Visual object tracking is one of the important tasks in computer vision， in order to achieve high-performance object tracking， a large number of object tracking methods have been proposed in recent years. Among them， Transformer-based object tracking methods become a hot topic in the field of visual object tracking due to their ability to perform global modeling and capture contextual information. Firstly， existing Transformer-based visual object tracking methods were classified based on their network structures， an overview of the underlying principles and key techniques for model improvement were expounded， and the advantages and disadvantages of different network structures were also summarized. Then， the experimental results of the Transformer-based visual object tracking methods on public datasets were compared to analyze the impact of network structure on performance. in which MixViT-L （ConvMAE） achieved tracking success rates of 73.3% and 86.1% on LaSOT and TrackingNet， respectively， proving that the object tracking methods based on pure Transformer two-stage architecture have better performance and broader development prospects. Finally， the limitations of these methods， such as complex network structure， large number of parameters， high training requirements， and difficulty in deploying on edge devices， were summarized， and the future research focus was outlooked， by combining model compression， self-supervised learning， and Transformer interpretability analysis， more kinds of feasible solutions for Transformer-based visual target tracking could be presented.

Now the integer Discrete Cosine Transform (DCT) algorithm of H.264 can not apply to Distributed Video Coding (DVC) framework directly because of its high complexity. In view of this, the authors presented a integer DCT algorithm and transform radix generating method based on fixed long step quantization which length was 2x (x was a plus integer). The transform radix in H.264 could be stretched. The authors took full advantage of this feature to find transform radix which best suits for working principle of hardware, and it moved the contracted-quantized stage from coder to decoder to reduced complexity of coder under the premise of "small" transform radix. In the process of "moving", this algorithm guaranteed image quality by saturated amplification for DCT coefficient, guaranteed reliability by overflow upper limit, and improved compression performance by reducing radix error. The experimental results show that, compared with corresponding module in H.264, the quantization method of this algorithm is convenient for bit-plane extraction. And it reduces calculating work of contracted-quantized stage of coder to 16 times of integer constant addition under the premise of quasi-lossless compression, raises the ratio of image quality and compression by 0.239. This algorithm conforms to DVC framework.

This paper researched the disadvantage of the traditional GIS and put forward a distributed coordinated management of spatial date based on division of GML vector map layer. GML was used to model the spatial date. Because GML was based on XML, the GML was parsed with XML technique. The algorithm of the division of the GML spatial date, and the distributed spatial data base and the metadata base were designed. The distributed spatial data base was sorted by regional geographic position, and the spatial metadata base was used to help manage the spatial data base and place the data while looking up data. In addition, the global coordinated module was designed to manage the release and the query of the spatial data, also to coordinate the storage and the getting of the spatial data, and we locked the data in use to deal with the multi-user concurrent.

On-line community has become an important place for people to retrieve information and deliver comments. It consists of a large number of topics delivered by registered users. Concerning the shortage of traditional method for calculating influential topic, a new method was presented. By calculating word's influence on re-comment chain, our method can discover the potential influential topic in on-line community based on clusters of influential words. It can timely, exactly and conveniently provide important topic information to user and forum manager.

To address the issues such as slow convergence， insufficient convergence accuracy， and the inability to jump out of the local optimum in Harris Hawks Optimization （HHO） algorithm， an improved algorithm based on Refracted Opposition-Based Learning （ROBL） and adaptive strategy was proposed. By introducing a refracted opposition-based learning strategy， opposition solutions were generated to broaden the search scope， thereby enhancing both the convergence speed and the global exploration capability of the algorithm. Concurrently， adaptive inertia weights and nonlinear energy decay factors were employed to adjust the exploration and exploitation capabilities of the algorithm dynamically. Besides， an improved adaptive t distribution variation was incorporated to mutate the optimal positions， thereby enhancing the algorithm's capacity to jump out of the local optimum. While preserving the population diversity， the improved algorithm accelerated convergence， enhanced global search capability and convergence accuracy. In comparative experiments conducted on 12 benchmark functions， it is validated that compared to swarm intelligence algorithms， the proposed HHO algorithm has the highest convergence accuracy on all the functions. Furthermore， in the benchmark test function experiments， the effectiveness of single improvement strategies was verified， as well as the superiority of employing combinations of multiple strategies over using single strategies alone.