Power line interference filtering on surface electromyography based on the stationary wavelet packet transform

Power line interference is one of the main problems in surface electromyogram signals (EMG) analysis. In this work, a new method based on the stationary wavelet packet transform is proposed to estimate and remove this kind of noise from EMG data records. The performance has been quantitatively evaluated with synthetic noisy signals, obtaining good results independently from the signal to noise ratio (SNR). For the analyzed cases, the obtained results show that the correlation coefficient is around 0.99, the energy respecting to the pure EMG signal is 98–104%, the SNR is between 16.64 and 20.40 dB and the mean absolute error (MAE) is in the range of −69.02 and −65.31 dB. It has been also applied on 18 real EMG signals, evaluating the percentage of energy respecting to the noisy signals. The proposed method adjusts the reduction level to the amplitude of each harmonic present in the analyzed noisy signals (synthetic and real), reducing the harmonics with no alteration of the desired signal.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time–period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

WALSHSTORE : the application of burst processing to fail-soft storage systems using Walsh transforms /

"UILU-ENG 77 1753."

Colftar: a real-time electro-optical system for two dimensional Fourier transforms /

Thesis--University of Illinois.

The relationship between the auditory brain-stem response and its reconstructed waveforms following discrete wavelet transformation

Objective: To examine the relationship between the auditory brain-stem response (ABR) and its reconstructed waveforms following discrete wavelet transformation (DWT), and to comment on the resulting implications for ABR DWT time-frequency analysis. Methods: ABR waveforms were recorded from 120 normal hearing subjects at 90, 70, 50, 30, 10 and 0 dBnHL, decomposed using a 6 level discrete wavelet transformation (DWT), and reconstructed at individual wavelet scales (frequency ranges) A6, D6, D5 and D4. These waveforms were then compared for general correlations, and for patterns of change due to stimulus level, and subject age, gender and test ear. Results: The reconstructed ABR DWT waveforms showed 3 primary components: a large-amplitude waveform in the low-frequency A6 scale (0-266.6 Hz) with its single peak corresponding in latency with ABR waves III and V; a mid-amplitude waveform in the mid-frequency D6 scale (266.6-533.3 Hz) with its first 5 waves corresponding in latency to ABR waves 1, 111, V, VI and VII; and a small-amplitude, multiple-peaked waveform in the high-frequency D5 scale (533.3-1066.6 Hz) with its first 7 waves corresponding in latency to ABR waves 1, 11, 111, IV, V, VI and VII. Comparisons between ABR waves 1, 111 and V and their corresponding reconstructed ABR DWT waves showed strong correlations and similar, reliable, and statistically robust changes due to stimulus level and subject age, gender and test ear groupings. Limiting these findings, however, was the unexplained absence of a small number (2%, or 117/6720) of reconstructed ABR DWT waves, despite their corresponding ABR waves being present. Conclusions: Reconstructed ABR DWT waveforms can be used as valid time-frequency representations of the normal ABR, but with some limitations. In particular, the unexplained absence of a small number of reconstructed ABR DWT waves in some subjects, probably resulting from 'shift invariance' inherent to the DWT process, needs to be addressed. Significance: This is the first report of the relationship between the ABR and its reconstructed ABR DWT waveforms in a large normative sample. (C) 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

From cardinal spline wavelet bases to highly coherent dictionaries

Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation.