Channel modeling of the closed environment plays an important role in many application scenarios. When there are many obstacles in the space, the traditional ray tracing algorithm has the problem of too many times of finding intersection points in the calculation process, which makes the algorithm calculation efficiency low. Therefore, a ray tracing acceleration method based on space division was proposed. In the method, according to the distribution of objects in three-dimensional space, the static and dynamic space division acceleration methods were combined reasonably, so as to greatly reduce the number of finding intersection points between rays and objects in space, and improve the calculation efficiency of the algorithm. Simulation analysis shows that in the three-dimensional environment with the same prediction accuracy, compared with the original algorithm, the ray tracing algorithm using static space division has the calculation efficiency improved by at least 50.2% as the division level is improved; and compared with the algorithm which only uses static space division, the acceleration method based on the combination of static and dynamic space divisions has the calculation efficiency improved by at least 8.9% on the basis of the improvement above.

Aiming at the problem that virtual-real registered accuracy and real-time performance are influenced by image texture and uneven illumination in Augmented Reality (AR), a method based on improved ORB (Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features)) algorithm was proposed to solve it. The method firstly optimized the dense region of image feature points by setting the number and distance threshold of it and used parallel algorithm to reserve N points of greater eigenvalue; Then, the method adopted discrete difference feature to enhance the stability of uneven illumination changes and combined the improved ORB with BOF (Bag-of-Features) model to realize quick retrieval of Benchmark image. Finally, it realized the virtual-real registration by using the homographics between images. Comparative experiments among the proposed method, original ORB, Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) algorithms were performed from the aspects of accuracy and efficiency, and the proposed method reduced the registration time to about 40% and reached the accuracy more than 95%. The experimental results show that the proposed method can get a better real-time performance and higher accuracy in different texture and uneven illumination.

To solve permutation flowshop scheduling problem, an improved particle swarm optimization was proposed. Improved algorithm introduced a method to judge the premature state of the particle swarm, and used reversion strategy to mutate the best particle after the particle swarm being trapped in premature convergence, and used simulated annealing method to accept the new particle. The mutation for best particle can guide the particle swarm to escape from the local best values limit and overcome the particles premature stagnation. The simulation results based on Car and Rec benchmarks of permutation flowshop scheduling problem prove the effectiveness of the proposed algorithm.

In IP network, tomography method can perform fault diagnosis by analyzing the end-to-end properties with low costs. However, most existing tomography based techniques have the following problems: 1) the end-to-end detected number is not sufficient to determine the state of each link; 2) as the scale of the network goes up, the diagnosis time may become unacceptable. To address these problems, a new congested link diagnosis algorithm based on Bayesian model was proposed in this paper. This method firstly modeled the problem as a Bayesian network, and then simplified the network by two steps and limited the number of multiple congested links. Therefore, the proposed method could greatly reduce the computational complexity and guarantee the diagnostic accuracy. Compared with the existing diagnosis algorithm which is called Clink, the proposed algorithm has higher diagnostic accuracy and shorter diagnosis time.