Concerning the problem that the surface defects of the coin are small， variable in shape， easily confused with the background and difficult to be detected， an improved algorithm of coin surface defect detection named DCA-YOLO （Deformable Convolution and Adaptive space feature fusion-YOLO） was proposed. First of all， due to the different shapes of defects， three network structures with deformable convolution modules added at different positions in the backbone network were designed， and the ability to extract defects was improved through convolution learning offset and adjusting parameters. Then， the adaptive spatial feature fusion network was used to learn the weight parameters to better adapt to targets with different scales by adjusting the contribution of each pixel in the feature maps of different scales. Finally， the anchor ratio was adjusted， the category weights were dynamically adjusted， the comparison network performance was optimized， thus， a model network to add deformable convolution before upsampling for multi-scale fusion of the output features of the backbone network was proposed. Experimental results show that on the coin defect dataset， the detection mAP （mean Average Precision） of DCA-YOLO algorithm reaches 92.8%， which is close to that of Faster-RCNN （Faster Region-based Convolutional Neural Network）； compared with YOLOv3， the proposed algorithm has the detection speed basically the same with 3.3 percentage points improvement on detection mAP， and 3.2 percentage points increase on F1-score.

To improve the computational efficiency of stereo matching on foreground disparity estimation tasks， aiming at the disadvantage that the general networks use the complete binocular image as input and the input information redundancy is large due to the small proportion of the foreground space in the scene， a real-time target stereo matching algorithm based on sparse convolution was proposed. In order to realize and improve the sparse foreground disparity estimation of the algorithm， firstly， the sparse foreground mask and scene semantic features were obtained by the segmentation algorithm at the same time. Secondly， the sparse convolution was used to extract the spatial features of the foreground sparse region， and scene semantic features were fused with them. Then， the fused features were input into the decoding module for disparity regression. Finally， the foreground truth graph was used as the loss to generate the disparity graph. The test results on ApolloScape dataset show that the accuracy and real-time performance of the proposed algorithm are better than those of the state-of-the-art algorithms PSMNet （Pyramid Stereo Matching Network） and GANet （Guided Aggregation Network）， and the single run time of the algorithm is as low as 60.5 ms. In addition， the proposed algorithm has certain robustness to the foreground occlusion， and can be used for the real-time depth estimation of targets.

In view of the existing bad information diffusion models do not consider the information diffusion between different social networks, the connectivity principle in graph theory was used to establish a dynamic model of the diffusion of bad information among multiple social networks, and the optimal control theory was applied to the model. Through the optimal control principle, the existence of the optimal control strategy was proved. Then the optimal control model of the diffusion of bad information was obtained. The experimental results show that the introduction of optimal control measures can effectively suppress the diffusion scale of bad information, and the strength of the control strategy can be dynamically adjusted as needed. In addition, by simulating the conditions with and without information transmitted between different social networks, it is found that the information transmission between social networks will increase the scale and speed of the diffusion of bad information.

Focusing on the issue that traditional open-loop power control algorithm normally aims to increase the throughput and ignores the interference to other cells, to achieve a tradeoff between edge users and whole system performance, an Uplink Joint Power Control algorithm of LTE system (UJPC), was proposed. In the algorithm, single base station and three sectors were adopted as system model, which aimed to maximize proportional fair index of system throughput. Firstly, the corresponding mathematical optimization model was obtained according to two constraints of the minimum Signal-to-Interference plus Noise Ratio (SINR) and the maximum transmit power of users. Then continuous convex approximation method was used to solve optimization problem to get optimal transmission power of all users in each cell. The simulation results show that, compared with open-loop scheme, UJPC can greatly improve spectrum utilization of cell edge while ensuring average spectrum utilization of system and its best performance gain can reach 50%.

Concerning that the existing networks virus propagation models do not consider the influence of interactive behaviors among the users in different social networks on network virus propagation, a dynamic model of differential equations was established. The stability theory was used to analyze the dynamical behaviors of the network virus propagation, and the accurate expression of the basic reproduction number was obtained, which is the threshold of controlling the network virus propagation. Furthermore, using Runge-Kutta numerical method, the correctness of theoretic analysis was verified by simulations. The results show that the basic reproduction number is the direct decisive factor of network virus prevalence situations. When the value of the basic reproduction number is less than or equal to one, the propagation of the network viruses will be controlled with the evolution of time. Additionally, the research reveals that it is helpful for distributing the users to different social networks to slow the prevalence of network viruses.

A distributed strategy based on Stackelberg game was proposed to allocate cooperative power for cooperative networks. A Stackelberg game model was built at first, and the source node decided the price according to the cooperative power. Considering the relay's available resources, channel state, location and the price determined by source node, the relay node allocated the cooperative power to construct a user utility function. Then, the utility function was demonstrated to satisfy the conditions of concave function to ensure the existence of equilibrium. Subsequently, each node maximized its utility by finding the Stackelberg Equilibrium (SE) of optimum power and price. Finally, the simulation results proved the existence of equilibrium point, and the node's price, cooperative power and each node's utility were analyzed when the source node was in a different position. In the experiments, the cooperative power and price of the closer user respectively were 1.29 times and 1.37 times of the farther user. The experimental results show that the proposed strategy is effective, and it can be used in cooperative network and some other distributed networks.

For deeply analyzing the factors that affect the prevalence of P2P botnets, the formation of a Peer-to-Peer (P2P) botnet was portrayed from dynamic perspective. Firstly, the differential equation model was formulated according to the formation of P2P botnets, which considered the effect of immunization on computer malware propagation. Furthermore, effective immune ratio of eliminating P2P botnets was calculated by analyzing steady condition of equilibrium in the model. Finally, the effective immune region was obtained and verified by deterministic simulation and stochastic simulation, respectively. The results show that the outbreak of P2P botnets can be effectively prevented by reasonable immune measures.

Five scheduling mechanisms have been proposed to ensure Quality of Service (QoS) in WiMAX network. Three of them are designed for real-time applications. However, fairness has not been considered while assigning resources in these scheduling mechanisms. In order to guarantee the existing services, new service requests will be rejected when lacking network resources. For resolving this problem, a new scheduling mechanism was proposed for Voice over Internet Protocol (VoIP) service, in which a Priority Decision Maker (PDM) was implanted. When new services and the existing services requested resources simultaneously, higher priority would be assigned to new service requests; and resource allocation would be achieved based on different level priorities by a resource distribution center for guaranteeing fairness. Detailed performance analysis was performed. The simulation results show the proposed scheduling mechanism could significantly increase number of VoIP connections and total throughput, about 15% and 11% respectively.