Evolutionary algorithms and de novo peptide design

One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the evolutionary algorithms implemented.     

利用框架理论对信号进行重建

在Hilbert空间中讨论了框架的一些重要的性质,定义了一个框架算子,并讨论了该算子的性质。根据实际问题构造了一框架,使信号在此框架下的投影正好为非均匀采样值。给出了一种框架界的估计方法和重建原信号的迭代算法,最后,用仿真验证了此算法的有效性。     

基于DOCSIS标准的HFC数据加密算法的分析

基于DOCSIS标准的增强型基线保密性接口（BPI＋）采用56bit DES加密分组数据，采用二密钥三重DES加密通信量加密密钥。这种多重加密技术的应用为HFC网络的数据传输提供了相当于或更好于专线级的网络安全与加密性能。     

一种有效的优化的多载波通信系统的功率分配算法

本文提出一种关于DMT系统传输纯数据流业务的最佳的功率分配算法，该算法使用了一种有效的表格查手工艺工和拉格朗日乘法器对分搜索办法，能够较快的收敛到最佳的功率点。同时，易于用硬件和软件实现。     

基于局部多项式逼近的变参数梯度估计算法

本文分析了梯度辨识算法跟踪时变系统的缺点，提出了一种新的基于局部多项式逼近的变参数梯度估计算法。本算法具有良好跟踪性能特别适用于时变系统跟踪且其对噪声的敏感程度与一般梯度估计方法相当。仿真结果验证了其良好性能。     

集群计算机上基于BSP模型的并行算法及其程序设计

文章分析了BSP模型和集群计算机的特点，给出了在集群计算机根据BSP模型设计并行算法及其软件的原则，举例说明了利用BSP模型代价公式定性分析并行算法性能的方法，并利用实验结果验证了得出的结论。结果表明模型的成功地抽象出了集群计算中影响算法性能的诸种因素，并通过代价公式反映出来，利用代价公式可以基本准确地估计并行算法代价，这对集群计算机上的并行算法及其软件及设计和优化具有重要的指导意义。     

第三代短波自动链路建立系统特性分析及仿真实现

分析了第三代短波自动链路建立系统的工作特性及技术特点，并对系统仿真实现的原则及一些细节问题进行了较深入的探讨，同时对仿真实现中涉及的链路质量分析（LQA）算法进行了研究。     

An EM‐based adaptive multiple target tracking filter

Tracking targets of interest is one of the major research areas in radar surveillance systems. We formulate the problem as incomplete data estimation and apply EM to the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The advantage is that the measurement‐update deals with multiple measurements in parallel and the parameter‐update estimates the system parameters on the fly. Experiments tracking separate targets in parallel show that tracking maintenance ratio of the proposed system is better than that of NNF and RMS position error is smaller than that of PDAF. Also, the system parameters are correctly obtained even from incorrect initial values. Copyright © 2001 John Wiley & Sons, Ltd.     

基于双因子认证技术的网络身份识别

本文提出了一种基于动态双因子认证技术的网络身份识别方法,用户每次登录的口令是利用系统时间和用户ID通过MD5加密算法计算得到,其通过MD5加密后生成的登录口令是随机的,这将进一步提高开放网络环境下身份识别的可靠性和安全性。同时,本文还提出网络身份识别技术可以应用到各类网站的服务器上,用来完成对用户身份的识别,以提高网络系统的安全性。     

Optimal ensemble construction via meta-evolutionary ensembles

In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.