In order to response the effect of sampling non-uniformity, based on the representative-based classification algorithm, a Leave-One-Out Ensemble Learning Classification Algorithm (LOOELCA) for symbolic data classification was proposed. Firstly, n small training sets were obtained through leave-one-out methods, where n is the initial training set size. Then independent representative-based classifiers were built by using training sets, and the misclassified classifiers and objects were marked out. Finally, the marked classifier and the original classifier formed a committee and the test set objects were classified. If the committee voted the same, the test object was directly labeled with a class label; otherwise, the test object was classified based on the k-Nearest Neighbor (kNN) algorithm and the marked objects. The experimental results on the UCI standard dataset show that the accuracy of LOOELCA improved 0.35-2.76 percentage points on average compared with the Representative-Based Classification through Covering-Based Neighborhood Rough Set (RBC-CBNRS); compared with ID3, J48, Naïve Bayes, OneR and other methods, LOOELCA also has higher classification accuracy.

The Adaptive Generalized Total Variation (AGTV) model for image denoising has the shortages that it cannot locate image edge accurately and extract enough edge information. In order to improve the effectiveness and Peak Signal-to-Noise Ratio (PSNR) of image denoising, an Improved AGTV(IAGTV) model for image denoising was presented. On the one hand, another gradient calculating method with higher accuracy was adopted, in order to locate image edge more accurately than AGTV. On the other hand, for optimizing the filtering of image preprocess, the united Gauss-Laplace conversion which was good at image edge information detection was chosen to take place of Gaussian smoothing filter, so as to prevent edge information from reduction while denoising. Numerical simulation experiments show that the restored image PSNR of IAGTV was increased approximately by 1 dB than that of GTV with the fixed value p and at least 0.2 dB than that of AGTV. The experimental results show that IAGTV has good ability of image denoising.

In target tracking applications, the moving targets have randomness and contingency, and the tracking nodes have limited energy and small communication radius. In order to improve the tracking accuracy with minimizing the energy consumption and extending the lifetime of network, a trilateration based clustering target tracking algorithm was proposed. It adopted trilateration technique for the target localization to improve the tracking accuracy. For the sake of achieving energy balance, on the basis of two parameters including the distance between the node and the target and the residual energy level of the node, the cluster head and members were elected in the stage of wake clustering establishment. The simulation results show that, compared with Prediction-based Energy Saving (PES) scheme and Hybrid Cluster-based Target Tracking (HCTT) protocol, the proposed algorithm has a better performance in lifetime of the network, predicting path and tracking accuracy.

In order to improve the operational efficiency of SemiDefinite Programming (SDP), a new nonmonotonic trust region algorithm was proposed. The SDP problem and its duality problem were transformed into unconstrained optimization problem and the trust region subproblem was constructed, the trust region radius correction condition was modified. When the initial search point was near the canyon, the global optimal solution still could be found. The experimental results show that the number of iterations of the algorithm is less than the classical interior point algorithm for small and medium scale semidefinite programming problems, and the proposed algorithm works faster.

The events correlation techniques in security integration management systems were introduced. A normal architecture of the correlation engine was introduced, and some discussions on the critical technologies and the main achievements in the field were put forward. The directions of the technology development were analyzed and evaluated, such as pattern obtainment, engine distribution and performance promotion. At last, a solution based on hierarchical rules to correlate events was presented.