Glioma segmentation depends on multi-modal Magnetic Resonance Imaging (MRI) images. Convolutional Neural Network (CNN)-based segmentation algorithms are often trained and tested on fixed multi-modal images, which ignores the problem of missing or increasing of modal images. To solve this problem, a method mapping images of different modalities to the same feature space by CNN and using the features in the same feature space to segment tumors was proposed. Firstly, the features of different modalities were extracted through the same deep CNN. Then, the features of different modal images were concatenated, and passed through the fully connected layer to realize the feature fusion. Finally, the fused features were used to segment the brain tumor. The proposed model was trained and tested on the BRATS2015 dataset, and verified with the Dice coefficient. The experimental results show that, the proposed model can effectively alleviate the problem of data missing. At the same time, compared with multi-modal joint method, this model is more flexible, and can deal with the problem of modal data increasing.

To solve the problem of retinal vascular structure division in fundus images, an adaptive breadth-first search algorithm was proposed. Firstly, based on the structure of retinal vessels, the concept of hierarchical features was proposed and feature extraction was carried out. Then, the segmented retinal vessels were analyzed and processed, and several undirected subgraphs were extracted. Finally, the adaptive breadth-first search algorithm was used to classify the hierarchical features in each subgraph. The division of retinal vascular structure was transformed into the classification of hierarchical features. By classifying the hierarchical features of retinal vessels, the hierarchical structures of retinal vascular segments containing these hierarchical features were able to be determined, thus realizing the division of retinal vascular structures. The algorithm has excellent performance when applied to public fundus image databases.

In order to solve the problem that the service composition in the logistics field has poor tolerance and unreliable service, a model of logistics service fault-tolerant composition for intelligent logistics system of Internet of Things (IoT) based on π-net was built. Firstly, after a brief introdution of IoT intelligent logistics system, a fault-tolerant service composition framework for the system was provided. Then, a model of logistics service fault-tolerant composition for the system based on π-net was built, and the correctness of fault tolerance and fitting degree of the model were analyzed. Finally, the service reliability and the fault-tolerant reliability of the model were tested, and the comparison with Petri-net, QoS (Quality of Service) dynamic prediction, fuzzy Kano model and modified particle swarm optimization methods in the service composition execution time, user satisfaction, reliability and optimal degree were carried out. The results show that the proposed model has high service reliability and fault-tolerant reliability, and has certain advantages in terms of service composition execution time, user satisfaction, reliability and optimal degree.

Since methods based on linked list in existing microscopic traffic simulation systems are not efficient and scalable to process Nearest Neighbor (NN) queries, a variation of B+ tree based method was proposed to resolve these problems. This method combined ideas from NN queries in database with advantages of linked list. By maintaining indices of nearby vehicles of each vehicle in the local lane, the performance of NN queries in that lane could be largely improved. Under the assumption of randomly distribution of vehicles, a mathematical model was also proposed to optimize the parameter setting according to multiple parameters for lanes and the amount of vehicles. This model calculated the minimized average query length of each NN query to optimize the parameter setting. The results of theoretical analysis and simulations showed that in common traffic conditions including sparse, normal and congestion, the main indicator, namely the average simulation time cost of each vehicle, could be reduced by 64.2% and 12.8% compared with linked list and B+ tree respectively. The results prove that the proposed method is suitable for larges-cale microscopic traffic simulation systems.

An index of network evolution speed and a network evolution model were put forward to analyze the effects of network evolution speed on propagation. The definition of temporal correlation coefficient was modified to characterize the speed of the network evolution; meanwhile, a non-Markov model of temporal networks was proposed. For every active node at a time step, a random node from network was selected with probability r, while a random node from former neighbors of the active node was selected with probability 1-r. Edges were created between the active node and its corresponding selected nodes. The simulation results confirm that there is a monotone increasing relationship between the network model parameter r and the network evolution speed; meanwhile, the greater the value of r, the greater the scope of the spread on network becomes. These mean that the temporal networks with high evolution speed are conducive to the spread on networks. More specifically, the rapidly changing network topology is conducive to the rapid spread of information, but not conducive to the suppression of virus propagation.

When Sunday algorithm is applied into the Chinese version of Unicode, there are some problems. On one hand, it causes the expansion of space if using Chinese characters directly to generate a failed jump table. On the other hand, it can reduce the space consumption at the cost of matching speed when Chinese characters are split into two bytes. Concerning the degradation of time performance produced by applying Sunday algorithm to the character-splitting environment of Chinese version of Unicode, in combination with internal relevance of Chinese unit in Unicode, the improved algorithm in this paper optimized the auxiliary jump table and matching rules in original Sunday algorithm in the character-splitting environment. Consequently, the proposed algorithm not only solves the problem of space expansion, but also improves time performance of Sunday algorithm in this environment. Finally, the improved time and space performance of the algorithm gets proved via simulation.

Current survivability models can hardly bring up a practical solution, nor reflect the properties of Web applications. Firstly, the properties of Web applications were analyzed, especially the differences between atomic Web applications and composite Web applications. Secondly, the mathematical model reflecting the invoking relationship of the atomic Web applications for the development of composite Web application was constructed. Lastly, a survivability model for atomic Web applications and a Markov-based survivability model for composite Web applications with regard to runtime environment were proposed. And on the basis of these models, a recovery approach for Web applications was given, when part or all of the functions failed in adverse environment. Besides, a case was analyzed using these models, and its recovery procedures were given, in which the high survivability was guaranteed.

Resource allocation strategy has been the hot and difficult research topic in the field of cloud computation. Research and analysis of current resource allocation strategies were carried out. Firstly, the challenges facing cloud platform resource allocation were analyzed. Then the formalization description of cloud platform resource allocation was given. The current mainstream of resource allocation strategies were described from the perspectives of heuristic allocation algorithms, economic theory based strategy, and other allocation strategies, and their advantages and disadvantages were discussed. Finally, a comprehensive comparison of the mainstream strategies based on specific indicators was made, and some future research directions were pointed out.