of Wavelets, Multiresolution and Information Processing, 2014A two-dimensional (2D) quaternion Fourier transform (QFT) defined with the kernel [Formula: see text] is proposed. Some fundamental properties, such as convolution, Plancherel and vector differential theorems, are established. The heat equation in quaternion algebra is presented as an example of the application of the QFT to partial differential ...
Bahri, Mawardi +2 more
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Lebedev–Skalskaya Transform Related Continuous Wavelet Transform
Results in MathematicsThe paper deals with the continuous wavelet transform (CWT) in the context of the Lebedev-Skalskaya transform (LS-transform). The authors propose the definition of the CTW, where the translation operator is specific to the LS-transform. The basic properties of the novel CWT are checked (linearity, convolution, etc.).
Ajay K. Gupt +2 more
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2010
The WFT localizes a signal simultaneously in time and frequency by "looking" at it through a window that is translated in time, then translated in frequency (i.e., modulated in time). These two operations give rise to the "notes" gω,t(u). The signal is then reconstructed as a superposition of such notes, with the WFT ƒ(tω,t) as the coefficient function.
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The WFT localizes a signal simultaneously in time and frequency by "looking" at it through a window that is translated in time, then translated in frequency (i.e., modulated in time). These two operations give rise to the "notes" gω,t(u). The signal is then reconstructed as a superposition of such notes, with the WFT ƒ(tω,t) as the coefficient function.
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Inverse continuous wavelet transform “Deconvolution”
SEG Technical Program Expanded Abstracts 2011, 2011Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that attempts to remove an estimated seismic wavelet from seismic data. Considering that seismic trace singularities are associated with acoustic impedance contrasts, and can be characterized by wavelet transform modulus maxima lines (WTMML), we ...
Marcilio Castro de Matos +1 more
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