Since Scale Invariant Local Ternary Pattern (SILTP) background modeling algorithm is of high complexity and slow computing speed, which is not suitable for real-time video processing, a new method named Uniform Scale Invariant Local Ternary Pattern (USILTP) background modeling algorithm was proposed. Firstly, the feature of USILTP was extracted by regulating the frequency of SILTP coding jump in order to reduce the feature dimension of SILTP. Secondly, a USILTP background modeling parallel algorithm based on Intel core graphics (Intel HD) and Open Computing Language technology (OpenCL) was designed and implemented to further accelerate USILTP background modeling algorithm. Finally, the foreground result of USILTP background modeling algorithm was optimized by combing multiple color channel models. The experimental result shows that the proposed algorithm can be applied to process 320×240 resolution video at a rate of 98 frame/s on the Intel HD 4600, which is 4 times faster than that of SILTP background modeling algorithm. In terms of foreground detection, the performance of the proposed algorithm is improved by 2.1% compared with SILTP background modeling algorithm on the public dataset.
Since the evaluation of Distributed Denial of Service (DDoS) is inaccurate and network security situational evaluation is not comprehensive, a new network security situational awareness model based on information fusion was proposed. Firstly, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed; Secondly, the original Common Vulnerability Scoring System (CVSS) was improved and the leak vulnerability was evaluated to make the assessment more comprehensive; Then, according to the combination of objective weight and subjective weight, the method of calculating the combined weights and optimizing the results by Sequence Quadratic Program (SQP) algorithm was raised to reduce the uncertainty of fusion; Finally, the network security situation was got by fusing three aspects evaluation. To verify the original evaluation of DDoS was inaccurate, a testing platform was built and the alarm of the same DDoS differed by 3 orders of magnitude. Compared to the original method based on alarm, the steady and accurate result of evaluation was obtained based on data packet. The experimental results show that the proposed method can improve the accuracy of evaluation results.
To minimize damage from network security problem, an improved network security situation assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) was proposed. First, a set of index system in conformity with actual environment which consists of index layer, criterion layer and decision layer was established in consideration of the large-scale network environment in the future. Aiming at the influence on evaluation by data distribution uncertainty and fuzziness in situation assessment, the proposed model used Fuzzy C-Means (FCM) clustering algorithm and the best clustering criterion for data preprocessing to get the optimal cluster number and cluster center. Finally, multi-factor secondary assessment model was established for situation assessment vector. The simulation results show that, compared with the present situation assessment method based on FAHP, the improved method takes the factors which have small weights into consideration better, so the standard deviation is smaller and evaluation results are more objective and accurate.
Classical regression algorithms for data set analysis of multiple models have the defects of long calculating time and low detecting accuracy of models. Therefore, a heuristic robust regression analysis method was proposed. This method mimicked the clustering principle of immune system. The B cell network was taken as classifier of data set and memory of model set. Conformity between data and model was used as the classification criteria, which improved the accuracy of the data classification. The extraction process of model set was divided into a parallel iterative trial including clustering, regressing and clustering again, by which the solution of model set was gradually approximated to. The simulation results show that the proposed algorithm needs obviously less calculating time and it has higher detecting accuracy of models than classical ones. According to the results of the eight-model data set analysis in this paper, among the classical algorithms, the best algorithm is the successive extraction algorithm based on Random Sample Consensus (RANSAC). Its mean model detecting accuracy is 90.37% and the calculating time is 53.3947s. The detecting accuracy of those classical algorithms which calculating time is below 0.5s is bellow 1%. By the contrary, the proposed algorithm needs only 0.5094s and its detecting accuracy is 98.25%.
Concerning the lackness of effective means in the feature evaluation of Advanced Radar Emitter Signals (ARES), and the excessive dependence on expert experience in Analytic Hierarchy Process (AHP), a new feature evaluation model of ARES named SPA-FAHP was proposed based on Set Pair Analysis (SPA) and Fuzzy Analytic Hierarchy Process (FAHP). In order to solve the uncertainty or fuzzy judgement of the judge people when they evaluate the large-capacity data of radar emitter signals, the traditional AHP was improved through the introduction of triangular fuzzy numbers, and the index weights of ARES feature evaluation system were analyzed by FAHP. Then, the expert decision matrix of traditional AHP was made improvement and identical degree analysis through the introduction of SPA theory to solve the problem that the decisions of AHP rely on experience of experts too much. Finally, ARES features were made comprehensive evaluation through the combination of index weights matrix and identical degree matrix of the decision. The calculation results show that the model is effective and feasible. It can achieve the characteristic analysis and evaluation of ARES features more objectively.
To solve the issue that, in wireless resource competition, the environment information which gamers get in power game is asymmetric, a power game mechanism based on hidden Markov prediction was proposed. By establishing a Hidden Markov Prediction Model (HMPM), the proposed mechanism estimated whether competitors would take part in the game to improve the information accuracy of the game. Then, the predicted information was used to calculate the best transmission power via the cost function. The simulation results show that, compared with MAP (Maximum A Posteriori) method and NP (No Predicting) method, the power game model based on hidden Markov prediction can not only meet the target capacity, but also improve the power efficiency of the unauthorized users.
Most of the variants of Graph Cut algorithm do not impose any shape constraints on the segmentations, rendering it difficult to obtain semantic valid segmentation results. As for pedestrian segmentation, this difficulty leads to the non-human shape of the segmented object. An improved Graph Cut algorithm combining shape priors and discriminatively learned appearance model was proposed in this paper to segment pedestrians in static images. In this approach, a large number of real pedestrian silhouettes were used to encode the a'priori shape of pedestrians, and a hierarchical model of pedestrian template was built to reduce the matching time, which would hopefully bias the segmentation results to be humanlike. A discriminative appearance model of the pedestrian was also proposed in this paper to better distinguish persons from the background. The experimental results verify the improved performance of this approach.
Concerning the shopping information Web page constructed by template, and the large number of Web information and complex Web structure, this paper studied how to extract the shopping information from the Web page template by not using the complex learning rule. The paper defined the Web page template and the extraction template of Web page and designed template language that was used to construct the template. This paper also gave a model of extraction based on template. The experimental results show that the recall rate of the proposed method is 12% higher than the Extraction problem Algorithm (EXALG) by testing the standard 450 Web pages; the results also show that the recall rate of this method is 7.4% higher than Visual information and Tag structure based wrapper generator (ViNTs) method and 0.2% higher than Augmenting automatic information extraction with visual perceptions (ViPER) method and the accuracy rate of this method is 5.2% higher than ViNTs method and 0.2% higher than ViPER method by testing the standard 250 Web pages. The recall rate and the accuracy rate of the extraction method based on the rapid construction template are improved a lot which makes the accuracy of the Web page analysis and the recall rate of the information in the shopping information retrieval and the shopping comparison system improve a lot .
Based on the theory of Restless Multi-Armed Bandit (RMAB) model, a novel mechanism of dynamic spectrum access was proposed for the problem that how to coordinate multiple user access multiple idle channels. Firstly, concerning the channel sensing error of the cognitive user being existed in the practical network, the Whittle index policy which can deal with sensing error effectively was derived. In this policy, the users achieved one belief value for every channel based on the historical experience accumulation and chose the channel, which was needed to sense and access, by considering the immediate and future rewards based on the belief values. Secondly, this paper used the multi-bid auction algorithm to deal with the collision among secondary users when they selected the channels to improve the spectrum utilization. The simulation results demonstrate that, in the same environment, the cognitive users with the proposed mechanism have higher throughtput than the mechanism without dealing with sensing error or without multi-bid.