Concerning the low energy efficiency and network energy imbalance of cluster head in Wireless Sensor Network (WSN), a sensor network clustering algorithm with Clustering Time Span Optimization (CTSO) was proposed. Firstly, the constraints within the cluster membership and cluster head spacing in cluster head election was considered to avoid overlapping between the various clusters as much as possible and optimize the energy of the cluster nodes. Secondly, the cluster head election cycle was optimized and divided into rounds by considering the task excution cycle as time span, by minimizing the cluster head election rounds, the cost for selecting cluster heads and the energy for broadcasting messages were reduced, and energy utilization of cluster nodes was improved. Simulation results showed that, compared to the homogeneous state data routing scheme based on multiple Agents and adaptive data aggregation routing policy, the average energy efficiency of CTSO was increased by 62.0% and 138.4% respectively, and the node life was increased by 17% and 9 % respectively. CTSO algorithm has a good effect on promoting the energy efficiency of cluster head node and balancing the energy of nodes in WSN.

In order to carry on the gait recognition fast and efficiently, a new feature relationship based feature representation was proposed in this paper, which utilized nonstationarity in the distribution of feature relationships. Firstly, relative direction between two adjacent edge pixels in 8-neighborhood region was labeled as one of the attributes characterizing relationship, and distance from edge pixel to shape centroid point as the other attribute. Joint probability function of the two attributes was estimated by normalized histogram of observed values. Secondly, Principal Component Analysis (PCA) was adopted for feature reduction. Finally, the nearest-neighbor classifier was adopted for classification. The experimental result demonstrates that the proposed method was used to CASIA gait database, and got the best recognition rate of more than 90%. Feature dimension of the attributes joint probability matrix is reduced from 900 to 240 with relatively lower computational cost.