一种基于ARQ技术的安全图像隐蔽通信方案

基于信息隐藏技术的图像隐蔽通信,需要解决隐藏信息不可检测和主动攻击条件下的通信可靠性两个问题.本文用广义信息熵来表示图像信息,给出了隐藏信息不可检测的条件.采用差错控制的ARQ技术解决主动攻击条件下的可靠通信问题,给出了ARQ技术所须解决的差错检测和检测编码,证明了给出的编码具有差错检测能力.本文所给出的方案适用于通信量较大的公共信道中进行图像隐蔽通信,在m位隐藏信息不全部出错的前提下,能保证可靠的隐蔽通信.     

基于DSP双相位检测微弱信号的仿真分析

针对数字信号处理器（DSP）系统集成度高、速度快、适合大量数据实时处理的特点,分析微弱信号的双相位相干检测原理,从应用的角度研究基于DSP实现的双相位检波模式的优点。利用DSP产生精确的相干波,从而使谐波的抑制能力可以达到一120dB。随机噪声中的信号幅值误差可以达到0．45％,相位误差0．228％。构建了一个以DSP为核心高精度的微弱信号检测系统。     

Protel 99SE在电液仿形系统电路设计中的应用

介绍了Protel 99SE应用软件的功能特点以及设计电路的一般方法步骤,结合实际在电液仿形系统电路设计中的应用,指出了设计中遇到的一些问题,并给出了具体的解决方法和注意事项,对学习和应用Protel?9SE会有一定的帮助.     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

基于JXTA平台P2P节点甄别的信誉机制研究

在分析P2P网络节点安全问题和JXTA平台具有的安全机制的基础上,构建一个基于JXTA的以节点甄别为目标的安全网络环境,并提出了一种基于信任和推荐的新型信誉模型.分析证明,该信誉模型通过采用信任和推荐两种机制来确定节点的信任度,成功地避免了单点失效问题并有效地遏制了P2P系统中虚假节点的欺骗行为.     

UAC技术在Windows XP中的应用与实现

管理员在系统中拥有极高的权限,这对系统的安全是极大的隐患.在Windows Vista中引入了UAC(User Account Control)技术以限制管理员的权限,以HIPS的形式在Windows XP系统中实现了UAC技术,对管理员权限进行了限制,防止恶意程序以管理员权限破坏系统.     

基于Web的罗非鱼养殖质量安全可追溯系统的设计与实现

通过对某罗非鱼养殖企业的实际调研,在分析罗非鱼养殖流程基础上,进一步结合HACCP原理对养殖中关系产品质量安全的关键信息进行了相关分析,并设计了系统的功能模块以及体系结构,建立基于Web的罗非鱼养殖质量安全可追溯系统.该系统既能够控制影响罗非鱼质量的关键环节,又减少了不必要的信息追溯,节约了成本,为消费者提供方便的查询平台,而且为罗非鱼养殖管理和罗非鱼质量安全监控提供了良好的操作平台.     

一种可扩展3D游戏脚本系统的设计与实现

通过运用现代软件工程的思想,本文提出一种可扩展、可复用脚本系统的设计方法,具有良好的层次性、封装性、易理解性.首先介绍脚本系统的整体框架设计,然后通过UML类图详细描述系统关键类的设计,并同时给出系统演示,效果良好.     

环境化合物降解预测模型的研究

为建立化合物降解的计算机预测模型,确定降解和非降解化合物显然不同的参数.选择389个有机分子作为数据集,选其中312个为训练集,其余77个为验证集,每个分子计算195个分子参数,分别采用逐步判别法和主成分分析法建模,并用外部验证集验证模型的预测能力.结果:逐步判别法分析结果中,训练集的降解和非降解化合物的正确率分别为90.6%和69.5%;验证集的降解和非降解化合物的正确率分别为83.9%和63.6%.主成分分析结果在测试集中,降解和非降解化合物的正确率分别为80.4%和31.8%.验证集的降解化合物和非降解化合物的正确率分别为67.9%和50.0%.因此,采用逐步判别法模拟而建立的数学模型,可作为预测化合物降解的模型.以卜研究可以为预测有机物降解提供参考.     

Dynamic file grouping for load balancing in streaming media clustered server systems

A dynamic file grouping strategy is presented to address the load balancing problem in streaming media clustered server systems. This strategy increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves the access hit ratio of cached files in delivery servers to alleviate the limitation of I/O bandwidth of storage node. First, the load balancing problem is formulated as a two layers semi-Markov switching state-space control process. This analytic model captures the behaviors of streaming media clustered server systems accurately, and is with constructional flexibility and scalability. Then, a policy iteration based reinforcement learning algorithm is proposed to optimize the file grouping policy online. By utilizing the features of the event-driven policy, the proposed optimization algorithm is adaptive and with less computational cost. Simulation results demonstrate the effectiveness of the proposed approach. Recommended by Editor Hyun Seok Yang. This work was supported by the National Natural Science Foundation of China under grant Nos. 60774038, 60574065, National 863 HI-TECH Research & Development Plan of China under grant Nos. 2006AA01Z114, 2008AA01A317, Natural Science Foundation of Anhui Province under grant No. 070412063, Graduate Student Innovation Foundation of USTC under grant No. KD2006036, and Science Research Development Foundation of HFUT under grant No. GDBJ2008-045. Qi Jiang received the B.S. degree in Industrial Electrical Automation from Southeast University in 1989 and the Ph.D. degree in Control Science and Engineering from University of Science and Technology of China in 2008. He is currently a Post-doc in USTC. His research interests include optimization and control of stochastic dynamic systems, and performance analysis and optimization of network communication systems. Hong-Sheng Xi received the M.S. degree in Applied Mathematics from University of Science and Technology of China in 1977. He is currently a Professor in Department of Automation, USTC. His research interests include discrete event dynamic systems, performance analysis and optimization of network communication systems, robust control, and network security. Bao-Qun Yin received the B.S. degree in Mathematics from Sichuan University in 1985, the M.S. degree in Applied Mathematics and the Ph.D. degree in Pattern Recognition and Intelligent Systems from University of Science and Technology of China in 1993 and 1998, respectively. He is currently a Professor in Department of Automation, USTC. His research interests include discrete event dynamic systems, and Markov decision processes.