To resolve the difficulties of forensics and determination of responsibility for traffic accidents, a blockchain-based e-forensics scheme under Internet Of Vehicles (IOV) communications architecture was proposed. In this scheme, the remote storage of digital evidence was implemented by using the decentralized storage mechanism of blockchain, and the fast retrieval of digital evidence and effective tracing of related evidence chain were realized by using the smart contracts. The access control of data was performed by using the token mechanism to protect the privacy of vehicle identities. Meanwhile, a new consensus mechanism was proposed to meet real-time requirements of IOV for forensics. Simulation results show that the new consensus algorithm in this proposed scheme has higher efficiency compared with the traditional Delegated Proof Of Stake (DPOS) consensus algorithm and the speed of forensics meets the requirements of IOV environment, which ensures the characteristics of electronic evidence such as non-tampering, non-repudiation and permanent preservation, so as to realize the application of blockchain technology in judicial forensics.

In order to solve the problem of low accuracy of video-based person re-identification caused by factors such as occlusion, background interference, and person appearance and posture similarity in video surveillance, a video-based person re-identification method of Evenly Sampling-random Erasing (ESE) and global temporal feature pooling was proposed. Firstly, aiming at the situation where the object person is disturbed or partially occluded, a data enhancement method of evenly sampling-random erasing was adopted to effectively alleviate the occlusion problem, improving the generalization ability of the model, so as to more accurately match the person. Secondly, to further improve the accuracy of video-based person re-identification, and learn more discriminative feature representations, a 3D Convolutional Neural Network (3DCNN) was used to extract temporal and spatial features. And a Global Temporal Feature Pooling (GTFP) layer was added to the network before the output of person feature representations, so as to ensure the obtaining of spatial information of the context, and refine the intra-frame temporal information. Lots of experiments conducted on three public video datasets, MARS, DukeMTMC-VideoReID and PRID-201l, prove that the method of jointing evenly sampling-random erasing and global temporal feature pooling is competitive compared with some state-of-the-art video-based person re-identification methods.

In order to solve the problems of low efficiency of centralized authentication and poor privacy protection in Internet of Vehicles (IOV), an efficient anonymous authentication scheme based on blockchain technology was proposed. According to the IOV's characteristics of openness, self-organization and fast movement, the tamper-proof and distributed features of blockchain technology were used to realize the generation and blockchain storing of temporary identities of the vehicles. Smart contract was implemented to make efficient anonymous two-way identity authentication while vehicles communicated with each other. Experimental results show that, in terms of authentication efficiency, the proposed scheme has the anonymous authentication with slower time delay growth and higher efficiency compared with traditional Public Key Infrastructure (PKI) authentication and identity authentication scheme with pseudonym authorization; in terms of safety performance, the temporary identity stored in the blockchain has characteristics of non-tampering, nondenying and traceability. In this scheme, the malicious vehicle identity and authority can be traced back and controlled respectively, and the public-key cryptography and digital signature technology ensure the confidentiality and integrity of communication data.

When police carry out traffic law enforcement using multi-target speed measuring radar, one of the most essential things is to judge which lane each vehicle belongs to, and only in this way the captured pictures can serve as the law enforcement evidence. To achieve lane division purpose, traditional way is to obtain a fixed threshold by manual measurement and sometimes the method of coordinate system rotation is also needed, but this method has a large error with difficulty in operating. A new lane division algorithm called Kernel Clustering algorithm based on Statistical and Density Features (K-CSDF) was proposed, which includes two steps: firstly, a feature extraction method based on statistical feature and density feature was used to process the vehicle data captured by radar; secondly, a dynamic clustering algorithm based on kernel and similarity was introduced to cluster the processed data. Simulations with Gaussian Mixture Model (GMM) algorithm and Self-Organizing Maps (SOM) algorithm were conducted. Simulation results show that the proposed algorithm and SOM algorithm can achieve a lane accuracy of more than 90% when only 100 sample points are used, while GMM algorithm cannot detect the lane center line. In terms of running time, when 1000 sample points are taken, the proposed algorithm and GMM algorithm spend less than one second, and the real-time performance can be guaranteed, while SOM algorithm takes about 2.5 seconds. The robustness of the proposed algorithm is better than GMM algorithm and SOM algorithm when sample points have a non-uniform distribution. When different amounts of sample points are used for clustering, the proposed algorithm can achieve an average lane division accuracy of more than 95%.

To deal with the output tracking problem of a hypersonic vehicle with parameters uncertainty, an adaptive controller which obtained H _∞ performance was proposed based on hierarchical fuzzy system. In order to solve the problem that the number of rules in a fuzzy controller increases exponentially with the number of variables involved, reduce the number of the parameters to be identified on-line and enhance the real-time performance of the control system, an adaptive controller was designed based on hierarchical fuzzy system. To weak the impact on the stability abused by approximation error of the fuzzy logic system, parameters uncertainty and the external disturbances, the robust compensation terms were introduced to improve the H _∞ performance of the system. The Lyapunov theory was applied to analyze and prove the stability of the system. The simulation results demonstrate that the system can not only track the input exactly, but also possess strong robustness.

The traditional Super-Resolution (SR) algorithm, based on sparse dictionary pairs, is slow in training speed, poor in dictionary quality and low in feature matching accuracy. In view of these disadvantages, a super-resolution algorithm based on the improved sparse coding was proposed. In this algorithm, a Morphological Component Analysis (MCA) method with adaptive threshold was used to extract picture feature, and Principal Component Analysis (PCA) algorithm was employed to reduce the dimensionality of training sets. In this way, the effectiveness of the feature extraction was improved, the training time of dictionary was shortened and the over-fitting phenomenon was reduced. An improved sparse K-Singular Value Decomposition (K-SVD) algorithm was adopted to train low-resolution dictionary, and the super-resolution dictionary was solved by utilizing overlapping relation, which enforced the effectiveness and self-adaptability of the dictionary. Meanwhile, the training speed was greatly increased. Through the reconstruction of color images in the Lab color space, the degradation of the reconstructed image quality, which may be caused by the color channel's correlation, was avoided. Compared with traditional methods, this proposed approach can get better high-resolution images and higher computational efficiency.

To improve the correctness and feasibility of the implementation of multilevel security in the distributed environment, a distributed multilevel security core architecture — Distributed Trusted Computing Base (DTCB) was proposed. DTCB was divided into three layers, TCB of System layer, TCB of Module layer and TCB of Partition layer, finer multilevel control granularity was realized step by step, greatly reducing the complexity of the implementation of multilevel security in the distributed environment. At last, based on the composable noninterference model, the security of DTCB was formally proved. The result shows that DTCB assures the multilevel security of distributed system as a whole.

The recognition of frontal facial appearance with illumination is a difficult task for face recognition. In this paper, a novel illumination invariant extraction method was proposed to deal with the illumination problem based on wavelet transform and denoising model. The illumination invariant was extracted in wavelet domain by using wavelet-based denoising techniques. Through manipulating the high frequency wavelet coefficient combined with denoising model, the edge features of the illumination invariants were enhanced and more useful information was restored in illumination invariants, which could lead to an excellent face recognition performance. The experimental results on Yale face database B and CMU PIE face database show that satisfactory recognition rate can be achieved by the proposed method.

The synchronization of video and audio is very important to streaming media system. First of all, (MPEG- 4) was introduced briefly, and the factors affecting the synchronization of video and audio in streaming media system were analyzed, Then a set of solutions about the synchronization were presented in detail. Finally, the implementation of a digital video surveillance system based on (MPEG- 4) was introduced.