Showing papers on "Wavelet packet decomposition published in 2008"
TL;DR: This technology provides another useful way to EEG feature extraction in BCIs and is evaluated by separability and pattern recognition accuracy using the datasets of BCI 2003 Competition.
334 citations
Book•
11 Aug 2008
TL;DR: This book has three main objectives: providing an introduction to wavelets and their uses in statistics, acting as a quick and broad reference to many developments in the area, and interspersing R code that enables the reader to learn the methods, to carry out their own analyses, and further develop their own ideas.
Abstract: Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book has three main objectives: (i) providing an introduction to wavelets and their uses in statistics; (ii) acting as a quick and broad reference to many developments in the area; (iii) interspersing R code that enables the reader to learn the methods, to carry out their own analyses, and further develop their own ideas. The book code is designed to work with the freeware R package WaveThresh4, but the book can be read independently of R. The book introduces the wavelet transform by starting with the simple Haar wavelet transform, and then builds to consider more general wavelets, complex-valued wavelets, non-decimated transforms, multidimensional wavelets, multiple wavelets, wavelet packets, boundary handling, and initialization. Later chapters consider a variety of wavelet-based nonparametric regression methods for different noise models and designs including density estimation, hazard rate estimation, and inverse problems; the use of wavelets for stationary and non-stationary time series analysis; and how wavelets might be used for variance estimation and intensity estimation for non-Gaussian sequences. The book is aimed both at Masters/Ph.D. students in a numerate discipline (such as statistics, mathematics, economics, engineering, computer science, and physics) and postdoctoral researchers/users interested in statistical wavelet methods.
329 citations
TL;DR: The proposed method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses is suitable for real-time implementation.
249 citations
TL;DR: In this article, a fault location procedure for distribution networks based on the wavelet analysis of the fault-generated traveling waves is presented, in which the proposed procedure implements the continuous wavelets analysis applied to the voltage waveforms recorded during the fault in correspondence of a network bus.
Abstract: The paper presents a fault location procedure for distribution networks based on the wavelet analysis of the fault-generated traveling waves. In particular, the proposed procedure implements the continuous wavelet analysis applied to the voltage waveforms recorded during the fault in correspondence of a network bus. In order to improve the wavelet analysis, an algorithm is proposed to build specific mother wavelets inferred from the fault-originated transient waveforms. The performance of the proposed algorithm are analyzed for the case of the IEEE 34-bus test distribution network and compared with those achieved by using the more traditional Morlet mother wavelet.
243 citations
TL;DR: A wavelet-based denoising technique for the recovery of signal contaminated by white additive Gaussian noise and a new thresholding procedure is proposed, called subband adaptive, which outperforms the existing thresholding techniques.
224 citations
01 Jan 2008
TL;DR: In the first step an attempt was made to generate ECG wave- forms by developing a suitable MATLAB simulator and in the second step, using wavelet transform, the ECG signal was denoised by removing the corresponding wavelet coefficients at higher scales.
Abstract: This paper deals with the study of ECG signals using wavelet trans- form analysis. In the first step an attempt was made to generate ECG wave- forms by developing a suitable MATLAB simulator and in the second step, using wavelet transform, the ECG signal was denoised by removing the corresponding wavelet coefficients at higher scales. Then QRS complexes were detected and each complex was used to find the peaks of the individual waves like P and T, and also their deviations.
214 citations
TL;DR: The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.
Abstract: The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.
192 citations
TL;DR: It was demonstrated that the new scheme was able to classify the normal and epileptic EEG epochs with 94.3% and 98% accuracy, respectively, and it was also shown that, if the GA was not used for the optimal feature selection, the classification accuracies dropped noticeably.
173 citations
TL;DR: The proposed algorithm decomposes the voltage/current waveforms into the uniform frequency bands corresponding to the odd-harmonic components of the signal and uses a method to reduce the spectral leakage due to the imperfect frequency response of the used wavelet filter bank.
Abstract: This paper proposes a new algorithm based on the wavelet-packet transform for the analysis of harmonics in power systems. The proposed algorithm decomposes the voltage/current waveforms into the uniform frequency bands corresponding to the odd-harmonic components of the signal and uses a method to reduce the spectral leakage due to the imperfect frequency response of the used wavelet filter bank. This paper studies the selection of the mother wavelet, the sampling frequency, and the frequency characteristics of the wavelet filter bank for the two most common wavelet functions used for harmonic analysis and compares the performance of the proposed method with the results obtained using the discrete Fourier transform (DFT) analysis and the harmonic-group concept introduced by the International Electrotechnical Commission (IEC) under different measurement conditions.
168 citations
TL;DR: This paper compares and contrasts this transform with the better known continuous wavelet transform, and defines a relation between both that allows a better understanding of the S-transform.
Abstract: The S-transform is becoming popular for time-frequency analysis and data-adaptive filtering thanks to its simplicity. While this transform works well in the continuous domain, its discrete version may fail to achieve accurate results. This paper compares and contrasts this transform with the better known continuous wavelet transform, and defines a relation between both. This connection allows a better understanding of the S-transform, and makes it possible to employ the wavelet reconstruction formula as a new inverse S-transform and to propose several methods to solve some of the main limitations of the discrete S-transform, such as its restriction to linear frequency sampling.
159 citations
TL;DR: In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model and a wavelet feature selection algorithm based on statistical dependence is proposed.
Abstract: Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.
TL;DR: A DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property.
Abstract: An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval's theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560-1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909-996.], the''db4'', ''db8'' and ''db20'' wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.
TL;DR: A new variational method for blind deconvolution of images and inpainting is constructed, motivated by recent PDE-based techniques involving the Ginzburg-Landau functional, but using more localized wavelet-based methods.
Abstract: We construct a new variational method for blind deconvolution of images and inpainting, motivated by recent PDE-based techniques involving the Ginzburg-Landau functional, but using more localized wavelet-based methods. We present results for both binary and grayscale images. Comparable speeds are achieved with better sharpness of edges in the reconstruction.
TL;DR: In this paper, a new viewpoint in ECG detection is presented using continuous wavelet transform (CWT), and the concept of dominant rescaled wavelet coefficients (DRWC) is defined to magnify QRS complex and reduce the effects of other peaks.
TL;DR: A vector/matrix extension of the denoising algorithm initially developed for grayscale images, in order to efficiently process multichannel images, indicates that despite being nonredundant, the algorithm matches the quality of the best redundant approaches, while maintaining a high computational efficiency and a low CPU/memory consumption.
Abstract: We propose a vector/matrix extension of our denoising algorithm initially developed for grayscale images, in order to efficiently process multichannel (e.g., color) images. This work follows our recently published SURE-LET approach where the denoising algorithm is parameterized as a linear expansion of thresholds (LET) and optimized using Stein's unbiased risk estimate (SURE). The proposed wavelet thresholding function is pointwise and depends on the coefficients of same location in the other channels, as well as on their parents in the coarser wavelet subband. A nonredundant, orthonormal, wavelet transform is first applied to the noisy data, followed by the (subband-dependent) vector-valued thresholding of individual multichannel wavelet coefficients which are finally brought back to the image domain by inverse wavelet transform. Extensive comparisons with the state-of-the-art multiresolution image denoising algorithms indicate that despite being nonredundant, our algorithm matches the quality of the best redundant approaches, while maintaining a high computational efficiency and a low CPU/memory consumption. An online Java demo illustrates these assertions.
TL;DR: Experimental results demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity, as well as theoretical exact expressions for the estimation of the GG parameters are derived.
Abstract: In this paper, a novel despeckling algorithm based on undecimated wavelet decomposition and maximum a posteriori estimation is proposed. Such a method represents an improvement with respect to the filter presented by the authors, and it is based on the same conjecture that the probability density functions (pdfs) of the wavelet coefficients follow a generalized Gaussian (GG) distribution. However, the approach introduced here presents two major novelties: 1) theoretically exact expressions for the estimation of the GG parameters are derived: such expressions do not require further assumptions other than the multiplicative model with uncorrelated speckle, and hold also in the case of a strongly correlated reflectivity; 2) a model for the classification of the wavelet coefficients according to their texture energy is introduced. This model allows us to classify the wavelet coefficients into classes having different degrees of heterogeneity, so that ad hoc estimation approaches can be devised for the different sets of coefficients. Three different implementations, characterized by different approaches for incorporating into the filtering procedure the information deriving from the segmentation of the wavelet coefficients, are proposed. Experimental results, carried out on both artificially speckled images and true synthetic aperture radar images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity.
TL;DR: This paper focuses on the optimization of a full wavelet compression system for hyperspectral images and shows that a specific fixed decomposition significantly improves the classical isotropic decomposition.
Abstract: Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.
TL;DR: The basis selection algorithm by Coifman and Wickerhauser is adapted, providing a solution to the basis selection problem for the DWPT, and it is shown how to extend the two-band to an -band using the same method.
Abstract: The two-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be ldquooptimalrdquo for a given signal. The discrete wavelet packet transform (DWPT) provides a dictionary of bases over which one can search for an optimal representation (without constraining the analysis to an octave-band one) for the signal at hand. However, it is well known that both the DWT and the DWPT are shift-varying. Also, when these transforms are extended to 2-D and higher dimensions using tensor products, they do not provide a geometrically oriented analysis. The dual-tree complex wavelet transform , introduced by Kingsbury, is approximately shift-invariant and provides directional analysis in 2-D and higher dimensions. In this paper, we propose a method to implement a dual-tree complex wavelet packet transform , extending the as the DWPT extends the DWT. To find the best complex wavelet packet frame for a given signal, we adapt the basis selection algorithm by Coifman and Wickerhauser, providing a solution to the basis selection problem for the . Lastly, we show how to extend the two-band to an -band (provided that ) using the same method.
TL;DR: Experiments show that the proposed texture analysis and classification approach with the linear regression model based on the wavelet transform significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these.
Abstract: The wavelet transform as an important multiresolution analysis tool has already been commonly applied to texture analysis and classification. Nevertheless, it ignores the structural information while capturing the spectral information of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, our method considers not only the frequency regions but also the correlation between these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these.
01 Jan 2008
TL;DR: This paper proposes to investigate the suitability of different wavelet bases and the size of different neighborhood on the performance of image de-noising algorithms in terms of PSNR, and the impact of wavelet coefficients arising from the standard discrete wavelet transform.
Abstract: Summary The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Additive random noise can easily be removed using simple threshold methods. De-noising of natural images corrupted by Gaussian noise using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values. The wavelet de-noising scheme thresholds the wavelet coefficients arising from the standard discrete wavelet transform. In this paper, it is proposed to investigate the suitability of different wavelet bases and the size of different neighborhood on the performance of image de-noising algorithms in terms of PSNR.
TL;DR: Wavelet packet energy entropy and weighted support vector machines are used to automatically detect and classify power quality (PQ) disturbances.
Abstract: In this paper, wavelet packet energy entropy and weighted support vector machines are used to automatically detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of power systems. The types of concerned disturbances include voltage sags, swells, interruptions. Wavelet packet are utilized to denoise the digital signals, to decompose the signals and then to obtain five common features for the sampling PQ disturbance signals. A weighted support vector machine is designed and trained by 5-dimension feature space points for making a decision regarding the type of the disturbance. Simulation cases illustrate the effectiveness.
TL;DR: The developed model is based on wavelet packet decomposition, entropy and neural network and is an efficient and a robust tool to predict WWTP performance.
Abstract: In this paper, an intelligent wastewater treatment plant model is developed to predict the performance of a wastewater treatment plant (WWTP). The developed model is based on wavelet packet decomposition, entropy and neural network. The data used in this work were obtained from a WWTP in Malatya, Turkey. Daily records of these WWTP parameters over a year were obtained from the plant laboratory. Wavelet packet decomposition was used to reduce the input vectors dimensions of intelligent model. The suitable architecture of the neural network model is determined after several trial and error steps. Total suspended solid is one of the measures of overall plant performance so the developed model is used to predict the total suspended solid concentration in plant effluent. According to test results, the developed model performance is at desirable level. This model is an efficient and a robust tool to predict WWTP performance.
01 Jan 2008
TL;DR: A new method for invariant pixel regions texture image classification is presented and Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively.
Abstract: Recently, significant of the robust texture image classification has increased. The texture image classification is used for many areas such as medicine image processing, radar image processing, etc. In this study, a new method for invariant pixel regions texture image classification is presented. Wavelet packet entropy adaptive network based fuzzy inference system (WPEANFIS) was developed for classification of the twenty 512x512 texture images obtained from Brodatz image album. There, sixty 32x32 image regions were randomly selected (overlapping or non-overlapping) from each of these 20 images. Thirty of these image regions and other 30 of these image regions are used for training and testing processing of the WPEANFIS, respectively. In this application study, Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively. In this way, effects to correct texture classification performance of these wavelet families were compared. Efficiency of WPEANFIS developed method was tested and a mean %93.12 recognition success was obtained.
TL;DR: This paper investigates the usage of Wavelet transform and Adaptive neuro-fuzzy inference system (ANFIS) for color texture classification problem and proposed scheme composed of a wavelet domain feature extractor and an ANFIS classifier.
Abstract: The wavelet domain features have been intensively used for texture classification and texture segmentation with encouraging results. More of the proposed multi resolution texture analysis methods are quite successful, but all the applications of the texture analysis so far are limited to gray scale images. This paper investigates the usage of Wavelet transform (WT) and Adaptive neuro-fuzzy inference system (ANFIS) for color texture classification problem. The proposed scheme composed of a wavelet domain feature extractor and an ANFIS classifier. Both entropy and energy features are used on wavelet domain. Different color spaces are considered in the experimental studies. The performed experimental studies show the effectiveness of the wavelet transform and ANFIS structure for color texture classification problem. The overall success rate is over 96%.
Book•
18 Jan 2008TL;DR: In this paper, the Haar Wavelet Transformation Daubechies Wavelet Transformations Orthogonality and Fourier Series Wavelet Shrinkage: An Application to Denoising Biorthogonal Filters Computing Bi-Logonal Wavelet transformations The JPEG2000 Image Compression Standard Appendix A: Basic Statistics References Index
Abstract: Preface Acknowledgements Introduction: Why Wavelets Vectors and Matrices An Introduction to Digital Images Complex Numbers and Fourier Series Convolution and Filters The Haar Wavelet Transformation Daubechies Wavelet Transformations Orthogonality and Fourier Series Wavelet Shrinkage: An Application to Denoising Biorthogonal Filters Computing Biorthogonal Wavelet Transformations The JPEG2000 Image Compression Standard Appendix A: Basic Statistics References Index
TL;DR: Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-basedWavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed.
Abstract: In this paper, a new weighted adaptive lifting (WAL)-based wavelet transform is presented. The proposed WAL approach is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach, such as mismatch between the predict and update steps, interpolation favoring only horizontal or vertical direction, and invariant interpolation filter coefficients for all images. The main contribution of the proposed approach consists of two parts: one is the improved weighted lifting, which maintains the consistency between the predict and update steps as far as possible and preserves the perfect reconstruction at the same time; another is the directional adaptive interpolation, which improves the orientation property of the interpolated image and adapts to statistical property of each image. Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-based wavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL-based wavelet transform, up to 1.22-dB improvement in PSNR is reported.
Journal Article•
TL;DR: In this article, the authors used wavelet analysis together with wavelet entropy principle to detect and classify the type of fault in power system and achieved successful identification of the type. But, this procedure is required to be precise with no time consumption.
Abstract: The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.
TL;DR: This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG) using a wavelet transform along with the instantaneous RR-interval, which offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.
Abstract: This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. Only 11 features are used for beat classification with the classification accuracy of approximately 99.5% through a KNN classifier. Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is approximately 4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.
TL;DR: This work investigates the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds.
TL;DR: In this article, wavelet transform (discrete wavelet and wavelet packet transform) was introduced into a fourth-order statistic, kurtosis, for fault diagnosis in rolling element bearings.
Abstract: Signal processing plays a pivotal role in fault diagnostics of mechanical systems. An approach, viz. wavelet transform-based higher-order statistics, was developed in this paper for fault diagnosis in rolling element bearings. In the approach, wavelet transform (discrete wavelet and wavelet packet transform) was introduced into a fourth-order statistic, kurtosis. Thereinto, discrete wavelet transform-based kurtosis (DWTK) was applied to signals to get a higher resolution in low-frequency bands1 on the other hand, wavelet packet transform-based kurtosis (WPTK) was applied to obtain a relatively high resolution in high-frequency bands in comparison with the DWTK. DWTK, WPTK and wavelet transform-based kurtosis (WTK) curves were introduced to calibrate the in-field signals in comparison with the benchmark signals, whereby the non-stationary transients and singularity in the vibration signals attributed to damage were detected. WTK curves of vibration signals collected from bearing with damage of different se...