Aiming at the quality and quantity problem of multi-type task completion in spatial crowdsourcing, a method of multi-type task assignment and scheduling was proposed. Firstly, in the task assignment process, by combining with the characteristics of multi-type tasks and users in spatial crowdsourcing and improving the greedy allocation algorithm, a Distance ε based Assignment ( ε-DA) algorithm was proposed. Then the tasks were assigned to the nearby user, in order to improve the quality of task completion. Secondly, the idea of Branch and Bound Schedule (BBS) was utilized, and the task sequences were scheduled according to the size of the professional matching scores. Finally, the best sequence of tasks was found. Due to the low running speed of the scheduling algorithm of branch and bound idea, the Most Promising Branch Heuristic (MPBH) algorithm was presented. Through the MPBH algorithm, local optimization was achieved in each task allocation process. Compared with the scheduling algorithm of branch and bound idea, the running speed of the proposed algorithm was increased by 30%. The experimental results show that the proposed method can improve the quality and quantity of task completion and raise the running speed and accuracy.

Focused on the issues of low accuracy, high energy consumption of target tracking network and short life cycle of network in Wireless Sensor Network (WSN), the moving target tracking technology based on dynamic clustering was proposed. Firstly, a Two-Ring Dynamic Clustering (TRDC) structure and the corresponding TRDC updating methods were proposed; secondly, based on centroid localization, considering energy of node, the Centroid Localization based on Power-Level (CLPL) algorithm was proposed; finally, in order to further reduce the energy consumption of the network, the CLPL algorithm was improved, and the random localization algorithm was proposed. The simulation results indicate that compared with static cluster, the life cycle of network increased by 22.73%; compared with acyclic cluster, the loss rate decreased by 40.79%; there was a little difference from Received Signal Strength Indicator (RSSI) algorithm in accuracy. The proposed technology can effectively ensure tracking accuracy and reduce energy consumption and loss rate.

Optimum Multi-user Detection (OMD) technique can achieve the theoretical minimum error probability, but it has been proven to be a Non-deterministic Polynomial (NP) problem. As a new swarm intelligence algorithm, Artificial Bee Colony (ABC) algorithm has been widely used in various optimization problems. However, the traditional Binary Artificial Bee Colony (BABC) algorithm has the shortcomings of slower convergence speed and falling into local optimum easily. Concerning the shortcomings, an improved binary artificial bee colony algorithm was proposed and used for optimum multi-user detection. The initialization process was simplified. The one-dimensional-reversal neighborhood search strategy was adopted. Compared with optimum multi-user detection, the computation complexity of the improved algorithm declines obviously. The simulation results show that the proposed scheme has significant performance improvement over the conventional detection in anti-multiple access interference and near-far resistance.

The algorithm of exemplar-based image inpainting was introduced to the digital protection of Thangka image due to the advantage that can repair the broken structure and texture effectively at the same time. This algorithm is good at repairing the specific broken images of Thangka， but is not appropriate to other kinds of broken images. Hence, this paper proposed two aspects of improvement for the deficiencies of exemplar-based broken Thangka image inpainting: the improvement of computing method of confidence and the improvement of computing method of isophote intensity. The problem that optimum exemplar block is not exclusive has been solved. The experimental results show that the improved algorithm can not only get a satisfied inpainting result but also improve repairing efficiency.