Single biometric identification technology can be easily affected by various external factors, thus the recognition rate and stability are poor. A palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain, namely NSCT-NBP, was proposed. Firstly, palmprint and palmvein images were decomposed by using Non-Subsampled Contourlet Transform (NSCT), then the obtained low-frequency and high-frequency sub-images were respectively merged by using the regional energy and image self-similarity principle. Secondly, the texture features were extracted from the fused images by using Neighbor based Binary Pattern (NBP), thus the eigenvector was got. Finally, the similarity of the fused images was calculated by Hamming distance of the feature vectors, to get Equal Error Rate (EER). The experiments were conducted on PolyU and a self-built database, the experimental results show that the lowest EER of NSCT-NBP algorithm were 0.72% and 0.96%, the identification time were only 0.0530 s and 0.0871 s. Compared with the current best palmprint-palmvein fusion method based on wavelet transform and Gabor filter, the EER of the two databases were reduced by 4% and 36.8%, respectively. The NSCT-NBP algorithm can effectively fuse the texture features of the palmprint-palmvein images and has good recognition performance. The fusion of palmprint-palmvein features can enhance the security of the recognition system.

In order to improve the energy efficiency of Wireless Sensor Network (WSN), a WSN routing algorithm based on Optimal Number of Clusters and Improved Gravitational Search (ONCIGS) was proposed. Firstly, the optimal number of clusters was calculated according to the idea of uneven clustering, and the improved AGglomerative NESting (AGNES) algorithm was adopted to realize the reasonable clustering of network. Secondly, reverse learning mechanism and elite strategy were introduced into the gravitational search algorithm, and the force was adjusted adaptively based on population density to improve the search precision and speed up the convergence. Then, the standard deviation of residual energy of cluster heads was taken as the objective function to search the energy-balanced inter-cluster data forwarding path. The experimental results show that, compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm and Distributed Energy Balanced Unequal Clustering (DEBUC) routing algorithm, the network life cycle of the proposed ONCIGS is prolonged by 41.94% and 5.77% respectively under the network scale of 100 m×100 m, and it is prolonged by 76.60% and 7.82% respectively under the network scale of 200 m×200 m. The proposed ONCIGS can effectively prolong network lifetime and improve energy efficiency.

Since indoor environment is complex and equal signal differences are assumed to equal physical distances in the traditional K Nearest Neighbor ( KNN) approach, a new Access Point (AP) selection method and KNN indoor positioning algorithm based on scaling weight were proposed. Firstly, in the improved AP selection method, box plot was used to filter Received Signal Strength (RSS) outliers and create a fingerprint database. The AP with high loss rate in the fingerprint database were removed. The standard deviation was used to analyze the variations of RSS, and TOP- N APs with less interference were selected. Secondly, the scaling weight was introduced into the traditional KNN algorithm to construct a scaling weight model based on RSS. Finally, the first K reference points which obtained the minimum effective signal distance were calculated to get the unknown position coordinates. In the localization simulation experiments, the mean of error distance by improved AP selection method is 21.9% lower than that by KNN. The mean of error distance by the algorithm which introduced scaling weight is 1.82 m, which is 53.6% lower than that by KNN.