Aiming at the problems of limited energy of nodes, short life cycle and low throughput of Wireless Sensor Network (WSN), a WSN Clustering Routing algorithm based on Genetic Algorithm (GA) and Fuzzy C-Means (FCM) clustering (GAFCMCR) was proposed, which adopted the method of centralized clustering and distributed cluster head election. Network clustering was performed by the base station using a FCM clustering algorithm optimized by GA during network initialization. The cluster head of the first round was the node closest to the center of the cluster. From the second round, the election of the cluster head was carried out by the cluster head of the previous round. The residual energy of candidate node, the distance from the node to the base station, and the mean distance from the node to other nodes in the cluster were considered in the election process, and the weights of these three factors were real-time adjusted according to network status. In the data transfer phase, the polling mechanism was introduced into intra-cluster communication. The simulation results show that, compared with the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and the K-means-based Uniform Clustering Routing (KUCR) algorithm, the life cycle of the network in GAFCMCR is prolonged by 105% and 20% respectively. GAFCMCR has good clustering effect, good energy balance and higher throughput.

For the fact that information groups arrive at the system in a continuous time, a two-level polling service model with different priorities was proposed for the business problems of different priorities in the polling system. Firstly, gated service was used in sites with low priority, and exhaustive service was used in sites with high priority. Then, when high priority turned into low priority, the transmission service and the transfer query were processed in parallel to reduce the time cost of server during query conversion, improving the efficiency of polling system. Finally, the mathematical model of system was established by using Markov chain and probabilistic parent function. By accurately analyzing the mathematical model, the expressions of average queue length and average waiting time of each station of continuous-time two-level service system were obtained. The simulation results show that the theoretical calculation value was approximately equal to the experimental simulation value, indicating that the theoretical analysis is correct and reasonable. The model provides high-quality services for high-priority sites while maintaining the quality of services in low-priority sites.

To improve the work efficiency of polling control model and distinguish network priorities, an Exhaustive-Threshold Two-stage Polling control model based on Site Status (ETTPSS) was proposed. Based on two levels of priority, parallel processing was used to only send information to busy sites according to busy and idle states of sites. The model could not only distinguish the priorities of transmission services but also avoid the queries to the idle sites without information packets, thereby improving model resource utilization and work efficiency. The method of probabilistic generating function and Markov chain was used to analyze the model theoretically, and the important performance parameters of the model were analyzed accurately. The simulation results show that the simulation values and the theoretical values are approximately equal, indicating that the theoretical analysis is correct and reasonable. Compared with normal polling model, the model performance is greatly improved.

Aiming at improving the robustness in pre-processing and extracting features sufficiently for Synthetic Aperture Radar (SAR) images, an automatic target recognition algorithm for SAR images based on Deep Belief Network (DBN) was proposed. Firstly, a non-local means image despeckling algorithm was proposed based on Dual-Tree Complex Wavelet Transformation (DT-CWT); then combined with the estimation of the object azimuth, a robust process on original data was achieved; finally a multi-layer DBN was applied to extract the deeply abstract visual information as features to complete target recognition. The experiments were conducted on three Moving and Stationary Target Acquisition and Recognition (MSTAR) databases. The results show that the algorithm performs efficiently with high accuracy and robustness.