The problem of joint eigenvalue estimation for the non-defective commuting set of matrices A is addressed. A procedure revealing the joint eigenstructure by simultaneous diagonalization of. A with simultaneous Schur d...The problem of joint eigenvalue estimation for the non-defective commuting set of matrices A is addressed. A procedure revealing the joint eigenstructure by simultaneous diagonalization of. A with simultaneous Schur decomposition （SSD） and balance procedure alternately is proposed for performance considerations and also for overcoming the convergence difficulties of previous methods based only on simultaneous Schur form and unitary transformations, it is shown that the SSD procedure can be well incorporated with the balancing algorithm in a pingpong manner, i. e., each optimizes a cost function and at the same time serves as an acceleration procedure for the other. Under mild assumptions, the convergence of the two cost functions alternately optimized, i. e., the norm of A and the norm of the left-lower part of A is proved. Numerical experiments are conducted in a multi-dimensional harmonic retrieval application and suggest that the presented method converges considerably faster than the methods based on only unitary transformation for matrices which are not near to normality.展开更多
为解决高性能视频编码标准H.265/HEVC中引入的样点自适应补偿技术的计算复杂度极高导致严重影响编码效率的问题,提出一种HEVC样点自适应补偿快速算法.首先根据编码单元(coding unit,CU)的划分深度确定亮度树形编码块(coding tree block,...为解决高性能视频编码标准H.265/HEVC中引入的样点自适应补偿技术的计算复杂度极高导致严重影响编码效率的问题,提出一种HEVC样点自适应补偿快速算法.首先根据编码单元(coding unit,CU)的划分深度确定亮度树形编码块(coding tree block,CTB)中需要提取边缘方向的区域,然后利用边缘方向提取算法获得亮度CTB的边缘方向列表,并根据此列表减少亮度CTB在模式判别过程中遍历的模式数量,最后利用亮度和色度CTB之间的相关性进一步简化色度CTB的模式判别过程.实验结果表明,在性能损失较小的情况下,本算法可以在全I帧(AI)、随机访问(RA)、低延时B帧(LB)、低延时P帧(LP)四种配置下分别节省31.75%、56.85%、52.81%、51.51%的样点自适应补偿编码时间.展开更多
Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in moun...Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.展开更多
The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The m...The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.展开更多
Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode an...Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the 6al level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.展开更多
基金The National Natural Science Foundation of China(No.60572072,60496311),the National High Technology Researchand Development Program of China (863Program ) ( No.2003AA123310),the International Cooperation Project on Beyond 3G Mobile of China (No.2005DFA10360).
文摘The problem of joint eigenvalue estimation for the non-defective commuting set of matrices A is addressed. A procedure revealing the joint eigenstructure by simultaneous diagonalization of. A with simultaneous Schur decomposition （SSD） and balance procedure alternately is proposed for performance considerations and also for overcoming the convergence difficulties of previous methods based only on simultaneous Schur form and unitary transformations, it is shown that the SSD procedure can be well incorporated with the balancing algorithm in a pingpong manner, i. e., each optimizes a cost function and at the same time serves as an acceleration procedure for the other. Under mild assumptions, the convergence of the two cost functions alternately optimized, i. e., the norm of A and the norm of the left-lower part of A is proved. Numerical experiments are conducted in a multi-dimensional harmonic retrieval application and suggest that the presented method converges considerably faster than the methods based on only unitary transformation for matrices which are not near to normality.
文摘为解决高性能视频编码标准H.265/HEVC中引入的样点自适应补偿技术的计算复杂度极高导致严重影响编码效率的问题,提出一种HEVC样点自适应补偿快速算法.首先根据编码单元(coding unit,CU)的划分深度确定亮度树形编码块(coding tree block,CTB)中需要提取边缘方向的区域,然后利用边缘方向提取算法获得亮度CTB的边缘方向列表,并根据此列表减少亮度CTB在模式判别过程中遍历的模式数量,最后利用亮度和色度CTB之间的相关性进一步简化色度CTB的模式判别过程.实验结果表明,在性能损失较小的情况下,本算法可以在全I帧(AI)、随机访问(RA)、低延时B帧(LB)、低延时P帧(LP)四种配置下分别节省31.75%、56.85%、52.81%、51.51%的样点自适应补偿编码时间.
基金supported jointly by Key Laboratory of Geo-special Information Technology, Ministry of Land and Resources (Grant No. KLGSIT2013-12)Knowledge Innovation Program (Grant No. KSCX1-YW-09-01) of Chinese Academy of Sciences
文摘Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.
基金Supported by the Project of National Key Laboratory Fund
文摘The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.
基金The Open Project of the State Key Laboratory of Robotics and System (HIT)the State Key Laboratory of Cognitive Neuroscience and Learning+3 种基金Natural Science Fund for Colleges and Universities in Jiangsu Provincegrant number:105TB51003Natural Science Fund in Changzhougrant number:CJ20110023
文摘Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the 6al level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.
