Towards capacity and profit optimization of video-on-demand services in a peer-assisted IPTV platform

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has three major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud” model and a “physical” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology. Second, we propose an algorithm called Zebra that pre-stripes content across multiple peers during idle hours to speed up P2P content delivery in an IPTV environment with limited upload bandwidth. We also perform simulations to measure Zebra’s effectiveness at reducing load on the content server during peak hours. Third, we provide a cost-benefit analysis of P2P video content delivery, focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model, while the conventional no-P2P model generates more profits when the request rate is low. The flat-reward model generally falls in between the usage-based model and the built-in model in terms of profitability except for low request rates. We also find that built-in and flat-reward models are more profitable than the usage-based model for a wide range of subscriber community sizes. Funding for J. Rahe’s research has been provided by AT&T Labs, the State of California under the MICR Oprogram, and by the Toshiba Corporation. Zhen Xiao is partially supported by China MOST project (2006BAH02A10).     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.     

Recognizing control chart patterns with neural network and numerical fitting

Control chart has been widely used to determine whether the state of machining process is stable or not, and pattern recognition technology is often used to automatically judge the changing modes of control chart. It is because that the abnormal patterns of a control chart can reveal the potential problem of machining quality. In order to improve the recognition rate and efficiency of control chart patterns, a neural network-numerical fitting (NN-NF) model is proposed to recognize different control chart patterns. A back propagation (BP) network is first used to recognize control chart patterns preliminarily. And then, numerical fitting method is adopted to estimate the parameters and specific types of the patterns, which is different from the traditional neural network-based control chart pattern recognition methods. Based on this, Monte Carlo simulation is used to generate training and testing data samples. The results of simulated experiment show that training time of this NN-NF model can be reduced. At the same time, the recognition rate can also be improved. At last, a real example is also provided to illustrate the NN-NF model.     

电磁流量计示值误差校准不确定度评定

针对目前电磁流量计在出厂时只标注有允许误差或准确度等级,其不确定度未予表述这一现状,依据<电磁流量计检定规程>JJG 1033-2007,进行水流量标准装置实流校验试验.分别介绍了评定的测量依据、环境条件、测量标准、被测对象、测量过程以及评定结果的使用.示值误差校准不确定度分析评定表明,该标定装置的不确定度对于电磁流量计的测量非常重要.     

基于MSP430的无功补偿系统设计与实现

针对电网中无功补偿问题,介绍了一种基于MSP430F449的无功补偿系统,简要地论述了系统的硬件和软件设计.系统能够通过430F449内部自带的A/DC12模块实现电流、电压的采集,同时利用定时器的捕获功能来计算信号相位差,求得输出控制量,控制自动投切并联补偿电容器组,实现系统的无功补偿.     

无线传感器网络TBCCA树形分簇算法

针对当前无线传感器网络分簇和拓扑控制算法中存在的问题,例如能耗过大、负载不均、计算复杂性高和存在额外开销等,提出了一种新型的TBCCA(tree-based clustering construction algorithm)分簇算法.该算法基于正三角形簇树结构,并利用RSSI(received signal strength indicator)值控制簇半径,从而产生3类集合--Near集、Common集和Medium集,及其相应的选择策略.其后,根据树的度数和结构,设计了簇树的建立算法.性能分析和实验仿真表明,相对于现有的几种算法,例如TopDisc和DLMST等,TBCCA算法具有较低的计算复杂性;与Leach协议和HEED协议相比,该算法能在保证较高连通性与覆盖率的同时,有效节约能量,均衡负载,延长网络的生命周期.     

JPEG隐密分析中的重压缩检测

重压缩检测是多级隐密分析中关键的预处理部分,高准确率的重压缩检测是隐密分析获得更高性能的重要前提条件.深入研究了重压缩对于JPEG图像各种特征的影响,基于此提出了一种融合直方图分布特征、Benford特征、DFT特征的重压缩检测算法.仿真实验表明,该算法具有更高的检测率,能够适用于JPEG多级隐密分析中的重压缩检测.     

基于VQ和LBG的移动AdHoc网络中分簇算法

应用信息论与数据压缩中的矢量量化理论和LBG算法,在簇和Voronoi胞腔、簇首和码矢、节点和矢量之间建立起——对应的关系,把分簇的过程转换成二维矢量量化中胞腔的分裂过程。设计一个以簇结构稳定性高和负载均衡度好为目标的移动自组网分簇算法,仿真结果显示,分簇算法能够迅速地进行分簇,并且簇的结构具有较高的稳定性。     

混合式P2P系统中基于缓存的多项查询方法研究

首先从混合式P2P网络拓扑结构出发,结合DHT思想,提出了基于DHT的层次化P2P网络模型.其次根据在文档集巨大的情况下,用户提交的查询不可能"面面俱到",实际用来回答查询的文档仅仅是文档集中很小的一部分这一思想,在层次化P2P模型的超级节点中建立了分布式缓存,运用分布式索引与缓存技术,提出一种新的方法来解决多项查询问题.即由多项查询中的某个关键字key,根据hash函数定位到负责该key的超级节点,查询该节点上的分布式索引得到缓存具体存储位置,最终将结果返回给用户,如若缓存中没有所要查询的内容,则广播该查询,同时根据系统中的历史广播查询信息来计算某个待选缓存项的利益值,利益最大的待选项加入缓存.一般针对多项查询的泛洪算法往往会造成巨大的网络信息量,提出的方法牺牲了超级节点上一小部分的存储力,缓解了多项查询造成的网络拥挤现象.同时,基于DHT的层次化P2P模型也具有很好的稳定性,不会因为大量节点的动态加入或者退出而无法进行多项查询.