Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recogn...Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing. The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.展开更多
Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and...Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.展开更多
Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation...Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.展开更多
In this paper, we propose the so-called continuous Fresnel-wavelet combinatorial transform which means that the mother wavelet undergoes the Fresnel transformation. This motivation can let the mother-wavelet-state its...In this paper, we propose the so-called continuous Fresnel-wavelet combinatorial transform which means that the mother wavelet undergoes the Fresnel transformation. This motivation can let the mother-wavelet-state itself vary from ｜ψ〉 to Ftr, s ｜ψ〉, except for variation within the family of dilations and translations. The Parseval＇s equality, admissibility condition and inverse transform of this continuous Fresnel-wavelet combinatorial transform are analysed. By taking certain parameters and using the admissibility condition of this continuous Fresnel-wavelet combinatorial transform, we obtain some mother wavelets. A comparison between the newly found mother wavelets is presented.展开更多
We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous...We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.展开更多
In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to e...In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.展开更多
An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimens...An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.展开更多
Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robus...Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robust fault detection,classification,and location based on the empirical wavelet transform-Teager energy operator(EWT-TEO)and artificial neural network(ANN)for hybrid transmission lines in VSC-HVDC systems.The operational scheme of the proposed protection method consists of two loops①an EWT-TEO based feature extraction loop,②and an ANN-based fault detection,classification,and location loop.Under the proposed protection method,the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform(EWT)method.The energy content extracted by the EWT is fed into the ANN for fault detection,classification,and location.Various fault cases,including the high-impedance fault(HIF)as well as noises,are performed to train the ANN with two hidden layers.The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB,respectively.The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave(TW)based protection method.The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems,where a mean percentage error of approximately 0.1%is achieved.展开更多
Apoptosis proteins have a central role in the develop-ment and homeostasis of an organism,and their function is related to their types.In this paper,we constructed the character vectors of apoptosis proteins from thei...Apoptosis proteins have a central role in the develop-ment and homeostasis of an organism,and their function is related to their types.In this paper,we constructed the character vectors of apoptosis proteins from their amino acid sequences by using the discrete wavelet transform,combined with support vector machine,to predict the type of given apoptosis proteins.For the widely used dataset z98,high success rates were obtained by Jackknife test,and the Matthews correlation coefficients were 0.92,0.90,0.81 and 0.80,respectively,which were higher than the other methods on average.展开更多
A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavele...A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.展开更多
A coding method of speech compression, which is based on Wavlet Transform and Vector Quantization (VQ), is developed and studied. The Wavlet Thansform or Wavlet Packet Thansform is used to process the speech signal, t...A coding method of speech compression, which is based on Wavlet Transform and Vector Quantization (VQ), is developed and studied. The Wavlet Thansform or Wavlet Packet Thansform is used to process the speech signal, then VQ is used to compress the coefficients of Wavlet Thansform, and the entropy coding is used to decrease the bit rate. The experimental results show that the speech signal, sampled by 8 kHz sampling rate and 8 bit quatisation,i.e., 64 kbit/s bit rate, can be compressed to 6 - 8 kbit/s, and still have high speech quality,and the low-delay, only 8 ms.展开更多
In this paper, we propose a VLSI architecture that performs the line-based discrete wavelet transform （DWT） using a lifting scheme. The architecture consists of row processors, column processors, an intermediate buf...In this paper, we propose a VLSI architecture that performs the line-based discrete wavelet transform （DWT） using a lifting scheme. The architecture consists of row processors, column processors, an intermediate buffer and a control module. Row processor and Column processor work as the horizontal and vertical filters respectively. Intermediate buffer is composed of five FIFOs to store temporary results of horizontal filter. Control module schedules the output order to external memory. Compared with existing ones, the presented architecture parallelizes all levels of wavelet transform to compute multilevel DWT within one image transmission time, and uses no external but one intermediate buffer to store several line results of horizontal filtering, which decreases resource required significantly and reduces memory efficiently. This architecture is suitable for various real-time image/video applications.展开更多
This paper presents a new technique by support vector machines after extracting the prominent iris features using discrete wavelet transformations,to achieve an optimal classification of energy system disturbances.A f...This paper presents a new technique by support vector machines after extracting the prominent iris features using discrete wavelet transformations,to achieve an optimal classification of energy system disturbances.A framework for iris recognition and protection of the recognition system from fake iris scenes was proposed.The scale-invariant feature transform is set as an algorithm to extract local features(key points)from iris images and their classification method.To elicit the prominent iris features,the test image is first pre-processed.This will facilitate confining and segmenting the region of interest hopefully,reducing the blurring and artifacts,especially those associated with the edges.The textural features can be exploited to partition irises into regions of interest in addition to providing necessary information in the spatial distribution of intensity levels in an iris neighborhood.Next,the detection efficiency of the proposed method is achieved through extracting iris gradients and edges in complex areas and in different orientations.Further,the iris diagonal edges were easily detected after calculating the variance of different blocks in an iris and the additive noise variance in a textured image.The vertical,horizontal,and diagonal iris image gradients with different directions were successfully extracted.These gradients were extracted after adjusting the threshold amplitude obtained from the histograms of these gradients.The average calculations of MAVs,peak signal-to-noise ratios(PSNRs),and mean square errors(MSEs)within the orientation angles(-45°,+45°and 90°)for both vertical and horizontal iris gradients had occurred within the rates of 1.9455-3.1266,36.388-39.863 dB and 0.0001-0.0026,respectively.展开更多
Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to im...Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to improve the accuracy of radar QPE,we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations(AWSs)in East China.Considering the time dependence and abrupt changes of precipitation,the data during the previous 30-min period were selected as the training data.To reduce the complexity of radar QPE,we transformed the weather data into the wavelet domain by means of the stationary wavelet transform(SWT)in order to extract high and low-frequency reflectivity and precipitation information.Using the wavelet coefficients,we constructed a support vector machine(SVM)at all scales to estimate the wavelet coefficient of precipitation.Ultimately,via inverse wavelet transformation,we obtained the estimated rainfall.By comparing the results of the proposed method(SWTSVM)with those of Z=300×R1.4,linear regression(LR),and SVM,we determined that the root mean square error(RMSE)of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score(TS)could exceed 40%with the exception of the downpour category,thus remaining at a high level.Generally speaking,the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.展开更多
This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet pa...This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.展开更多
This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients...This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with展开更多
A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages ...A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.展开更多
The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Ex...The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.展开更多
文摘Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing. The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.
基金National Natural Science Foundation of China(No.51205043)the Special Fundamental Research Funds for Central Universities of China(No.DUT14QY21)
文摘Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.
基金Supported by the National Natural Science Foundation of China,no.69672039
文摘Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.
基金supported by the Startup Research Fund for Introducing Talents of Anhui Polytechnic University (Grant No. 2009YQQ006)the Research Foundation of the Education Department of Anhui Province of China (Grant No. KJ2011B031)
文摘In this paper, we propose the so-called continuous Fresnel-wavelet combinatorial transform which means that the mother wavelet undergoes the Fresnel transformation. This motivation can let the mother-wavelet-state itself vary from ｜ψ〉 to Ftr, s ｜ψ〉, except for variation within the family of dilations and translations. The Parseval＇s equality, admissibility condition and inverse transform of this continuous Fresnel-wavelet combinatorial transform are analysed. By taking certain parameters and using the admissibility condition of this continuous Fresnel-wavelet combinatorial transform, we obtain some mother wavelets. A comparison between the newly found mother wavelets is presented.
基金the Natural Science Foundation of China (No. 60472037).
文摘We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.
基金Sponsored by the Projects of International Cooperation and Exchange of the National Natural Science Foundation of China(Grant No.51561135003)the Scientific Research Foundation of Graduated School of Southeast University(Grant No.YBJJ1842)
文摘In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.
文摘An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.
文摘Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robust fault detection,classification,and location based on the empirical wavelet transform-Teager energy operator(EWT-TEO)and artificial neural network(ANN)for hybrid transmission lines in VSC-HVDC systems.The operational scheme of the proposed protection method consists of two loops①an EWT-TEO based feature extraction loop,②and an ANN-based fault detection,classification,and location loop.Under the proposed protection method,the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform(EWT)method.The energy content extracted by the EWT is fed into the ANN for fault detection,classification,and location.Various fault cases,including the high-impedance fault(HIF)as well as noises,are performed to train the ANN with two hidden layers.The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB,respectively.The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave(TW)based protection method.The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems,where a mean percentage error of approximately 0.1%is achieved.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2006AA102108)the Youth Fund of College of Science,Huazhong Agriculture University Research Launching Funds(06033)
文摘Apoptosis proteins have a central role in the develop-ment and homeostasis of an organism,and their function is related to their types.In this paper,we constructed the character vectors of apoptosis proteins from their amino acid sequences by using the discrete wavelet transform,combined with support vector machine,to predict the type of given apoptosis proteins.For the widely used dataset z98,high success rates were obtained by Jackknife test,and the Matthews correlation coefficients were 0.92,0.90,0.81 and 0.80,respectively,which were higher than the other methods on average.
基金This work was supported by the National Natural Science Foundation of China (No. 60472081) the Avigation Science Foundation (No. 05F07001)the 985 Innovation Project on Information Technique of Xiamen University (2004-2007).
文摘A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.
文摘A coding method of speech compression, which is based on Wavlet Transform and Vector Quantization (VQ), is developed and studied. The Wavlet Thansform or Wavlet Packet Thansform is used to process the speech signal, then VQ is used to compress the coefficients of Wavlet Thansform, and the entropy coding is used to decrease the bit rate. The experimental results show that the speech signal, sampled by 8 kHz sampling rate and 8 bit quatisation,i.e., 64 kbit/s bit rate, can be compressed to 6 - 8 kbit/s, and still have high speech quality,and the low-delay, only 8 ms.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60532060 and 60507012.
文摘In this paper, we propose a VLSI architecture that performs the line-based discrete wavelet transform （DWT） using a lifting scheme. The architecture consists of row processors, column processors, an intermediate buffer and a control module. Row processor and Column processor work as the horizontal and vertical filters respectively. Intermediate buffer is composed of five FIFOs to store temporary results of horizontal filter. Control module schedules the output order to external memory. Compared with existing ones, the presented architecture parallelizes all levels of wavelet transform to compute multilevel DWT within one image transmission time, and uses no external but one intermediate buffer to store several line results of horizontal filtering, which decreases resource required significantly and reduces memory efficiently. This architecture is suitable for various real-time image/video applications.
文摘This paper presents a new technique by support vector machines after extracting the prominent iris features using discrete wavelet transformations,to achieve an optimal classification of energy system disturbances.A framework for iris recognition and protection of the recognition system from fake iris scenes was proposed.The scale-invariant feature transform is set as an algorithm to extract local features(key points)from iris images and their classification method.To elicit the prominent iris features,the test image is first pre-processed.This will facilitate confining and segmenting the region of interest hopefully,reducing the blurring and artifacts,especially those associated with the edges.The textural features can be exploited to partition irises into regions of interest in addition to providing necessary information in the spatial distribution of intensity levels in an iris neighborhood.Next,the detection efficiency of the proposed method is achieved through extracting iris gradients and edges in complex areas and in different orientations.Further,the iris diagonal edges were easily detected after calculating the variance of different blocks in an iris and the additive noise variance in a textured image.The vertical,horizontal,and diagonal iris image gradients with different directions were successfully extracted.These gradients were extracted after adjusting the threshold amplitude obtained from the histograms of these gradients.The average calculations of MAVs,peak signal-to-noise ratios(PSNRs),and mean square errors(MSEs)within the orientation angles(-45°,+45°and 90°)for both vertical and horizontal iris gradients had occurred within the rates of 1.9455-3.1266,36.388-39.863 dB and 0.0001-0.0026,respectively.
基金Supported by the National Natural Science Foundation of China(41575046)Project of Commonweal Technique and Application Research of Zhejiang Province of China(2016C33010)Project of Shanghai Meteorological Center of China(SCMO-ZF-2017011)。
文摘Currently,Doppler weather radar in China is generally used for quantitative precipitation estimation(QPE)based on the Z–R relationship.However,the estimation error for mixed precipitation is very large.In order to improve the accuracy of radar QPE,we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations(AWSs)in East China.Considering the time dependence and abrupt changes of precipitation,the data during the previous 30-min period were selected as the training data.To reduce the complexity of radar QPE,we transformed the weather data into the wavelet domain by means of the stationary wavelet transform(SWT)in order to extract high and low-frequency reflectivity and precipitation information.Using the wavelet coefficients,we constructed a support vector machine(SVM)at all scales to estimate the wavelet coefficient of precipitation.Ultimately,via inverse wavelet transformation,we obtained the estimated rainfall.By comparing the results of the proposed method(SWTSVM)with those of Z=300×R1.4,linear regression(LR),and SVM,we determined that the root mean square error(RMSE)of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score(TS)could exceed 40%with the exception of the downpour category,thus remaining at a high level.Generally speaking,the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.
基金Supported by the National Basic Research Program("973"Program, No2005CB724303 )
文摘This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.
文摘This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with
文摘A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.
文摘The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.
