Showing papers on "Wavelet packet decomposition published in 2013"
TL;DR: This paper presents a new approach to build adaptive wavelets, the main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank, which leads to a new wavelet transform, called the empirical wavelets transform.
Abstract: Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank. This construction leads us to a new wavelet transform, called the empirical wavelet transform. Many experiments are presented showing the usefulness of this method compared to the classic EMD.
1,398 citations
TL;DR: In this paper, the authors proposed an enhanced Kurtogram based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths, which measured the protrusion of the sparse representation.
323 citations
TL;DR: A new intelligent fault diagnosis scheme based on the extraction of statistical parameters from the paving of a wavelet packet transform (WPT), a distance evaluation technique (DET) and a support vector regression (SVR)-based generic multi-class solver is proposed.
222 citations
TL;DR: In this paper, the sparsogram is constructed using the sparsity measurements of the power spectra from the envelopes of wavelet packet coefficients at different wavelet decomposition depths.
204 citations
TL;DR: The proposed method for classification of fault and prediction of degradation of components and machines in manufacturing system and the result indicates its higher efficiency and effectiveness comparing to traditional methods.
Abstract: This paper proposes a method for classification of fault and prediction of degradation of components and machines in manufacturing system. The analysis is focused on the vibration signals collected from the sensors mounted on the machines for critical components monitoring. The pre-processed signals were decomposed into several signals containing one approximation and some details using Wavelet Packet Decomposition and, then these signals are transformed to frequency domain using Fast Fourier Transform. The features extracted from frequency domain could be used to train Artificial Neural Network (ANN). Trained ANN could predict the degradation (Remaining Useful Life) and identify the fault of the components and machines. A case study is used to illustrate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.
196 citations
TL;DR: In this paper, the authors presented an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert-Huang transform (HHT).
Abstract: Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert–Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert–Huang spectrum, which is a full time–frequency–energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert–Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively.
182 citations
TL;DR: A review on the mother wavelet selection methods with particular emphasis on the quantitative approaches is presented in this paper, where a new technique to determine the optimum mother wavelets specifically for machinery faults diagnosis is also presented.
Abstract: Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.
168 citations
TL;DR: The sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.
Abstract: A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.
159 citations
TL;DR: In this article, a stochastic ground motion model with time and frequency nonstationarity is developed using wavelet packets, which can be used for non-linear dynamic structural analysis as the input ground motions.
Abstract: For performance-based design, non-linear dynamic structural analysis for various types of input ground motions is required. Stochastic (simulated) ground motions are sometimes useful as input motions, because unlike recorded motions they are not limited in number and because their properties can be varied systematically to understand the impact of ground motion properties on structural response. Here a stochastic ground motion model with time and frequency nonstationarity is developed using wavelet packets. Wavelet transform is a tool for analyzing time-series data with time and frequency nonstationarity, as well as simulating such data. Wavelet packet transform is an operation that decomposes time-series data into wavelet packets in the time and frequency domain, and its inverse transform reconstructs a time-series from wavelet packets. The characteristics of a nonstationary ground motion therefore can be modeled intuitively by specifying the amplitudes of wavelet packets at each time and frequency. In the proposed model, 13 parameters are sufficient to completely describe the time and frequency characteristics of a ground motion. These parameters can be computed from a specific target ground motion recording or by regression analysis based on a large database of recordings. The simulated ground motions produced by the proposed model reasonably match the target ground motion recordings in several respects including the spectral acceleration, inelastic response spectra, duration, bandwidth, and time and frequency nonstationarity. In addition, the median and logarithmic standard deviation of the spectral acceleration of the simulated ground motions match those of the published empirical ground motion prediction. These results suggest that the syntheti c ground motions generated by the proposed model can be used for the non-linear dynamic structural analysis as the input ground motions.
136 citations
TL;DR: In this paper, a fault detection method for gearboxes using blind source separation (BSS) and nonlinear feature extraction techniques is presented, where the nonstationary vibration signals were analyzed to reveal the operation state of the gearbox.
134 citations
TL;DR: The proposed texture descriptor is built on the wavelet-based spatial-frequency analysis of two complementary wavelet pyramids: standard multiscale and wavelet leader, and enjoys both high discriminative power and robustness against many environmental changes.
Abstract: In this paper, we propose a new texture descriptor for both static and dynamic textures. The new descriptor is built on the wavelet-based spatial-frequency analysis of two complementary wavelet pyramids: standard multiscale and wavelet leader. These wavelet pyramids essentially capture the local texture responses in multiple high-pass channels in a multiscale and multiorientation fashion, in which there exists a strong power-law relationship for natural images. Such a power-law relationship is characterized by the so-called multifractal analysis. In addition, two more techniques, scale normalization and multiorientation image averaging, are introduced to further improve the robustness of the proposed descriptor. Combining these techniques, the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes. We apply the descriptor for classifying both static and dynamic textures. Our method has demonstrated excellent performance in comparison with the state-of-the-art approaches in several public benchmark datasets.
TL;DR: It is demonstrated through experimental results that the use of the lower sampling rate does not affect the performance of SWPT to detect BRB, while requiring much less computation and low cost implementation.
TL;DR: Multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold.
TL;DR: Analysis and results show that the proposed dynamic watermarking scheme has better visual quality under a higher embedding capacity and outperforms the existing schemes in the literature.
Abstract: In this paper, a novel watermarking scheme based on quantum wavelet transform (QWT) is proposed. Firstly, the wavelet coefficients are extracted by executing QWT on quantum image. Then, we utilize a dynamic vector for controlling embedding strength instead of a fixed parameter for embedding process in other schemes. Analysis and results show that the proposed dynamic watermarking scheme has better visual quality under a higher embedding capacity and outperforms the existing schemes in the literature.
TL;DR: A novel encryption scheme is proposed for securing multiple images during communication and transmission over insecure channel by discretizing continuous fractional wavelet transform and chaotic maps.
TL;DR: Wang et al. as mentioned in this paper explored the wavelet packet energy (WPE) flow characteristics of vibration signals by using the manifold learning technique, which reveals the nonlinear WPE flow structure among various redundant time-frequency subspaces.
TL;DR: The proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.
Abstract: A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project-Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.
TL;DR: In this article, an automatic selection process for finding the optimal complex Morlet wavelet filter with the help of genetic algorithm that maximizes the sparsity measurement value was presented, and the modulus of the wavelet coefficients obtained by the optimal wavelet filtering was used to extract the envelope.
TL;DR: The proposed algorithm can be used for extracting fetal ECG from abdominal signals based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm and the performance is proven quantitatively by SNR calculation.
TL;DR: A frame for classifying polarimetric synthetic aperture radar (PolSAR) data is presented based on the combination of wavelet polarization information, textons, and sparse coding, which confirms that the proposed method effectively classifies PolSAR data.
Abstract: This paper presents a frame for classifying polarimetric synthetic aperture radar (PolSAR) data. The frame is based on the combination of wavelet polarization information, textons, and sparse coding. Polarimetric synthesis unites with the discrete wavelet frame to obtain wavelet polarization variance through the calculation of the wavelet variance in the space of polarization states. The K-means cluster algorithm is implemented to cluster the wavelet polarization variance vectors of the training samples for the purpose of constructing a texton dictionary. A patch, in which all the wavelet polarization variance vectors match those in the texton dictionary, is used to obtain a statistical histogram. Sparse coding is applied to describe the histogram feature and generate a new texture feature called sparse coding of a wavelet polarization texton. Finally, support vector machine is used for the classification. All experiments are carried out on five sets of PolSAR data. The experimental results confirm that the proposed method effectively classifies PolSAR data.
TL;DR: It can be deduced that the performance of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets, however, the computational complexity of the proposed technique is the price paid for the improvement in the compression performance measures.
TL;DR: The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.
Abstract: We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS), music player, short message service (SMS), and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT) and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor) was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5). The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.
TL;DR: The proposed 2-D dual-mode LDWT architecture has the merits of low transpose memory (TM), low latency, and regular signal flow, making it suitable for very large-scale integration implementation, and can be applied to real-time visual operations such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding applications.
Abstract: Memory requirements (for storing intermediate signals) and critical path are essential issues for 2-D (or multidimensional) transforms. This paper presents new algorithms and hardware architectures to address the above issues in 2-D dual-mode (supporting 5/3 lossless and 9/7 lossy coding) lifting-based discrete wavelet transform (LDWT). The proposed 2-D dual-mode LDWT architecture has the merits of low transpose memory (TM), low latency, and regular signal flow, making it suitable for very large-scale integration implementation. The TM requirement of the $N\times N$ 2-D 5/3 mode LDWT and 2-D 9/7 mode LDWT are $2N$ and $4N$ , respectively. Comparison results indicate that the proposed hardware architecture has a lower lifting-based low TM size requirement than the previous architectures. As a result, it can be applied to real-time visual operations such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding applications.
20 Jun 2013
TL;DR: The promising results indicate that the selective frequency and orientation properties of Gabor filters are extremely useful for providing multiscale texture description.
Abstract: A computer aided diagnostic system to characterise normal and cirrhotic liver by multiresolution texture descriptors is proposed in this paper The study is carried out in 120 segmented regions of interest extracted from 31 clinically acquired B-mode liver ultrasound images Mean and standard deviation multiresolution texture descriptors derived by using 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform are considered for analysis and exhaustive search with J3 criterion of class separability is used for feature selection The performance of subset of five most discriminative texture descriptors obtained from 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform is compared by using a support vector machine classifier It is observed that only five mean multiresolution texture descriptors obtained from 2D-Gabor wavelet transform at selective scale and orientations provide highest classification accuracy of 9833% and sensitivity of 100% by using a support vector machine classifier The promising results indicate that the selective frequency and orientation properties of Gabor filters are extremely useful for providing multiscale texture description
TL;DR: In this article, a method based on the wavelet ridges of continuous wavelet transform is proposed for the instantaneous frequency identification of time-varying structures, where a penalty function is imposed first, and then the dynamic optimization technique is implemented for wavelet ridge extraction.
09 Mar 2013
TL;DR: An efficient algorithm for Content Based Image Retrieval (CBIR) based on Discrete Wavelet Transform (DWT) and Edge Histogram Descriptor (EHD) feature of MPEG-7 and compared with various other proposed schemes to show the superiority of this scheme.
Abstract: This paper describes an efficient algorithm for Content Based Image Retrieval (CBIR) based on Discrete Wavelet Transform (DWT) and Edge Histogram Descriptor (EHD) feature of MPEG-7. The proposed algorithm is explained for image retrieval based on shape and texture features only not on the basis of color information. Here input image is first decomposed into wavelet coefficients. These wavelet coefficients give mainly horizontal, vertical and diagonal features in the image. After wavelet transform, Edge Histogram Descriptor is then used on selected wavelet coefficients to gather the information of dominant edge orientations. The combination of DWT and EHD techniques increases the performance of image retrieval system for shape and texture based search. The performance of various wavelets is also compared to find the suitability of particular wavelet function for image retrieval. The proposed algorithm is trained and tested for Wang image database. The results of retrieval are expressed in terms of Precision and Recall and compared with various other proposed schemes to show the superiority of our scheme.
TL;DR: A strategy combining the complementary wavelet and curvelet transforms to address the issue of resolution loss associated with standard denoising and could become an alternative solution to filters currently used after image reconstruction in clinical systems such as the Gaussian filter.
Journal Article•
01 Jan 2013-International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy
TL;DR: A comparative study of different wavelet denoising techniques and the results obtained were examined and it was observed that „rigrsure„ method gives optimum performance.
Abstract: This paper presents a comparative study of different wavelet denoising techniques and the results obtained were examined. The denoising process rejects noise by thresholding in the wavelet domain. It is observed that „rigrsure„ method gives optimum performance. Discrete wavelet transform has the benefit of giving a joint timefrequency representation of the signal. Also it is suitable for both stationary and non stationary signals and is the most appropriate system in the field of signal detection. Discrete wavelet transform is implemented through multi resolution analysis and digital filter banks
TL;DR: A novel decentralized modal identification method based on a multi-linear algebra tool called PARAllel FACtor (PARAFAC) decomposition enables source separation in wavelet packet coefficients with considerable mode mixing, thereby relaxing the conditions to generate over-complete bases, thus reducing the computational burden.
TL;DR: In this paper, a multiscale slope feature extraction method using wavelet-based multiresolution analysis for rotating machinery fault diagnosis is proposed, which reveals an inherent structure within the power spectra of vibration signals.