The real-time monitoring data generated by high-speed trains during running are usually processed by variable-length coding compression technology, which is convenient for transmission and storage. However, this method will complicate the internal structure of the compressed data, so that the corresponding data decompression process must follow the composition order of the compressed data, which is inefficient. In order to improve the decompression efficiency of high-speed train monitoring data, a parallel decompression algorithm for high-speed train monitoring data was proposed with the help of the speculation technology. Firstly, the structural characteristics of high-speed train monitoring data were studied, and the internal dependence that affects data division was analyzed. Secondly, the speculation technology was used to clean up internal dependence, and then, the data were divided into different parts tentatively. Thirdly, the division results were decompressed in a distributed computing environment in parallel. Finally, the parallel decompression results were combined together. Through this way, the decompression efficiency of high-speed train monitoring data was improved. Experimental results showed that on the computing cluster composed of 7 computing nodes, compared with the serial algorithm, the speedup of the proposed speculative parallel algorithm was about 3, showing a good performance of this algorithm. It can be seen that this algorithm can improve the monitoring data decompression efficiency significantly.

Aiming at the credibility problem, a secure multi-party blockchain auditable signature scheme was proposed. In the proposed scheme, the trust vector with timestamp was introduced, and a trust matrix composed of multi-dimensional vector groups was constructed for regularly recording the trustworthy behavior of participants, so that a credible evaluation mechanism for the participants was established. Finally, the evaluation results were stored in the blockchain as a basis for verification. On the premise of ensuring that the participants are trusted, a secure and trusted signature scheme was constructed through secret sharing technology. Security analysis shows that the proposed scheme can effectively reduce the damages brought by the malicious participants, detect the credibility of participants, and resist mobile attacks. Performance analysis shows that the proposed scheme has lower computational complexity and higher execution efficiency.

In order to solve the problem of low positioning accuracy of binocular vision system, a binocular vision target positioning method based on coarse-fine stereo matching was proposed. The coarse-fine matching strategy was adopted in the proposed method, firstly the random fern algorithm based on Canny-Harris feature points was used to identify the target in the left and right images at the stage of coarse matching, and the center points of target rectangular regions were extracted to achieve the center matching. Then, a binary feature descriptor based on image gradient information was established at the stage of fine matching, and the right center point obtained by center matching was used as an estimated value to set a pixel search range, in which the best matching point of left center point was found. Finally, the center matching points were substituted into the mathematical model of parallel binocular vision to achieve target positioning. The experimental results show that the proposed method has the positioning error controlled in 7 mm within 500 mm distance, and the average relative positioning error of 2.53%. Compared with other methods, the proposed method has the advantages of high positioning accuracy and short running time.

There are some problems in the Artificial Bee Colony (ABC) algorithm, such as the slow convergence speed, low solution precision and easy to fall in local optimum. In this paper, the scout bees firstly explored the food source by a random motivation. Along with the process of colony bee foraging behavior, the elite swarm was constructed to guide the colony bee to achieve better solutions. Hence, the paper proposed a continuous optimization algorithm based on elite swarm search strategy, which simulated the foraging behavior of scout bees. The search mechanism of the algorithm was enhanced by constructing elite swarm strategy, improving the scout bee search mechanism and selecting the best solution based on the objective function value. The numerical experiment results show that the proposed algorithm has high searching precision, success rate and fast convergence speed. It is also suitable for solving high-dimensional space optimization problems.

The performance requirements of application layer multicast are not identical in different business areas, and the network environment is more complex as follows: the multicast node is diversified, the communication channel is complex, the node scale is large, the amount of data is magnified and so on. The multicast programs should be optimized by analyzing the existing application layer multicast and combining new applications demands. By analyzing the evaluating indicator of application layer multicast, application layer multicast optimization method would be divided into the coding features optimization, the hierarchical clustering optimization, the node performance optimization, the optimal parent selection optimization and the routing information maintenance optimization. Through comparing the performance indicators of different types of optimization methods, the applicable environments were introduced separately, and further research directions were discussed finally.

Cross-sectional interpolation is one of the key steps in 3D reconstruction of medical images. Considering the shortcomings of current interpolation methods, such as blurring the object boundary and low computational efficiency, an interslice interpolation method for medical images based on voxel similarity was proposed in this paper. According to the principle of voxel relativity in cross-section images and structures feature, this method calculated the relativity of voxels to classify pixels and interpolated it. The experimental results illustrate that the new method has less computational complexity and better improves the quality of image than the previous interpolation methods.

Data dissemination is a novel active service architecture based on the IP multicasting or IP broadcasting technology. It can be widely applied in LAN,WAN and digital broadcasting network,and transmits the hot-information to massive consumers. The services and transmission characteristic of data dissemination was concluded,re-transmission ratio and computing methods which can improve the errors of communication channels was submited.The sending and receiving algorithms and implementation codes for data dissemination were discussed.