Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the...Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the authors.DOI:10.31209/2019.100000097 URL:https://www.techscience.com/iasc/v25n2/39663 The article duplicates significant parts of a paper published in Journal of Intelligent&Fuzzy Systems[2].展开更多
With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at lo...With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.展开更多
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has establishe...With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.展开更多
In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS...In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).展开更多
In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum ...In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum mean square error (MSE) and the steady state excess MSE of the WTD adaptive equalizer are obtained. Constant and time varying convergence factor adaptive algorithms are studied respectively. Computational complexities of WTD LMS equalizers are given. The equalizer in WTD shows much better convergence performance than that of the conventional in time domain.展开更多
Investigating source parameters of small and moderate earthquakes plays an important role in seismology research. For small and moderate earthquakes, the mechanisms are usually obtained by first motion of P-Wave, surf...Investigating source parameters of small and moderate earthquakes plays an important role in seismology research. For small and moderate earthquakes, the mechanisms are usually obtained by first motion of P-Wave, surface wave spectra method in frequency-domain or the waveform inversion in time-domain, based on the regional waveform records. We applied the wavelet domain inversion method to determine mechanism of regional earthquake. Using the wavelet coefficients of different scales can give more information to constrain the inversion. We determined the mechanisms of three earthquakes occurred in California, the United States. They are consistent with the previous results (Harvard Centroid Moment Tensor and United States Geological Service). This proves that the wavelet domain inversion method is an efficient method to determine the source parameters of small and moderate earthquakes, especially the strong aftershocks after a large, disastrous earthquake.展开更多
We focus on the single layer formulation which provides an integral equation of the first kind that is very badly conditioned. The condition number of the unpreconditioned system increases exponentially with the multi...We focus on the single layer formulation which provides an integral equation of the first kind that is very badly conditioned. The condition number of the unpreconditioned system increases exponentially with the multiscale levels. A remedy utilizing overlapping domain decompositions applied to the Boundary Element Method by means of wavelets is examined. The width of the overlapping of the subdomains plays an important role in the estimation of the eigenvalues as well as the condition number of the additive domain decomposition operator. We examine the convergence analysis of the domain decomposition method which depends on the wavelet levels and on the size of the subdomain overlaps. Our theoretical results related to the additive Schwarz method are corroborated by numerical outputs.展开更多
Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based method...Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.展开更多
Conventional analytical methods in the wavelet domain are used to present an analysis of terahertz （THz） waveguide modes.To obtain THz radiation pulses passing through a waveguide,we build a simple experimental syst...Conventional analytical methods in the wavelet domain are used to present an analysis of terahertz （THz） waveguide modes.To obtain THz radiation pulses passing through a waveguide,we build a simple experimental system with a 5-mm-long,230-μm-inner-diameter stainless steel waveguide.The single-mode guided signal from the experiments and the multi-mode signal of a similar THz waveguide reported in the literature are analyzed using the continuous wavelet transform （CWT）.The results demonstrate that analyzing THz waveguide modes in the wavelet domain not only possesses all the functionality of the traditional THz time-domain spectroscopy （TDS） data processing but also has the ability to unscramble quantitatively and intuitively detailed information about the target samples,such as mode type,cut-off frequency,amplitude distinction,and group velocity.展开更多
An efficient wavelet-based finite-difference time-domain(FDTD)method is implemented for analyzing nanoscale optical devices,especially optical resonator.Because of its highly linear numerical dispersion properties the...An efficient wavelet-based finite-difference time-domain(FDTD)method is implemented for analyzing nanoscale optical devices,especially optical resonator.Because of its highly linear numerical dispersion properties the high-spatial-order FDTD achieves significant reduction in the number of cells,i.e.used memory,while analyzing a high-index dielectric ring resonator working as an add/drop multiplexer.The main novelty is that the wavelet-based FDTD model is extended in a parallel computation environment to solve physical problems with large dimensions.To demonstrate the efficiency of the parallelized FDTD model,a mirrored cavity is analyzed.The analysis shows that the proposed model reduces computation time and memory cost,and the parallel computation result matches the theoretical model.展开更多
Based on strain signals, a new time-domain methodology for detecting the beam local damage has been developed. The pseudo strain energy density （PSED） is defined and used to build two major damage indexes, the avera...Based on strain signals, a new time-domain methodology for detecting the beam local damage has been developed. The pseudo strain energy density （PSED） is defined and used to build two major damage indexes, the average pseudo strain energy density （APSED） and the average pseudo strain energy density rate （APSEDR）. Probability and mathematical statistics are utilized to derive a standardized damage index. Furthermore, by applying the analytic relation between the strain energy release rate and the stress intensity factor, an analytic solution of crack depth is derived. For the dynamic strain signals, the wavelet packet transform is used to pre-process measured data. Finally, a numerical simulation indicates that this method can effectively identify the damage location and its absolute severity.展开更多
We propose a normalizing flow based on the wavelet framework for super-resolution(SR)called WDFSR.It learns the conditional distribution mapping between low-resolution images in the RGB domain and high-resolution imag...We propose a normalizing flow based on the wavelet framework for super-resolution(SR)called WDFSR.It learns the conditional distribution mapping between low-resolution images in the RGB domain and high-resolution images in the wavelet domain to simultaneously generate high-resolution images of different styles.To address the issue of some flowbased models being sensitive to datasets,which results in training fluctuations that reduce the mapping ability of the model and weaken generalization,we designed a method that combines a T-distribution and QR decomposition layer.Our method alleviates this problem while maintaining the ability of the model to map different distributions and produce higher-quality images.Good contextual conditional features can promote model training and enhance the distribution mapping capabilities for conditional distribution mapping.Therefore,we propose a Refinement layer combined with an attention mechanism to refine and fuse the extracted condition features to improve image quality.Extensive experiments on several SR datasets demonstrate that WDFSR outperforms most general CNN-and flow-based models in terms of PSNR value and perception quality.We also demonstrated that our framework works well for other low-level vision tasks,such as low-light enhancement.The pretrained models and source code with guidance for reference are available at https://github.com/Lisbegin/WDFSR.展开更多
This paper is concerned with estimation of electrical conductivity in Maxwell equations. The primary difficulty lies in the presence of numerous local minima in the objective functional. A wavelet multiscale method is...This paper is concerned with estimation of electrical conductivity in Maxwell equations. The primary difficulty lies in the presence of numerous local minima in the objective functional. A wavelet multiscale method is introduced and applied to the inversion of Maxwell equations. The inverse problem is decomposed into multiple scales with wavelet transform, and hence the original problem is reformulated to a set of sub-inverse problems corresponding to different scales, which can be solved successively according to the size of scale from the shortest to the longest. The stable and fast regularized Gauss-Newton method is applied to each scale. Numerical results show that the proposed method is effective, especially in terms of wide convergence, computational efficiency and precision.展开更多
文摘Intelligent Automation&Soft Computing has retracted the article titled“Line Trace Effective Comparison AlgorithmBased onWavelet Domain DTW”[1],Intell Automat Soft Comput.2019;25(2):359–366 at the request of the authors.DOI:10.31209/2019.100000097 URL:https://www.techscience.com/iasc/v25n2/39663 The article duplicates significant parts of a paper published in Journal of Intelligent&Fuzzy Systems[2].
基金This study was funded by the National Natural Science Foundation of China(Grant No.41975027)the Natural Science Foundation of Jiangsu Province(Grant No.BK20171457)the National Key R&D Program on Monitoring,Early Warning and Prevention of Major Natural Disasters(Grant No.2017YFC1501401).
文摘With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
文摘With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
文摘In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).
文摘In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum mean square error (MSE) and the steady state excess MSE of the WTD adaptive equalizer are obtained. Constant and time varying convergence factor adaptive algorithms are studied respectively. Computational complexities of WTD LMS equalizers are given. The equalizer in WTD shows much better convergence performance than that of the conventional in time domain.
基金supported by National Natural Science Foundation of China (Grant Nos. 40974028 and 41030319)National Basic Research Program of China (Grant No. 2008CB425701)
文摘Investigating source parameters of small and moderate earthquakes plays an important role in seismology research. For small and moderate earthquakes, the mechanisms are usually obtained by first motion of P-Wave, surface wave spectra method in frequency-domain or the waveform inversion in time-domain, based on the regional waveform records. We applied the wavelet domain inversion method to determine mechanism of regional earthquake. Using the wavelet coefficients of different scales can give more information to constrain the inversion. We determined the mechanisms of three earthquakes occurred in California, the United States. They are consistent with the previous results (Harvard Centroid Moment Tensor and United States Geological Service). This proves that the wavelet domain inversion method is an efficient method to determine the source parameters of small and moderate earthquakes, especially the strong aftershocks after a large, disastrous earthquake.
文摘We focus on the single layer formulation which provides an integral equation of the first kind that is very badly conditioned. The condition number of the unpreconditioned system increases exponentially with the multiscale levels. A remedy utilizing overlapping domain decompositions applied to the Boundary Element Method by means of wavelets is examined. The width of the overlapping of the subdomains plays an important role in the estimation of the eigenvalues as well as the condition number of the additive domain decomposition operator. We examine the convergence analysis of the domain decomposition method which depends on the wavelet levels and on the size of the subdomain overlaps. Our theoretical results related to the additive Schwarz method are corroborated by numerical outputs.
文摘Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.
基金supported by the National "973" Project of China(No. 2007CB310408)the National Natural Science Foundation of China(No. 60578037)+3 种基金the National Natural Science Foundation of China-Rassian Foundation for Basic Research Program 2007-2008(No. 60711120198)the Major Project of Tianjin Sci-Tech Support Program(No. 08ZCKFZC28000)the International Joint Program in Tianjin(No. 07ZCGHH01100)the Project 985 Program of Tianjin University
文摘Conventional analytical methods in the wavelet domain are used to present an analysis of terahertz （THz） waveguide modes.To obtain THz radiation pulses passing through a waveguide,we build a simple experimental system with a 5-mm-long,230-μm-inner-diameter stainless steel waveguide.The single-mode guided signal from the experiments and the multi-mode signal of a similar THz waveguide reported in the literature are analyzed using the continuous wavelet transform （CWT）.The results demonstrate that analyzing THz waveguide modes in the wavelet domain not only possesses all the functionality of the traditional THz time-domain spectroscopy （TDS） data processing but also has the ability to unscramble quantitatively and intuitively detailed information about the target samples,such as mode type,cut-off frequency,amplitude distinction,and group velocity.
基金Supported by the Scientific Research Foundation of Nanjing University of Posts and Telecommunications(NY212008,NY213116)the National Science Foundation of Jiangsu Province(BK20131383)
文摘An efficient wavelet-based finite-difference time-domain(FDTD)method is implemented for analyzing nanoscale optical devices,especially optical resonator.Because of its highly linear numerical dispersion properties the high-spatial-order FDTD achieves significant reduction in the number of cells,i.e.used memory,while analyzing a high-index dielectric ring resonator working as an add/drop multiplexer.The main novelty is that the wavelet-based FDTD model is extended in a parallel computation environment to solve physical problems with large dimensions.To demonstrate the efficiency of the parallelized FDTD model,a mirrored cavity is analyzed.The analysis shows that the proposed model reduces computation time and memory cost,and the parallel computation result matches the theoretical model.
基金The National Natural Science Foundation of China (Nos.50778077 and 50608036)
文摘Based on strain signals, a new time-domain methodology for detecting the beam local damage has been developed. The pseudo strain energy density （PSED） is defined and used to build two major damage indexes, the average pseudo strain energy density （APSED） and the average pseudo strain energy density rate （APSEDR）. Probability and mathematical statistics are utilized to derive a standardized damage index. Furthermore, by applying the analytic relation between the strain energy release rate and the stress intensity factor, an analytic solution of crack depth is derived. For the dynamic strain signals, the wavelet packet transform is used to pre-process measured data. Finally, a numerical simulation indicates that this method can effectively identify the damage location and its absolute severity.
基金grateful to Zhejiang Gongshang University for its valuable computing resources and outstanding laboratory facilities,and support from the National Natural Science Foundation of China(Grant No.62172366)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY22F020013)+1 种基金“Pioneer”and“Leading Goose”R&D Program of Zhejiang Province(Grant No.2023C01150),Major Sci-Tech Innovation Project of Hangzhou City(Grant No.2022AIZD0110)“Digital+”Discipline Construction Project of Zhejiang Gongshang University(Grant No.SZJ2022B009).
文摘We propose a normalizing flow based on the wavelet framework for super-resolution(SR)called WDFSR.It learns the conditional distribution mapping between low-resolution images in the RGB domain and high-resolution images in the wavelet domain to simultaneously generate high-resolution images of different styles.To address the issue of some flowbased models being sensitive to datasets,which results in training fluctuations that reduce the mapping ability of the model and weaken generalization,we designed a method that combines a T-distribution and QR decomposition layer.Our method alleviates this problem while maintaining the ability of the model to map different distributions and produce higher-quality images.Good contextual conditional features can promote model training and enhance the distribution mapping capabilities for conditional distribution mapping.Therefore,we propose a Refinement layer combined with an attention mechanism to refine and fuse the extracted condition features to improve image quality.Extensive experiments on several SR datasets demonstrate that WDFSR outperforms most general CNN-and flow-based models in terms of PSNR value and perception quality.We also demonstrated that our framework works well for other low-level vision tasks,such as low-light enhancement.The pretrained models and source code with guidance for reference are available at https://github.com/Lisbegin/WDFSR.
基金supported by the Program of Excellent Team of Harbin Institute of Technology
文摘This paper is concerned with estimation of electrical conductivity in Maxwell equations. The primary difficulty lies in the presence of numerous local minima in the objective functional. A wavelet multiscale method is introduced and applied to the inversion of Maxwell equations. The inverse problem is decomposed into multiple scales with wavelet transform, and hence the original problem is reformulated to a set of sub-inverse problems corresponding to different scales, which can be solved successively according to the size of scale from the shortest to the longest. The stable and fast regularized Gauss-Newton method is applied to each scale. Numerical results show that the proposed method is effective, especially in terms of wide convergence, computational efficiency and precision.
