Focusing on the issue that extractive summarization needs to automatically produce a short summary of a document by concatenating several sentences taken exactly from the original material. A single document automatic summarization algorithm based on word-sentence co-ranking was proposed, named WSRank for short, which integrated the word-sentence relationship into the graph-based sentences ranking model. The framework of co-ranking in WSRank was given, and then was converted to a quite concise form in the view of matrix operations, and its convergence was theoretically proved. Moreover, a redundancy elimination technique was presented as a supplement to WSRank, so that the quality of automatic summarization could be further enhanced. The experimental results on real datasets show that WSRank improves the performance of summarization by 13% to 30% in multiple Rouge metrics, which demonstrates the effectiveness of the proposed method.

Focusing on the admission control problems of media delivery network in Software Defined Network (SDN), an admission control scheme of comprehensively optimizing the service performance of service nodes and transmission links was proposed. The proposed scheme used SDN controller's abilities of directly controlling routers and perception of entire network, to jointly optimize the service performance of the service nodes on the application layer and the transmission links between the service nodes and the users on the network layer, and the influences of link congestion to data transmission and quality of service were reduced. Firstly, the admission control process of the SDN service system was modeled as a Partially Observable Markov Decision Process (POMDP). Secondly, the observation-based randomized policy was used as the admission control policy of the system. Finally, the policy-gradient algorithm was used to optimize the admission control policy, and the optimal policy of the model was obtained. The simulation results show that, compared with the best effort service policy, the POMDP-based optimal admission control policy improves system performance by 10%, which illustrates the effectiveness of the proposed approach.

The problem of cloud computing processors failure cannot be ignored in the cloud environment. Fault-tolerance becomes a key requirement in the design and development of cloud computing systems. Aiming at the problem of low scheduling efficiency and single type of task in most fault-tolerant scheduling algorithms, the fault-tolerant scheduling method based on processors, primary-backup copies of hybrid tasks grouped was proposed. A method to determine whether two backup copies can overlap was presented. What's more, the calculation formulas of periodic task worst-case response time and completion time of aperiodic tasks preemptive execution were given. The simulation result shows that the proposed algorithm has a remarkable saving of cloud computing system processors needed and scheduling computation time compared with Hybrid real time task Fault Tolerant Scheduling (HFTS) algorithm. It is of great significance for improving the reliability of cloud system and the schedulability of real-time tasks set, as well as the processor efficiency.

A lattice signature scheme was proposed and some parameter choosing rules were illustrated concerning Small Integer Solution (SIS) problem and random oracle model of lattice. Then the results of the length of the keys that were generated under different parameter circumstances were compared. Afterwards the security and efficiency with the signature scheme were verified. At last, for the purpose of fairness, and reliability in multipartite authentication, the signature scheme was combined with key distribution and escrow, a new authentication scheme with the Singular Value Decomposition (SVD) algorithm based on mathematical matrix decomposition theory was proposed.

Complex background, especially when the object is similar to the background in color or the target gets blocked, can easily lead to tracking failure. Therefore, a particle filter integrating color feature and texture feature was proposed. An object tracking algorithm based on adaptive multiple cues fusion was proposed too. When occlusion occurred, the fusion rules would be switched in time. Mean-shift algorithm was applied to each particle to overcome degeneracy. The experimental results show that this algorithm is robust to occlusion and is able to track object under complex background.

The Standard Particle Filter (SPF) is a typical method of solving the tracking problem of non-linear/non-Gaussian model system. However, updating process strictly depends on parameters selection, and it cannot handle the changes in curve topology. In regard to this, a new particle filter target tracking algorithm based on geometric active contours was proposed, which made a good deal with the changes of curve topology using level set theory. The algorithm improved the resampling techniques and increased the diversity of particles. The simulation results indicate that the proposed method can effectively improve the state estimation precision with more flexibility.