Zhan Yiju

A Minimalist Mutual Authentication Protocol for RFID System & BAN Logic Analysis

This paper proposes a new security protocol with satisfying the lightweight requirements of the security of RFID system - A Minimalist Mutual Authentication Protocol for RFID System Based on EPC C1G2 Standard. The scheme makes sufficiently use of the limited resources of RFID system to encrypt, decrypt and implement mutual authentication between readers and tags, Then the paper gives qualitative analysis against various security attacks and BAN logic analysis of the security of the protocol. The results demonstrate that the protocol can effectively solve the security problem of RFID system based on C1G2 standard.

Application of arti cial bee colony optimization algorithm in complex blind separation source

Aiming at the key technologies of number estimation and ordered recovery of complex-valued signals, a method of complex blind source separation with an unknown number of sources based on artificial bee colony optimization was proposed. Firstly, we introduced an algorithm based on cross validation technology to estimate number of sources. Then, the optimally extracted vectors were determined through maximizing absolute value of kurtosis by using artificial bee colony optimization, so as to separate complex-valued signals one by one. The simulation results show that the proposed method can achieve the blind separation for source signals of any distribution in decreasing order of absolute kurtosis, and compared to other conventional algorithms, this method has preferable estimating performancee. In addition, we considered the underdetermined complex blind source separation problem of instantaneous mixtures based on kurtosis. The cost function of the extracted vector in the underdetermined mixed case was constructed by exploiting the statistics properties of complex-valued source signals, and then artificial bee colony optimization algorithm was used to maximize the function to determine the optimally extracted vectors. The underdetermined complex blind source separation was achieved through many times of extracting. The simulation result of blind separation of different types of signals validates the feasibility of the proposed method.

A method of underdetermined blind source separation in time-domain

In this article, we consider the problem of sources separation from their underdetermined instantaneous mixtures via time-domain. First, we introduced an algorithm based on differential kurtosis to separate non-stationary sources one by one under underdetermined case. Then, the rest sources are recovered using an algorithm based on second-order statistics. The simulation results show that the proposed method can separate sources of super- and sub-Gaussian distributions, and it has superior performance compared to other conventional methods.

Fault diagnosis technology based on Wigner–Ville distribution in power electronics circuit

Based on Wigner–Ville distribution similarity, a method of fault diagnosis is proposed for power electronics circuits. After setting up the module time–frequency matrices of Wigner–Ville distribution for all fault signals, the similarities between the module time–frequency matrices of the fault signal and the former standard are then calculated, thus arriving at the diagnosis according to the principle of maximum similarity. The simulation result of fault diagnosis of a thyristor in a three-phase full-bridge-controlled rectifier shows that the method can accurately identify the fault types as well as the location of the fault elements for power electronics circuits, and it has an excellent performance for noise robustness and calculation complexity. Therefore, it is quite practically valuable in the solution to the fault problems for power electronics circuits.

Activity Recognition in Smart Homes

In recent years, activity recognition in smart homes is an active research area. It is an important problem of Human Computer Interaction (HCI), and has many applications in HCI, such as assistive living and healthcare. Recognition of users’ common behaviors allows an environment to provide personalized service. Unlike activity recognition in computer vision which uses cameras, it studies activities by embedded sensors in smart homes. In this paper, we propose a method to extract latent features from sensor data by Beta Process Hidden Markov Model (BP-HMM). The contributions of our method are twofold: 1, we extend BP-HMM by dependent Beta process, and integrate state constraints of sensors into the sampling process of BP-HMM. 2, we extract latent features automatically by our dependent BP-HMM, and train a structural support vector machine (SVM) by these features in a supervised way for activity recognition. To evaluate the proposed method, we performed experiments on the real-world smart home datasets. Our results suggest that extracting latent features from sensor data leads to good performance for activity recognition.

Recognizing human actions by two-level Beta process hidden Markov model

We propose a method for human action recognition using latent topic models. There are two main differences between our method and previous latent topic models used for recognition problems. First, our model is trained in a supervised way, and we propose a two-level Beta process hidden Markov model which automatically identifies latent topics of action in video sequences. Second, we use the human skeleton to refine the spatial–temporal interest points that are extracted from video sequences. Because latent topics are derived from these interest points, the refined interest points can improve the precision of action recognition. Experimental results using the publicly available “Weizmann”, “KTH”, “UCF sport action”, “Hollywood2”, and “HMDB51” datasets demonstrate that our method outperforms other state-of-the-art methods.

A class of blind source extraction method using second-order statistics

A class of second-order statistics-based algorithms consisting of the offline BSE, online BSE algorithms, and they are proposed for extracting stationary and nonstationary signals. Within our algorithm, the new cost functions were given by exploiting the prosperities of signals, and a steepest descent update method of parameter-free adaptive steps-size was proposed to obtain optimal extracted weighted vector. Simulation results for stationary and nonstationary show that the proposed algorithms have the ability to restore original signals one by one, and its superior performances to the higher-order statistics-based BSE algorithm. Moreover, the steepest descent update method of parameter-free adaptive steps-size has the following merits: no parameters to be adjusted manually, low computational cost and no additional preprocessing requirements.

A Design of Message Transferring Based on Mailbox in NetGAP

NetGAP always constitutes with two parts: the trusted side and the non-trusted side. It is implemented in both hardware and software. Among its software modules carrying out it is very important that message transferring between them, we bring forward a method based on mailbox to solve it. The mailbox is a communication channel between two software modules. It not only solves the difficulty of information exchange between networks, but also breaks through the application difficulty caused by security.

A Reflective NetGAP Logic Framework Design

Adopting expert hardware to physical isolate the trusted network from the non-trusted network can prevent all kinds of attack basing on network layer and operating system layer etc. This paper comes up with network isolation system model based on reflective architecture. The reflective GAP is hardware-based and its purpose is to rapidly mirror buffers between the non-trusted memory and the trusted memory. We realize it by LVDS bus and high speed double switch technique. Its activities are achieved using store & forward of memory blocks. Its software system comprises with seven main basic modules. The Reflective GAP can keep good security and implement high real-time data transmission.