It is difficult to judge the image multiply distortion type. In order to solve the problem， based on the idea of deep learning multi-label classification， a new multiply distortion type judgement method based on multi-scale and multi-classifier Convolutional Neural Network （CNN） was proposed. Firstly， the image block containing high-frequency information was obtained from the image， and the image block was input into the convolution layers of different receptive fields to extract the shallow feature maps of the image. Then， the shallow feature maps were input into the structure of each sub-classifier for deep feature extraction and fusion， and the fused features were judged by the Sigmoid classifier. Finally， the judgment results of different sub-classifiers were fused to obtain the multiply distortion type of image. Experimental results show that， on the Natural Scene Mixed Disordered Images Database （NSMDID）， the average judgment accuracy of the proposed method can reach 91.4% for different types of multiply distortion types in the images， and most of them are above 96.8%， illustrating that the proposed method can effectively judge the types of distortion in multiply distortion images.

With the rapid development of E-learning system, E-learning resources grow explosively. How to effectively organize E-learning resources is a key factor of constructing efficient E-learning system. Concerning the existing resources organization deficiency of E-learning resource library, this paper proposed an E-learning resource retrieval model based on domain ontology. This model built a domain knowledge library by making use of the domain knowledge and constructed E-learning resources metadata database by extracting resources metadata, realized semantic organization of E-learning resources through mapping relations, and constructed a semantic retrieval model on this basis, in order to effectively solve the problem of the loss of semantic background in the E-learning resource retrieving. The model has also enhanced the recall rate and the precision rate on the retrieval results, and it is more in line with the needs of the users.

VIrtual Scanning Algorithm（VISA) is unable to fully take advantage of the number of nodes, in order to prolong the network lifetime, it must be built on the basis of dense nodes deployment which makes the time for finding average target increase. Therefore, based on low dutycycle Wireless Sensor Network (WSN) by combining the ideology of virtual scan wave, the Multiple VIrtual Scan Algorithm (M-VISA) was proposed for road surveillance. This algorithm adopted the way of fixing points, deploying the same location with multinodes to let the nodes to be worked in order and in batches, hence, the network lifetime could be greatly extended. Simulation result demonstrates that M-VISA can prolong network lifetime by 180% when compared with VISA, improving the network performance effectively.

To improve the quality of video surveillance outdoors and to automatically acquire the weather situations, a method to recognize weather situations in outdoor images is presented. It extracted such parameters as power spectrum slope, contrast, noise, saturation as features to realize the multi-classification of weather situations with Support Vector Machine (SVM). Then a decision tree was constructed in accordance with the distance between these features. The experimental results on WILD image base and our image set of eight hundred samples show that the proposed method can recognize sunny, overcast, foggy weather more than 85%, and recognize rainy weather more than 75%.

This paper made a systematic analysis on a few of current trust models, provided a new mixed authenticated trust model, and gave a particular frame and function design. Then the study carried out a simulation and property analysis of the new model. The simulation results indicate that the new mixed authentication model can resolve the defaults of the static key mechanism and improve the security of the grid authentication.

In order to resolve the widely existing problem of Non-Transitive Connectivity (NTC) in networks, a uncertainty Distributed Hash Tables （DHT） method to resolve the NTC problem in overlay network was proposed. The relationship of bottom node ID and logical space location was lifted to avoid infection of network structure by NTC nodes, and redirection route mechanism was used to implement the message routing of network. Simulation shows that uncertainty DHT could effectively resolve the NTC problem in overlay network.