基于RSA的数字签名算法及其快速实现

在基于RSA的数字签名算法中,直接决定实现效率的是大数模幂运算。对基于二进制的Montgomery算法进行了改进,并将其应用于大数的模幂运算中。改进后的算法在保证算法快速实现的同时,又节省了算法运算空间。     

基于Eclipse的警务系统密码平台

该文提出基于Eclipse的密码系统平台,作为警务平台的辅助工具,为警务系统中消息和文件的传播提供了一个加密解密系统,用户可以在密码系统中定制不同的加密算法将内部资源进行加密和解密,加强了信息在传播中的安全性,同时,这也是一个很好的学习密码学以及应用各种加密解密算法的平台。     

基于Chan算法的无源雷达定位精度分析

该文对无源雷达时差定位中的Chan算法进行分析研究,主要分析了非线性定位方程组的求解技术及其与之相关的定位精度、定位模糊问题,讨论不同布站对定位模糊的影响,时差测量与站址测量误差对定位精度的影响。仿真结果表明,时差测量误差,站址测量误差．布站形式、目标辐射源与各接收站几何位置关系都对定位精度有很大影响。     

A single layer zero skew clock routing in X architecture

With its advantages in wirelength reduction and routing flexibility compared with conventional Manhattan routing, X architecture has been proposed and applied to modern IC design. As a critical part in high-performance integrated circuits, clock network design meets great challenges due to feature size decrease and clock frequency increase. In order to eliminate the delay and attenuation of clock signal introduced by the vias, and to make it more tolerant to process variations, in this paper, we propose an algorithm of a single layer zero skew clock routing in X architecture (called Planar-CRX). Our Planar-CRX method integrates the extended deferred-merge embedding algorithm (DME-X, which extends the DME algorithm to X architecture) with modified Ohtsuki’s line-search algorithm to minimize the total wirelength and the bends. Compared with planar clock routing in the Manhattan plane, our method achieves a reduction of 6.81% in total wirelength on average and gets the resultant clock tree with fewer bends. Experimental results also indicate that our solution can be comparable with previous non-planar zero skew clock routing algorithm. Supported in part by the National Natural Science Foundation of China (Grant No. 60876026), and the Specialized Research Fund for the Doctoral Program of Higher Education (Crant No. 200800030026)     

浅析无线局域网的安全性

无线局域网已经成为网络体系结构的重要组成部分,同时无线局域网的安全性也越来越受到人们的重视。文章主要介绍了无线局域网安全的相关概念和基本机制,从加密和认证两个方面,重点分析了无线局域网的安全缺陷,最后讨论了改进的安全措施和方案。     

数据挖掘在入侵检测中的应用探析

介绍了入侵检测系统的基本概念和相关技术,阐述了数据挖掘在入侵检测系统研究中常用的技术,提出了基于数据挖掘的入侵检测系统和一种改进的Apriori算法,并详细介绍了其中的检测分析系统。     

中文分词算法解析

中文分词是计算机中文信息处理中的难题,而中文分词算法是其中的核心,但由于中英文环境中语素的不同特点,使得中文必须要解决分词的问题。这篇文章较为深刻的阐述了中分分词的算法,基于对分词算法的理解和对匹配法分词的分析,对最大匹配分词方法进行了较深入的研究探讨,提出了什么算法是解决分词效率的最佳方法以及各种方法的比较优劣等问题,及可能产生的歧义．对不同的算法给予了充分的解释,通过对各种算法的比较,总结出了比较常用和效率较高的算法。     

基于黄金分割法的遗传算法改进

该文受到黄金分割的思想的启发,将二维黄金分割算法的形式推广到三维,再将三维的形式推广到高维,从而得到一种数值结果很好的变形的黄金分割混合遗传算法。     

Multivariable direct adaptive decoupling controller using multiple models and a case study

In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive controller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured disturbance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system. Supported by the National Natural Science Foundation of China (Grant Nos. 60504010, 60864004), the National High-Tech Research and Development Program of China (Grant No. 2008AA04Z129), the Key Project of Chinese Ministry of Education (Grant No. 208071), and the Natural Science Foundation of Jiangxi Province (Grant No. 0611006)     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)