Concerning the problem that in complex and non-cooperative situations the number of matching feature pairs and the accuracy of feature matching results in video stitching can not meet the requirements of subsequent image stabilization and stitching at the same time, a method of constructing matching model to match features accurately after feature points being scored by grayscale tower was proposed. Firstly, the phenomenon that the similiar grayscales would merged together after grayscale compression was used to establish a grayscale tower to realize the scoring of feature points. Then, the feature points with high score were selected to establish the matching model based on position information. Finally, according to the positioning of the matching model, regional block matching was performed to avoid the influence of global feature point interference and large error noise matching, and the feature matching pair with the smallest error was selected as the final result of matching pair. In addition, in a motion video stream, regional feature extraction could be performed by using the information of previous and next frames to establish a mask, and the matching model could be selectively passed on to the next frame to save the computation time. The simulation results show that after using this matching model based on grayscale tower score, the feature matching accuracy is about 95% and the number of matching feature pairs of the same frame is nearly 10 times higher than that of the traditional method. The proposed method has good robustness to environment and illumination while guaranteeing the matching number and the matching accuracy without large error matching result.

Mobile application crowdsourcing testers are anonymous, non-contractual, which makes it difficult for task publishers to accurately evaluate the ability of crowdsourcing testers and quality of test results.To solve these problems, a new evaluation model of Analytic Hierarchy Process (AHP) for mobile application crowdsouring testers was proposed. The ability of crowdsourcing testers was evaluated comprehensively and hierarchically by using the multiple indexes, such as activity degree, test ability and integrity degree. The combination weight vector of each level index was calculated by constructing the judgment matrix and consistency test. Then, the proposed model was improved by introducing the requirement list and description list, which made testers and crowdsourcing tasks match better. The experimental results show that the proposed model can evaluate the ability of testers accurately, support the selection and recommendation of crowdsourcing testers based on the evaluation results, and improve the efficiency and quality of mobile application crowdsourcing testing.

Most existing algorithms for data streams mining utilize the true label of testing data to detect concept drift and adjust current model according to requirements. It is impractical in real-world applications as manual labeling of instances which arrive continuously at a high speed requires a lot of human and material resources. Therefore, a concept drift detection method with limited amount of labeled data was proposed. The proposed method used the model clusters generated by the fast KNNModel algorithm to classify instances. It was able to detect concept drift on whether the number of instances which were not covered by any model clusters on the current block increased remarkably at a certain significance level than that of the prior block. Once concept drift happened, the domain experts were asked to label a few instances which were not covered by the model clusters and these representative instances were used to update the current model. The experimental results show that, compared with the traditional classification algorithms, the proposed method not only adapts to the situation of concept drift, but also acquires approximate or better classification accuracy.

Current urban traffic noise mapping systems are not ideal for big scale project distributed computing in dynamic network. This paper proposed a noise mapping distributed computation method based on loosely-coupled services and the mechanism of Service Object Oriented Architecture (SOOA), investigated the generation approach of noise propagation calculation service, and introduced the deployment and management of services in the proposed system. At last, a demonstration indicated that the distributed computation approach considerably reduced the overhead of calculation and supplied flexible system architecture at the same time. The experimental results show that the imbalance of parallel subtasks will affect the parallel efficiency. Under normal circumstances, parallel efficiency can reach over 85%.

The algorithms for mining data streams have to make fast response and adapt to the concept drift at the premise of light demands on memory resources. This paper proposed an ensemble classification algorithm for high speed data stream. After dividing a given data stream into several data blocks, it computed the central point and subspace for every class on each block which were integrated as the classification model. Meanwhile, it made use of statistics to detect concept drift. The experimental results show that the proposed method not only classifies the data stream fast and adapt to the concept drift with higher speed, but also has a better classification performance.