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.     

基于BIBD（4，2，1）的RFID防碰撞算法

标签防碰撞技术是射频识别系统的关键技术之一,它决定着标签的读取速率和正确率。以二进制搜索算法和平衡不完全区组设计BIBD（4,2,1）为基础,提出一种新型确定性RFID标签防碰撞算法。将标签分节,每节只包含BIBD（4,2,1）的子集,通过逐节识读达到快速识别的目的。数学分析和仿真结果表明,该算法识别速度优于二进制算法和动态二进制算法,可达到二进制算法的6倍以上,适用于标签数量多、UID长度较长的识别环境。     

基于离散ID序列的RFID标签认证协议

针对RFID的安全问题,提出一种基于离散ID序列的RFID标签认证协议——DSTAP,在此基础上,将DSTAP协议与其他认证协议进行比较分析,桑用BAN逻辑对所提出的协议进行形式化分析。分析结果表明,DSTAP协议达到指定的安全设计要求,同时可减少计算量。     

一种基于查询前缀的快速抗冲突算法

基于阅读器发送的查询前缀和电子标签的响应后缀,提出一种新的射频识别（RFID）标签识别算法,用以解决RFID仲裁过程中的零标签响应问题。通过实验验证,与Memoryless抗冲突算法相比,该识别算法所需查询次数、查询信息量、标签响应次数及标签发送的信息量更少,从而降低功耗,加快标签识别速度,提高识别效率。     

基于模糊辨识算法的蓄电池荷电状态测量方法与模块设计

针对蓄电池荷电状态在线监测中对准确度和测量速度的要求,本文提出采用模糊辨识算法对蓄电池进行系统辨识。并通过对蓄电池的荷电状态与内阻、端电压数据的分析,建立了蓄电池荷电状态的模糊规则模型,并以此进行蓄电池荷电状态的测量,得到均方误差为0.0052。测量结果表明基于模糊辨识算法的蓄电池荷电状态测量能够满足蓄电池在线监测的要求,且易于硬件实现。文章还使用DSP Builder设计了蓄电池荷电状态测量模块,其中内阻测量采用了特征分解谱估计的信号提取方法,荷电状态测量则实现了模糊辨识算法所得出的模糊规则模型的运用。     

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; the state-space equations are then computed by solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order model.     

On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling

Research on the robustness of fuzzy logic systems (FLSs), an imperative factor in the design process, is very limited in the literature. Specifically, when a system is subjected to small deviations of the sampling points (operating points), it is of great interest to find the maximum tolerance of the system, which we refer to as the system’s robustness. In this paper, we present a methodology for the robustness analysis of interval type-2 FLSs (IT2 FLSs) that also holds for T1 FLSs, hence, making it more general. A procedure for the design of robust IT2 FLSs with a guaranteed performance better than or equal to their T1 counterparts is then proposed. Several examples are performed to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications, and preference one or the other, in general, is application-dependant. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The methodologies presented in this paper lay the foundation for the design of FLSs with robust properties that will be very useful in many practical modeling and control applications.     

标签运动状态下的RFID系统反碰撞算法

针对现实应用中标签经常处于运动状态的情况,对传送带模型下的无线射频识别(RFID)系统进行了分析,并使用Matlab做了相应的仿真实验。结果证明,当标签密度D与传送带速度V保持不变时,识别率P在帧长度N与作用范围内未识别标签数n相等时最高。当时隙持续时间一定时,识别率P只与V和D有关,而与作用区域内传送带长度L无关。同时针对该模型,对现有的标签估算方法进行了修正,在此基础上提出了一种新的估算方法,该方法的准确度在标签数目较大时有着明显的提高。     

情感变异的语音语料库的设计和构建

针对情感变异的语音分类与识别研究的需要,给出了情感变异的语音语料库的构建流程和制作规范,设计并建立了情感变异的语音语料库。现已录制的语料包含自然、高兴、悲伤和愤怒四种情感类型,共1 600条语句。为了证明该情感变异的语音语料库的有效性,对其所有数据进行了听辨实验检测。结果表明,设计并构建的情感变异的语音语料库具有较高的实用价值,为情感变异的语音的分类与识别的研究奠定了夯实的基础。