In order to solve the problems of Discriminative Correlation Filter(DCF) tracking algorithm such as model drift, rough scale and tracking failure when the tracking object suffers from rotation or non-rigid deformation, an accurate object tracking algorithm based on Distance Weighting Overlap Prediction and Ellipse Fitting Optimization(DWOP-EFO) was proposed. Firstly, the overlap and center-distance between bounding-boxes were both used as the basis for the evaluation of dynamic anchor boxes, which can narrow the spatial distance between the prediction result and the object region,easing the model drift problem. Secondly,in order to further improve the tracking accuracy,a lightweight object segmentation network was applied to segment the object from background, and the ellipse fitting algorithm was applied to optimize the segmentation contour result and output stable rotated bounding box, achieving accurate estimation of the object scale. Finally, a scale-confidence optimization strategy was used to realize gating output of the scale result with high confidence. The proposed algorithm can alleviate the problem of model drift, enhance the robustness of the tracker, and improve the accuracy of the tracker. Experiments were conducted on two widely used evaluation datasets Visual Object Tracking challenge(VOT2018) and Object Tracking Benchmark(OTB100). Experimental results demonstrate that the proposed algorithm improves Expected-Average-Overlap(EAO) index by 2.2 percentage points compared with Accurate Tracking by Overlap Maximization(ATOM) and by 1.9 percentage points compared with Learning Discriminative Model Prediction for tracking(DiMP). Meanwhile, on evaluation dataset OTB100, the proposed algorithm outperforms ATOM by 1.3 percentage on success rate index and shows significant performance especially on attribute of non-rigid deformation. the proposed algorithm runs over 25 frame/s averagely on evaluation datasets which realizes real-time tracking.

The Most Valuable Player Algorithm (MVPA) is a new intelligent optimization algorithm that simulates sports competitions. It has the problems of low precision and slow convergence. An adaptive most valuable player algorithm considering multiple training methods (ACMTM-MVPA) was proposed to solve these problems. MVPA has a single initialization method, which is random and blind, reducing the convergence speed and accuracy of the algorithm. In order to enhance the level of the initial player and improve the overall strength of the initial team, the training phase was added before the competition phase of MVPA, and the neighborhood search algorithm and chaotic sequence and reverse learning algorithms were used to train and screen players; in order to enhance the player’s ability to self-explore and learn from the best player to make the player have the qualification to compete for the most valuable player trophy, an adaptive player evolution factor was added during the team competition phase. Experimental results on 15 benchmark functions show that the proposed algorithm outperforms MVPA, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) in optimization accuracy and convergence speed. Finally, an application example of ACMTM-MVPA in parameter optimization of storm intensity formula was given. The results show that this proposed algorithm is superior to accelerated genetic algorithm, traditional regression method and preferred regression method.

Aiming at the problems that it is difficult to improve performance and reduce power consumption in the process of High Level Synthesis (HLS), an automatic custom instructions identification method for high level synthesis was proposed. The enumeration and selection of custom instructions were implemented before high level synthesis, so as to provide a universal automatic custom instructions identification method for high level synthesis. Firstly, the high level source code was transformed into a Control Data Flow Graph (CDFG), and the source code was preprocessed. Secondly, a subgraph enumeration algorithm was used to enumerate all the connected convex subgraphs in a bottom-up manner from the Data Flow Graph (DFG) based on control data flow graph, which effectively improved the user's ability to flexibly modify the constraints. Then, considering the area, performance and code size, the subgraph selection algorithms were used to select partial optimal subgraphs as the final custom instructions. Finally, a new code was regenerated by incorporating the selected custom instructions as the input of high level synthesis. Compared with the traditional high level synthesis, the pattern selection based on frequency of occurrence reduced the area by an average of 19.1%. Meanwhile, the subgraph selection based on critical paths reduced the latency by an average of 22.3%. In addition, compared with Transitive Digraph (TD) algorithm, the enumeration efficiency of the proposed algorithm was increased by an average of 70.8%. The experimental results show that the automatic custom instructions identification method can significantly improve performance and reduce area and code size for high level synthesis in circuit design.

Concerning that there is no research about the relationship between disease classification and functional homogeneity analysis of functional protein module in network medicine, the following research work was carried out. Firstly, a gene relationship network was constructed based on the Mesh database and String9 database. Secondly, the gene relationship network was divided by using optimized modularity-based module classification method (such as BGLL, Nonnegtive Matrix Factorization (NMF) and other clustering algorithms). Thirdly, the GO enrichment analysis was carried out for divided modules, and through the comparison of GO enrichment analysis to the high and low pathogenic topology module, important biology suggests for disease classification could be found from protein functional module characteristics in the aspects of biological process, cellular component, molecular function and so on. Finally, the functional characteristics of topological module for disease classification were analyzed, and the data about the functional features of each module was obtained by the analysis to the properties of the network topology such as average degree, density, and average shortest path length, and further correlativity between disease classification and functional module was revealed.

Focusing on the cold-start problem existed in the recommendation algorithm, a micro-blog Recommendation algorithm combined Tag and Artificial Bee Colony, namely TABC-R, was proposed. Firstly, the tag information for user was defined, and the tag set was used as user's interest. Secondly, the fitness function of Artificial Bee Colony (ABC) algorithm was established by three variables including tag weight, tag attribute weight and the similarity of the micro-blog words and the tags. Finally, the micro-blog with the best fitness value was obtained and recommended to users according to the search strategy of ABC algorithm. Compared with Tag-based Recommendation (T-R) algorithm and the Recommendation algorithm based on ABC (ABC-R), TABC-R algorithm has a light increase in the precision and recall, which proves the effectiveness of TARC-R.

The coordination problem between Agents in traffic intersections is a gambling problem. On the basis of bounded rationality, this paper tentatively made use of game learning thought to build the multi-Agent coordinate game learning algorithm. This learning coordination algorithm analyzed travelers' unreasonable behavior and corrected it to realize the urban traffic intersections unimpeded, so as to achieve regional and global transportation optimization. At last, its feasibility is verified by means of an example and simulation.

The Artificial Fish Swarm Algorithm (AFSA) has low search speed and it is difficult to obtain accurate value. To solve the problems, an improved algorithm based on social learning mechanism was proposed. In the latter optimization period, the authors used convergence and divergence behaviors to improve the algorithm. The two acts had fast search speed and high optimization accuracy, meanwhile, the divergence behavior enhanced the population diversity and the ability of skipping over the local extremum. To a certain extent, the improved algorithm enhanced the search performance. The experimental results show that the proposed algorithm is feasible and efficacious.

A 2.5G SmartPhone based Remote Home Surveillance System was presented,and the system structure and design principles were described.Based on the analysis of current GPRS bandwidth and SmartPhone’s computing power,an MJPEG based surveillance scheme and the design of related transfer/control protocol were introduced. The realization and analysis of proto type system was presented at the end of the paper.

A kind of technique for detection of multiple moving objects based on fussy clustering and Kalman filtering was brought forward, and has been applied to vehicle detection and tracking. An improved fussy C mean clustering algorithm was used, in which the matrix of grade of membership was modified in order to speed up convergent velocity. Kalman filtering was used to track moving target. Corresponding state equation and plus matrix were constructed based on video sequence to track multiple moving objects at the same time. It can achieve fine object searching with more reliability and efficiency.