Currently, Convolutional Neural Network (CNN) is applied in the field of pneumonia classification. Aiming at the hardness to improve the accuracy of pneumonia recognition of convolution network with shallow layers and simple structure, deep learning method was adopted; and concerning the problem that the deep learning method often consumes a lot of system resources, which makes the convolution network difficult to be deployed at user end, an classification method based on optimized convolution neural network was proposed. Firstly, according to the features of pneumonia images, AlexNet and Inception V3 models with good image classification performance were selected. Then, the characteristics of medical images were used to re-train the Inception V3 model with deeper layers and more complex structure. Finally, through knowledge distillation method, the trained "knowledge" (effective information) was extracted into AlexNet model, so as to reduce the occupancy of system resources and improve the accuracy. The experimental data show that after knowledge distillation, AlexNet model has the accuracy, specificity and sensitivity improved by 4.1, 7.45 and 1.97 percentage points respectively, and has the Graphics Processing Unit (GPU) occupation reduced by 51 percentage points compared with InceptionV3 model.

In order to automatically register the skulls that differ a lot in pose with the reference skull, or miss a large part of bones, an automatic nonrigid 3D skull registration method based on boundary correspondence was proposed. First, all the boundaries of target skull were calculated, and according to the edge length and the shortest distance between the edges, the edge type was identified automatically, and the correspondence between the registered skull and the reference skull was established. Based on that, the initial position and attitude of the skull were adjusted to realize the coarse registration. Finally, Coherent Point Drift (CPD) algorithm was used twice to realize the accurate registration of two skulls from the edge region to all regions. The experimental results show that, compared with the automatic registration method based on Iterative Closest Point (ICP) and Thin Plate Spline (TPS), the proposed method has stronger robustness in pose, position, resolution and defect, and has more availability.

In order to improve the accuracy and reliability of cognitive radio spectrum sensing, an optimal linear cooperation spectrum sensing method based on Chaos Harmony Search (CHS) algorithm was proposed in this paper. This algorithm is based on the linear weighted cooperative spectrum sensing model with energy detection, using the optimization capability of Harmony Search (HS) and the traverse and randomness of chaotic system to find the optimal weight values and then improve the performances of spectrum sensing for cognitive radio networks. The simulation results show that the proposed algorithm has better optimal performance and convergence speed than the traditional HS algorithm. This CHS algorithm can obtain optimal weight values and improve the probability of detection in complex communications environment. Besides, cooperation spectrum sensing performance based on the proposed algorithm is better than the existing Modified Deflection Coefficient (MDC) method with the same false probability.

Molnar protocol is a scheme for mutual authentication between tags and readers in Radio Frequency Identification (RFID) system, which emphasizes protecting privacy for the tag; however, its security has not been proved formally. By using the eHa model, a formal proof was given, in which the output of the Molnar protocol maintain unpredictable, denoted as un-privacy. Moreover, the accurate security boundary of the Molnar protocol was computed. The privacy of protocol was reduced tightly on the assumption that the output of pseudorandom functions was indistinguishable from the output of random functions in polynomial time by utilizing the game-based technique. This technique is a powerful tool for analyzing and solving the privacy problem of RFID system, and provides an effective and universal solution.