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Asymptotic Expansions of the Wavelet Transform for Large and Small Values of b  [PDF]
R. S. Pathak,Ashish Pathak
International Journal of Mathematics and Mathematical Sciences , 2009, DOI: 10.1155/2009/270492
Abstract: Asymptotic expansions of the wavelet transform for large and small values of the translation parameter are obtained using asymptotic expansions of the Fourier transforms of the function and the wavelet. Asymptotic expansions of Mexican hat wavelet transform, Morlet wavelet transform, and Haar wavelet transform are obtained as special cases. Asymptotic expansion of the wavelet transform has also been obtained for small values of when asymptotic expansions of the function and the wavelet near origin are given.
Moment Asymptotic Expansions of the Wavelet Transforms  [PDF]
R S Pathak,Ashish Pathak
Mathematics , 2014,
Abstract: Using distribution theory we present the moment asymptotic expansion of continuous wavelet transform in different distributional spaces for large and small values of dilation parameter $a$. We also obtain asymptotic expansions for certain wavelet transform.
Quasiasymptotic expansion of distributions from S+' and the asymptotic expansion of the distributional Stieltjes transform  [cached]
S. Pilipovic
International Journal of Mathematics and Mathematical Sciences , 1989, DOI: 10.1155/s0161171289000839
Abstract: By following the approach of Dr oinov, Vladimirov and Zavialov we investigate the quasiasymptotic expansion of distributions and give Abelian type results for the ordinar asymptotic behaviour of the distributional Stieltjes transform of a distribution with appropriate quasiasymptotic expansion.
[On the Asymptotic Expansion of the $q$-Dilogarithm  [PDF]
Fethi Bouzeffour
Mathematics , 2015,
Abstract: In this work, we study some asymptotic expansion of the $q$-dilogarithm at $q=1$ and $q=0$ by using Mellin transform and adequate decomposition allowed by Lerch functional equation.
On the Analytic Wavelet Transform  [PDF]
Jonathan M. Lilly,Sofia C. Olhede
Mathematics , 2007, DOI: 10.1109/TIT.2010.2050935
Abstract: An exact and general expression for the analytic wavelet transform of a real-valued signal is constructed, resolving the time-dependent effects of non-negligible amplitude and frequency modulation. The analytic signal is first locally represented as a modulated oscillation, demodulated by its own instantaneous frequency, and then Taylor-expanded at each point in time. The terms in this expansion, called the instantaneous modulation functions, are time-varying functions which quantify, at increasingly higher orders, the local departures of the signal from a uniform sinusoidal oscillation. Closed-form expressions for these functions are found in terms of Bell polynomials and derivatives of the signal's instantaneous frequency and bandwidth. The analytic wavelet transform is shown to depend upon the interaction between the signal's instantaneous modulation functions and frequency-domain derivatives of the wavelet, inducing a hierarchy of departures of the transform away from a perfect representation of the signal. The form of these deviation terms suggests a set of conditions for matching the wavelet properties to suit the variability of the signal, in which case our expressions simplify considerably. One may then quantify the time-varying bias associated with signal estimation via wavelet ridge analysis, and choose wavelets to minimize this bias.
Uniqueness for the continuous wavelet transform  [PDF]
H. -Q. Bui,R. S. Laugesen
Mathematics , 2011,
Abstract: Injectivity of the continuous wavelet transform acting on a square integrable signal is proved under weak conditions on the Fourier transform of the wavelet, namely that it is nonzero somewhere in almost every direction. For a bounded signal (not necessarily square integrable), we show that if the continuous wavelet transform vanishes identically, then the signal must be constant.
On Inversion of Continuous Wavelet Transform  [PDF]
Lintao Liu, Xiaoqing Su, Guocheng Wang
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57071
Abstract:

This study deduces a general inversion of continuous wavelet transform (CWT) with timescale being real rather than positive. In conventional CWT inversion, wavelet’s dual is assumed to be a reconstruction wavelet or a localized function. This study finds that wavelet’s dual can be a harmonic which is not local. This finding leads to new CWT inversion formulas. It also justifies the concept of normal wavelet transform which is useful in time-frequency analysis and time-frequency filtering. This study also proves a law for CWT inversion: either wavelet or its dual must integrate to zero.

On the Discrete Harmonic Wavelet Transform  [PDF]
Carlo Cattani,Aleksey Kudreyko
Mathematical Problems in Engineering , 2008, DOI: 10.1155/2008/687318
Abstract: The discrete harmonic wavelet transform has been reviewed and applied towards given functions. The absolute error of reconstruction of the functions has been computed.
An Algorithm for the Continuous Morlet Wavelet Transform  [PDF]
Richard Buessow
Physics , 2007, DOI: 10.1016/j.ymssp.2007.06.001
Abstract: This article consists of a brief discussion of the energy density over time or frequency that is obtained with the wavelet transform. Also an efficient algorithm is suggested to calculate the continuous transform with the Morlet wavelet. The energy values of the Wavelet transform are compared with the power spectrum of the Fourier transform. Useful definitions for power spectra are given. The focus of the work is on simple measures to evaluate the transform with the Morlet wavelet in an efficient way. The use of the transform and the defined values is shown in some examples.
WAVELET TRANSFORM AND LIP MODEL  [cached]
Guy Courbebaisse,Frederic Trunde,Michel Jourlin
Image Analysis and Stereology , 2002, DOI: 10.5566/ias.v21.p121-125
Abstract: The Fourier transform is well suited to the study of stationary functions. Yet, it is superseded by the Wavelet transform for the powerful characterizations of function features such as singularities. On the other hand, the LIP (Logarithmic Image Processing) model is a mathematical framework developed by Jourlin and Pinoli, dedicated to the representation and processing of gray tones images called hereafter logarithmic images. This mathematically well defined model, comprising a Fourier Transform "of its own", provides an effective tool for the representation of images obtained by transmitted light, such as microscope images. This paper presents a Wavelet transform within the LIP framework, with preservation of the classical Wavelet Transform properties. We show that the fast computation algorithm due to Mallat can be easily used. An application is given for the detection of crests.
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