To facilitate the communication security of Wireless Sensor Network (WSN), an Agent-based multi-layered security detection method was proposed. Nodes were divided into three layers based on the different functions, and each layer performed the appropriate detection method to maintain the security of network. In order to reduce the energy consumption, mobile Agent technology was applied to collect data efficiently. And then, the Agent nodes completed the underlying security detection tasks to reduce the energy and prolong network life of head nodes. Finally, the experimental results show that, compared with the methods of Su and eHIDS, the proposed algorithm can increase detection rate by at most 35% and decrease false alarm rate by at most 15% respectively, as well as better performance from perspectives of energy consumption, showing an effective method to detect attack in WSN.

To improve the recognition rate of Principal Component Analysis (PCA) algorithm in face recognition, a new algorithm combining the image texture spectrum feature with PCA was proposed. Firstly, the texture unit operator was used to extract the texture spectrum feature of the face image. Secondly, PCA approach was used to reduce the dimensions of the texture spectrum feature. Finally, K-Nearest Neighbor (KNN) classification was chosen to recognize the face. ORL and Yale face database were used to test the proposed algorithm, and the recognition accuracies were 96.5% and 95% respectively, which were higher than those of PCA and Modular Two-Dimensional PCA (M2DPCA). The experimental results demonstrate the efficiency and accuracy of the proposed algorithm.