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Turbulent Signal Processing Based on Wavelet Transform De-noising Technique

LI Shi-xin,LIU Lu-yuan,SHU Wei,

实验力学 , 2001,
Abstract: Based on the characteristics of random noise wavelet transform on different scales and the relationship between the noise Lipschitz and its wavelet modulus maximum, an algorithm was presented where the wavelet transform coefficients of noise-turbulent signal were filtered by changeable threshold so as to reduce the noise in turbulent signal. Some computer de-noising simulation results were given. The results showed that the method was effective both in removing the noise and in enhancing the SNR of the signal.
Asymptotic Expansion of Wavelet Transform  [PDF]
Ashish Pathak, Prabhat Yadav, Madan Mohan Dixit
Advances in Pure Mathematics (APM) , 2015, DOI: 10.4236/apm.2015.51002
Abstract: In the present paper, we obtain asymptotic expansion of the wavelet transform for large value of dilation parameter a by using López technique. Asymptotic expansion of Shannon wavelet, Morlet wavelet and Mexican Hat wavelet transform are obtained as special cases.
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

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.
Asymptotic expansion of the wavelet transform with error term  [PDF]
R S Pathak,Ashish Pathak
Mathematics , 2014,
Abstract: UsingWong's technique asymptotic expansion for the wavelet transform is derived and thereby asymptotic expansions for Morlet wavelet transform, Mexican Hat wavelet transform and Haar wavelet transform are obtained.
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.
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.
Digital Watermarking in Wavelet Transform Domain
M. Candik,E. Matus,D. Levicky
Radioengineering , 2001,
Abstract: This paper presents a technique for the digital watermarking ofstill images based on the wavelet transform. The watermark (binaryimage) is embedded into original image in its wavelet domain. Theoriginal unmarked image is required for watermark extraction. Themethod of embedding of digital watermarks in wavelet transform domainwas analyzed and verified on grey scale static images.
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.
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