片上网络中一种单周期2GHz无缓冲路由器

近年来,无缓冲路由器由于不需要缓冲器而成为片上网络低开销的解决方案。为了提高无缓冲路由器的性能,提出一种单周期高性能无缓冲片上网络路由器。该路由器使用一个简单的置换网络替换串行化的交换分配器与交叉开关以实现高性能。虚通道路由器与基准无缓冲路由器相比,该路由器在TSMC65nm工艺下可以以较小的面积开销达到2GHz的时钟频率。在合成通信负载与真实应用负载下的模拟结果表明,该路由器的包平均延迟远小于虚通道路由器和其他无缓冲路由器。     

协同通信中的基于信道统计信息的联合中继选择的功率分配

分析Rayleigh信道下分集/无分集的AF( Amplify and Forward)和DF( Decode and Forward)模式单中继传输的平均中断概率,给出解析表达式.在总功率约束下推导了4种模式下的统计最优功率分配的解析表达式来最小化系统的平均中断概率,并提出通过最佳中继选择进一步降低系统的平均中断概率...     

基于复杂网络的联合作战指挥体系研究

以作战指挥理论需求为牵引,从复杂网络的角度对联合作战指挥体系问题进行网络抽象,将指挥体系中的指挥机构作为网络的节点,将指挥关系作为网络的边,按照指挥体系历史发展的规律,从简单到复杂,分别构建了传统层级纵长树状型的指挥体系网络模型,在树状模型基础上,增加了横向联系但依然具有层级结构的信息共享改进型的指挥体系网络模型,以及...     

导弹两种蛇形机动对反导舰炮突防效果比较

为探讨反舰导弹的近距离机动突防能力,用仿真方法计算了反舰导弹末端作水平蛇形机动和垂直蛇形机动时对近程反导舰炮武器系统的突防概率,以图示方式给出了两种机动周期、两种舰炮射速、多种机动幅值条件下的计算结果.结果表明,短周期大幅度的频繁机动可显著提高反舰导弹的突防能力,且两种蛇形机动的突防效果大致相同;而提高舰炮射速则是削弱...     

一种新的避免航迹合并的比例联合概率关联算法

通过对传统的联合概率数据关联算法(JPDA)在平行临近及小角度交叉目标的关联跟踪中引起的航迹合并问题的分析,提出了一种基于最大交互概率的改进比例联合概率数据关联算法.该算法找出距离两逼近目标的交互概率中心最近的有效观测,引入一比例因子对该观测与所有目标的关联概率进行修正,将逼近目标“拉开”,从而抑制了航迹的合并.仿真结...     

扩展信念网络模型的军事情报检索系统设计

着眼信息化战争条件下军事情报分类、处理困难的现实,在深入分析军事情报检索系统特点基础上,提出一个基于分类的自动存储和利用查询术语同义词关系扩展信念模型的检索军事情报检索原型系统,详细描述了其存储和检索设计思路.     

无线Mesh网络技术研究

无线Mesh网络是新近出现的一种无线多跳网状拓扑网络,在多领域被广泛的应用。简要介绍了无线Mesh网络的结构、特点及应用,并重点分析了影响无线Mesh网性能的主要技术因素。     

IED casualties mask the real problem: it's us

Casualty figures suggest that the US/Allied Counter Improvised Explosive Device (C-IED) policy and the present allocation of national assets, resources, and intellectual capital have not been very successful. A number of explanations for why this has been the case are discussed and critiqued here.     

Crime facilitation purposes of social networking sites: A review and analysis of the ‘cyberbanging’ phenomenon

There have been growing claims in media circles and law-enforcement settings that street gangs and criminal groups are turning to Internet-based social networking sites for various reasons ranging from the showcasing of their images and exploits to the suspected recruitment of members. The present study investigates whether such a trend is, in fact, in place. The presence of street gangs on these Internet sites is referred to as cyberbanging. While there is some anecdotal evidence suggesting that gangs are turning to social networking sites, there is little available research on exactly how street gangs and criminal groups use the Internet. Our main argument is that gang culture is in many ways an individualized phenomenon and this feature ties in directly with recent assessments of the Internet as a setting that is governed by a process of networked individualism. This theoretical link between the individualized gang setting and the presence of gang members on social networking sites helps us understand why recruitment is improbable even in a context where people are openly diffusing their image and exploits to a growing number of Internet users. The empirical segment of this research adds to this general outlook. Based on a keyword search of over 50 street gang names, the three main social networking sites (Twitter, Facebook, and MySpace) were monitored for street gang presence. Results illustrate that gang presence on social networking sites is linked primarily to promoting a general gang or street culture through individual displays. In regard to the visitors to such sites, there is no evidence that they are being tricked or manipulated in any way. They are, however, showing their curiosity in regard to such groups and, for those who share their comments and opinions, signs of support are evident. Thus, whereas criminal gangs are not proactively using the Internet to convert anyone into being gang members, social networking sites are creating a new venue for people who share or are sensitive to the values underlying street gang lifestyle to come together. These sites essentially create a new convergence setting for gang members to interact with a wider number of people who would probably never have been exposed to their lifestyles and exploits through physical interactions. The study's conclusion extends these findings toward further research in this area, as well as outlining the more relevant implications for law-enforcement monitoring of this growing phenomenon.     

信息推荐系统中的朋友关系预测算法设计

伴随着互联网规模的不断扩展,信息过载问题越来越突出。信息推荐系统被视为解决信息过载问题的最有效方法。然而目前的方法大多数仅考虑用户独立的反馈,而忽略用户的社会属性对推荐的重要作用,这对信息推荐系统的性能会造成巨大的影响。为此,本文提出了基于朋友关系预测的信息推荐算法,将用户的社会关系预测引入信息推荐过程中,分别基于用户的拓扑信息及历史交互信息建立用户社会关系的存在性判定及关系类型判定,并利用线性回归分析方法和逻辑回归分析方法实现了基本特征的融合。最后,通过在Epinions和Slashdot真实数据集上的实验证明,本方法能够有效提高用户社会关系预测的准确性。