Proceedings ArticleDOI
A study of wavelet thresholding denoising
Dai-Fei Guo,Wei-Hong Zhu,Zhen-Ming Gao,Jian-Qiang Zhang +3 more
- Vol. 1, pp 329-332
TLDR
According to the computer simulation, the best rule of estimating the threshold is found, which is combined with three kinds of threshold processing methods to denoise the same noisy signal.Abstract:
This paper introduces the principle of wavelet multiresolution analysis. Four kinds of threshold selection rules and three methods of threshold processing are given. According to the computer simulation, the best rule of estimating the threshold is found, which is combined with three kinds of threshold processing methods to denoise the same noisy signal. The best method of threshold processing is obtained by comparing the performance of three methods of threshold processing that are applied to denoising.read more
Citations
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Journal ArticleDOI
Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine
TL;DR: A multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery and the effectiveness of the proposed approach is effectively applied to battery monitoring and prognostics.
Journal ArticleDOI
Speech signal enhancement through adaptive wavelet thresholding
TL;DR: Overall results indicate that SNR and SSNR improvements for the proposed approach are comparable to those of the Ephraim Malah filter, with BWT enhancement giving the best results of all methods for the noisiest (-10db and -5db input SNR) conditions.
Journal ArticleDOI
Denoising of partial discharge signals in wavelet packets domain
TL;DR: In this paper, a wavelet packet transform-based method is proposed for effective denoising of on-site partial discharge data from a power generator and a gas-insulated substation.
Journal ArticleDOI
Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks
TL;DR: A Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies, yielding very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks.
Journal ArticleDOI
Noise reduction of nuclear magnetic resonance (NMR) transversal data using improved wavelet transform and exponentially weighted moving average (EWMA).
TL;DR: A method of combing the improved wavelet thresholding with the EWMA is proposed for noise reduction of decay data and it is demonstrated that the proposed approach can reduce the noise of T(2) decay data perfectly.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
An introduction to wavelets
TL;DR: An Overview: From Fourier Analysis to Wavelet Analysis, Multiresolution Analysis, Splines, and Wavelets.
Book
Characterization of Signals From Multiscale Edges
Stéphane Mallat,S. Zhong +1 more
TL;DR: The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.