With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for l...With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for large-scale multi layer satellite networks.A vector forwarding path is built based on the location between the source and the destination.Data packets are forwarded along this vector path,shielding the influence of satellite motion on routing forwarding.Then,a dynamic route maintenance strategy is suggested.In a multi layer satellite network,the low-orbit satellites are in charge of computing the routing tables for one area,and the routing paths are dynamically adjusted in the area in accordance with the network.The medium-orbit satellites maintain the connectivity of vector paths in multiple segmented areas.The forwarding mode based on the source and destination location improves the forwarding efficiency,and the segmented route maintenance mode decreases the routing overhead.The simulation results indicate that vector segment routing has significant performance advantages in end-to-end delay,packet loss rate,and throughput in a multi layer satellite network.We also simulate the impact of routing table update mechanism on network performance and overhead and give the performance of segmented vector routing in multi layer low-orbit satellite networks.展开更多
Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all t...Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.展开更多
In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentati...In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.展开更多
Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effec...Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.展开更多
In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model ca...In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.展开更多
Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmenta...Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.展开更多
The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodica...The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodically time-varying mesh stiffness, the nonlinear tooth backlash, the lump-parameter model of the gear system is developed with one rotational and two translational freedoms of each gear. The eigen-values and eigenvectors are derived and analyzed on the basis of the real modal theory. The sensitivities of natural frequencies to design parameters including supporting and meshing stiffnesses, gear masses, and moments of inertia by the direct differential method are also calculated. The results show the quantitative and qualitative impact of the parameters to the natural characteristics of the gear system. Furthermore, the periodic steady state solutions are obtained by the numerical approach based on the nonlinear model. These results are employed to gain insights into the primary controlling parameters, to forecast the severity of the dynamic response, and to assess the acceptability of the gear design.展开更多
Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法...在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.展开更多
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806100in part by the Natural Science Foundation of China under Grant U19B2025 and Grant 62001347+1 种基金in part by the Key Research and Development Program of Shaanxi under Grants 2022ZDLGY05-02 and 2021KWZ-05in part by the Fundamental Research Funds for the Central Universities under Grant QTZX22161
文摘With the expansion of satellite constellation,routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency.This paper proposes a vector segment routing method for large-scale multi layer satellite networks.A vector forwarding path is built based on the location between the source and the destination.Data packets are forwarded along this vector path,shielding the influence of satellite motion on routing forwarding.Then,a dynamic route maintenance strategy is suggested.In a multi layer satellite network,the low-orbit satellites are in charge of computing the routing tables for one area,and the routing paths are dynamically adjusted in the area in accordance with the network.The medium-orbit satellites maintain the connectivity of vector paths in multiple segmented areas.The forwarding mode based on the source and destination location improves the forwarding efficiency,and the segmented route maintenance mode decreases the routing overhead.The simulation results indicate that vector segment routing has significant performance advantages in end-to-end delay,packet loss rate,and throughput in a multi layer satellite network.We also simulate the impact of routing table update mechanism on network performance and overhead and give the performance of segmented vector routing in multi layer low-orbit satellite networks.
文摘Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572127）, the Development Foundation of Shanghai Municipal Commission of Education （Grant No.05AZ43）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.
基金Supported by the National Natural Science Foundation of China (No. 60475024)
文摘Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.
文摘In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.
文摘Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.
文摘The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodically time-varying mesh stiffness, the nonlinear tooth backlash, the lump-parameter model of the gear system is developed with one rotational and two translational freedoms of each gear. The eigen-values and eigenvectors are derived and analyzed on the basis of the real modal theory. The sensitivities of natural frequencies to design parameters including supporting and meshing stiffnesses, gear masses, and moments of inertia by the direct differential method are also calculated. The results show the quantitative and qualitative impact of the parameters to the natural characteristics of the gear system. Furthermore, the periodic steady state solutions are obtained by the numerical approach based on the nonlinear model. These results are employed to gain insights into the primary controlling parameters, to forecast the severity of the dynamic response, and to assess the acceptability of the gear design.
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
文摘在机载锂电池失效识别等样本不平衡的应用场景中,支持向量机(support vector machine,SVM)算法存在分离超平面偏移的问题,为此,提出分段惩罚参数支持向量机(segmented penalty parameters support vector machine,SPP-SVM)算法.该算法在训练过程中对样本进行分段,并根据各段内样本的识别误差自动调整惩罚参数,从而抑制超平面偏移;基于容量增量分析和灰色关联分析等方法提取并筛选特征,进而基于SPP-SVM算法建立锂电池失效识别模型;以NASA锂电池数据集和加州大学欧文分校(University of California Irvine,UCI)数据集为对象,开展对比实验.研究结果表明:与结合寻优算法的SVM相比,SPP-SVM算法识别性能更好,在不平衡程度较大的锂电池数据上,查准率和查全率的调和平均数(F1值)提升11.7%;在锂电池数据集和UCI数据集上的训练耗时缩短,减少幅度超过10倍;证明在样本不平衡情况下,使用SPP-SVM算法能够有效抑制分离超平面偏移,提升识别效果.
