Abstract: In this article, we propose a method to implement the management of the role based access controlling by using layered ontology. By modeling elements of collaboration system, we can formally and explicitly describe the relationships and rules of collaboration activity. We can clearly and easily define the authentication by managing role based access controlling models. By reasoning based on description logic on ontology models, we can discover the immediate and implicit relationship. In this way we make authentication of collaboration system more flexible and scalable.
Abstract: Position service is essential for positionbased ad-hoc routing, which is one popular routing mechanism for Vehicle ad-hoc networks (VANETs). In this paper, a controllable privacy protection scheme for position service in VANETs is proposed and evaluated. Our scheme integrates a vehicle authentication mechanism, and a Random activation proxy (RAP) mechanism with the common VHR-based position service. The authentication mechanism can achieve a conditional anonymity to the vehicles by using the group signature, and guarantee the security of the position service. RAP can defend the local tracking privacy attacks in the VANET where the identifiable areas are widespread. Privacy protection analysis indicates that our scheme achieves location privacy protection to the participant vehicles at a reasonable cost. Evaluation results show that the random silent period in our scheme can improve and configure the anonymity of the participant vehicles.
Abstract: Mobility models play an important role on VANET simulation, which has been discussed in many studies. However, few studies have analyzed the impact of maps. We find that maps can greatly influence simulation results. We present a framework to help researchers analyze the impact of maps. We regard intersections in the map as virtual nodes and propose Virtual node coverage (VNC) and Virtual connected components (VCC) as two metrics to estimate the impact of maps. Through theoretical analysis and extensive simulations, we show that our approach can be applied to various scenarios and researchers can use our approach to analyze the impact of maps on VANET simulation.
Abstract: Secure sum protocol is a significant secure multiparty computation protocol and it has various applications in privacy-preserving distributed multiparty computation. However, most existing secure sum protocols rarely considered how to resist underlying collusion which is a significant practical problem. Urabe et al. proposed a collusion-resistant secure sum protocol, but too much cost of communication and computation results in its low performance efficiency. In this paper, we propose security definitions to measure secure multiparty computation protocol’s capability of resisting potential collusion. Then, we precisely analyze several previous secure sum protocols’ capability of resisting collusion. In addition, considering realistic requirement to resist collusion and performance efficiency needs, we present a novel collusion-resisting secure sum protocol. Theoretical analysis and experimental results confirm that our secure sum protocol is efficient and has strong capability of resisting potential collusion such that it is much superior to previous ones. The communication overheads and computation complexity of our scheme both are linearity of the number of participants. Besides, our protocol’s capability of resisting collusion is adjustable according to different security needs.
Abstract: To solve the curse of dimensionality and structure credit assignment in multi-agent reinforcement learning, a learning method based on K-Means is proposed in this paper. With this method, state space explosion is avoided by classifying states into different clusters using K-Means. The roles are dynamic assigned to agents and the corresponding set of characteristic behaviours is established by using K-Means algorithm. The credit assignment function is designed according to factors like the weight of roles. The experimental results of the multi-robot cooperation show that our scheme improves the team learning ability efficiently. Meanwhile, the cooperation efficiency can be enhanced successfully.
Abstract: The codebook is an intermediate level representation which has been proven to be very powerful for addressing scene categorization problems. However, in most scene categorization methods, a scene is characterized by a single histogram based on the sole universal codebook, which is lack of enough discriminative ability to separate the similar images among different categories and results in low classification accuracy. In order to solve this problem, in this paper, we propose a novel scene categorization method that constructs class-specific codebooks based on feature selection strategy. Specifically, feature selection method mutual information is adopted to measure the visual word's contribution to each category and construct class-specific codebooks. Then, an image is characterized by a set of combined histograms (one histogram per category), each of which is generated by concentrating the traditional histogram based on universal codebook and the class-specific histogram grounded on class-specific codebook with an adaptive weighting coefficient. The improved combined-histogram provides useful information or cue to overcome the similarity of inter-class images. The proposed method is sufficiently evaluated over three wellknown scene classification datasets, and experimental results show that our proposed scene categorization method outperforms the state-of-the-art approaches.
Abstract: Trustworthy service flow is an important content of the trustworthy software theory, and it is also a key point to achieve service-oriented computing currently. For meeting the diverse trustworthy requirements of consumers, using fuzzy set to describe the trustworthiness attribute of service is proposed. According to the theory of fuzzy set, the trustworthiness of service flow is computed and evaluated, and the similar service and similar service flow are defined. Constrained by the quantitative assessed trustworthiness and normalized QoS attributes, trustworthy service flow problem is formalized as the NPC problem, which is proved in terms of NPC problem definition. Then, a novel algorithm Hybrid QPSO is put forward for solving the trustworthy service flow problem. To illustrate the feasibility, and effectiveness of our approach, we take the travel service flow as an example to test, and compare the execution result of HQPSO algorithm with the backtracking algorithm and greedy algorithm.
Abstract: The aim of MDA is to increase the quality and speed of system development by using modeling techniques. However, the lack of accurate semantic language support has hampered the wide adoption of MDA. This paper presents an executable meta-modeling language xKL design method and a mapping method from xKL to the Java language. metaKernel based on CMOF model is the core of xKL language, which enriches its data types by expanding DataType element, increases expression types by adding ExpType element, improves Operations by adding a variety of basic action information. metaOCL expanded from metaKernel can express constraints between elements. Model mapping tool metaMap provides mapping method from xKL language to Java language and illustrates the mapping rules. The created domain model using xKL language can be directly mapped to the target language using metaMap, so the domain model will never become obsolete if technology changes, and can be developed and reused continuously.
Abstract: Wireless sensor-actuator networks can bring flexibility to building control. We develop a building control prototype system to exhibit how to apply the wireless sensor-actuator networks technologies to the building control. The general purpose is energy saving and emission reduction through the collaboration among a mass of lowcost sensor nodes and actuator nodes. In this paper, we detail the hardware and software in our building control prototype system, and then we address the following key technical challenges: (1) a lightweight trust management framework for discrete data; (2) self-adaptive audio sampling and combining reception algorithm; (3) hierarchical synchronization strategy. Finally, a set of experiments are presented to evaluate the performance of our key technologies.
Abstract: Ethernet passive optical networks (EPONs) are promising communication technologies for Distribution automation system (DAS). However, EPONs have very specific security requirements, due to the broadcast character of the transmission medium. Based on the hierarchical network model, this paper proposes an access control scheme for DAS using EPON, where the mutual authentication and key establishment between the OLT and the ONU is accomplished. The proposed scheme utilizes identity-based cryptosystem and is compatible with the ONU’s auto-discovery process in EPON. The analysis results show that the proposed scheme satisfies the strong security, sound scalability and efficiency simultaneously.
Abstract: A novel feature points based gait authentication method is introduced in this paper, which uses the acceleration signals acquired from an ankle-mounted 3-axis accelerometer. Feature points are extracted from original gait samples. A dynamic time wrapping algorithm is employed to match the feature points of different samples and calculate the distortions. Based on these distortions, a multi-criterion model is designed for authentication. The experimental result shows that the extracted feature points can represent the original signals good enough in authenticating with one accelerometer, and the equal error rate of this method is only 3.27%, better than that of the previous literatures reported.
Abstract: The traditional tracking algorithms for continuous-time nonlinear dynamic system face two problems: linearization and discretization, this greatly degrades the tracking performance, especially for the cases of stronger dynamic nonlinearity or longer revisit time. In this paper, a novel algorithm called Numerical integration based Kalman filter (NIKF) which can avoid the linearization and discretization steps is presented. The NIKF use numerical integration to predict the state and covariance instead of the series expansion based methods. By using a simple fourth-order Runge-Kutta numerical integration, the prediction error is reduced to a considerably lower level. An example of re-entry target tracking indicates that the algorithm significantly outperforms the traditional algorithms (e.g., an Extended Kalman filter (EKF)) in the tracking accuracy and filtering robustness.
Abstract: To enhance the efficiency of eavesdropping detection in the “Ping-pong” protocol, an improved “Pingpong” protocol based on four-qubit genuine entangled state is presented. The four-qubit genuine entangled state is used to detect eavesdroppers. In the security analysis, the method of the entropy theory is introduced, and three detection strategies are contrasted quantitatively by using the constraint between the information eavesdropper can obtain and the interference introduced. If the eavesdropper wants to obtain the same amount of information, she must face a larger detection probability in the proposed scheme than the other two. At last, the security of the proposed protocol is discussed. The analysis results indicate that the improved “Ping-pong” protocol in this paper is more secure than the other two.
Abstract: One of the problems in OFDM system is that bit errors are normally concentrated in a few severely faded subcarriers, which therefore normally restrict the performance of the whole system. Adaptation algorithms are able to identify these subcarriers and improve the overall bit error rate of the whole OFDM frame by choosing different modulation schemes, code rates and other parameters for different subcarriers. Power allocation is another technique that can improve the performance of OFDM systems. A combination of a power allocation algorithm with the adaptive OFDM system is proposed in this paper to further improve performance. We determine the best possible power distribution over subcarriers for OFDM systems by power allocation algorithms, and choose modulation schemes and code rate for subcarriers using adaptation algorithms, and hence a better trade-off between the Bit error rate (BER) and throughput performance of OFDM system is achieved.
Abstract: Kernel principal component analysis (KPCA) has been widely applied in pattern recognition areas, but it endures the high store space and time consuming problems on feature extraction in the practical applications. In this paper, we propose a novel Refined kernel principal component analysis (RKPCA) based feature extraction with adaptively choosing the few samples from the training sample set but with less influence on recognition performance in the practical applications. Experimental results on seven datasets show the proposed algorithm achieves the approximate error rates but only about 20%–30% training samples. RKPCA performs well on the conditions of high computation efficiency but not a strict on recognition accuracy.
Abstract: Elliptic curve cryptosystems (ECC) provide the highest strength per bit of any cryptosystem known today, which makes them especially well suited to computation resource-restricted devices. However, at the ECC implementation stage, a major concern is securing ECC scalar multiplications against Side-channel attacks (SCA). Existing solutions reached the goal by inserting dummy operations, which largely increase the computational costs and prohibit the deployment of ECC in computation resource-restricted devices. In this paper, we propose an efficient and secure scalar multiplication method by partitioning the bit string of the scalar in half and extracting the common substring from the two parts based on propositional logic operations. The computations for common substring are thus saved. Computational results demonstrate the proposed method is approximately 50% more efficient than almost all existing secure solutions. The power measurement experiments prove that the proposed method is secure against SCA.
Abstract: To distinguish factual and uncertain information in biological texts, hedged information detection has received considerable interest in the biomedical natural language processing, which remains a challenging task due to the complexity of the syntactic and semantic analysis. This paper presents an approach to hedges scope detection using a composite kernel which combines structured and flat features. The composite kernel consists of two individual kernels: a polynomial kernel that exploits the flat features widely used in hedges scope detection and a tree kernel that captures the syntactic structured features. Four structured features over a parse tree are explored for hedges scope learning to investigate the effect of the structured features. Experiments on the CoNLL-2010 evaluation data show that our model achieves F-scores of 87.34% on hedge identification and 57.47% on scope detection respectively, which are better than those of the previous reported systems. The analysis results show that structured syntactic features with the tree kernel is more effective for hedges scope detection than the traditional flat syntactic features without the labor of detailed features designing.
Abstract: In order to improve the generation method in vision-grounded language model ViMac, a core-based visual semantic representation is proposed. With core-based semantic representation, ViMac can work with Compounds generation method to output more accurate compounds instead of single words. Compounds generation method can describe unseen visual feature values by creating new compounds and overcome the subjective variabilities imported during the learning phase. In the experiment, three generation methods are compared by the generation error rate. Gaussian model based generation method gets the result of 82%, KNN generation method gets the result of 69%, and Compounds method gets the result of 54%, which reduces at least 15% on the generation error rate. In another comparison experiment on execution time of nonparametric generation methods, KNN method gets the result of 35.2s. Compound method gets the result of 15.7s, which is almost half of the time cost by KNN method. Experimental results indicate that Compounds generation method can greatly reduce both the generation error rate and the computational complexity compared with KNN method and Gaussian model based method.
Abstract: Imbalanced data sets in real-world applications have a majority class with normal instances and a minority class with abnormal or important instances. Learning from such data sets usually generates biased classifiers that have a higher predictive accuracy over the majority class, but a rather poorer predictive accuracy over the minority class. The Synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced data sets. This paper presents a novel approach for learning from imbalanced data sets, based on an improved SMOTE algorithm. The approach deals with noise data by a hierarchical filtering mechanism, employs a selection strategy of the minority instances and makes full use of dynamic distribution density of the minority followed by the SMOTE process. This empirical analysis of the approach showed quantitatively competitive with SMOTE and series of its improved algorithm in terms of the receiver operating characteristic curve when applied to several highly and moderately imbalanced data sets.
Abstract: This paper investigates how to learn the distance between multilinear samples. First, for tensor data, we present a new distance metric called as tensorbased Mahalanobis distance. Then the distance is learned through solving a model of tensor-based maximally collapsing metric learning. The proposed metric learning technique has the advantage of few parameters. At the same time, it is also employed to perform dimensionality reduction. Finally, face recognition experiments demonstrate the superiority of the learned distance over the Euclidean distance.
Abstract: This paper presents a Novel version of real-coded quantum evolutionary algorithm (NRQEA) to solve global numerical optimization with continuous variables. Complementary mutation operator, which is designed based on the specific configuration of real-coded chromosome and the gradient informance of objective function, is used to update chromosomes and realize a balance between exploration and exploitation. Technique of reducing the search space, which is implemented based on the evolutionary process of algorithm, is adopted to improve the convergence rate. Simulation results on benchmark functions show that the algorithm proposed is more suitable for global numerical optimization with continuous variables than the compared algorithms, and has the characteristics of rapid convergence, good global search ability and stability.
Abstract: We analyzed the relationship between the nearest neighbor algorithm and the arrangement of reference tags used in LANDMARC, and propose an isosceles triangular placement of reference tags. Results of simulations and experiments reveal that the precision of indoor localization can be improved by using isosceles triangular arrangement from 9.1% to 14.2% over square arrangement without data pre-processing.
Abstract: This paper analyzes a new multivariate public key encryption scheme which we name as PTH+. It is an improved version of the TH scheme by the internal perturbation and plus methods. The inventors of PTH+ claimed that it can resist all known types of attacks including differential attack, and to ensure it achieves a security level higher than 280, they suggested its parameter is taken as (l, r,m) = (47, 6, 11). We utilizes a distinguishing property on its differentials and combines the linearization equation attack to present a ciphertext-only attack on PTH+ of complexity 2l+r+1(2l)w ≈ 272, which is independent on the number m, and disproves a claim in their original paper that the larger is the m, the securer is PTH+. Simulation results of small-scale parameters demonstrate our attack works.
Abstract: A new intra coding method based on one dimensional line prediction with different transform is presented. The H.264/AVC design includes a multidirectional spatial prediction method to reduce spatial redundancy by using neighboring samples as a predictor for all the samples in a block of data to be encoded. In the proposed intra coding method, the spatial prediction is performed line-based in horizontal or vertical direction instead of in the block-based manner used in the current H.264/AVC standard, while the block structure is either retained or one dimensional based for the residual difference transform and entropy coding process. Experimental results on CIF (352 × 288) and 720P (1280 × 720) sequences show that the new line based intra coding framework reduces the bit rate by approximately 7% and up to 12% at the same PSNR in comparison with H.264/AVC baseline profile.
Abstract: The impacts of imperfect channel estimation on the Symbol-error-rate (SER) are investigated for multi-node Decode-and-forward (DF) cooperative communications, in which all the channels are described by Nakagami-m fading. The approximate SER expression is firstly provided by employing the Moment generating function (MGF) approach. By choosing the two most significant terms in the approximate SER, asymptotic SER is also derived at a high SNR regime for the insight of our approach. According to our analysis, an approximated diversity order of K ×m + mSR can be achieved under the assumption of independent and identical distribution (i.i.d) channels and ideal channel estimation. In addition, error floor caused by channel estimation error is provided for the better understanding of channel estimation error. The exactness of theoretic SER expressions has been proved by numerical studies. Results also illustrate the negative impact of channel estimation error can be reduced by increasing the number of relays.
Abstract: In the coverage region of Distributed antenna systems (DAS), two Remote antennas (RA) are assumed at the Base station (BS) for receiving the signal transmitted from the Mobile station (MS) with a single antenna. Since the locations of the two RAs at BS are fixed, a constraint, which is exploited to associate the time delays from the MS to the two RAs, is derived from the known locations of the two RAs. With this constraint, we propose a maximum-likelihood (ML)-based Cooperative timing acquisition (CTA) method. Compared to the Independent timing acquisition (ITA) for each RA, the analysis and simulation results show that the Probability of correct acquisition (PCA) for each RA can be improved by the cooperation of the two RAs.
Abstract: The optimization of national Air route network (ARN) has become an effective method to improve the safety and efficiency of air transportation. The Crossing waypoints location (CWL) problem is a crucial problem in the design of ARN. This paper formulates a multi-objective model for the CWL problem, and presents a Comprehensive learning multi-objective particle swarm optimizer (CLMOPSO) to minimize both airlines cost and flight conflicts. The application to redesign national ARN of China shows the proposed optimizer valid and effective by comparison with the conventional optimization algorithms. The application of the proposed methodology can also serve as a benchmark application as shown in the paper.
Abstract: In close-loop MIMO system, transmitter can obtain instantaneous CSI by a feedback link from receiver. Then the CSI is used to beamform or precode the transmitting signal, achieving performance gain. In real application, limited codebook will lead to quantized error and loss of performance, so optimized feedback design becomes the key of close-loop MIMO system. Current feedback schemes are analyzed in this paper. To the questions of them, layered feedback is proposed. These feedback schemes are simulated in link level simulation to compare the performance of MIMO system. Simulation results show that, for different spatial correlations, comparing to current feedback schemes, the proposed feedback can obtain lower PER and higher capacity, with 1 or 2dB SNR gain, generally.
Abstract: In this paper, we study the impact of spatial correlation on the performance of Unitary space time codes (USTC). We derived the exact expression and upper bound on the Pair-wise error probability (PEP) for USTC over quasi-static Rayleigh flat fading channel when Generalize likelihood ratio test (GLRT) decoder is employed. This bound, which has a closed form expression, is more tight and simpler than other existing bound. In addition, we establish a theorem that the spatial correlation has no impact on the diversity gain, whereas it merely reduces the coding gain and thereby degrades the performance curve. Simulation results confirm the theoretical analysis.
Abstract: A novel adaptive equalizer algorithm is proposed based on an array pattern synthesis algorithm, which provides a fast convergence speed with low complexity compared to RLS algorithm. By incorporating an interference suppression mechanism in the iterative procedure, the algorithm is able to achieve a performance that is very close to that of RLS algorithm. Theoretical derivation is shown and the proposed algorithm is demonstrated through computer simulation.
Abstract: Two serious problems existing in Particle filter (PF) are the degeneracy phenomenon and the sample impoverishment caused by simple random resampling. In this paper, based on the Extended Kalman particle filter (EKPF) which selects the importance distribution of PF by the Extended Kalman filter (EKF), we propose a new resampling method from niching genetic algorithm to inhibit the degeneracy phenomenon and avoid the sample impoverishment problem, and name the improved particle filtering algorithm as Niching genetic algorithm Extended Kalman particle filter (NGA-EKPF). According to the theoretical analysis and computer simulation of three algorithms in the Global positioning system (GPS), i.e. EKF, EKPF with simple random resampling and NGA-EKPF, the performance of the proposed algorithm has improvement compared with other algorithms not only in positioning accuracy, but also by Cramér-Rao low bound (CRLB) which provides a theoretical bound on the filtering performance.
Abstract: The Multi-frequency complementary phase-coded (MCPC) signal has caught much attention of researchers recently as a new type radar signal. The narrow band characteristic implied in its synthesized wide band enables it to be more suitable for Moving target detection (MTD) than traditional wideband signals. It is significant to research the MTD of MCPC for realization of multi-function wideband radar. Based on separation of sub-carriers from multi-frequency structure, this paper provides integration method of MTD for such radar signal. After analyzing the gain of Signal-to-noise ratio (SNR) after Doppler processing, the detection threshold and two improved measurements are presented. Processing the MCPC signal as one chip overcomes sampling shift caused by range walk and enables phase-coherent integration. And integrating the peek of Doppler spectrum along velocity lines promotes the effect of integration. The simulation results show that this method can detect the fastmoving target efficiently and guarantee detection performance against low SNR.
Abstract: Formulas of field intensity and mutual impedance of two probes with six specific orientations in an elliptic waveguide are given and discussed. The waveguide is semi-infinite. The reflection coefficient at terminal plane is Γ.
Abstract: The importance of loaded quality factor QL was analyzed and the method of reducing phase noise on the basis of improving QL was performed. The formula of QL was derived from analysis of the Pierce oscillator circuit, and calculated by MATLAB. According to the results, we can draw a conclusion that QL is explicitly related to circuit parameters. Based on this conclusion, the prototype 120MHz crystal oscillator was designed and the experiments were carried out. The crystal resonator we use is AT-cut 5th overtone crystal resonator with UM- 1 resistance welding package, its unloaded quality factor Q0 is about 1.06 × 105. The measurement results of near carrier frequency phase noise are -103dBc/Hz@10Hz and -134dBc/Hz@100Hz. Experimental results show that it is feasible to design low phase noise crystal oscillators based on improving QL.
Abstract: A new approach is proposed to estimate the two-dimensional (2-D) scattering centers. The approach combines the idea of Total least squares (TLS) with the 2-D ESPRIT type method. Numerical results show that the approach provides lower estimation errors than the 2-D ESPRIT type method and the Matrix enhancement and matrix pencil (MEMP) method with a new pairing procedure. The approach is successfully used for estimating the scattering centers for real radar data.
Abstract: A novel fusion filtering algorithm is proposed to resolve the problems of the hybrid filtering algorithm based on advanced D-S theory of evidence. The hybrid filtering algorithm judge image edges. However, for the image pixel, the hybrid filtering algorithm adopts the single method that is linear or non-linear, and it is not a real fusion filtering algorithm. Accordingly, the proposed algorithm based on fusion theory judges image edges, but also makes fusion filtering, which improves the performance of filtering. The experimental results indicate that the proposed algorithm is effective.