A novel algorithm based on tabu search and combined with simulated annealing was proposed to solve random Constraint Satisfaction Problem (CSP) with growing domain. Firstly, tabu search was used to obtain a set of initial heuristic assignments, which meant a set of candidate solutions were constructed based on a randomly initialized feasible solution through neighborhood, and then the tabu table was used to move the candidate solutions to the direction of minimizing the objective function value. If the obtained optimal assignment was not the solution of the problem, the assignment would be used as the initial heuristic assignment and then simulated annealing was performed to correct the set of assignments until the global optimal solution was obtained. The numerical experiments demonstrate that, the proposed algorithm can effectively find the solution of problem when approaching the theoretical phase transition threshold of problem, and it shows obvious superiority compared with other local search algorithms. The proposed algorithm can be applied to the algorithm design of random CSP.

Aiming at the optimization of servers' energy consumption in data center and the reasonable migration time of Virtual Machine (VM), a VM migration algorithm based on Dynamic Adjusting Threshold (DAT) was proposed. Firstly, the migration threshold was dynamically adjusted by analyzing the historical load data acquired from Physical Machine (PM), then the time for migrating VMs was decided by the delay trigger mechanism and the PM load trend prediction. The VM migration algorithm based on DAT was tested on datacenter platform in the laboratory. Experimental results indicate that compared with the static threshold method, the number of the shut down PMs of the proposed algorithm is larger, and the energy consumption of the data center is lower. The VM migration algorithm based on DAT can dynamically migrate VMs according to the variation of PM load, thus improving the utilization of resources and the efficiency of VM migration, reducing the energy consumption of the data center.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.

Robust fragments-based tracking cannot update template, so the paper proposed a template updating method based on hue detecting. First, hue histogram distance between template and current target was calculated, and then the distance was compared with the threshold. If the distance is more than the threshold, the template should be fixed in next frames, or the template should be updated every frame. The experimental results on challenging sequences demonstrate the method can track targets when illumination changes as well as improve tracking accuracy.

This paper aims at reducing the influence of ranging error on localization accuracy. A Wireless Sensor Network (WSN) localization algorithm was proposed, which was based on Kernel Locality Preserving Projection (KLPP). The algorithm took transmission time vector between nodes as input, and a model was established by KLPP, which can reflect partial information network topology structure. The simulation results indicate that compared with Kernel Principle Component Analysis (KPCA), the algorithm can achieve higher localization accuracy in Time of Arrival (TOA) localization and reduce the influence of ranging error on localization accuracy effectively in complex environment.

Based on the medial axis transform of the upright maximal squares, a new robust binary image skeletonization was proposed in this paper. By computing the numbers of the maximal neighboring pixels in an object, the transform image was obtained, and then skeleton was extracted. If the threshold was initialized, this method could extract the skeleton from the noisy image. Experimental results show that this method can excellently extract the skeletons. Moreover, compared with the traditional medial axis transform (MAT) method and morphology method to skeletonization, this method is robust to background noise.