Single object tracking is an important research direction in the field of computer vision， and has a wide range of applications in video surveillance， autonomous driving and other fields. For single object tracking algorithms， although a large number of summaries have been conducted， most of them are based on correlation filter or deep learning. In recent years， Siamese network-based tracking algorithms have received extensive attention from researchers for their balance between accuracy and speed， but there are relatively few summaries of this type of algorithms and it lacks systematic analysis of the algorithms at the architectural level. In order to deeply understand the single object tracking algorithms based on Siamese network， a large number of related literatures were organized and analyzed. Firstly， the structures and applications of the Siamese network were expounded， and each tracking algorithm was introduced according to the composition classification of the Siamese tracking algorithm architectures. Then， the commonly used datasets and evaluation metrics in the field of single object tracking were listed， the overall and each attribute performance of 25 mainstream tracking algorithms was compared and analyzed on OTB 2015 （Object Tracking Benchmark） dataset， and the performance and the reasoning speed of 23 Siamese network-based tracking algorithms on LaSOT （Large-scale Single Object Tracking） and GOT-10K （Generic Object Tracking） test sets were listed. Finally， the research on Siamese network-based tracking algorithms was summarized， and the possible future research directions of this type of algorithms were prospected.

The unique advantages of Named Data Networking （NDN） make it a candidate for the next generation of new internet architecture. Through the analysis of the communication principle of NDN and the comparison of it with the traditional Transmission Control Protocol/Internet Protocol （TCP/IP） architecture， the advantages of the new architecture were described. And on this basis， the key elements of this network architecture design were summarized and analyzed. In addition， in order to help researchers better understand this new network architecture， the successful applications of NDN after years of development were summed up. Following the mainstream technology， the support of NDN for cutting-edge blockchain technology was focused on. Based on this support， the research and development of the applications of NDN and blockchain technology were discussed and prospected.

Focusing on the problem that the existing log classification method is only applicable to the formative log, and the performance is closely related to the structure of the log, the existing log parsing algorithm LogSig (Log Signature) was extended and improved based on machine learning, and a log clustering analysis system was designed by combining data processing and result analysis in one, including raw data preprocessing, log analysis, clustering analysis and evaluation, scatter diagram display of results. This system was tested on the open source firewall log data set in VAST 2011 challenge. The experimental results show that the average accuracy of the improved algorithm in the classification of the event log reaches more than 85%; compared with the original LogSig algorithm, the log parsing accuracy is improved by 50%, and the parsing time is only 25% of the original algorithm. The proposed algorithm can be used to analyze multi-source unstructured log data efficiently and accurately in large data environment.

To promote the model reuse efficiency of 3D Computer Aided Design (CAD), aiming at the flaw of semantic expression in previous 3D model retrieval systems, a similarity measure method based on model semantic representation of Web Ontology Language (OWL) was presented. Firstly, 3D CAD master model was transformed into structuralized representation model with class-property feature as its basic semantic object. And then, the feature semantic information for matching two models was extracted from OWL representation model to be a quantitative similarity unit. And a method of total weight combining subgraph isomorphism and Tversky algorithm was proposed for similarity measure. Finally, the experiment verified the feasibility and effectiveness of the proposed method. The comprehensive quantitative assessments of the experiment show that the proposed method makes the evaluation benchmark switch from the object itself to the set of semantic description of two object properties, and can objectively reflect the similar degree of the two contrast models.

Most conventional software can not implement communication protection by modifying compiled binary code. Concerning this problem, this paper proposed a general LSP-based confidential communication model. The model introduces the optimization of the linear Hash table to store communication information, and realizes communication protection function on LSP layer by converted Winsock network model and hooked system function. The experimental results show that the proposed model can efficiently realize confidential communication between client and server.