基于选择性马尔可夫模型的缓存预取策略

通过分析研究现有流媒体缓存管理算法和用户的访问行为特征,提出了一种新的基于选择性马尔可夫模型的缓存预取策略.该策略通过序列合并方法对用户访问拖曳行为进行建模,采用状态剪枝优化方法FP_Vlike得到选择性马尔可夫模型FPMM_Vlike,并在此之上结合替换算法LRU-2构建出一种流媒体代理服务器缓存预取机制FPVlike_LRU_2.仿真结果表明,在访问延时降低量方面,FPVlike_LRU-2要比FP_LRU-2、SP_LRU-2、LRU-2分别高出10%、12%、17%,且在最佳的情况下该值能够达到60%以上.     

基于灰色马尔可夫模型的人体健康PH值检测

文章在灰色预测的基础上，引入马尔可夫预测，建立起灰色马尔可夫预测组合模型。把此模型应用到人体健康尿液PH值的分析预测中。灰色模型预测尿液PH值总的变化趋势，而马尔可夫链预测则适合描述随机波动性较大的预测问题，两者优势互补，形成对PH值的良好预测。     

基于缓存行为特征的线程数据预取距离控制策略

针对目前大多数面向指针应用程序的线程数据预取方法在预取距离控制方面的不足,该文提出一种基于缓存行为特征的数据预取距离控制策略。该策略利用指针应用程序执行时的数据缓存特征构建预取距离控制模型,以避免共享缓存污染,降低系统资源竞争,并通过忽略对部分非循环依赖数据预取平衡帮助线程与主线程间的执行任务,提高线程数据预取的时效性。实验结果表明,通过该策略控制线程数据预取距离能进一步提高线程预取性能。     

预取控制模型的改进

现有的预取控制模型一般都是基于预取而言,缺少对缓存数量和命中率的探讨,缺少考虑预取与缓存对存储空间的竞争问题,而先进的预取控制技术会减少不必要的预取,会使系统资源得到更合理的利用,文章在现有预取控制模型度量的基础上进行了改进,在加入缓存数量和缓存命中率的模型的基础下,推导出了改进的代价函数和门限函数,并仿真得出了相应的关系图,对预取控制模型进行了较好的改进,改进的模型对预取技术会起到很好的指导作用。     

面向按序执行处理器的预执行指导的数据预取方法

为提高按序执行处理器的访存性能,本文提出一种预执行指导的数据预取方法(PEDP).PEDP利用跨距预取器对规则的访存模式进行预取,并在发生L2 Cache失效后通过预执行后续指令对不规则的访存模式进行精确的预取,从而结合两者的优势提高预取覆盖率.同时,PEDP利用预执行过程中提前捕获的真实访存信息指导跨距预取器的预取过程.在预执行的指导下,跨距预取器可以对预执行能够产生的符合跨距访存模式的地址更早地发起预取请求,从而改善预取及时性.此外,为进一步优化上述指导过程,PEDP使用更新过滤器有效去除指导过程中对跨距预取器的有害更新,从而提高预取准确率.实验结果表明,在平均情况下,PEDP将基准处理器的性能提升33.0%.与跨距预取和预执行各自单独使用相比,PEDP将性能分别提高16.2%和7.3%.     

基于隐马尔可夫模型局部最优状态路径的数据重建算法

该文提出了基于隐马尔可夫模型局部最优状态路径的数据重建(LOPDI)算法。该算法假设语音特征矢量是一个L状态隐马尔可夫模型的输出序列,基于局部最优状态路径估计产生语音特征矢量的次最优状态序列,并按最大后验概率准则(MAP)重建出缺失矢量。实验表明,LOPDI算法能够显著提高语音识别系统对加性噪声的鲁棒性。     

马尔可夫及隐马尔可夫模型的应用

通过对马尔可夫模型进行深入的分析的基础上对隐马尔科夫模型做了详细的讨论,对马尔科夫模型在语音识别、疾病分析等方面的应用做了介绍,同时针对隐马尔科夫模型在估值问题、解码问题和学习问题等经典问题上的应用做了研究。最后讨论了马尔科夫模型其隐马尔可夫模型的缺陷,并提出相关的改进建议。     

基于GA-BP神经网络的VOD代理节目预取模型

VOD代理服务器的节目预取方法决定了园区网VOD系统的整体运行效率。提出一种节目预取模型,采用BP神经网络构建分类器并对VOD节目进行分类,再根据分类结果采用基于分组的方法实现代理服务器的节目预取。模型中引入遗传算法对已建立的分类模型进行改进,以克服局部极小值问题。仿真实验表明,该预取模型具有较高的命中率,能有效提高代理服务器的利用率。     

基于位置的服务中数据预取策略的应用研究

由于受网络环境和嵌入式移动数据库等自身工作环境和软硬件条件的限制,用户在申请基于位置的服务时,存在数据传输不稳定,频繁连接服务数据库,服务响应时间过长等缺点.为了能更好的为用户服务,减少服务等待时间,提高服务质量,文章改进关联规则方法,挖掘位置服务中用户的使用模式,预测用户下一步可能申请的服务类型,从而可以预取相关服务数据到用户移动设备,能够减少用户等待数据调用和传输的时间,优化服务质量,提高嵌入式移动数据库的工作效率.     

VoiceXML语音平台中预取方案的研究

该文在分析目前主要预取算法优劣的基础上,根据VoiceXML语音平台与基于HTML的WWW之间的区别,认为在VoiceXML语音平台中应该预取其引用的语音资源,提出一种自适应的多用户共享的Markov预测模型,统一预测所有在线用户下一步所需的资源及其访问概率,有助于提高预测的准确率。最后,该文还提出抢占式优先级模型来调度预取任务,将资源的访问概率映射为优先级。仿真研究表明,与单用户预测算法和循环调度模型比较,该预取算法和调度模型都能很好地减少用户请求的访问延迟,提高响应速度。     

基于隐马尔科夫模型的RCS识别方法研究

雷达散射截面(RCS)时间序列由目标电磁散射特性和姿态运动特性共同决定,包含了雷达目标的材质、尺寸和结构等信息,是实现雷达目标识别的重要测量量.隐马尔科夫模型(HMM)是一种用参数表示的用于描述随机过程统计特性的概率模型,是一个无记忆的非平稳随机过程,具有很强的表征时变信号的能力,非常适合作为动态模式分类器,对具有不同变化特性的时变信号进行分类识别.文中利用HMM表征雷达目标RCS序列变化模式(规律),根据不同类别目标RCS序列变化模式的差异对雷达目标进行分类识别.实测数据验证结果表明,该算法具有较高的识别概率.     

Energy-Efficient Data Caching and Prefetching for Mobile Devices Based on Utility

In mobile computing environments, vital resources like battery power and wireless channel bandwidth impose significant challenges in ubiquitous information access. In this paper, we propose a novel energy and bandwidth efficient data caching mechanism, called GreedyDual Least Utility (GD-LU), that enhances dynamic data availability while maintaining consistency. The proposed utility-based caching mechanism considers several characteristics of mobile distributed systems, such as connection-disconnection, mobility handoff, data update and user request patterns to achieve significant energy savings in mobile devices. We develop an analytical model for energy consumption of mobile devices in a dynamic data environment. Based on the utility function derived from the analytical model, we propose algorithms for cache replacement and passive prefetching of data objects. Our comprehensive simulation experiments demonstrate that the proposed caching mechanism achieves more than 10% energy saving and near-optimal performance tradeoff between access latency and energy consumption. Huaping Shen received his M.S. and B.S. degrees in computer science from Fudan University, China, in 2001 and 1998, respectively. He is currently a Ph.D. student in the Department of Computer Science and Engineering at the University of Texas at Arlington. His research interests include data management in mobile networks, mobile computing, peer-to-peer networks, and pervasive computing. Mohan Kumar is an Associate Professor in Computer Science and Engineering at the University of Texas at Arlington. His current research interests are in pervasive computing, wireless networks and mobility, active networks, mobile agents, and distributed computing. Recently, he has developed or co-developed algorithms for active-network based routing and multicasting in wireless networks and caching prefetching in mobile distributed computing. He has published over 90 articles in refereed journals and conference proceedings and supervised Masters and doctoral theses in the areas of pervasive computing, caching/prefetching, active networks, wireless networks and mobility, and scheduling in distributed systems. Kumar is on the editorial board of The Computer Journal and he has guest edited special issues of several leading international journals including MONET and WINET issues and the IEEE Transactions on Computers. He is a co-founder of the IEEE International Conference on pervasive computing and communications (PerCom)—served as the program chair for PerCom 2003, and is the vice general chair for PerCom 2004. He has also served in the technical program committees of numerous international conferences/workshops. He is a senior member of the IEEE. Mohan Kumar obtained his PhD (1992) and MTech (1985) degrees from the Indian Institute of Science and the BE (1982) from Bangalore University in India. Prior to joining The University of Texas at Arlington in 2001, he held faculty positions at the Curtin University of Technology, Perth, Australia (1992–2000), The Indian Institute of Science (1986-1992), and Bangalore University (1985–1986). Dr. Sajal K. Das is currently a Professor of Computer Science and Engineering and also the Founding Director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington (UTA). Prior to 1999, he was a professor of Computer Science at the University of North Texas (UNT), Denton where he founded the Center for Research in Wireless Computing (CReW) in 1997, and also served as the Director of the Center for Research in Parallel and Distributed Computing (CRPDC) during 1995–97. Dr. Das is a recipient of the UNT Student Association’s Honor Professor Award in 1991 and 1997 for best teaching and scholarly research; UNT’s Developing Scholars Award in 1996 for outstanding research; UTA’s Outstanding Faculty Research Award in Computer Science in 2001 and 2003; and the UTA College of Engineering Research Excellence Award in 2003. An internationally-known computer scientist, he has visited numerous universities, research organizations, government and industry labs worldwide for collaborative research and invited seminar talks. He is also frequently invited as a keynote speaker at international conferences and symposia.Dr. Das’ current research interests include resource and mobility management in wireless networks, mobile and pervasive computing, wireless multimedia and QoS provisioning, sensor networks, mobile internet architectures and protocols, parallel processing, grid computing, performance modeling and simulation. He has published over 250 research papers in these areas, directed numerous industry and government funded projects, and holds four US patents in wireless mobile networks. He received the Best Paper Awards in the 5th Annual ACM International Conference on Mobile Computing and Networking (MobiCom’99), 16th International Conference on Information Networking (ICOIN-16), 3rd ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2000), and 11th ACM/IEEE International Workshop on Parallel and Distributed Simulation (PADS’97). Dr. Das serves on the Editorial Boards of IEEE Transactions on Mobile Computing, ACM/Kluwer Wireless Networks, Parallel Processing Letters, Journal of Parallel Algorithms and Applications. He served as General Chair of IEEE PerCom 2004, MASCOTS’02 ACM WoWMoM 2000-02; General Vice Chair of IEEE PerCom 2003, ACM MobiCom-2000 and IEEE HiPC 2000-01; Program Chair of IWDC 2002, WoWMoM 1998-99; TPC Vice Chair of ICPADS 2002; and as TPC member of numerous IEEE and ACM conferences. He is Vice Chair of the IEEE TCPP and TCCC Executive Committees and on the Advisory Boards of several cutting-edge companies.Dr. Sajal K. Das received B.S. degree in 1983 from Calcutta University, M.S. degree in 1984 from Indian Institute of Science, Bangalore, and Ph.D. degree in 1988 from the University of Central Florida, Orlando, all in Computer Science. Zhijun Wang received the M.S degree in Electrical Engineering from Pennsylvania State University, University Park, PA, 2001. He is working toward the Ph.D. degree in Computer Science and Engineering Department at the University of Texas at Arlington. His current research interests include data management in mobile networks and peer-to-peer networks, mobile computing and networking processors.This revised version was published online in August 2005 with a corrected cover date.     

基于小波隐性马尔可夫模型的人脸检测

本文提出了一种基于小波隐性马尔可夫模型(WHMM)的人脸检测方法,根据隐性马尔可夫模型对人脸拓扑结构的约束以及小波变换的多尺度性和局部定位性,采用3状态的WHMM进行从粗到精的人脸检测。实验结果表明这种方法具有较高的检测速度与正确率及鲁棒性。     

基于HMM的系统可靠性建模研究

传统的系统可靠性分析需要检测系统中所有元件的故障状态，并不适用予系统的定期维护和保养检查。隐马尔可夫模型(HMM)是一种双重随机过程，能够解决随机不确定问题。通过对系统关键点的检测，经过复杂的网络运算综合得到系统状态的检测参数，给出了实现检测的相关网络模型以及相应的算法。     

基于最大熵的隐马尔可夫模型文本信息抽取

文本信息抽取是处理海量文本的重要手段之一.最大熵模型提供了一种自然语言处理的方法.提出了一种基于最大熵的隐马尔可夫模型文本信息抽取算法.该算法结合最大熵模型在处理规则知识上的优势,以及隐马尔可夫模型在序列处理和统计学习上的技术基础,将每个观察文本单元所有特征的加权之和用来调整隐马尔可夫模型中的转移概率参数,实现文本信息抽取.实验结果表明,新的算法在精确度和召回率指标上比简单隐马尔可夫模型具有更好的性能.     

基于混合高斯模型的三马尔可夫场红外图像分割

针对马尔可夫随机场在红外图像分割方面存在的问题,给出了一种基于混合高斯模型的三马尔可夫场红外图像分割算法.三马尔可夫场在马尔可夫随机场的基础上通过引入一个附加随机场和全体随机变量服从马尔可夫性假设,克服了马尔可夫场算法中对条件概率分布相互独立的要求,并赋予该附加随机场对目标和背景区域的标识作用,其中采用混合高斯模型作为三马尔可夫随机场的先验模型.仿真结果表明,文中提出的基于混合高斯模型的三马尔可夫场红外图像分割算法能够实现复杂背景的红外图像准确分割,得到较为理想的分割效果.     

基于领域模型的Web数据抽取与集成

Web数据抽取与集成的目的是提供面向领域的增值服务,结合领域数据特征,提出Web数据模式与领域数据模型.给出了基于Web数据模式的数据定位、抽取与基于领域数据模型的集成算法,并结合行业领域的需求,验证了模型和算法的有效性.     

针对电信运营商的大数据价值化经营研究

针对通信信息产业中的基础服务提供商——电信运营商展开分析,在结合其运营过程中掌握的大量数据的分类及分析的基础上,提出了运营商大数据价值化的核心途径：一是针对现有业务,运用大数据开展精细化运营,改善运营效率;二是针对潜在业务,进行基于大数据的资源性开发利用,通过与其他企业合作等方式挖掘数据内涵,创造新的价值。最后,提出了国内电信运营商基于大数据的价值化路径选择。