System Wide Information Management (SWIM) is a distributed, large-scale network system that provides uninterrupted aviation information data sharing and transmission services to air traffic management departments, airports and airlines in real time. In order to guarantee the continuity of SWIM services, the emergency response mechanism of SWIM based on subscription/release service was studied. Firstly, by real-time monitoring various performance indicators of SWIM network, a network survivability evaluation method based on improved fuzzy analytic hierarchy process was proposed. Secondly, when the network survivability index fell below the boundary value of the parameter, the corresponding information was published to the subscriber. It was determined by the subscriber whether to perform the service migration. Finally, an Emergency Response Model based on Subscribe/Publish service (ERMSP) for SWIM was proposed for natural disasters and Distributed Denial of Service (DDoS) attacks. The model is based on subscribe, publish and trust management mechanisms. Simulation experimental results show that the resistibility is improved by 8.9% and the business continuity is improved by 18.2% by real-time monitoring of network performance indicators and deployment of ERMSP, which can realize the emergency response of SWIM.

The generation of massive data brings a huge storage and computational burden to users, and the emergence of cloud servers solves this problem well. However, data outsourcing brings convenience to users while it also causes some security problems. In order to solve the security problem of data in the outsourcing process, a simpler and more efficient cloud outsourcing data security auditing protocol throughout whole lifecycle was designed and implemented, which was combined with classical distributed string equality checking protocol and Rank-based Merkel Hash Tree (RMHT) algorithm. The protocol not only can protect the integrity of outsourced storage data, allowing users periodically audit its integrity, but also can guarantee the secure transfer of cloud data. Besides, the copy of transfer data can avoid being reserved by malicious cloud servers, protecting users' privacy well. The analyses of security and efficiency show that the proposed protocol is sufficiently secure and comparatively efficient, the security of outsourcing data throughout its whole lifecycle will be protected well.

A novel Energy-Balanced Clustering Routing algorithm based on Ring Partition (EBCR-RP) was proposed to solve the network lifetime problem of unbalanced energy consumption and low energy efficiency in Wireless Sensor Network (WSN). Firstly, the one-hop distance with minimize energy consumption was calculated and regarded as ring spacing. Secondly, the number of clusters was optimized and each ring was partitioned uniformly, and the node with highest energy in each block was chosen as cluster header to balance energy consumption. Finally, a cost function was designed to search optimal data transform path to improve energy efficiency. The simulation results show that network lifetime of EBCR-RP is increased by 51.4% and 8.6% compared with Fuzzy Logic Cluster Formation Protocol (FLCFP) and Improved Uneven Clustering Routing (IUCR) algorithms. EBCR-RP can effectively prolong network lifetime, balance energy consumption and improve energy efficiency.

At present, the video key information extraction technology mainly focuses on the extraction of key frames according to the characteristics of video low-level, and ignores the semantic information related to users' interests. Semantic modeling of video requires a large number of marked video training samples, which is time consuming and laborious. To alleviate this problem, a large amount of Internet image data was used to construct a semantic model based on users' interests, which was rich in content and covered a large amount of event information. However, the images obtained from the Internet were diversed and often accompanied by image noise, the final extraction of video would be greatly affected by brute force migration. The synonym-weight model was used to measure the differences of the semantically similar image groups on the Internet, and these image groups were used to construct a semantic model. The weight of each image group in knowledge migration was determined by the weight value. The experimental results on several challenging video datasets demonstrate that semantic modeling based on users' interests combined with weights is more comprehensive and accurate, so as to effectively guide the video key frame extraction.

Aiming at real-time requirements and elimination ghost produced by moving object in video stitching, a method based on circular Region Of Interest (ROI) image registration was proposed by using the simplified process and Graphics Processing Unit (GPU) acceleration. Firstly, the feature extraction only occured in the ROI area, which improved the detection speed and the feature matching accuracy. Secondly, to further reduce the time cost and meet the real-time requirements for video processing, two strategies were used. On one hand, only the first frame was used for matching, while the subsequent frames used the same homography matrix to blend. On the other hand, GPU was adopted to realize hardware acceleration. Besides, when there are dynamic objects in the field of view, the graph-cut and multi-band blending algorithms were used for image blending, which can effectively eliminate ghost image. When stitching two videos of 640×480, the processing speed of the proposed method was up to 27.8 frames per second. Compared with the Speeded Up Robust Features (SURF) and Oriented features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB), the efficiency of the proposed method was increased by 26.27 times and 11.57 times respectively. Experimental results show the proposed method can be used to stitch multi-channel videos into a high quality video.

The log-based index update mechanism in flash-based database system has following shortage: low query efficiency, expensive update cost, unreasonable space allocation and merge for the log. In order to solve these problems, a new adaptive index structure named LM-B+TREE was proposed. LM-B+TREE can map the page for index update buffer into corresponding node of traditional B+ TREE. Furthermore, according to the read/write workload and read/write overhead, LM-B+TREE can dynamically maintain the update buffer and adjust the index frame adaptively. The experimental results show that LM-B+ TREE can dynamically adjust the index structure to adapt to the read-write workload, significantly reduce the overhead of index update and improve the query performance.

Considering the importance of selecting Kernel parameters, the Particle Swarm Optimization (PSO) model search algorithm was proposed to search optimal parameters. This method combined the global search capability of PSO algorithm and the good local convergence of mode search, that making PSO model search algorithm displays higher performance, and applied to an the practice of agricultural technological project classification. The results of experiment show that this method is not only efficient, but also catches the optimal parameters that have achieved higher accuracy.

It has been a new approach that flash memory is used as storage media to improve database system performance. In the current flash database system, to solve the problems of low search performance, improper log region allocation, and high index update cost in storage management, a forecasting algorithm based on the latest version of Bloom Filter was proposed, record locator and log summary structure were introduced, and a self-adaptive mechanism based on flash search and update cost estimate model were given. The experimental results prove that it can make a proper log region allocation, efficiently improve search performance, and reduce non-clustered index update cost.