面向分组密码的NCL电路处理模型

根据分组密码操作方式的特点,提出一种面向分组密码处理的NCL电路模型。采用双异步流水线并行工作方式、线性握手和非线性握手混合的握手方式以及基于NCL闭环流水线的控制方式,将AES算法在该模型上进行映射,使用异步电路综合工具Balsa在1 µm工艺下进行综合和仿真,结果证明该模型有效。     

A bandpass filter with adjustable bandwidth and predictable transmission zeros

In this article, a microstrip bandpass filter with an adjustable bandwidth and predictable transmission zeros is proposed. The proposed filter is implemented by combining two hairpin edge‐coupled resonators with interdigital capacitors. Compared to typical edge‐coupled filters, the proposed filter provides a wider bandwidth resulting from a higher coupling strength between its resonators. To further increase the coupling and consequently the bandwidth, a pair of etched slots in the ground plane is used. By adjusting the geometrical parameters of the interdigital capacitors and etched slots, the bandwidth can be easily adjusted. The filter features two transmission zeros, which are determined by means of the semi‐analytical model developed as part of this work. Furthermore, the proposed filters can be cascaded to obtain a sharper cutoff frequency response. Frequency responses of the filters from measurements are in good agreement with those simulated using IE3D in the 5–9 GHz range. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

多时间粒度序列模式挖掘

序列模式挖掘是数据挖掘的一个重要问题.传统的序列模式仅能揭示频繁出现的项目以及出现的顺序,但不能揭示在前续项目出现的情况下,后续项目出现的时间.在本文中,引入一种新的多时间粒度序列模式,模式中相邻项目之间的转换时间采用从原数据集中导出的、多时间粒度下的最小有界时间区间和平均时间标注.建立了多时间粒度序列模式挖掘模型,提出了一种新的多时间序列模式挖掘算法MG-PrefixSpan.实验表明,算法是有效的.     

广义指定多个验证者有序多重签名方案

广义指定多个验证者签名允许签名持有者指定多个签名验证者,有序多重签名则可满足多个签名者以严格的次序进行签名的要求。根据上述特性,提出一种基于身份的广义指定多个验证者有序多重签名方案,采用类短签名的形式构造签名,并在随机预言模型下证明方案的安全性。分析结果表明,该方案具有较高的效率,可抵抗适应性选择消息和身份攻击。     

基于闭合序列模式的减量挖掘算法

针对数据库减量时不断重复挖掘的问题,在已有闭合序列模式算法PosD*的基础上,提出一种减量挖掘算法 DePosD*。通过移动频繁和非频繁闭合序列集合之间的数据,在原有挖掘结果上直接进行更新,减少挖掘的时间。实验结果证明,在减量过程中该算法的时间效率与PosD*相比有所提高。     

A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA

We have developed a neural-network-based fault diagnosis approach of analog circuits using maximal class separability based kernel principal components analysis (MCSKPCA) as preprocessor. The proposed approach can detect and diagnose faulty components efficiently in the analog circuits by analyzing their time responses. First, using wavelet transform to preprocess the time responses obtains the essential and reduced candidate features of the corresponding response signals. Then, the second preprocessing by MCSKPCA further reduces the dimensionality of candidate features so as to obtain the optimal features with maximal class separability as inputs to the neural networks. This simplifies the architectures reasonably and reduces the computational burden of neural networks drastically. The simulation results show that our resulting diagnostic system can classify the faulty components of analog circuits under test effectively and achieves a competitive classification performance.     

基于序列分析的僵尸网络检测模型

现有的IRC botnet检测技术不适合控制命令交互不频繁的botnet检测。为了实现小规模隐秘僵尸网络的检测,提出了一种基于序列分析的僵尸网络检测模型,对现有的被动检测技术进行补充。讨论了几种探测技术和检测算法,根据客户端响应类型选择检测算法,分析了平均检测轮数,只须观察少量的命令控制交互,能够对单个或多个IRC僵尸主机进行检测。实验结果表明,在保证误报率和漏报率的前提下该方法能在预定检测轮数内完成判定。     

改进工作集选择策略的序贯最小优化算法

针对标准序贯最小优化(sequential minimal optimization, SMO)算法采用可行方向工作集选择策略所带来的缓存命中率低下问题,给出了SMO类型算法每次迭代所带来的目标函数下降量的二阶表达式,并据此提出了一种改进的工作集选择策略.新策略综合考虑算法收敛所需的迭代次数及缓存效率,从总体上减少了核函数计算次数,因此极大提高了训练效率,并且,它在理论上具有严格的收敛保障.实验结果表明,核函数越复杂,样本维度越高,缓存容量相对训练样本的规模越小,改进工作集选择策略的SMO算法相较于标准SMO算法的性能提高就越显著.     

Job-shop排序问题的遗传强化学习算法

针对Job-shop排序问题的复杂性,提出一种遗传强化学习算法对其求解。通过引入多个随机变量,把Job-shop排序问题转换成多阶段决策问题,通过仿真手段构建作业排序问题模型环境,求取系统性能指标并保证解的可行性。设计一个多智能体Q-Learning算法和遗传算法相结合的算法用于解决Job-shop排序问题。仿真优化实验结果验证了该算法的有效性。     

红外多气体传感器设计

针对甲烷、二氧化碳和一氧化碳三种气体的同时检测,设计出一种红外多气体传感器.该传感器将探测器和红外光源集成在光学气室内.在对气室进行设计时,首先确定气室高度为25 mm,随后通过计算和软件仿真分析确定气室的半径为10 mm.设计完成外围电路并对传感器进行测试,结果显示:对甲烷、一氧化碳和二氧化碳的检测范围可达到(0~40 000)×10-6.