基于服务器系统模型的光网络容量分析

In this paper, a new architecture of optical networks -- the optical network based on server system is considered. From the point of this new architecture, the network can be modeled as a server system with three type servers -- the access server, the node server and the link server. The network performances such as cost, energy consume and network capacity can be affected by the capability of these three type servers. New ILP formulations are proposed to analyze the network capacity under two types of node severs, with and without wavelength converter. Computer simulations are conducted to evaluate the effectiveness of these new formulations. The study has shown that the network can achieve the same throughput under the two types of node servers and the network throughput increases when the maximum allowed variation increases.     

Parallel Image Processing Technology of Surface Detection System

To improve image processing speed and detection precision of a surface detection system on a strip surface, based on the analysis of the characteristics of image data and image processing in detection system on the strip surface, the design of parallel image processing system and the methods of algorithm implementation have been studied. By using field programmable gate array（FPGA） as hardware platform of implementation and considering the characteristic of detection system on the strip surface, a parallel image processing system implemented by using multi IP kernel is designed. According to different computing tasks and the load balancing capability of parallel processing system, the system could set different calculating numbers of nodes to meet the system＇s demand and save the hardware cost.     

MULTI-AGENT SYSTEM FOR WORM DETECTION AND CONTAINMENT IN METROPOLITAN AREA NETWORKS

Active worms can cause widespread damages at so high a speed that effectively precludes humandirected reaction, and patches for the worms are always available after the damages have been caused, which has elevated them self to a first-class security threat to Metropolitan Area Networks （MAN）. Multi-agent system for Worm Detection and Containment in MAN （MWDCM） is presented to provide a first-class automatic reaction mechanism that automatically applies containment strategies to block the propagation of the worms and to protect MAN against worm scan that wastes a lot of network bandwidth and crashes the routers. Its user agent is used to detect the known worms. Worm detection agent and worm detection correlation agent use two-stage based decision method to detect unknown worms. They adaptively study the accessing in the whole network and dynamically change the working parameters to detect the unknown worms. MWDCM confines worm infection within a macro-cell or a micro-cell of the metropolitan area networks, the rest of the accesses and hosts continue functioning without disruption. MWDCM integrates Worm Detection System （WDS） and network management system. Reaction measures can be taken by using Simple Network Management Protocol （SNMP） interface to control broadband access server as soon as the WDS detect the active worm. MWDCM is very effective in blocking random scanning worms. Simulation results indicate that high worm infection rate of epidemics can be avoided to a degree by MWDCM blocking the propagation of the worms.     

Analysis of group multiuser detection based on coalition game theory

Coalition game theory is introduced to investigate the performance, fairness and stability of decorrelating group multiuser detection receiver, not only from the perspective of individual nodes, but also various coalitions and the whole system as well. Firstly, to derive how the system scale with coalition size, a stochastic model with transferable payoffs (stochastic TU-model) is provided. Secondly, to find the most pre-ferred coalition structures from the view point of individual nodes, a model with Non-Transferable payoffs (NTU-model) is presented. Theoretical analysis and simulation results suggest that stochasticaly the grand coalition is payoff maximizing for the system as a whole, while individual nodes with good-conditioned channels may prefer local “win-win coalitions”.     

Converting a Display Screen into a Touchscreen

A method is presented to convert any display screen into a touchscreen by using a pair of cameras. Most state of art touchscreens make use of special touch-sensitive hardware or depend on infrared sensors in various configurations. We describe a novel computer-vision-based method that can robustly identify fingertips and detect touch with a precision of a few millimeters above the screen. In our system, the two cameras capture the display screen image simultaneously. Users can interact with a computer by the fingertip on the display screen. We have two important contributions: first, we develop a simple and robust hand detection method based on predicted images. Second, we determine whether a physical touch takes places by the homography of the two cameras. In this system, the appearance of the display screen in camera images is inherently predictable from the computer output images. Therefore, we can compute the predicted images and extract human hand precisely by simply subtracting the predicted images from captured images.     

Study of Intrusion Detection Systems

Modern network systems have much trouble in security vulnerabilities such as buffer overflow, bugs in Microsoft Internet, sensor network routing protocol too simple, security flaws of applications, and operating systems. Moreover, wireless devices such as smart phones, personal digital assistants （PDAs）, and sensors have become economically feasible because of technological advances in wireless communication and manufacturing of small and low-cost sensors. There are typologies of vulnerabilities to be exploited in these devices. In order to improve securities, many mechanisms are adopted, including authentication, cryptography, access control, and intrusion detection systems （IDS）. In general, intrusion detection techniques can be categorized into two groups： misuse detection and anomaly detection. The misuse detection systems use patterns of weB-known attacks or weak spots of the systems to identify intrusions. The weakness of misuse detection systems is unable to detect any future （unknown） intrusion until corresponding attack signatures are intruded into the signature database. Anomaly detection methods try to determine whether the deviation is from the established normal usage patterns or not. The critical success of anomaly detection relies on the model of normal behaviors.     

AN IMMUNITY-BASED SECURITY ARCHITECTURE FOR MOBILE AD HOC NETWORKS

This paper focuses on investigating immunological principles in designing a multi-agent security architecture for intrusion detection and response in mobile ad hoc networks. In this approach, the immunity-based agents monitor the situation in the network. These agents can take appropriate actions according to the underlying security policies. Specifically, their activities are coordinated in a hierarchical fashion while sensing, communicating, decision and generating responses. Such an agent can learn and adapt to its environment dynamically and can detect both known and unknown intrusions. The proposed intrusion detection architecture is designed to be flexible, extendible, and adaptable that can perform real-time monitoring. This paper provides the conceptual view and a general framework of the proposed system. In the end, the architecture is illustrated by an example to show it can prevent the attack efficiently.     

Reliability of NFV Using COTS Hardware

This paper describes a study on the feasibility of using commercial off- the- shelf(COTS) hardware for telecom equipment. The study outlines the conditions under which COTS hardware can be utilized in a network function virtualization environment. The concept of silent-error probability is introduced to account for software errors and/or undetectable hardware failures, and is included in both the theoretical work and simulations. Silent failures are critical to overall system availability. Site-related issues are created by combined site maintenance and site failure. Site maintenance does not noticeably limit system availability unless there are also site failures. Because the theory becomes extremely involved when site failure is introduced, simulation is used to determine the impact of those facts that constitutes the undesirable features of using COTS hardware.     

Design and Implementation of Network Communication Interface Based on μCLinux

In order to meet the requirements of real-time and high efficient data transmission in data acquisition and industrial control fields, a scheme of network communication interface is presented. The design of hardware circuit and the realization of software program are mainly introduced. The result of the experiment shows that this embedded system has high reliability and stability. It can realize high-speed data transmission, and also can satisfy the requirements of network communication in the industrial control fields.     

Research on intelligent fault diagnosis based on time series analysis algorithm

Aiming to realize fast and accurate fault diagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent fault diagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio （GLR） test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced. Secondly, the intelligent fault diagnosis model is established by introducing neural network technology, and thereby, the anomaly information of each node in end-to-end network is integrated and processed in parallel to intelligently diagnose the fault cause. Finally, server backup solution in enterprise information network is taken as the simulation scenario. The results demonstrate that the proposed method can not only detect fault occurrence in time, but can also implement online diagnosis for fault cause, and thus, real-time and intelligent fault management process is achieved.     

Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.     

Construction of parallel and distributed static simulation system based on augmented reality

This article puts forward a kind of parallel and distributed static augmented scene system structure to improve the performance of real time augmented simulation system.Based on static registration technique,several groups of processing nodes do parallel scene pictures taking,3D registration and virtual-real merging.Process on different nodes is controlled by uniform synchronization mechanism and network transmitting.Wide field of view image can be obtained from image mosaic operation and displayed by wide view display system.Detailed system architecture,registration algorithm,method how to determine camera position and synchronization mechanism between each process node are introduced.The experiment result can validate the good performance of the designed system.     

A Virtual Walk along Street Based on View Interpolation

Image morphing is a powerful tool for visual effect. In this paper, a view interpolation algorithm is proposed to simulate a virtual walk along a street from start position to end position. To simulate a virtual walking view needs to create new appearing scene in the vision-vanishing point and disappearing scene beyond the scope of view. To attain these two aims we use two enhanced position parameters to match pixels of source images and target images. One enhanced position parameter is the angular coordinates of pixels. Another enhanced position parameter is the distances from pixels to the vision-vanishing point. According to the parameter values, pixels beyond the scope of view can be ＂moved＂ out in linear interpolation. Result demonstrates the validity of the algorithm. Another advantage of this algorithm is that the enhanced position parameters are based on real locations and walking distances, so it is also an approach to online virtual tour by satellite maps of virtual globe applications such as Google Earth.     

Early warning model of network intrusion based on D-S evidence theory

Application of data fusion technique in intrusion detection is the trend of nextgeneration Intrusion Detection System (IDS). In network security, adopting security early warning technique is feasible to effectively defend against attacks and attackers. To do this, correlative information provided by IDS must be gathered and the current intrusion characteristics and situation must be analyzed and estimated. This paper applies D-S evidence theory to distributed intrusion detection system for fusing information from detection centers, making clear intrusion situation, and improving the early warning capability and detection efficiency of the IDS accordingly.     

Microblog Burst Keywords Detection Based on Social Trust and Dynamics Model

Microblog has emerged as a popular medium for providing new sources of information and rapid communications, particularly during burst topics. Burst keywords detection from real-time microblog streams is important for burst topics detection. The exiting algorithms may detect fake burst keywords without taking into account the trustworthiness of the users and human＇s daily timetable. Our work is the first to combine the trustworthiness of the users with burst keywords detection. We propose a novel approach to detect burst keywords based on social trust and dynamics model. We adapt basic notions of dynamics from physics and model keywords bursts as momentum change of the keywords. On the analogy of physical dynamics model, this approach defines mass as the trustworthiness of user and position as the frequency of keywords. We compute each keyword＇s burst value by using Moving average convergence/divergence （MACD） and determine whether it is a burst keyword in a given time window. The experimental results on large-scale Sina microblog dataset show that the proposed approach can avoid detecting fake burst keywords.     

DPHM： A FAULT DETECTION PROTOCOL BASED ON HEARTBEAT OF MULTIPLE MASTER-NODES

In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resource and potentially reduce the availability of system. A fault Detection Protocol based on Heartbeat of multiple Master-nodes (DPHM) is proposed, which can immediately and accurately detect and locate faulty links by adopting voting and electing mechanism among master-nodes. Thus, DPHM can effectively improve availability of system. In addition, in contrast with other detection protocols, DPHM reduces greatly the detection cost due to the structure of master-nodes.     

An Interactive Network Laboratory for Electronic Engineering Education

The advantage of the network laboratory is the better flexibility of lab experiments by allowing remote control from different locations at a freely chosen time. In engineering education, the work should not only be focused on the technical realization of virtual or remote access experiments, but also on the achievement of its pedagogical goals. In this paper, an interactive laboratory is introduced which is based on the online tutoring system, virtual and remote access experiments. It has been piloted in the Department of Electronic Science and Technology, HUST. Some pedagogical issues for electronic engineering laboratory design, the development of a multi-server- based distributed architecture for the reduction of network latency and implementations of the function module are presented. Finally, the system is proved valid by an experiment.     

Data-mining based fault detection

This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Г-test, by which the quasi-optimal embedding dimension and time delay can be obtained. The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.     

Robust hybrid beamforming design for millimeter-wave massive MIMO systems

Hybrid analog-digital beamforming is recognized as a promising solution for a practical implementation of massive multiple-input multiple-output(MIMO) systems based on millimeter-wave(mmWave) technology. In view of the overwhelming hardware cost and excessive power consumption and the imperfection of the channel state information(CSI), a robust hybrid beamforming design is proposed for the mmWave massive MIMO systems, where the robustness is defined with respect to imperfect knowledge or error of the CSI at the transmitter due to limited feedback and/or imperfect channel estimation. Assuming the errors of the CSI are bounded, the optimal hybrid beamforming design with robustness is formulated to a mean squared error(MSE) minimization problem. An iterative semidefinite programming(SDP) based algorithm is proposed to obtain the beamforming matrices. Simulation results show that the proposed robust design can provide more than 4 dB performance gain compared to that of non-robust design.