On the Design and Temporal Petri Net Verification of Grid Commerce Architecture

The emergence of Grid Computing settings lays a solid foundation for designing and constructing a next generation E-commerce environment. This work presents a new E-commerce architecture based on a Grid Computing environment, and models and analyzes the architecture using Temporal Petri nets （TPNs）. It can make sufficiently use of the Grid resources to implement various electronic bargains between the geographically dispersed users. By means of its TPN model, some main temporal properties are represented and verified formally.     

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.     

3D Reconstruction of Periodic Human Walking Trajectories Based on Single View

Periodicity of a moving body is one of important characteristics in activity monitoring. We present a method to estimate the trajectory of human gait in 3D space from a single camera by exploring the periodicity of the human movement. Geometric constraints are established to characterize the periodic motion, which are invariant under viewing geometry variations. According to our analysis, these geometric constraints reduce the overall computational complexity of evaluating periodicity. Meanwhile, with the help of these geometric constraints, we develop a novel method to reconstruct the 3D motion trajectory from a single camera. Experimental results demonstrate the accuracy and robustness of the algorithm.     

Statically-Directed Dynamic Taint Analysis

Taint analysis is a popular method in soft- ware analysis field including vulnerability/malware analy- sis. By identifying taint source and making suitable taint propagation rules, we could directly know whether vari- ables in software have any relationship with input data. Static taint analysis method is efficlent, but it is imprecise since runtime information is lacked. Dynamic taint anal- ysis method usually instruments every instruction in soft- ware to catch the taint propagation process. However, this is inefficient since it usually takes lots of time for context switches between original code and instrumenting code. In this papery we propose a statically-directed dynamic taint analysis method to increase the efficiency of taint analysis process without any loss of accuracy. In this ways there is no need to instrument every instruction. Several experi- ments are made on our prototype SDTaint and the results show that our method is several times more efficient than traditional dynamic taint analysis method.     

On the Negative Effects of Trend Noise and Its Applications in Side-Channel Cryptanalysis

Most existing noise reduction methods in side-channel cryptanalysis treat all noises as a whole~ in- stead of identifying and dealing with each of them individ- ually. Motivated by this, this paper investigates the feasi- bility and implications of identifying trend noise from any other noises in side-channel acquisitions and then dealing with it accordingly. We discuss the effectiveness of apply- ing least square method to remove inherent trend noise in side-channel leakages~ and also clarify the limited capa- bility of existing noise reduction methods in dealing with trend noise. We performed a series of correlation power analysis attacks against real power traces publicly available in DPA contest v2 from an unprotected FPGA implemen- tation of Advanced encryption standard （AES） encryption. The experimental results firmly confirmed the soundness and validity of our analysis and observations.     

Geometrical-Analysis-Based Algorithm for Stereo Matching of Single-Lens Binocular and Multi-Ocular Stereovision System

A geometrical analysis based algorithm is proposed to achieve the stereo matching of a single-lens prism based stereovision system. By setting the multi- face prism in frontal position of the static CCD （CM-140MCL） camera, equivalent stereo images with different orientations are captured synchronously by virtual cameras which are defined by two boundary lines： the optical axis and CCD camera field of view boundary. Subsequently, the geometrical relationship between the 2D stereo images and corresponding 3D scene is established by employing two fundamentals： ray sketching in which all the pertinent points, lines, and planes are expressed in the 3D camera coordinates and the rule of refraction. Landing on this relationship, the epipolar geometry is thus obtained by fitting a set of corresponding candidate points and thereafter, stereo matching of the prism based stereovision system is obtained. Moreover, the unique geometrical properties of the imaging system allow the proposed method free from the complicated camera calibration procedures and to be easily generalized from binocular and tri-oeular to multi-ocular stereovision systems. The performance of the algorithm is presented through the experiments on the binocular imaging system and the comparison with a conventional projection method demonstrates the efficient assessment of our novel contributions.     

Non-Invasive Measurement of Brain Neurotransmitter： One Novel Model

Brain sciences require tools that can noninvasively measure the blo-energy of Brain neurotransmitters （BNs）. First we review the key models behind these tools, then to report one novel model OIIWA on otopoint＇s infrared image and wavelet analysis. Based on our model we provide a new investigative tool probe with 10-order wavelet coefficients corresponding to BNs in mHz level fluctuating, at last to select the medicament inducement as validity experiments for verifying the accuracy through the brain window of OIIWA. The data come from 10-subjective confirmed that our model validity be 90%.     

Design of Two-phase SABL Flip-flop for Resistant DPA Attacks

Differential power analysis （DPA） poses a great threat to cipher security circuit since it exploits the dependency between the processed data and the power consumption. Two-phase Sense amplifier based logic （Two- phase SABL） suitable to DPA resistant logic style has been introduced under unbalanced load conditions. The pro- posed logic obtains constant energy consumption per clock cycle with pre-charge and evaluation phases. In this paper, Two-phase SABL cell and flip-flop are designed and simu- lated to confirm the energy balancing characteristic. Using TSMC 0.13μm CMOS technique, simulation results show that the power consumption fluctuations in the flip-flop is decreased by 15.1% under unbalanced condition and the area is reduced by 38.4%.     

NIR Chemical-Image Analysis Based on Adaptive Local Optimization PSO

Principal component analysis （PCA） com- bined with cluster analysis has become an effective ap- proach for Near-infrared （NIR） chemical image analysis. Traditional cluster algorithms are sensitive to initial start- ing conditions and can be trapped into local optimal so- lutions. To overcome the drawbacks, we develop a new algorithm in this paper which improves Particle swarm op- timization with Adaptive local optimization （ALO-PSO）. Simulation experiments performed on NIR image of tablet verify the feasibility and effectiveness of the proposed algo- rithm. Experimental results of the clustering performances indicate that ALO-PSO algorithm offers an alternative ap- proach for solving data clustering problems in NIR chem- ical image analysis.     

A Dynamic Quantitative Analysis Model for Network Survivability

A Dynamic quantitative analysis model for network survivability （DQAMNS） is proposed to measure survivability of large-scale distributed information systems based on ANP. Starting with the quantification and normalization of survivability factors （Resistance, recognition, recovery and adaptation, R3A）, DQAMNS makes use of probability model and dimensionless representation to ac- cess the degree of each factor. Then, DQAMNS applies ANP to measure the weights of survivability factors based on experts＇ experience and gets a number between 0 and 1 which synthetically indicates network survivability. Analysis results show that DQAMNS can actually evaluate the four survivability properties, and precisely access survivability in different environments, thus they could be theoretical instructions to improve survivability in designing and implementing LSDIS.     

Dependability Analysis for Fault-Tolerant Computer Systems Using Dynamic Fault Graphs

Dependability analysis is an important step in designing and analyzing safety computer systems and protection systems. Introducing multi-processor and virtual machine increases the system faults＇ complexity, diversity and dynamic, in particular for software-induced failures, with an impact on the overall dependability. Moreover, it is very different for safety system to operate successfully at any active phase, since there is a huge difference in failure rate between hardware-induced and software- induced failures. To handle these difficulties and achieve accurate dependability evaluation, consistently reflecting the construct it measures, a new formalism derived from dynamic fault graphs （DFG） is developed in this paper. DFG exploits the concept of system event as fault state sequences to represent dynamic behaviors, which allows us to execute probabilistic measures at each timestamp when change occurs. The approach automatically combines the reliability analysis with the system dynamics. In this paper, we describe how to use the proposed methodology drives to the overall system dependability analysis through the phases of modeling, structural discovery and probability analysis, which is also discussed using an example of a virtual computing system.     

A LSA Based Image Classification Framework Utilizing Relative Spatial Arrangement

This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.     

Boosting Bootstrap FLD Subspaces in Classification

Propose a new ensemble learning algo- rithm based on Fisher linear discriminant analysis （FLD or LDA） and Adaboost.M2 for multi-class classification problem. On each iteration of boosting, a number of bootstrap training subsets are first sampled from the original training set according to the sample weights, and FLD is employed in each subset to construct a number of bootstrap FLD subspaces. Then the subspace with the minimum weighted K- nearest neighbor （KNN） pseudo-loss was selected to form the final strong classifier and update the sample weights. The main advantage of our algorithm is that its application is not restricted to the Gaussian assumption of data distribution, and the learning criterion is directly related to the minimum training error. Through the experiments on two real-world data sets, we demonstrate that our algorithm is superior to other techniques, especially for complex data distribution.     

Real-time Facial Features Tracker Based on Topologic Information and Similarity Analysis

Real-time facial features tracking of video can be widely used in face recognition, video surveillance, face animation and Human-Computer Interaction. We present a fast tracking approach, and our method only requires simple device---a digital camera and a PC, and our approach needs limited user interactions. We first use eigenface and topologic information to detect the position and the size of face from the first frame, and facial features of the first frame are acquired automatically. The successor of the first frame can be tracked by using similarity analysis and motion estimation, the automatic tracker of first frame is also used to resolve the features occlusion problem when the tracked features disappear which is a difficult issue for tracking. Experimental results show that our approach is easily implemented, and the analysis also shows the high robustness of our method.     

Grey Entropy Relation Algorithm of Choosing the Optimum Diagnostic Nodes in Analogue Circuits

Choosing the optimum diagnostic nodes is helpful to increase the accuracy and the efficiency of circuits fault diagnosis. Grey entropy relation algorithm is proposed to choose the optimum diagnostic nodes of analogue circuits in this paper. Analogue circuits are regarded as grey system. Grey relation analysis and grey entropy analysis are combined to grey entropy relation algorithm. Grey entropy relation algorithm is used to quantify the relationship between the diagnostic nodes and fault components. The relevance between the diagnostic nodes and fault components can be evaluated by grey entropy relation degrees. According to the rank order of grey entropy relation degrees, the optimum diagnostic nodes of analogue circuits can be selected objectively and accurately. An example of fault diagnosis is presented to verify the validity of the optimum diagnostic nodes.     

Real-Time Method for DDoS Attacks Detection Based on Self-Similarity and Wavelet Analysis

Traditional wavelet methods are not efficient to detect the real-time traffic anomaly. To address this drawback, on the basis of self-similarity and wavelet analysis, this paper proposes one real-time method for DDoS attacks detection. Firstly, to effectively analyze new network state, we extracted data packets from network monitor in backward time direction. Secondly, for reducing admissible computing time, we applied time scale selfadjust according to last packets arrival speed. Finally, we adopted three parallel computing strategies to improve real-time performance. Experimental results show that proposed method can quickly and accurately detect DDoS attacks.     

A New Affine Projection Algorithm and Its Statistical Behavior

An Affine projection algorithm with Direction error （AP-DE） is presented by redefining the iteration error. Under a measurement-noise-free condition, the iteration error is directly caused by the direction vector. A statistical analysis model is used to analyze the AP-DE algorithm. Deterministic recursive equations for the mean weight error and for the Mean-square error （MSE） in iteration direction are derived. We also analyze the stability of MSE in iteration direction and the optimal step-size for the AP-DE algorithm. Simulation results are provided to corroborate the analytical theory.     

A Novel Event Model for Performance Analysis of IEEE 802.11 DCF

After considering the memory effect among series events occurring on the channel, we propose a novel event model to analyze the channel status more precisely. The memory effect is caused by the backoff freezing regulation of IEEE 802.11 Distributed coordination function （DCF）, which has been ignored before and thus resulted in the inaccurate evaluation of the network performance. Based on our new event model, the network performance of IEEE 802.11 DCF, including throughput, packet delay distribution and energy efficiency is analyzed. Simulation results show that our model is highly accurate.