基于混沌加密的无线射频识别安全机制

为使无线射频识别(RFID)系统安全机制同时具有较高安全性和较好的运算效率,提出一种将混沌技术应用于RFID系统信息保护的方案。采用一种初始值可变的Logistic映射产生混沌密码序列,并对数据加密。经相关分析和仿真,证明该RFID安全机制实现简单、运行速度快、成本低,完全符合RFID系统的安全要求。     

基于几何分布的RFID标签数目估算方法

已有的标签数目估算方法准确率低、估算范围小,且标签数目增加时,时隙数目须成比例增加,否则估算误差率将大幅提高。针对上述问题,提出一种基于几何分布的无线射频识别(RFID)标签数目估算方法。在估算标签数目时,将帧长设定为固定值,并在标签中采用多个哈希函数,以减少几何分布偏差,降低估算误差率。实验结果表明,在时间复杂度、空间复杂度不高于其他算法的前提下,该方法能降低估算误差率。     

基于代理随机数的低成本RFID安全协议

针对低成本电子标签安全隐私保护能力和防跟踪能力较弱的问题,采用读写器代理生成随机数的方法,提出一种面向大容量被动型标签的新型无线射频识别(RFID)安全认证协议。理论分析证明,该协议具有前向安全性,能够防止位置隐私攻击、窃听攻击,保障数据的保密性、可靠性和一致性,且硬件复杂度较低,适用于低成本电子标签。     

基于模糊匹配的专用库函数识别技术

针对传统库函数识别方法无法有效识别专用库函数的问题,提出基于模糊匹配的专用库函数识别技术。在库文件快速识别与鉴定技术(FLIRT)的函数签名机制的基础上做出改进,提取目标文件的有效函数集并利用专用库函数特征库进行模糊匹配,确定需要加载的库签名,加载签名完成精确匹配。实验结果证明,该技术在专用库函数的识别方面效果较好。     

脑电信号中眼电伪迹的自动去除算法

为实现眼电伪迹的自动去除,提高算法的有效性和稳健性,提出一种眼电伪迹自动去除算法。采用样本熵和一种通用的伪迹判决方法对眼电伪迹进行自动识别,通过脑电信号的重构实现眼电伪迹的去除。实验结果表明,对于不同长度的真实脑电信号,该算法均能准确地去除眼电伪迹,较好地保留其他的脑电信号成分,且可以完全自动地去除眼电伪迹,适用于实时场合。     

The efficiency of identifying timed automata and the power of clocks

We develop theory on the efficiency of identifying (learning) timed automata. In particular, we show that: (i) deterministic timed automata cannot be identified efficiently in the limit from labeled data and (ii) that one-clock deterministic timed automata can be identified efficiently in the limit from labeled data. We prove these results based on the distinguishability of these classes of timed automata. More specifically, we prove that the languages of deterministic timed automata cannot, and that one-clock deterministic timed automata can be distinguished from each other using strings in length bounded by a polynomial. In addition, we provide an algorithm that identifies one-clock deterministic timed automata efficiently from labeled data.Our results have interesting consequences for the power of clocks that are interesting also out of the scope of the identification problem.     

The truncation method for identifying the heat source dependent on a spatial variable

This paper deals with the ill-posed problem of determining an unknown source depending only on the spatial variable in the heat equation. In this problem, the truncation method is employed to get the regularization solution. Moreover, the Hölder type error estimate is also obtained between this regularization solution and the exact solution. Various typical examples are given to verify the efficiency and accuracy of the proposed method.     

RFID技术在铁路列车信息自动识别系统中的应用

无线射频识别技术（RFID Technology）是近些年来发展起来并已得到逐步推广的一种先进的物件识别技术。该文介绍RFID在铁路车辆信息自动识别系统中的应用,主要对该系统的构成和基本工作原理进行了描述。     

A novel user identification scheme with key distribution preserving user anonymity for distributed computer networks

Recently, Yang et al. proposed an efficient user identification scheme with key distribution, in which it is possible for the user to anonymously log into a system and establish a secret key shared with the system. Mangipudi and Katti later demonstrated a Deniable-of-Service (DoS) attack on the Yang et al. scheme and then proposed an improvement to withstand such an attack. However, this paper demonstrates an identity disclosure attack to show that neither schemes’ claimed user anonymity requirement can be achieved. We further propose a novel user identification scheme with key distribution preserving user anonymity for distributed computer networks. The proposed scheme not only withstands the attacks mentioned above, but also achieves the following: (i) user anonymity, (ii) key distribution, (iii) mutual authentication, and (iv) key confirmation. The performance of our scheme is of greater efficiency than that of previously proposed schemes in terms of communication costs and computational complexities.     

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.