ECO布局中的电源线/地线网络局部SOR分析方法

供电电压直接决定芯片性能,在IC设计的各个阶段考虑供电电压约束具有重要的意义.受制于电源线/地线(P/G)网络分析的高复杂性,尽管供电电压已成为布图规划设计中的一个设计约束,但目前在布局设计中还未考虑供电电压约束.有别于ICCG,SOR等经典的全局分析算法,提出了一种局部的连续过松弛方法(SORPECO),并在ECO布局过程中对P/G网电压约束进行高效的分析.基于前一个布局的P/G网电压分布,针对ECO试探布局中某些轻微设计变动,SORPECO只需对这些设计变动的局部变化周边区域进行松弛,以更新P/G网电压分布.受益于P/G网络分析的局部性,SORPECO拥有局部、高效和高精度等优点.实验结果表明,与通常用于布图规划的传统高效的ICCG算法相比,SORPECO不仅精度损耗几乎可以忽略(最大误差0.062%),而且可以加速2个数量级.     

基于Kd树递归区域划分的分布式空间连接查询

随着空间信息应用需求的不断增长,分布式空间查询处理已经成为空间数据库领域一个重要的研究问题,其中应用最广也是最复杂的一类查询是分布式空间连接查询,分布式空间连接操作的计算代价与传输代价都非常高。目前处理该问题的策略大都要求空间数据集上存在索引并且对数据分布敏感,然而在某些情况下,这个前提并不存在。面对这个问题,本文提出一种基于Kd树递归区域划分的分布式空间连接策略,该策略以最小化网络数据传输代价为目标,基于任务分治的思想对连接区域进行递归划分。实验表明,该策略在不同数据分布情况下均优于传统查询策略,能有效地减小网络传输代价,表现出较好的性能。     

浅谈校园网络结构的规划与设计

随着信息化时代到来,校园网建设越来越受到各高校重视.本文从校园网设计原则出发,着重描述校园网架构以及各网络层次结构的选择和建议.     

利用Word与Excel轻松制作考号标签

在教务处干了一年多,繁琐的事特别多。这不,又到会考了,我们教务处又得布置考场。光好几百人的考场标签,就得写很久。最近上网,看到了几篇文章,综合运用一下,就把这个事解决了。具体怎么解决的,还是听我细细道来。     

VB程序设计中控制结构的应用分析

VB程序设计中控制结构是关键的编写程序的基础,只有在程序设计中很好的将三种控制结构很好的结合起来,才能避免错误的出现,导致死循环。     

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.     

球形全景图像的自动拼接

近年来,球形全景的虚拟现实方法开始发展起来.在已有的球形全景图像拼接方法中,基本上都是"半自动"的拼接方法,需要很多人工操作,效率低下、成本高昂.提出一种球面自寻匹配的拼接算法.该算法通过把多幅图像映射到一个合适的球面上,自动调整每幅图像的插入点,使得重合位置的差值图像灰度累积平均值最小,然后球面的图像反映射成平面形式,最终得到拼合图像.该算法可使程序实现完全自动化,使人工操作降至最低,从而降低全景图像的制作成本.     

Network traffic classification based on ensemble learning and co-training

Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.     

Cryptanalysis of RSA for a special case with d > e

In this paper, we study the RSA public key cryptosystem in a special case with the private exponent d larger than the public exponent e. When N ^0.258 ⩽ e ⩽ N ^0.854, d > e and satisfies the given conditions, we can perform cryptanalytic attacks based on the LLL lattice basis reduction algorithm. The idea is an extension of Boneh and Durfee’s researches on low private key RSA, and provides a new solution to finding weak keys in RSA cryptosystems. Supported partially by the National Basic Research Program of China (Grant No. 2003CB314805), the National Natural Science Foundation of China (Grant Nos. 90304014 and 60873249), and the Project funded by Basic Research Foundation of School of Information Science and Technology of Tsinghua