Focused on the issue that U-Net has a poor segmentation effect for small-volume pulmonary nodules, a segmentation method based on deep transfer learning was proposed, and Block Superimposed Fine-Tuning (BSFT) strategy was used to assist the segmentation of pulmonary nodules. Firstly, convolutional neural network was used to learn the feature information of large natural image datasets. Then, the learned features were transferred to the network for the segmentation of small pulmonary nodule image datasets. From the last sampling layer of the network, the network was released block by block and fine-tuned until the network completed the superimposition of the last layer. Finally, the similarity coefficient of Dice was quantitatively analyzed to determine the optimal segmentation network. The experimental results show that the Dice value of BSFT on LUNA16 pulmonary nodule open dataset reaches 0.917 9, which is obviously better than that of the mainstream pulmonary nodule segmentation algorithms.

Minimally invasive endoscopic surgery always remains a challenge due to the complexity of the anatomical location and the limitations of endoscopic vision. An Augmented Reality (AR) navigation system was designed for simulation of pelvic minimally invasive surgery. Firstly, a 3D model of pelvis which was segmented and reconstructed from the preoperative CT (Computed Tomography) was textured mapping with the real pelvic surgical video, and then a surgical video with the ground truth pose was simulated. The blank model was initially registered with the intraoperative video by a 2D/3D registration based on color consistency of visible surface points. After that, an accurate tracking of intraoperative endoscopy was performed using a stereoscopic tracking algorithm. According to the multi-DOFs (Degree Of Freedoms) transformation matrix of endoscopy, the preoperative 3D model could then be fused to the intraoperative vision to achieve an AR navigation. The experimental results show that the root mean square error of the estimated trajectory compared to the ground truth is 2.3933 mm, which reveals that the system can achieve a good AR display for visual navigation.

ElectroCardioGram (ECG) heart beat classification plays an important role in clinical diagnosis.However, there is a serious imbalance of the available data among four types of ECG, which restricts the improvement of heart beat classification performance. In order to solve this problem, a class information extracting method based on Convolutional Neural Network (CNN) was proposed. Firstly, an general CNN model based on equivalent data of four ECG types was constructed. And then based on the general CNN model, four CNN models that more effectively express the propensity information of the four heart beat categories were constructed. Finally, the outputs of the four categories of CNN models were combined to discriminate the heart beat type. The experimental results show that the average sensitivity of the proposed method is 99.68%, the average positive detection rate is 98.58%, and the comprehensive index is 99.12%; which outperform the two-stage cluster analysis method.

Focusing on the spectrum allocation problem in cognitive radio network, an allocation model considering spectrum availability was proposed. When handling the constraint, the higher availability spectrum was assigned to the cognitive users. A chaotic artificial physics optimization was proposed based on NP (Non-deterministic Polynomial) feature of spectrum allocation problem. The ergodicity of chaos was used to initiate population and the force equation between particles was improved to avoid the algorithm falling into local optimum. The simulation results show that the proposed algorithm can get better network revenue and improve the spectrum usage.

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.

In order to improve the parameter optimization results of cognitive wireless network, an immune optimization based parameter adjustment algorithm was proposed. Engine parameter adjustment of cognitive wireless network is a multi-objective optimization problem. Intelligent optimization method is suitable for solving it. Immune clonal optimization is an effective intelligent optimization algorithm. The mutation probability affects the searching capabilities in immune optimization. Cloud droplets have randomness and stable tendency in normal cloud model, so an adaptive mutation probability adjustment method based on cloud model was proposed, and it was used in parameter optimization of cognitive radio networks. The simulation experiments were done to test the algorithm under multi-carrier system. The results show that, compared with relative algorithms, the proposed algorithm has better convergence, and the parameter adjustment results are consistent with the preferences for the objectives function. It can get optimal parameter results of cognitive engine.

The traditional Canny operator uses the global threshold method, but when the gray of the input image's background and foreground change largely, the global threshold method will lose some weak edge. Concerning this issue, an improved adaptive Canny operator was put forward. Firstly, the image was divided into blocks according to the gradient variance. Secondly, the threshold of the sub blocks was obtained by Otsu method. Then, the threshold matrix was got by interpolation. Finally, an improved edge connection algorithm was proposed to extract the edge with the threshold matrix. The experimental results show that the improved Canny algorithm has not only a good anti-noise function, but also a very good precision.