A method for integer ambiguity resolution in the global positioning system （GPS） multi-reference station network real time kinematic （RTK） is proposed. First, the barycenter of the triangle of reference stations f...A method for integer ambiguity resolution in the global positioning system （GPS） multi-reference station network real time kinematic （RTK） is proposed. First, the barycenter of the triangle of reference stations for ambiguity resolution is taken as a reference point. The satellite which has the largest elevation angle with the reference point is selected as a reference satellite. The parameters for constructing the weight matrix of carrier phase observation and the criteria for checking the correctness of integer ambiguity resolution of a network are obtained. Then, the wide ambiguity is calculated by a linear combination method of dualband observation. And the LI ambiguity is obtained by a nonionosphere combination method. The Kalman filter is introduced to refine the floating-point solution of ambiguity and estimate the real-time tropospheric delay. Finally, the cofactor matrix of ambiguity is de-correlated by Z-transformation to reduce the searching space of the integer ambiguity solution and improve the efficiency of the least-squares ambiguity decorrelation adjustment （LAMBDA） algorithm. The experimental results show that this method can reliably obtain the integer ambiguity solution among multi-reference stations with 40 epochs.展开更多
A novel bandwidth prediction and control scheme is proposed for video transmission over an ad boc network. The scheme is based on cross-layer, feedback, and Bayesian network techniques. The impacts of video quality ar...A novel bandwidth prediction and control scheme is proposed for video transmission over an ad boc network. The scheme is based on cross-layer, feedback, and Bayesian network techniques. The impacts of video quality are formulized and deduced. The relevant factors are obtained by a cross-layer mechanism or Feedback method. According to these relevant factors, the variable set and the Bayesian network topology are determined. Then a Bayesian network prediction model is constructed. The results of the prediction can be used as the bandwidth of the mobile ad hoc network （MANET）. According to the bandwidth, the video encoder is controlled to dynamically adjust and encode the right bit rates of a real-time video stream. Integrated simulation of a video streaming communication system is implemented to validate the proposed solution. In contrast to the conventional transfer scheme, the results of the experiment indicate that the proposed scheme can make the best use of the network bandwidth; there are considerable improvements in the packet loss and the visual quality of real-time video.K展开更多
Accurate and real-time classification of network traffic is significant to network operation and management such as QoS differentiation, traffic shaping and security surveillance. However, with many newly emerged P2P ...Accurate and real-time classification of network traffic is significant to network operation and management such as QoS differentiation, traffic shaping and security surveillance. However, with many newly emerged P2P applications using dynamic port numbers, masquerading techniques, and payload encryption to avoid detection, traditional classification approaches turn to be ineffective. In this paper, we present a layered hybrid system to classify current Internet traffic, motivated by variety of network activities and their requirements of traffic classification. The proposed method could achieve fast and accurate traffic classification with low overheads and robustness to accommodate both known and unknown/encrypted applications. Furthermore, it is feasible to be used in the context of real-time traffic classification. Our experimental results show the distinct advantages of the proposed classifi- cation system, compared with the one-step Machine Learning (ML) approach.展开更多
In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to hi...In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.展开更多
基金The National Key Technology R&D Program of Chinaduring the11th Five-Year Plan Period (No2008BAJ11B05)
文摘A method for integer ambiguity resolution in the global positioning system （GPS） multi-reference station network real time kinematic （RTK） is proposed. First, the barycenter of the triangle of reference stations for ambiguity resolution is taken as a reference point. The satellite which has the largest elevation angle with the reference point is selected as a reference satellite. The parameters for constructing the weight matrix of carrier phase observation and the criteria for checking the correctness of integer ambiguity resolution of a network are obtained. Then, the wide ambiguity is calculated by a linear combination method of dualband observation. And the LI ambiguity is obtained by a nonionosphere combination method. The Kalman filter is introduced to refine the floating-point solution of ambiguity and estimate the real-time tropospheric delay. Finally, the cofactor matrix of ambiguity is de-correlated by Z-transformation to reduce the searching space of the integer ambiguity solution and improve the efficiency of the least-squares ambiguity decorrelation adjustment （LAMBDA） algorithm. The experimental results show that this method can reliably obtain the integer ambiguity solution among multi-reference stations with 40 epochs.
基金The National High Technology Research and Development Program of China (863Program) (No.2003AA1Z2130)the Scienceand Technology Project of Zhejiang Province(No.2005C11001-02)
文摘A novel bandwidth prediction and control scheme is proposed for video transmission over an ad boc network. The scheme is based on cross-layer, feedback, and Bayesian network techniques. The impacts of video quality are formulized and deduced. The relevant factors are obtained by a cross-layer mechanism or Feedback method. According to these relevant factors, the variable set and the Bayesian network topology are determined. Then a Bayesian network prediction model is constructed. The results of the prediction can be used as the bandwidth of the mobile ad hoc network （MANET）. According to the bandwidth, the video encoder is controlled to dynamically adjust and encode the right bit rates of a real-time video stream. Integrated simulation of a video streaming communication system is implemented to validate the proposed solution. In contrast to the conventional transfer scheme, the results of the experiment indicate that the proposed scheme can make the best use of the network bandwidth; there are considerable improvements in the packet loss and the visual quality of real-time video.K
基金Supported in part by the National 863 Project of China (No.2006AA01Z232)Zhejiang Natural Science Founda-tion (No.Y1080935)Research Innovation Program Project for Graduate Students in Jiangsu Province ( No.CX07B_110zF)
文摘Accurate and real-time classification of network traffic is significant to network operation and management such as QoS differentiation, traffic shaping and security surveillance. However, with many newly emerged P2P applications using dynamic port numbers, masquerading techniques, and payload encryption to avoid detection, traditional classification approaches turn to be ineffective. In this paper, we present a layered hybrid system to classify current Internet traffic, motivated by variety of network activities and their requirements of traffic classification. The proposed method could achieve fast and accurate traffic classification with low overheads and robustness to accommodate both known and unknown/encrypted applications. Furthermore, it is feasible to be used in the context of real-time traffic classification. Our experimental results show the distinct advantages of the proposed classifi- cation system, compared with the one-step Machine Learning (ML) approach.
文摘In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.
