Mobile free space optical networks have aroused much attention due to the ability of providing high speed connectivity over long distance using the wireless laser links,while requiring relatively high available bandwi...Mobile free space optical networks have aroused much attention due to the ability of providing high speed connectivity over long distance using the wireless laser links,while requiring relatively high available bandwidth resource and less energy consumption.However,maintaining the network with laserlinks is quite challenging due to a number of issues,such as the link fragility,the difficulty in pointingand tracking of the link,which also raises the great difficulty in the control of the network.In this paper,we present the methodology for the deployment of the mobile freespace optical networks based on our proposed OpenFlow-based control architecture.In addition,a new routing scheme is proposed and demonstrated on the testbed based on this control architecture.Delivery ratio,average delivery delay and time complexity are given to verify the performance of the OpenFlow-based control architecture.展开更多
Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structu...Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.展开更多
基金supported in part by 863 program(2012AA011301)973 program (2010CB328204)+3 种基金NSFC project(61271189, 61201154)RFDP Project(20120005120019)the Fundamental Research Funds for the Central Universities(2013RC1201)Fund of State Key Laboratory of Information Photonics and Optical Communications(BUPT)
文摘Mobile free space optical networks have aroused much attention due to the ability of providing high speed connectivity over long distance using the wireless laser links,while requiring relatively high available bandwidth resource and less energy consumption.However,maintaining the network with laserlinks is quite challenging due to a number of issues,such as the link fragility,the difficulty in pointingand tracking of the link,which also raises the great difficulty in the control of the network.In this paper,we present the methodology for the deployment of the mobile freespace optical networks based on our proposed OpenFlow-based control architecture.In addition,a new routing scheme is proposed and demonstrated on the testbed based on this control architecture.Delivery ratio,average delivery delay and time complexity are given to verify the performance of the OpenFlow-based control architecture.
基金Supported by the National Key Research and Development Program of China(No.2016YFB0201305)National Science and Technology Major Project(No.2013ZX0102-8001-001-001)National Natural Science Foundation of China(No.91430218,31327901,61472395,61272134,61432018)
文摘Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.
