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.

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.