Jun Tan’s research while affiliated with Chongqing University of Technology and other places

This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

Because of the DV-Hop algorithm has a big error in the estimation of the average hop distance, this paper proposed a weighted hop distance correction localization algorithm. The improved algorithm is carried out by introducing the average hop distance error correction value of the weighting processing, thereby reducing the hop distance error, and avoid the accumulation of errors in the subsequent computation process. The simulation results show that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices; therefore, it is a practical localization solution for WSN.

In the Wireless Sensor Network (WSN), the operation reliability is usually evaluated by processing the measured data of the network nodes. As the problems of the large energy consumption and complex calculation in traditional algorithms, a method for fault diagnosis of nodes in WSN based on particle swarm optimization is proposed in the paper. The range of threshold value is obtained by optimizing the measured data of nodes according to the fast convergence rate and simple rules of characteristics of the PSO. The judgment of the nodes’ malfunction is determined by analyzing the relationship between the measured data and the range of threshold value. The experimental results show that the method of fault diagnosis can find the fault nodes promptly and effectively and improve the reliability of WSN greatly.