基于IPTV的网络教学系统的设计

以IPTV技术为支持的远程教育模式具有投入少且效果好的特点,在远程教育中发挥着越来越重要的作用.利用P2P和流媒体技术为支撑,设计和实现了具有IPTV交互特点的远程教育系统.该系统包括课后练习,测试以及答疑等学习活动,实现集在线学习,游戏娱乐,生活服务于一体的互动数字化教育,并有效解决实时课堂传输中视频质量的问题.     

基于 Django 的编程题互评系统的设计与实现

学好《程序设计》这门课的重中之重就是编程练习。然而,程序作业的判分是一项单调且繁琐的工作,学生数量上的扩大使这一问题更加严重。解决这个问题的方法是学生之间互相评分,这样,学生可以通过电脑提交程序作业并不受时间和地点的约束,可以立即得到反馈。主要介绍基于 Django 的编程题互评系统,通过对作业分配管理事务的流程化处理,实现对网络互评资源的系统化配置与管理。     

P2P技术分析与流量管理研究

随着P2P应用的日益流行,P2P流量消耗了网络的大量带宽,已经影响到了互联网传统业务的服务质量。为解决这一问题,网络运营者和研究人员在P2P流量的管理技术上进行了大量的探索研发工作,主要有P2P流量阻塞、缓存和本地化疏导等技术。首先对P2P技术做了简单概述,然后分析了影响P2P流量分布的重要技术因素,之后综述了最新的P2P流量管理技术,最后做了总结。     

一种彩色图像去噪的快速PGF滤波算法

提出了一种去除彩色图像噪声的快速PGF滤波算法.首先介绍了基本的PGF滤波算法,然后在分析PGF滤波算法的基础上,提出了一种简化的PGF滤波算法.最后,对该算法进行了仿真研究,仿真结果表明该算法在完成图像的噪声平滑和边缘保持的同时实现了图像的快速滤波.     

基于内容分组与能力匹配的邻居选择算法

邻居选择算法是影响P2P文件共享系统的整体吞吐量和带宽利用率的关键技术之一.目前BT类P2P文件共享系统中的一些常用邻居选择算法一般存在着邻居节点间的内容可交换性差和带宽利用率低等问题.提出一种新的邻居选择算法,将节点按内容分组,由节点上报的上传、下载的字节数计算出带宽能力,让能力匹配的节点成为邻居.计算机仿真实验表明,新算法显著地提高文件共享系统的整体吞吐量,减少用户的平均下载时间,从而有效地改善P2P文件共享系统的整体性能.     

CoBTx-Net: A model for business collaboration reliability verification

Collaborative business process can become unreliable when business partners collaborate in a peer- based fashion without central control. Therefore, reliability checking becomes an important issue that needs to be dealt with for any generic solution in managing business collaboration. In this paper, we propose a novel Choreographical Business Transaction Net (CoBTx-Net) to model collaborative business process and to manage the collaboration by individual participants. Furthermore three reliability properties named Time-embedded dead marking freeness, Inter-organizational dead marking freeness, and Collaborative soundness are defined based on CoBTx-Net to verify (1) the violation of time constraint, (2) collaborative logic conflicts, and (3) the improper termination from individual organizations.

过程质量管理实践的组合应用

随着项目的复杂度日趋复杂,设计及实现的重构日趋频繁。项目对质量管理的要求也越来越高。项目质量管理所涉及的内容是多方面的,本文根据实践积累,结合作者所管理项目的质量提升过程,讲述了几种项目过程质量管理方法的组合应用,并对项目质量管理工作的一些心得及经验进行了总结。     

基于PGF检测的改进彩色图像滤波方法

针对彩色图像中的噪声污染问题,提出一种改进的开关自适应矢量滤波方法。通过对噪声图像进行同组滤波器检测得到滤波窗口内满足检测条件的噪声像素个数,当满足条件的像素个数较少时,直接对检测出的噪声进行矢量中值滤波,当满足条件的像素个数较多时,采用改进的自适应矢量中值滤波器进行2次检测后再滤波。实验结果表明,该方法能提高噪声检测的准确性,并能更好保护滤波的细节。     

基于多智能体的大众生产系统稳定性研究

采用多智能体建模与仿真的方法,基于REPAST平台分别研究大众生产系统在智能体数量不能增加的封闭系统中以及智能体数量能够增长、具有生命期限且网络结构是动态变化的开放系统中的稳定性。分析结果表明,在封闭系统中,智能体数量无法大量增长,外部干扰会通过智能体之间的网络拓扑结构扩大化,从而使系统走向崩溃;而在开放系统中,智能体数量能够快速增长,并具有生命期限时,系统最终稳定时会运行在较低水平上,但整个系统不会出现崩塌现象。     

A general distributed scalable grid scheduler for independent tasks

We consider non-preemptively scheduling a bag of independent mixed tasks (hard, firm and soft) in computational grids. Based upon task type, we construct a novel generalized distributed scheduler (GDS) for scheduling tasks with different priorities and deadlines. GDS is scalable and does not require knowledge of the global state of the system. It is composed of several phases: a multiple attribute ranking phase, a shuffling phase, and a task-resource matched peer to peer dispatching phase. Results of exhaustive simulation demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms existing approaches by 10%–25% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS is slightly better. Thus, GDS not only maximizes the number of mission-critical tasks meeting deadlines, but it does so without degrading the overall performance. The results have been further confirmed by examining each component phase of GDS. Given that fully known global information is time intensive to obtain, the performance of GDS is significant. GDS is highly scalable both in terms of processors and number of tasks—indeed it provides superior performance over existing algorithms as the number of tasks increase. Also, GDS incorporates a shuffle phase that moves hard tasks ahead improving their temporal fault tolerance. Furthermore, since GDS can handle mixed task types, it paves the way to open the grid to make it amenable for commercialization. The complexity of GDS is O(n²m)$O\left(n^{2}m\right)$ where n$n$ is the number of tasks and m$m$ the number of machines.