Working memory complex network analysis methods mostly use channels as nodes to analyze from the perspective of space, while rarely analyze channel networks from the perspective of time. Focused on the high time resolution characteristics of ElectroEncephaloGraph (EEG) and the difficulty of time series segmentation, a method of constructing and analyzing network from the time perspective was proposed. Firstly, the microstate was used to divide EEG signal of each channel into different sub-segments as nodes of the network. Secondly, the effective features in the sub-segments were extracted and selected as the sub-segment effective features, and the correlation between sub-segment feature vectors was calculated to construct channel time sequence complex network. Finally, the attributes and similarity analysis of the constructed network were analyzed and verified on the schizophrenic EEG data. The experimental results show that the analysis of schizophrenia data by the proposed method can make full use of the time characteristics of EEG signals to understand the characteristics of time series channel network constructed in working memory of patients with schizophrenia from a time perspective, and explain the significant differences between patients and normals.

When the appearance of an object changes rapidly, most of the weak learners can not capture the new feature distributions which will lead to tracking failure. In order to deal with that issue, a Gaussian weighted online multiple classifiers algorithm boosting for object tracking was proposed. This algorithm defined one weak classifier which included a simple visual feature and a threshold for each domain problem. Gaussian weighting function was introduced to weigh each weak classifier's contribution in a particular sample, therefore the tracking performance was improved through joint learning of multiple classifiers. In the process of object tracking, online multiple classifiers can not only simultaneously determine the location and estimate the pose of the object, but also successfully learn multi-modal appearance models and track an object under rapid appearance changes. The experimental results show that, after a short initial training phase, the average tracking error rate of the proposed algorithm is 12.8%, which proves that the tracking performance has enhanced significantly.

Abstract：To the blemish of PERT in the project plan management, this thesis introduces a kind of optimization arithmetic which is called PSO and offers an advanced technique to PERT on PSO. This technique using the method of treating with the time of the tasks in project and the theory of PSO made a advantage to the traditional PERT. The experimental results show that the technique show a more advantage, more exact ration controlling standard, can get better controlling and regulating ability to the whole project process compared with the traditional PERT.

Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms, a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed. To guarantee the prediction accuracy of the new users, the user nearest neighbor model based on user data and profile information was used. Meanwhile, large data sets and the problem of matrix sparsity would significantly increase the time and space complexity. Therefore, matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy. The experimental results show that the new algorithm can improve the recommendation accuracy effectively, and solve the problems of data sparsity and new user.

The traditional ontology matching methods that use the ontology's structure to find the matches do not really make good use of the ontology's structural feature, which leads to considerable computation redundancies during the entire matching process. Therefore, a modified method named TARA was proposed to improve the matching process in this paper. The method firstly casted matching process by strictly using the ontology's structural information, and then a re-match process was applied to overcome the inevitable defect that caused by the matching process before. The experimental results show that the method has good performances in both recall and precision.

Though many DWT-based watermarking algorithms have been presented, few of them discuss the issue that which detail subband is the better choice to watermark. This paper presented a novel DWT-based watermarking algorithm. This algorithm selected the detail subband with the highest RMS value for watermarking and got good results. Experiment results show that watermarking in this subband is more robust than the other two detail subbands to Gaussian noise and JPEG compression.