When noise and intensity distortion exist in brain images, the method based on structural information cannot accurately extract image intensity information, edge and texture features at the same time. In addition, the computational complexity of continuous optimization is relatively high. To solve these problems, according to the structural information of the image, a non-rigid multi-modal brain image registration method based on Improved Zernike Moment based Local Descriptor (IZMLD) and Graph Cuts (GC) discrete optimization was proposed. Firstly, the image registration problem was regarded as the discrete label problem of Markov Random Field (MRF), and the energy function was constructed. The two energy terms were composed of the pixel similarity and smoothness of the displacement vector field. Secondly, a smoothness constraint based on the first derivative of the deformation vector field was used to penalize displacement labels with sharp changes between adjacent pixels. The similarity metric based on IZMLD was used as a data item to represent pixel similarity. Thirdly, the Zernike moments of the image patches were used to calculate the self-similarity of the reference image and the floating image in the local neighborhood and construct an effective local descriptor. The Sum of Absolute Difference (SAD) between the descriptors was taken as the similarity metric. Finally, the whole energy function was discretized and its minimum value was obtained by using an extended optimization algorithm of GC. The experimental results show that compared with the registration method based on the Sum of Squared Differences on Entropy images (ESSD), the Modality Independent Neighborhood Descriptor (MIND) and the Stochastic Second-Order Entropy Image (SSOEI), the mean of the target registration error of the proposed method was decreased by 18.78%, 10.26% and 8.89% respectively; and the registration time of the proposed method was shortened by about 20 s compared to the continuous optimization algorithm. The proposed method achieves efficient and accurate registration for images with noise and intensity distortion.

For the problem that task scheduling program in cloud computing environments usually takes high response time and communication costs, a Honey Bee Behavior inspired Load Balancing (HBB-LB) algorithm was proposed. Firstly, the load was balanced across Virtual Machines (VMs) for maximizing the throughput. Then the priorities of tasks on the machines were balanced. Finally, HBB-LB algorithm was used to improve the overall throughput of processing, and priority based balancing focused on reducing the wait time of tasks on a queue of the VM. The experiments were carried out in cloud computing environments simulated by CloudSim. The experiment results showed that HBB-LB algorithm respectively reduced average response time by 5%, 13%, 17%, 67% and 37% compared with Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Dynamic Load Balancing (DLB), First In First Out (FIFO) and Weighted Round Robin (WRR) algorithms, and reduced maximum completion time by 20%, 23%, 18%, 55% and 46%. The result indicates that HBB-LB algorithm is suitable for cloud computing system and helpful to balancing non-preemptive independent tasks.

The number of components is continuously growing in the network component library, it is hard for users to select high-quality components from the massive uneven-quality components. In order to solve the problem, a reuse trust evaluation method based on component use dependency relations was proposed, in which component base was used as an evidence base. Firstly, component dependency relations were collected from evidence base. Secondly, the basic trust function was defined for each component, and the different believable weight value was set up for each evidence according to the different sources of component dependency relations on the above basis. Finally, the final trust value of component was generated by a specific conversion algorithm with the obtained results. In instance analysis, the component evaluation result of the proposed method was consistent with the expectation and the conclusion which was gotten by the internal and external quality model of the reference components. However, the proposed method greatly reduced the workload of component's credible evaluation and improved the evaluation efficiency. The results of analysis show that the proposed method can objectively reflect the credibility of components, and can be used as a trusted measurement mechanism of component retrieval in the component library, which helps to realize the high quality retrieval and reuse of components.