A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning ...A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.展开更多
Under some conditions on probability, the author obtains some results on the complete convergence for partial sums of not necessary identically distributed p-mixing se- quences, and the complete convergence for partia...Under some conditions on probability, the author obtains some results on the complete convergence for partial sums of not necessary identically distributed p-mixing se- quences, and the complete convergence for partial sums of B-valued martingale differences is also studied. As application the author gives the corresponding results on the complete convergence for randomly indexed partial sums.展开更多
Considering a sequence of standardized stationary Gaussian random variables, a universal result in the almost sure central limit theorem for maxima and partial sum is established. Our result generalizes and improves t...Considering a sequence of standardized stationary Gaussian random variables, a universal result in the almost sure central limit theorem for maxima and partial sum is established. Our result generalizes and improves that on the almost sure central limit theory previously obtained by Marcin Dudzinski [1]. Our result reaches the optimal form.展开更多
Deep sequencing of small RNAs（sR NA） is widely used in sR NAs studies in plants. In order to investigate the sequencing frequency variation of sR NAs, the same sR NA samples from rice grains were sequenced twice usi...Deep sequencing of small RNAs（sR NA） is widely used in sR NAs studies in plants. In order to investigate the sequencing frequency variation of sR NAs, the same sR NA samples from rice grains were sequenced twice using deep sequencing technique. The sR NAs were classified into three categories, high abundance（〉 100 RPM）, medium abundance（10–100 RPM） and low abundance（1–10 RPM）. According to the repeat sequencing data of the same sample, highly expressed sR NAs（〉 100 RPM） were less subject to random drift, and 95% of the sR NAs Log2 ratio between two samples fell between-0.649 and 0.558. The same trend was observed in mediumly expressed sR NAs（10–100 RPM）, and 95% of the Log2 ratio fell between-0.535 and 0.759. As to lowly expressed sR NAs（1–10 RPM）, 95% of the Log2 ratio varied between-1.009 and 1.011. These results can be used as a theoretical guide to find differentially expressed s RNAs in sR NA studies in plants.展开更多
In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing prec...In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.展开更多
To speed up the reconstruction of 3D dynamic scenes in an ordinary hardware platform, we propose an efficient framework to reconstruct 3D dynamic objects using a multiscale-contour-based interpolation from multi-view ...To speed up the reconstruction of 3D dynamic scenes in an ordinary hardware platform, we propose an efficient framework to reconstruct 3D dynamic objects using a multiscale-contour-based interpolation from multi-view videos. Our framework takes full advantage of spatio-temporal-contour consistency. It exploits the property to interpolate single contours, two neighboring contours which belong to the same model, and two contours which belong to the same view at different times, corresponding to point-, contour-, and model-level interpolations, respectively. The framework formulates the interpolation of two models as point cloud transport rather than non-rigid surface deformation. Our framework speeds up the reconstruction of a dynamic scene while improving the accuracy of point-pairing which is used to perform the interpolation. We obtain a higher frame rate, spatio-temporal-coherence, and a quasi-dense point cloud sequence with color information. Experiments with real data were conducted to test the efficiency of the framework.展开更多
文摘A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.
文摘Under some conditions on probability, the author obtains some results on the complete convergence for partial sums of not necessary identically distributed p-mixing se- quences, and the complete convergence for partial sums of B-valued martingale differences is also studied. As application the author gives the corresponding results on the complete convergence for randomly indexed partial sums.
文摘Considering a sequence of standardized stationary Gaussian random variables, a universal result in the almost sure central limit theorem for maxima and partial sum is established. Our result generalizes and improves that on the almost sure central limit theory previously obtained by Marcin Dudzinski [1]. Our result reaches the optimal form.
基金supported by the National Natural Science Foundation of China(Grant No.31200973)Key Scientific Research Program of Henan Province,China(Grant No.141100110600)
文摘Deep sequencing of small RNAs（sR NA） is widely used in sR NAs studies in plants. In order to investigate the sequencing frequency variation of sR NAs, the same sR NA samples from rice grains were sequenced twice using deep sequencing technique. The sR NAs were classified into three categories, high abundance（〉 100 RPM）, medium abundance（10–100 RPM） and low abundance（1–10 RPM）. According to the repeat sequencing data of the same sample, highly expressed sR NAs（〉 100 RPM） were less subject to random drift, and 95% of the sR NAs Log2 ratio between two samples fell between-0.649 and 0.558. The same trend was observed in mediumly expressed sR NAs（10–100 RPM）, and 95% of the Log2 ratio fell between-0.535 and 0.759. As to lowly expressed sR NAs（1–10 RPM）, 95% of the Log2 ratio varied between-1.009 and 1.011. These results can be used as a theoretical guide to find differentially expressed s RNAs in sR NA studies in plants.
基金supported by the iMinds visualization research program(HIVIZ)
文摘In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.
基金Project supported by the National Basic Research Program(973)of China(No.2012CB725305)the Natural Science Foundation of Guizhou Province,China(No.20132094)+1 种基金the National Social Science Fund,China(No.12&ZD32)the Introducing Talents Foundation of Guizhou University,China(No.2012028)
文摘To speed up the reconstruction of 3D dynamic scenes in an ordinary hardware platform, we propose an efficient framework to reconstruct 3D dynamic objects using a multiscale-contour-based interpolation from multi-view videos. Our framework takes full advantage of spatio-temporal-contour consistency. It exploits the property to interpolate single contours, two neighboring contours which belong to the same model, and two contours which belong to the same view at different times, corresponding to point-, contour-, and model-level interpolations, respectively. The framework formulates the interpolation of two models as point cloud transport rather than non-rigid surface deformation. Our framework speeds up the reconstruction of a dynamic scene while improving the accuracy of point-pairing which is used to perform the interpolation. We obtain a higher frame rate, spatio-temporal-coherence, and a quasi-dense point cloud sequence with color information. Experiments with real data were conducted to test the efficiency of the framework.
