EMAP: An efficient mutual authentication protocol for passive RFID tags

Radio frequency identification (RFID) system is a contactless automatic identification system, which uses small and low cost RFID tags. The primary problem of current security and privacy preserving schemes is that, in order to identify only one single tag, these schemes require a linear computational complexity on the server side. We propose an efficient mutual authentication protocol for passive RFID tags that provides confidentiality, untraceability, mutual authentication, and efficiency. The proposed protocol shifts the heavy burden of asymmetric encryption and decryption operations on the more powerful server side and only leaves lightweight hash operation on tag side. It is also efficient in terms of time complexity, space complexity, and communication cost, which are very important for practical large-scale RFID applications.     

Robust stability and stabilization of discrete singular systems with interval time-varying delay and linear fractional uncertainty

The robust stability and robust stabilization problems for discrete singular systems with interval time-varying delay and linear fractional uncertainty are discussed. A new delay-dependent criterion is established for the nominal discrete singular delay systems to be regular, causal and stable by employing the linear matrix inequality (LMI) approach. It is shown that the newly proposed criterion can provide less conservative results than some existing ones. Then, with this criterion, the problems of robust stability and robust stabilization for uncertain discrete singular delay systems are solved, and the delay-dependent LMI conditions are obtained. Finally, numerical examples are given to illustrate the e?ectiveness of the proposed approach.     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

Design of robotic visual servo control based on neural network and genetic algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.     

一种新的修正恒模算法

阐述了恒模算法(CMA)、修正恒模算法(MCMA)和判决引导(DD)算法的基本原理。针对CMA和MCMA收敛速度慢,固定步长条件下收敛速度和剩余误差之间存在矛盾的缺陷,在分析CMA误差特性的基础上,利用DD算法误差函数的模值和判决器输出构造新的误差函数,提出了一种新的变步长MCMA。新算法在加快收敛速度的同时保持小的剩余误差。仿真结果表明,新算法比CMA和MCMA收敛速度快,均衡输出剩余码间干扰(ISI)小而且能克服相位偏移,具有很好的实用性。     

表面贴装过程仿真系统的研究与开发

文章从表面贴装技术(SMT)现状及特点开始,介绍多品种小批量电子产品中使用表面贴装技术存在的问题。针对这一情况对表面贴装过程仿真系统的需求进行了分析,并详细论述了仿真系统的文件预处理、仿真以及数据库维护三大主要模块设计,主要介绍采用OpenGL实现仿真模块的方法,同时展示仿真系统实现结果。最后总结了仿真系统的特点。     

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为载体的铁离子(Ⅲ)电极的研究

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为中性载体制备了PVC膜Fe3+离子选择性电极。该电极对Fe3+呈现出良好的选择性和近Nernst电位响应性能。电极斜率为21mV/dec,线性范围为3.0×10-5～1.0×10-1mol/L,检出限为1.0×10-5mol/L。采用交流阻抗技术研究了电极的响应机理,并将电极作为指示电极初步用于EDTA的电位滴定。     

基于2,4-二羟基苯甲酸铜(Ⅱ)为载体的硫氰酸根电极的电位响应

制备了基于一种简单的金属铜配合物2,4-二羟基苯甲酸铜(Ⅱ)(Cu(Ⅱ)DHBA)为载体的PVC膜硫氰酸根离子(SCN-)选择性电极。该电极在1.0×10-1～1.0×10-6mol/LSCN-浓度范围内呈现斜率为-59.5mV/dec的近Nernst电位响应,检测下限为9.1×10-7mol/L。利用紫外可见光谱及交流阻抗技术初步探讨了电极对SCN-呈现的选择性电位响应机理。该电极作为直接电位分析法的指示电极,成功运用于实验室废水中硫氰酸盐含量的测定。     

化学气相沉积法制备碳化铬薄膜的研究

以三（2,2,6,6-四甲基-3,5-庚二酮）化铬为前驱体,采用化学气相沉积（CVD）法在氧化铝（Al₂O₃）陶瓷基板上制备碳化铬（Cr₃C₂）薄膜,其中沉积温度为723 K至923 K,沉积时间为1 200 s。研究了不同沉积温度对Cr₃C₂薄膜的相组成、择优取向、宏观表面、微观结构及电学性能的影响。结果表明,在723 K至923 K下制备得到具有高度（130）择优取向的Cr₃C₂薄膜。随着沉积温度的升高,Cr₃C₂薄膜表面先由光滑变粗糙,后逐渐变光滑;薄膜晶粒呈椭球型生长;薄膜的厚度先增加后减小,从而导致电阻先减小后增大。在798 K时制备得到厚度最大且电阻最小的（130）择优取向的最佳Cr₃C₂薄膜。同时,在实验条件下Cr₃C₂薄膜表面存在少量的碳和Cr₂O₃。     

重芳烃轻质化催化剂n（Ni）/n（Ni+Mo）的优化与分析

以β分子筛为载体,在保持金属总负载量不变的情况下,采用等体积浸渍法制备了4种不同n（Ni）/n（Ni+Mo）的催化剂。分别采用X射线衍射（XRD）、比表面积测试（BET）、氨程序升温脱附（NH₃-TPD）、氢程序升温还原（H₂-TPR）、氢程序升温脱附（H₂-TPD）和热重-差热分析（TG-DTG）等方法对催化剂进行了表征。结果表明,4种催化剂的酸量和酸强度相近,在n（Ni）/n（Ni+Mo）等于基准+0.2时,Mo与载体之间的相互作用最弱,其氢气吸附量最多且积炭量最少;采用某炼厂重整C₁₀⁺ 重芳烃对4种催化剂进行评价,结果表明n（Ni）/n（Ni+Mo）等于基准+0.2催化剂具有最优的催化活性和稳定性。上述结果表明,影响重芳烃轻质化催化剂活性和稳定性的关键因素是催化剂氢气吸附量的多少,氢气吸附量越多金属表面的溢流氢效应越明显,积炭前驱体被溢流氢及时消除,从而保护了催化剂的加氢活性中心不被积炭覆盖,有助于催化剂在较高活性下保持稳定。