In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on m...In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.展开更多
In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, whi...In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.展开更多
A novel scheme of M-ary multi-carrier spread spectrum based on wavelet packet is proposed. Its performance is investigated for a multipath, slow Rayleigh fading channel. The performance advantages of the system over t...A novel scheme of M-ary multi-carrier spread spectrum based on wavelet packet is proposed. Its performance is investigated for a multipath, slow Rayleigh fading channel. The performance advantages of the system over that based on DFT are demonstrated by both analytical and simulation methods.展开更多
Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral reso...Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspectral image. According to the characteristics and 3-Dimensional (3-D) feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures: Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient inte- gration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling (RIBSR) method, is proposed to accomplish data resampling in spectral domain by util- izing the property of ratio image. And a new fusion rule, Average and Substitution (A&S) rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral character- istics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A&S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by hig...This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.展开更多
The discrete scalar data need prefiltering when transformed by discrete multi-wavelet, but prefiltering will make some properties of multi-wavelets lost. Balanced multi-wavelets can avoid prefiltering. The sufficient ...The discrete scalar data need prefiltering when transformed by discrete multi-wavelet, but prefiltering will make some properties of multi-wavelets lost. Balanced multi-wavelets can avoid prefiltering. The sufficient and necessary condition of p-order balance for multi-wavelets in time domain, the interrelation between balance order and approximation order and the sampling property of balanced multi-wavelets are investigated. The algorithms of 1-order and 2-order balancing for multi-wavelets are obtained. The two algorithms both preserve the orthogonal relation between multi-scaling function and multi-wavelets. More importantly, balancing operation doesnt increase the length of filters, which suggests that a relatively short balanced multi-wavelet can be constructed from an existing unbalanced multi-wavelet as short as possible.展开更多
BACKGROUND: Recent studies have focused on various methods of wavelet transformation for electroencephalogram (EEG) signals. However, there are very few studies reporting characteristics of multi-scale phase waves ...BACKGROUND: Recent studies have focused on various methods of wavelet transformation for electroencephalogram (EEG) signals. However, there are very few studies reporting characteristics of multi-scale phase waves during epileptic discharge.OBJECTIVE: To extract multi-scale phase average waveforms from childhood absence epilepsy EEG signals between time and frequency domains using wavelet transformation, and to compare EEG signals of absence seizure with pre-epileptic seizure and normal children, and to quantify multi-scale phase average waveforms from childhood absence epilepsy EEG signals. DESIGN, TIME AND SETTING: The case-comparative experiment was performed at the Department of Neuroelectrophysiology, Tianjin Medical University from August 2002 to May 2005. PARTICIPANTS: A total of 15 patients with childhood absence epilepsy from the General Hospital of Tianjin Medical University were enrolled in the study. The patients were not administered anti-epileptic drugs or sedatives prior to EEG testing. In addition, 12 healthy, age- and gender-matched children were also enrolled.METHODS: EEG signals were tested on 15 patients with childhood absence epilepsy and 12 normal children. Epileptic discharge signals during clinical and subclinical seizures were collected 10 and 20 times, respectively. The collected EEG signals were treated with wavelet transformation to extract multi-scale characteristics during absence epilepsy seizure using a conditional sampling method. Multi-scale phase average waveforms were collected using a conditional phase averaging technique. Amplitude of phase average waveform from EEG signals of epilepsy seizure, subclinical epileptic discharge, and EEG signals of normal children were compared and statistically analyzed in the first half-cycle.MAIN OUTCOME MEASURES: Multi-scale wavelet coefficient and the evolution of EEG signals were observed during childhood absence epilepsy seizures using wavelet transformation. Multi-scale phase average waveforms from EEG signals were observed using a conditional sampling method and phase averaging technique.RESULTS: Multi-scale characteristics of EEG signals demonstrated that 12-scale (3 Hz) rhythmical activity was significantly enhanced during childhood absence epilepsy seizure and co-existed with background structure (〈1 Hz, low frequency discharge). The phase average wave exhibited opposed phase abnormal rhythm at 3 Hz. Prior to childhood absence epilepsy seizure, EEG detected opposed abnormal a rhythm and 3 Hz composition, which were not detected with traditional EEG. Compared to EEG signals from normal children, epileptic discharges from clinical and subclinical childhood absence epilepsy seizures were positive and amplitude was significantly greater (P〈0.05).CONCLUSION: Wavelet transformation was used to analyze EEG signals from childhood absence epilepsy to obtain multi-scale quantitative characteristics and phase average waveforms. Multi-scale wavelet coefficients of EEG signals correlated with childhood absence epilepsy seizure, and multi-scale waveforms prior to epilepsy seizure were similar to characteristics during the onset period. Compared to normal children, EEG signals during epilepsy seizure exhibited an opposed phase model.展开更多
The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses L...The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses LMS algorithm to implement the adaptive multiuser detection. The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence. White noise can be wiped off under the wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analyses and simulations demonstrate that the algorithm converges faster than the conventional adaptive multiuser detection algorithm, and has the better performance. Simulation results reveal that the algorithm convergence relates to the wavelet base, and show that the algorithm convergence gets better with the increasing of regularity for the same series of the wavelet base. Finally the algorithm shows that it can be easily implemented.展开更多
The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wav...The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.展开更多
The single 2 dilation orthogonal wavelet multipliers in one dimensional case and single A-dilation(where A is any expansive matrix with integer entries and|det A|=2) wavelet multipliers in high dimensional case were c...The single 2 dilation orthogonal wavelet multipliers in one dimensional case and single A-dilation(where A is any expansive matrix with integer entries and|det A|=2) wavelet multipliers in high dimensional case were completely characterized by the Wutam Consortium(1998) and Z. Y. Li, et al.(2010). But there exist no more results on orthogonal multivariate wavelet matrix multipliers corresponding integer expansive dilation matrix with the absolute value of determinant not 2 in L~2(R~2). In this paper, we choose 2I2=(~2~0)as the dilation matrix and consider the 2 I2-dilation orthogonal multivariate waveletΨ = {ψ, ψ, ψ},(which is called a dyadic bivariate wavelet) multipliers. We call the3 × 3 matrix-valued function A(s) = [ f(s)], where fi, jare measurable functions, a dyadic bivariate matrix Fourier wavelet multiplier if the inverse Fourier transform of A(s)( ψ(s), ψ(s), ψ(s)) ~T=( g(s), g(s), g(s))~ T is a dyadic bivariate wavelet whenever(ψ, ψ, ψ) is any dyadic bivariate wavelet. We give some conditions for dyadic matrix bivariate wavelet multipliers. The results extended that of Z. Y. Li and X. L.Shi(2011). As an application, we construct some useful dyadic bivariate wavelets by using dyadic Fourier matrix wavelet multipliers and use them to image denoising.展开更多
We propose a multi-symplectic wavelet splitting equations. Based on its mu]ti-symplectic formulation, method to solve the strongly coupled nonlinear SchrSdinger the strongly coupled nonlinear SchrSdinger equations can...We propose a multi-symplectic wavelet splitting equations. Based on its mu]ti-symplectic formulation, method to solve the strongly coupled nonlinear SchrSdinger the strongly coupled nonlinear SchrSdinger equations can be split into one linear multi-symplectic subsystem and one nonlinear infinite-dimensional Hamiltonian subsystem. For the linear subsystem, the multi-symplectic wavelet collocation method and the symplectic Euler method are employed in spatial and temporal discretization, respectively. For the nonlinear subsystem, the mid-point symplectic scheme is used. Numerical simulations show the effectiveness of the proposed method during long-time numerical calculation.展开更多
To solve the inter carrier interference (ICI) elimination problem of an M-band wavelet multi-carrier modulation system, this paper analyzes the principle of the ICI caused by the Doppler frequency shift and its math...To solve the inter carrier interference (ICI) elimination problem of an M-band wavelet multi-carrier modulation system, this paper analyzes the principle of the ICI caused by the Doppler frequency shift and its mathematical expression based on the M-band wavelet multi-carrier modulation system model. Through the analysis of the mathematical expression and combining with the perfect reconstruction conditions of the filter banks, we propose the design conditions of an M-band filter to reduce and eliminate the ICI. The impulse response model of the filter design conditions and an iterative algorithm is also established. The simulation results show that the proposed ICI reduction and elimination methods can effectively improve the system performance.展开更多
Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame...Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.展开更多
The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins includ...The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.展开更多
An improved wavelet packet domain least mean square (IWPD-LMS) based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the ...An improved wavelet packet domain least mean square (IWPD-LMS) based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate(BER) performance as a function of the signal power to noise power ratio(SNR) are presented finally to prove the validity of the proposed algorithm.展开更多
On the basis of the absolute and relative gravity observations in North China,spatial dynamic variation of regional gravity fields is obtained. A multi-scale decomposition technique is used to separate anomalies at di...On the basis of the absolute and relative gravity observations in North China,spatial dynamic variation of regional gravity fields is obtained. A multi-scale decomposition technique is used to separate anomalies at different depths,and give some explanation to gravity variation at different time space scales. Gravity variation trends in North China are improved. Based on this result and the analysis of wavelet power spectrum,the images of the depth of wavelet approximation and detail are obtained. The results obtained are of scientific significance for the deep understanding of potential seismic risk in North China from gravity variations in different time space scales.展开更多
A new finite element method (FEM) of B-spline wavelet on the interval (BSWI) is proposed. Through analyzing the scaling functions of BSWI in one dimension, the basic formula for 2D FEM of BSWI is deduced. The 2D F...A new finite element method (FEM) of B-spline wavelet on the interval (BSWI) is proposed. Through analyzing the scaling functions of BSWI in one dimension, the basic formula for 2D FEM of BSWI is deduced. The 2D FEM of 7 nodes and 10 nodes are constructed based on the basic formula. Using these proposed elements, the multiscale numerical model for foundation subjected to harmonic periodic load, the foundation model excited by external and internal dynamic load are studied. The results show the pro- posed finite elements have higher precision than the tradi- tional elements with 4 nodes. The proposed finite elements can describe the propagation of stress waves well whenever the foundation model excited by extemal or intemal dynamic load. The proposed finite elements can be also used to con- nect the multi-scale elements. And the proposed finite elements also have high precision to make multi-scale analysis for structure.展开更多
基金Project(61301095)supported by the National Natural Science Foundation of ChinaProject(QC2012C070)supported by Heilongjiang Provincial Natural Science Foundation for the Youth,ChinaProjects(HEUCF130807,HEUCFZ1129)supported by the Fundamental Research Funds for the Central Universities of China
文摘In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
基金Sponsored by the Nation Nature Science Foundation of China(Grant No.61275010,61201237)the Fundamental Research Funds for the Central Universities(Grant No.HEUCFZ1129,No.HEUCF120805)
文摘In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.
基金Supported by the National Natural Science Foundation of China(No.60172029)
文摘A novel scheme of M-ary multi-carrier spread spectrum based on wavelet packet is proposed. Its performance is investigated for a multipath, slow Rayleigh fading channel. The performance advantages of the system over that based on DFT are demonstrated by both analytical and simulation methods.
文摘Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspectral image. According to the characteristics and 3-Dimensional (3-D) feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures: Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient inte- gration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling (RIBSR) method, is proposed to accomplish data resampling in spectral domain by util- izing the property of ratio image. And a new fusion rule, Average and Substitution (A&S) rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral character- istics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A&S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
基金Science Research Foundation of Yunnan Fundamental Research Foundation of Applicationgrant number:2009ZC049M+1 种基金Science Research Foundation for the Overseas Chinese Scholars,State Education Ministrygrant number:2010-1561
文摘This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.
基金Supported by the Scientific Research Foundation for Returned Overseas Chinese Scholars from the State Education Ministry (No. [2002]247) and the Young Key Teachers Foundation of Chongqing University.
文摘The discrete scalar data need prefiltering when transformed by discrete multi-wavelet, but prefiltering will make some properties of multi-wavelets lost. Balanced multi-wavelets can avoid prefiltering. The sufficient and necessary condition of p-order balance for multi-wavelets in time domain, the interrelation between balance order and approximation order and the sampling property of balanced multi-wavelets are investigated. The algorithms of 1-order and 2-order balancing for multi-wavelets are obtained. The two algorithms both preserve the orthogonal relation between multi-scaling function and multi-wavelets. More importantly, balancing operation doesnt increase the length of filters, which suggests that a relatively short balanced multi-wavelet can be constructed from an existing unbalanced multi-wavelet as short as possible.
基金the National Natural Science Foundation of China,No. 60703045
文摘BACKGROUND: Recent studies have focused on various methods of wavelet transformation for electroencephalogram (EEG) signals. However, there are very few studies reporting characteristics of multi-scale phase waves during epileptic discharge.OBJECTIVE: To extract multi-scale phase average waveforms from childhood absence epilepsy EEG signals between time and frequency domains using wavelet transformation, and to compare EEG signals of absence seizure with pre-epileptic seizure and normal children, and to quantify multi-scale phase average waveforms from childhood absence epilepsy EEG signals. DESIGN, TIME AND SETTING: The case-comparative experiment was performed at the Department of Neuroelectrophysiology, Tianjin Medical University from August 2002 to May 2005. PARTICIPANTS: A total of 15 patients with childhood absence epilepsy from the General Hospital of Tianjin Medical University were enrolled in the study. The patients were not administered anti-epileptic drugs or sedatives prior to EEG testing. In addition, 12 healthy, age- and gender-matched children were also enrolled.METHODS: EEG signals were tested on 15 patients with childhood absence epilepsy and 12 normal children. Epileptic discharge signals during clinical and subclinical seizures were collected 10 and 20 times, respectively. The collected EEG signals were treated with wavelet transformation to extract multi-scale characteristics during absence epilepsy seizure using a conditional sampling method. Multi-scale phase average waveforms were collected using a conditional phase averaging technique. Amplitude of phase average waveform from EEG signals of epilepsy seizure, subclinical epileptic discharge, and EEG signals of normal children were compared and statistically analyzed in the first half-cycle.MAIN OUTCOME MEASURES: Multi-scale wavelet coefficient and the evolution of EEG signals were observed during childhood absence epilepsy seizures using wavelet transformation. Multi-scale phase average waveforms from EEG signals were observed using a conditional sampling method and phase averaging technique.RESULTS: Multi-scale characteristics of EEG signals demonstrated that 12-scale (3 Hz) rhythmical activity was significantly enhanced during childhood absence epilepsy seizure and co-existed with background structure (〈1 Hz, low frequency discharge). The phase average wave exhibited opposed phase abnormal rhythm at 3 Hz. Prior to childhood absence epilepsy seizure, EEG detected opposed abnormal a rhythm and 3 Hz composition, which were not detected with traditional EEG. Compared to EEG signals from normal children, epileptic discharges from clinical and subclinical childhood absence epilepsy seizures were positive and amplitude was significantly greater (P〈0.05).CONCLUSION: Wavelet transformation was used to analyze EEG signals from childhood absence epilepsy to obtain multi-scale quantitative characteristics and phase average waveforms. Multi-scale wavelet coefficients of EEG signals correlated with childhood absence epilepsy seizure, and multi-scale waveforms prior to epilepsy seizure were similar to characteristics during the onset period. Compared to normal children, EEG signals during epilepsy seizure exhibited an opposed phase model.
文摘The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses LMS algorithm to implement the adaptive multiuser detection. The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence. White noise can be wiped off under the wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analyses and simulations demonstrate that the algorithm converges faster than the conventional adaptive multiuser detection algorithm, and has the better performance. Simulation results reveal that the algorithm convergence relates to the wavelet base, and show that the algorithm convergence gets better with the increasing of regularity for the same series of the wavelet base. Finally the algorithm shows that it can be easily implemented.
文摘The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.
基金partially supported by the National Natural Science Foundation of China (Grant No. 11101142 and No. 11571107)
文摘The single 2 dilation orthogonal wavelet multipliers in one dimensional case and single A-dilation(where A is any expansive matrix with integer entries and|det A|=2) wavelet multipliers in high dimensional case were completely characterized by the Wutam Consortium(1998) and Z. Y. Li, et al.(2010). But there exist no more results on orthogonal multivariate wavelet matrix multipliers corresponding integer expansive dilation matrix with the absolute value of determinant not 2 in L~2(R~2). In this paper, we choose 2I2=(~2~0)as the dilation matrix and consider the 2 I2-dilation orthogonal multivariate waveletΨ = {ψ, ψ, ψ},(which is called a dyadic bivariate wavelet) multipliers. We call the3 × 3 matrix-valued function A(s) = [ f(s)], where fi, jare measurable functions, a dyadic bivariate matrix Fourier wavelet multiplier if the inverse Fourier transform of A(s)( ψ(s), ψ(s), ψ(s)) ~T=( g(s), g(s), g(s))~ T is a dyadic bivariate wavelet whenever(ψ, ψ, ψ) is any dyadic bivariate wavelet. We give some conditions for dyadic matrix bivariate wavelet multipliers. The results extended that of Z. Y. Li and X. L.Shi(2011). As an application, we construct some useful dyadic bivariate wavelets by using dyadic Fourier matrix wavelet multipliers and use them to image denoising.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.10971226,91130013,and 11001270)the National Basic Research Program of China(Grant No.2009CB723802)+1 种基金the Research Innovation Fund of Hunan Province,China (Grant No.CX2011B011)the Innovation Fund of National University of Defense Technology,China(Grant No.B120205)
文摘We propose a multi-symplectic wavelet splitting equations. Based on its mu]ti-symplectic formulation, method to solve the strongly coupled nonlinear SchrSdinger the strongly coupled nonlinear SchrSdinger equations can be split into one linear multi-symplectic subsystem and one nonlinear infinite-dimensional Hamiltonian subsystem. For the linear subsystem, the multi-symplectic wavelet collocation method and the symplectic Euler method are employed in spatial and temporal discretization, respectively. For the nonlinear subsystem, the mid-point symplectic scheme is used. Numerical simulations show the effectiveness of the proposed method during long-time numerical calculation.
基金supported by the National Natural Science Foundation of China (Grant No.60872114)
文摘To solve the inter carrier interference (ICI) elimination problem of an M-band wavelet multi-carrier modulation system, this paper analyzes the principle of the ICI caused by the Doppler frequency shift and its mathematical expression based on the M-band wavelet multi-carrier modulation system model. Through the analysis of the mathematical expression and combining with the perfect reconstruction conditions of the filter banks, we propose the design conditions of an M-band filter to reduce and eliminate the ICI. The impulse response model of the filter design conditions and an iterative algorithm is also established. The simulation results show that the proposed ICI reduction and elimination methods can effectively improve the system performance.
基金Supported by the National Natural Science Foundation of China (No. 60803036)the Scientific Research Fund of Heilongjiang Provincial Education Department (No.11531013)
文摘Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.
文摘The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.
基金Sponsored by the National"863"Program Projects (2007AA012293)
文摘An improved wavelet packet domain least mean square (IWPD-LMS) based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate(BER) performance as a function of the signal power to noise power ratio(SNR) are presented finally to prove the validity of the proposed algorithm.
基金funded by the Special Fund for Earthquake Scientific Research of China(201308004,201308009)
文摘On the basis of the absolute and relative gravity observations in North China,spatial dynamic variation of regional gravity fields is obtained. A multi-scale decomposition technique is used to separate anomalies at different depths,and give some explanation to gravity variation at different time space scales. Gravity variation trends in North China are improved. Based on this result and the analysis of wavelet power spectrum,the images of the depth of wavelet approximation and detail are obtained. The results obtained are of scientific significance for the deep understanding of potential seismic risk in North China from gravity variations in different time space scales.
基金supported by the National Natural Science Foundation of China (51109029,51178081,51138001,and 51009020)the State Key Development Program for Basic Research of China (2013CB035905)
文摘A new finite element method (FEM) of B-spline wavelet on the interval (BSWI) is proposed. Through analyzing the scaling functions of BSWI in one dimension, the basic formula for 2D FEM of BSWI is deduced. The 2D FEM of 7 nodes and 10 nodes are constructed based on the basic formula. Using these proposed elements, the multiscale numerical model for foundation subjected to harmonic periodic load, the foundation model excited by external and internal dynamic load are studied. The results show the pro- posed finite elements have higher precision than the tradi- tional elements with 4 nodes. The proposed finite elements can describe the propagation of stress waves well whenever the foundation model excited by extemal or intemal dynamic load. The proposed finite elements can be also used to con- nect the multi-scale elements. And the proposed finite elements also have high precision to make multi-scale analysis for structure.