Concerning the poor privacy and flexibility of traditional lifetime estimation for human motion, a lifetime estimation system for human motion was proposed, by analyzing the amplitude variation of WiFi Channel State Information (CSI). In this system, the continuous and complex lifetime estimation problem was transformed into a discrete and simple human motion detection problem. Firstly, the CSI was collected with filtering out the outliers and noise. Secondly, Principal Component Analysis (PCA) was used to reduce the dimension of subcarriers, obtaining the principal components and the corresponding eigenvectors. Thirdly, the variance of principal components and the mean of first difference of eigenvectors were calculated, and a Back Propagation Neural Network (BPNN) model was trained with the ratio of above two parameters as eigenvalue. Fourthly, human motion detection was achieved by the trained BP neural network model, and the CSI data were divided into some segments with equal width when the human motion was detected. Finally, after the human motion detection being performed on all CSI segments, the human motion lifetime was estimated according to the number of CSI segments with human motion detected. In real indoor environment, the average accuracy of human motion detection can reach 97% and the error rate of human motion lifetime is less than 10%. The experimental results show that the proposed system can effectively estimate the lifetime of human motion.

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.

Trajectory privacy protection is critical to the development of Internet of Vehicles (IoV), which makes it important to summarize and analyze existing research methods. Existing IoV trajectory privacy protection methods can be divided into three categories: trajectory obfuscation, pseudonym change and trajectory encryption. Trajectory obfuscation can be achieved based on users' real trajectory or dummy trajectory. Pseudonym change can be achieved based on mix-zone or path confusion. Trajectory encryption can be achieved based on Private Information Retrieval (PIR) protocol or spatial transformation. Firstly, the research background and common attacks were introduced and summarized in this paper. Secondly, existing IoV trajectory privacy protection methods were surveyed from the aspects of methodology, scientific problem and method evolution. The problems need to be further studied also were elaborated. Furthermore, the performances of representative schemes were summarized, such as privacy protection, attack resistance and complexity. Finally, the future research directions of IoV trajectory privacy protection was prospected.

In order to ensure the correctness of program execution in the safety critical system, the error control theory is used to encode the computer instructions, but the algorithm involves the modular operation, resulting in high additional complexity and difficulty to use in real-time systems. Aiming at reducing the additional complexity, delta code's multiplication and division algorithm was improved. The idea of redundancy encoding and differentiated ideology was introduced to ensure security, while the inverse element was introduced into division to transform division into multiplication, thus avoiding the overhead of the modular operation and reducing the additional complexity while improving the security of the algorithm. Theoretical analysis shows that the undetected error rate is proved to be 2.3*10 ^-10. Simulation results show that the undetected error rate of the proposed algorithm is consistent with the theoretical value, and the complexity is 6.4-7.2 times of the original algorithm, but 7%-19% lower than original delta code. The proposed algorithm satisfies the requirements of safety critical application systems in terms of error detection rate and complexity.