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Wavelet Transform (wavelet + transform)
Kinds of Wavelet Transform Selected AbstractsFeature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant AnalysisCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000A. Samant To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident-related features from the traffic patterns. A robust feature-extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature-extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection. [source] 4122: Exploring new strategies to record and analyse clinical electroretinogramsACTA OPHTHALMOLOGICA, Issue 2010P LACHAPELLE Purpose Investigate if the combination of time-frequency domain analysis and ERG dipole rotation reveals hidden features of the normal ERG that could be instrumental in the interpretation of nearly extinguished ERG responses. Methods Analyses were conducted on photopic ERGs (Photopic Hills: PH) obtained from normal subjects (n=75) and patients (n=65) affected with various retinopathies. A Discrete Wavelet Transform (DWT) was done on each ERGs and key descriptors (Holder exponent and wavelet coefficient maxima) were calculated. Dipole rotation was obtained by combining 11 gaze positions (0, 8, 16, 24, 32 and 40 degrees nasal or temporal to center) with 4 electrode locations [corneal (CE), lower lid (LL), external (EC) and internal canthi (IC)]. Results The Holder exponent follows a parabola, while some of the local wavelet maxima seem to follow a PH-like like distribution (b-wave and OPs) or a logistic growth function (a-wave). In still recordable pathological ERGs, the wavelet maxima matched that found in normal ERGs evoked at low stimulus intensities while in nearly extinguished ERGs (<10% of normal) the wavelet coefficients were significantly lower. Irrespective of the direction of gaze, there was little variation in DTL ERGs. EC ERGs were the only ones to reverse in polarity (seen 5 degrees nasal to fixation in nasal to temporal shift). Conclusion The parameters obtained with the DWT offers useful and reproducible tools to help identify subtle features of residual ERGs and therefore should allow for a more accurate quantification of low-voltage ERGs responses. Finally, our results suggest that varying the gaze and electrode positions would represent a valuable addition to the recording of clinical ERGs. Funded by NSERC. [source] Combined Wavelet Transform with Curve-fitting for Objective Optimization of the Parameters in Fourier Self-deconvolutionCHINESE JOURNAL OF CHEMISTRY, Issue 10 2001Xiu-Qi Zhang Abstract Fourier self-deconvolution was the most effective technique in resolving overlapping bands, in which deconvolution function results in deconvolution and apodization smoothes the magnified noise. Yet, the choice of the original half-width of each component and breaking point for truncation is often very subjective. In this paper, the method of combined wavelet transform with curve fitting was described with the advantages of an enhancement of signal to noise ratio as well as the improved fitting condition, and was applied to objective optimization of the original half-widths of components in unresolved bands for Fourier self-deconvolution. Again, a noise was separated from a noisy signal by wavelet transform, therefore, the breaking point of apodization function can be determined directly in frequency domain. Accordingly, some artifacts in Fourier self-deconvolution were minimized significantly. [source] Wavelet Transforms for System Identification in Civil EngineeringCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2003T. Kijewski Although challenges did not surface in prior applications concerned with mechanical systems, which are characterized by higher frequency and broader-band signals, the transition to the time-frequency domain for the analysis of civil engineering structures highlighted the need to understand more fully various processing concerns, particularly for the popular Morlet wavelet. In particular, as these systems may possess longer period motions and thus require finer frequency resolutions, the particular impacts of end effects become increasingly apparent. This study discusses these considerations in the context of the wavelet's multi-resolution character and includes guidelines for selection of wavelet central frequencies, highlights their role in complete modal separation, and quantifies their contributions to end-effect errors, which may be minimized through a simple padding scheme. [source] An Initial Analysis of River Discharge and Rainfall in Coastal New South Wales, Australia Using Wavelet TransformsGEOGRAPHICAL RESEARCH, Issue 3 2001H. Kirkup In many coastal catchments of south eastern New South Wales, Australia, changes in river morphology are a response to human impact superimposed on spatial and temporal patterns of variability in precipitation and discharge. Understanding, and preferably quantifying, spatial and temporal patterns of hydrologic variability are essential to understanding natural changes, and to separate these from artificial changes in river systems. Prediction and management of water resources are also dependent upon this understanding. We assess the variability in precipitation and discharge using the wavelet transform which projects the time series of data into a three dimensional surface of frequency, amplitude and time. The analysis reveals that changes across time often reflect changes in individual seasons and may be linked to changes in particular seasonal atmospheric circulation systems. Strong perturbations in the analysis of one catchment are consistent with documented, geomorphically-effective, flooding sequences. The characteristics of the series in the transformed data reveal interesting differences at certain times and scales which may be a reflection of changes in larger scale atmospheric processes. [source] An Adaptive Conjugate Gradient Neural Network,Wavelet Model for Traffic Incident DetectionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000H. Adeli Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways. [source] Wavelet transform in denoising magnetic archaeological prospecting dataARCHAEOLOGICAL PROSPECTION, Issue 2 2007B. Tsivouraki Abstract Magnetic measurements in archaeological prospecting are often affected by cultural noise having the same high-frequency content as anomalies arising from buried antiquities. Also, in many cases the microrelief of the ground surface causes a noise that is coherent, pseudorandom and periodic. The main cause of this kind of noise is the ploughing of the earth. In this paper the efficiency of a wavelet denoising scheme is tested with respect to these types of unwanted disturbances. The proposed scheme combines the cyclospinning algorithm with a variable threshold calculated in each cycle of the algorithm. Tests on synthetic and real data show a satisfactory performance of the technique in suppressing both the white noise and the coherent noise caused by the systematic undulations of the ground surface. Copyright © 2007 John Wiley & Sons, Ltd. [source] Exploiting statistical properties of wavelet coefficient for face detection and recognitionPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007Naseer Al-Jawad Wavelet transforms (WT) are widely accepted as an essential tool for image processing and analysis. Image and video compression, image watermarking, content-base image retrieval, face recognition, texture analysis, and image feature extraction are all but few examples. It provides an alternative tool for short time analysis of quasi-stationary signals, such as speech and image signals, in contrast to the traditional short-time Fourier transform. The Discrete Wavelet Transform (DWT) is a special case of the WT, which provides a compact representation of a signal in the time and frequency domain. In particular, wavelet transforms are capable of representing smooth patterns as well anomalies (e.g. edges and sharp corners) in images. We are focusing here on using wavelet transforms statistical properties for facial feature detection, which allows us to extract the image facial feature/edges easily. Wavelet sub-bands segmentation method been developed and used to clean up the non-significant wavelet coefficients in wavelet sub-band (k) based on the (k-1) sub-band. Moreover, erosion which is considered as one of the fundamental operation in morphological image processing, been used to reduce the unwanted edges in certain directions. For face detection, face template profiles been built for both the face and the eyes for different wavelet sub-band levels to achieve better computational performance, these profiles used to match the extracted profiles from the wavelet domain of the input image using the Dynamic Time Warping technique DTW. The DTW smallest distance allows identifying the face and the eyes location. The performance of face features distances and ratio has been also tested for face verification purposes. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Estimation of Frequency-Dependent Strong Motion Duration Via Wavelets and Its Influence on Nonlinear Seismic ResponseCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2008Luis A. Montejo The proposed procedure utilizes the continuous wavelet transform and is based on the decomposition of the earthquake record into a number of component time histories (named "pseudo-details") with frequency content in a selected range. The "significant" strong motion duration of each pseudo-detail is calculated based on the accumulation of the Arias intensity (AI). Finally, the FDSMD of the earthquake record in different frequency ranges is defined as the strong motion duration of the corresponding pseudo-detail scaled by a weight factor that depends on the AI of each pseudo-detail. The efficiency of this new strong motion definition as an intensity measure is evaluated using incremental dynamic analysis (IDA). The results obtained show that the proposed FDSMD influence the peak response of short-period structures with stiffness and strength degradation. [source] Numerical Treatment of Seismic Accelerograms and of Inelastic Seismic Structural Responses Using Harmonic WaveletsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2007Pol D. Spanos The effectiveness of the harmonic wavelets for capturing the temporal evolution of the frequency content of strong ground motions is demonstrated. In this regard, a detailed study of important earthquake accelerograms is undertaken and smooth joint time-frequency spectra are provided for two near-field and two far-field records; inherent in this analysis is the concept of the mean instantaneous frequency. Furthermore, as a paradigm of usefulness for aseismic structural purposes, a similar analysis is conducted for the response of a 20-story steel frame benchmark building considering one of the four accelerograms scaled by appropriate factors as the excitation to simulate undamaged and severely damaged conditions for the structure. The resulting joint time-frequency representation of the response time histories captures the influence of nonlinearity on the variation of the effective natural frequencies of a structural system during the evolution of a seismic event. In this context, the potential of the harmonic wavelet transform as a detection tool for global structural damage is explored in conjunction with the concept of monitoring the mean instantaneous frequency of records of critical structural responses. [source] Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern AnalysisCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2004Xiaomo Jiang A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models. [source] Enhancing Neural Network Traffic Incident-Detection Algorithms Using WaveletsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2001A. Samant Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially. [source] Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant AnalysisCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000A. Samant To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident-related features from the traffic patterns. A robust feature-extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature-extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection. [source] An Adaptive Conjugate Gradient Neural Network,Wavelet Model for Traffic Incident DetectionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2000H. Adeli Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways. [source] Estimate of input energy for elasto-plastic SDOF systems during earthquakes based on discrete wavelet coefficientsEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 15 2005Jun Iyama Abstract The response of an elasto-plastic single degree of freedom (SDOF) system to ground motion is estimated based on wavelet coefficients calculated by discrete wavelet transform. Wavelet coefficients represent both the time and frequency characteristics of input ground motion, and thus can be considered to be directly related to the dynamic response of a non-linear system. This relationship between the energy input into an elastic SDOF system and wavelet coefficients is derived based on the assumption that wavelets deliver energy to the structure instantaneously and the quantity of energy is constant regardless of yielding. These assumptions are shown to be valid when the natural period of the system is in the predominant period range of the wavelet, the most common scenario for real structures, through dynamic response analysis of a single wavelet. The wavelet-based estimation of elastic and plastic energy transferred by earthquake ground motion is thus shown to be in good agreement with the dynamic response analysis when the natural period is in the predominant range of the input. Copyright © 2005 John Wiley & Sons, Ltd. [source] Noise removal for medical X-ray images in wavelet domainELECTRICAL ENGINEERING IN JAPAN, Issue 3 2008Ling Wang Abstract Many important problems in engineering and science are well-modeled by Poisson noise, and the noise of medical X-ray images is Poisson noise. In this paper, we propose a method for noise removal for degraded medical X-ray images using improved preprocessing and an improved BayesShrink (IBS) method in the wavelet domain. First, we preprocess the medical X-ray image. Second, we apply the Daubechies (db) wavelet transform to medical X-ray images to acquire scaling and wavelet coefficients. Third, we apply the proposed IBS method to process wavelet coefficients. Finally, we compute the inverse wavelet transform for the threshold coefficients. Experimental results show that the proposed method always outperforms traditional methods. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 37, 46, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20486 [source] Decomposition and reconstruction of signal in real-time spectral analysisELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 11 2008Hideto Nakatsuji Abstract In recent years, wavelet transform which gives expression in the time,frequency domain has been adopted for a nonstationary process. The authors have proposed a new spectral analytical method in the time,frequency domain. In the conventional spectral analytical methods, the decomposition and the reconstruction of signals have been obtained analytically. The proposed method consists of two approaches. We call these approaches Approach 1 and Approach 2. In this paper, we show the decomposition and reconstruction of signals based on Approach 1. First, we show that the signal is decomposed to the signal elements. They are called the decomposition waves, and all of these decomposed waves are added to obtain the reconstructed wave. Next, we show the magnitude characteristic and the phase characteristic between the original signal and the reconstructed wave. Then the conditions between the signal and the reconstructed wave are derived to realize a sufficiently approximated wave. By a numeric calculation example, we show the approximation ability by the proposed method. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 91(11): 37,45, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10182 [source] Signal denoising and baseline correction by discrete wavelet transform for microchip capillary electrophoresisELECTROPHORESIS, Issue 18 2003Bi-Feng Liu Abstract Signal denoising and baseline correction using discrete wavelet transform (DWT) are described for microchip capillary electrophoresis (MCE). DWT was performed on an electropherogram describing a separation of nine tetramethylrohodamine-5-isothiocyanate labeled amino acids, following MCE with laser-induced fluorescence detection, using Daubechies 5 wavelet at a decomposition level of 6. The denoising efficiency was compared with, and proved to be superior to, other commonly used denoising techniques such as Fourier transform, Savitzky-Golay smoothing and moving average, in terms of noise removal and peak preservation by directly visual inspection. Novel strategies for baseline correction were proposed, with a special interest in baseline drift that frequently occurred in chromatographic and electrophoretic separations. [source] Spatiotemporal generation of long-range dependence models and estimationENVIRONMETRICS, Issue 2 2006M. P. Frías Abstract A parametric family of spatiotemporal models displaying separable isotropic long-range dependence, in space and time, is introduced in a fractional generalized framework. The weak-sense implementation of estimation methods based on the integrated periodogram, the variogram and the wavelet transform, to estimate the long-memory parameter vector is discussed. The construction of separable and non-separable anisotropic long-range dependence spatiotemporal processes is also described considering fractional integration filters. Copyright © 2005 John Wiley & Sons, Ltd. [source] Wavelet analysis of the scale- and location-dependent correlation of modelled and measured nitrous oxide emissions from soilEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2005A. E. Milne Summary We used the wavelet transform to quantify the performance of models that predict the rate of emission of nitrous oxide (N2O) from soil. Emissions of N2O and other soil variables that influence emissions were measured on soil cores collected at 256 locations across arable land in Bedfordshire, England. Rate-limiting models of N2O emissions were constructed and fitted to the data by functional analysis. These models were then evaluated by wavelet variance and wavelet correlations, estimated from coefficients of the adapted maximal overlap discrete wavelet transform (AMODWT), of the fitted and measured emission rates. We estimated wavelet variances to assess whether the partition of the variance of modelled rates of N2O emission between scales reflected that of the data. Where the relative distribution of variance in the model is more skewed to coarser scales than is the case for the observation, for example, this indicates that the model predictions are too smooth spatially, and fail adequately to represent some of the variation at finer scales. Scale-dependent wavelet correlations between model and data were used to quantify the model performance at each scale, and in several cases to determine the scale at which the model description of the data broke down. We detected significant changes in correlation between modelled and predicted emissions at each spatial scale, showing that, at some scales, model performance was not uniform in space. This suggested that the influence of a soil variable on N2O emissions, important in one region but not in another, had been omitted from the model or modelled poorly. Change points usually occurred at field boundaries or where soil textural class changed. We show that wavelet analysis can be used to quantify aspects of model performance that other methods cannot. By evaluating model behaviour at several scales and positions wavelet analysis helps us to determine whether a model is suitable for a particular purpose. [source] Analysing soil variation in two dimensions with the discrete wavelet transformEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2004R. M. Lark Summary Complex spatial variation in soil can be analysed by wavelets into contributions at several scales or resolutions. The first applications were to data recorded at regular intervals in one dimension, i.e. on transects. The theory extends readily to two dimensions, but the application to small sets of gridded data such as one is likely to have from a soil survey requires special adaptation. This paper describes the extension of wavelet theory to two dimensions. The adaptation of the wavelet filters near the limits of a region that was successful in one dimension proved unsuitable in two dimensions. We therefore had to pad the data out symmetrically beyond the limits to minimize edge effects. With the above modifications and Daubechies's wavelet with two vanishing moments the analysis is applied to soil thickness, slope gradient, and direct solar beam radiation at the land surface recorded at 100-m intervals on a 60 × 101 square grid in south-west England. The analysis revealed contributions to the variance at several scales and for different directions and correlations between the variables that were not evident in maps of the original data. In particular, it showed how the thickness of the soil increasingly matches the geological structure with increasing dilation of the wavelet, this relationship being local to the strongly aligned outcrops. The analysis reveals a similar pattern in slope gradient, and a negative correlation with soil thickness, most clearly evident at the coarser scales. The solar beam radiation integrates slope gradient and azimuth, and the analysis emphasizes the relations with topography at the various spatial scales and reveals additional effects of aspect on soil thickness. [source] Changes in variance and correlation of soil properties with scale and location: analysis using an adapted maximal overlap discrete wavelet transformEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2001R. M. Lark Summary The magnitude of variation in soil properties can change from place to place, and this lack of stationarity can preclude conventional geostatistical and spectral analysis. In contrast, wavelets and their scaling functions, which take non-zero values only over short intervals and are therefore local, enable us to handle such variation. Wavelets can be used to analyse scale-dependence and spatial changes in the correlation of two variables where the linear model of coregionalization is inadmissible. We have adapted wavelet methods to analyse soil properties with non-stationary variation and covariation in fairly small sets of data, such as we can expect in soil survey, and we have applied them to measurements of pH and the contents of clay and calcium carbonate on a 3-km transect in Central England. Places on the transect where significant changes in the variance of the soil properties occur were identified. The scale-dependence of the correlations of soil properties was investigated by calculating wavelet correlations for each spatial scale. We identified where the covariance of the properties appeared to change and then computed the wavelet correlations on each side of the change point and compared them. The correlation of topsoil and subsoil clay content was found to be uniform along the transect at one important scale, although there were significant changes in the variance. In contrast, carbonate content and pH of the topsoil were correlated only in parts of the transect. [source] Modelling volatility clustering in electricity price return series for forecasting value at riskEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2009R. G. Karandikar Abstract Modelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia. Copyright © 2007 John Wiley & Sons, Ltd. [source] A novel selectivity technique for high impedance arcing fault detection in compensated MV networksEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 4 2008Nagy I. Elkalashy Abstract In this paper, the initial transients due to arc reignitions associated with high impedance faults caused by leaning trees are extracted using discrete wavelet transform (DWT). In this way, the fault occurrence is localized. The feature extraction is carried out for the phase quantities corresponding to a band frequency 12.5,6.25,kHz. The detection security is enhanced because the DWT corresponds to the periodicity of these transients. The selectivity term of the faulty feeder is based on a novel technique, in which the power polarity is examined. This power is mathematically processed by multiplying the DWT detail coefficients of the phase voltage and current for each feeder. Its polarity identifies the faulty feeder. In order to reduce the computational burden of the technique, the extraction of the fault features from the residual components is examined. The same methodology of computing the power is considered by taking into account the residual voltage and current detail coefficients where the proposed algorithm performs best. Test cases provide evidence of the efficacy of the proposed technique. Copyright © 2007 John Wiley & Sons, Ltd. [source] Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signalsEXPERT SYSTEMS, Issue 2 2009Elif Derya Übeyli Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%). [source] Detection of electrocardiogram beats using a fuzzy similarity indexEXPERT SYSTEMS, Issue 2 2007Elif Derya Übeyli Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time,frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat). [source] Combined neural network model to compute wavelet coefficientsEXPERT SYSTEMS, Issue 3 2006nan Güler Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients. [source] An Initial Analysis of River Discharge and Rainfall in Coastal New South Wales, Australia Using Wavelet TransformsGEOGRAPHICAL RESEARCH, Issue 3 2001H. Kirkup In many coastal catchments of south eastern New South Wales, Australia, changes in river morphology are a response to human impact superimposed on spatial and temporal patterns of variability in precipitation and discharge. Understanding, and preferably quantifying, spatial and temporal patterns of hydrologic variability are essential to understanding natural changes, and to separate these from artificial changes in river systems. Prediction and management of water resources are also dependent upon this understanding. We assess the variability in precipitation and discharge using the wavelet transform which projects the time series of data into a three dimensional surface of frequency, amplitude and time. The analysis reveals that changes across time often reflect changes in individual seasons and may be linked to changes in particular seasonal atmospheric circulation systems. Strong perturbations in the analysis of one catchment are consistent with documented, geomorphically-effective, flooding sequences. The characteristics of the series in the transformed data reveal interesting differences at certain times and scales which may be a reflection of changes in larger scale atmospheric processes. [source] Search for direct empirical spatial correlation signatures of the critical triggering earthquake modelGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 3 2004G. Ouillon SUMMARY We propose a new test of the critical earthquake model based on the hypothesis that precursory earthquakes are ,actors' that create fluctuations in the stress field which exhibit an increasing correlation length as the critical large event becomes imminent. Our approach constitutes an attempt to build a more physically based time-dependent indicator (cumulative scalar stress function), in the spirit of, but improving on, the cumulative Benioff strain used in previous works documenting the phenomenon of accelerating seismicity. Using a simplified scalar space and time-dependent viscoelastic Green's function in a two-layer model of the Earth's lithosphere, we compute spatiotemporal pseudo-stress fluctuations induced by a series of events before four of the largest recent shocks in southern California. Through an appropriate spatial wavelet transform, we then estimate the contribution of each event in the series to the correlation properties of the simplified pseudo-stress field around the location of the mainshock at different scales. This allows us to define a cumulative scalar pseudo-stress function which reveals neither an acceleration of stress storage at the epicentre of the mainshock nor an increase of the spatial stress,stress correlation length similar to those observed previously for the cumulative Benioff strain. The earthquakes we studied are thus either simple ,witnesses' of a large-scale tectonic organization, or are simply unrelated, and/or the Green's function describing interactions between earthquakes has a significantly longer range than predicted for standard viscoelastic media used here. [source] Modelling elastic media with the wavelet transformGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 2 2001João Willy Corrêa Rosa Summary We present a new method for modelling 2-D elastic media with the application of the wavelet transform, which is also extended to cases where discontinuities simulate geological faults between two different elastic media. The basic method consists of the discretization of the polynomial expansion for the boundary conditions of the 2-D problem involving the stress and strain relations for the media. This parametrization leads to a system of linear equations that should be solved for the determination of the expansion coefficients, which are the model parameters, and their determination leads to the solution of the problem. The wavelet transform is applied with two main objectives, namely to decrease the error related to the truncation of the polynomial expansion and to make the system of linear equations more compact for computation. This is possible due to the properties of this finite length transform. The method proposed here was tested for six different cases for which the analytical solutions are known. In all tests considered, we obtained very good matches with the corresponding known analytical solutions, which validate the theoretical and computational parts of the project. We hope that the new method is useful for modelling real media. [source] | ||||||||||||||||||||||||||||