In order to generate a three-dimensional virtual human animation with rich expression and smooth movement, a method for generating three-dimensional virtual human animation based on synchronous capture of facial expression and human pose with two cameras was proposed. Firstly, the Transmission Control Protocol (TCP) network timestamp method was used to realize the time synchronization of the two cameras, and the ZHANG Zhengyou's calibration method was used to realize the spatial synchronization of the two cameras. Then, the two cameras were used to collect facial expressions and human poses respectively. When collecting facial expressions, the 2D feature points of the image were extracted and the regression of these 2D points was used to calculate the Facial Action Coding System (FACS) facial action unit in order to prepare for the realization of expression animation. Based on the standard head 3D coordinate, according to the camera internal parameters, the Efficient Perspective- n-Point (EP nP) algorithm was used to realize the head pose estimation. After that, the facial expression information was matched with the head pose estimation information. When collecting human poses, the Occlusion-Robust Pose-Map (ORPM) method was used to calculate the human poses and output data such as the position and rotation angle of each bone point. Finally, the established 3D virtual human model was used to show the effect of data-driven animation in the Unreal Engine 4 (UE4). Experimental results show that this method can simultaneously capture facial expressions and human poses and has the frame rate reached 20 fps in the experimental test, so it can generate natural and realistic three-dimensional animation in real time.

In cognitive radio networks, for the robust joint optimization problem of multiuser admission control and power beamforming, a joint optimization scheme based on smooth approximation of entropy function was proposed. Firstly, the two optimization problems of admission control and transmit power beams were converted into a joint optimization problem by L ₀-norm minimization. Secondly, the method of smoothing approximation based on entropy function was used to optimize the non-convexity and discontinuity of L ₀-norm. Finally, since the objective function was smooth, differentiable and unimodal function, the problem was transformed into the Lagrange function, and Armijo gradient descent method was used to get the optimal solution. The numerical results show that by using the proposed algorithm, the number of admitted uses is not significantly increased when the Signal-to-Interference-plus-Noise Ratio (SINR) is relatively low, but the transmission power consumption is decreased and the number of admitted uses is increased when SINR is relatively high. The uncertain Channel State Information (CSI) of model is analyzed, which can make the network better adapt to the changes of the outside world and improve the reliability of the network. The proposed algorithm can effectively realize the optimal allocation of the network resources and improve the network performance.

Aiming at the resource allocation problem for the uplink in cognitive radio networks, a game-theoretic algorithm for joint power control and rate allocation adapted to multi-cell cognitive radio networks was proposed. To control user's power and rate more reasonably and reduce interference among Secondary Users (SUs), firstly, the different cost factors for power and rate were set respectively, so as to control user more reasonably and avoid user excessively increasing transmission power. Then, the existence and uniqueness of the Nash Equilibrium (NE) for the proposed algorithm were proved, the convergence demonstration of the proposed algorithm was given. Finally, for solving the optimization problem of the transmission power and transmission rate, the iterative updating flowchart of the proposed algorithm for the joint power control and rate allocation was presented. The theoretical analysis and simulation results show that, compared with the similar game algorithms, on the premise of guaranteeing the quality of communication, the proposed algorithm can make user acquire higher transmission rate and higher Signal to Interference plus Noise Ratio (SINR) at lower transmission power, reduce the interference among users, and improve the system capacity of SUs.

Concerning the problem that the Hidden Markov Model (HMM) parameters are difficult to configure, an improved method of situation assessment based on HMM was proposed to reflect the security of the network. The proposed method used the output of intrusion detection system as input, classified the alarm events by Snort manual to get the observation sequence, and established the HMM model, the improved Simulated Annealing (SA) algorithm combined with the Baum_Welch (BW) algorithm to optimize the HMM parameters, and used the method of quantitative analysis to get the security situational value of the network. The experimental results show that the proposed method can improve the accuracy and convergence speed of the model.

To reduce the time complexity of situational element acquisition and cope with the low detection accuracy of small class samples caused by imbalanced class distribution of attack samples in large-scale networks, a situation element extraction mechanism based on deep auto-encoder network was proposed. In this mechanism, the improved deep auto-encoder network was introduced as basic classifier to identify data type. On the one hand, in the training of the auto-encoder network, the training rule based on Cross Entropy (CE) function and Back Propagation (BP) algorithm was adopted to overcome the shortcoming of slow weights updating by the traditional variance cost function. On the other hand, in the stage of fine-tuning and classification of the deep network, an Active Online Sampling (AOS) algorithm was applied in the classifier to select the samples online for updating the network weights, so as to eliminate redundancy of the total samples, balance the amounts of all sample types, improve the classification accuracy of small class samples. Simulation and analysis results show that the proposed scheme has a good accuracy of situation element extraction and small communication overhead of data transmission.

Since the evaluation of Distributed Denial of Service (DDoS) is inaccurate and network security situational evaluation is not comprehensive, a new network security situational awareness model based on information fusion was proposed. Firstly, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed; Secondly, the original Common Vulnerability Scoring System (CVSS) was improved and the leak vulnerability was evaluated to make the assessment more comprehensive; Then, according to the combination of objective weight and subjective weight, the method of calculating the combined weights and optimizing the results by Sequence Quadratic Program (SQP) algorithm was raised to reduce the uncertainty of fusion; Finally, the network security situation was got by fusing three aspects evaluation. To verify the original evaluation of DDoS was inaccurate, a testing platform was built and the alarm of the same DDoS differed by 3 orders of magnitude. Compared to the original method based on alarm, the steady and accurate result of evaluation was obtained based on data packet. The experimental results show that the proposed method can improve the accuracy of evaluation results.

To minimize damage from network security problem, an improved network security situation assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) was proposed. First, a set of index system in conformity with actual environment which consists of index layer, criterion layer and decision layer was established in consideration of the large-scale network environment in the future. Aiming at the influence on evaluation by data distribution uncertainty and fuzziness in situation assessment, the proposed model used Fuzzy C-Means (FCM) clustering algorithm and the best clustering criterion for data preprocessing to get the optimal cluster number and cluster center. Finally, multi-factor secondary assessment model was established for situation assessment vector. The simulation results show that, compared with the present situation assessment method based on FAHP, the improved method takes the factors which have small weights into consideration better, so the standard deviation is smaller and evaluation results are more objective and accurate.

To solve the issue that, in wireless resource competition, the environment information which gamers get in power game is asymmetric, a power game mechanism based on hidden Markov prediction was proposed. By establishing a Hidden Markov Prediction Model (HMPM), the proposed mechanism estimated whether competitors would take part in the game to improve the information accuracy of the game. Then, the predicted information was used to calculate the best transmission power via the cost function. The simulation results show that, compared with MAP (Maximum A Posteriori) method and NP (No Predicting) method, the power game model based on hidden Markov prediction can not only meet the target capacity, but also improve the power efficiency of the unauthorized users.

Based on the theory of Restless Multi-Armed Bandit (RMAB) model, a novel mechanism of dynamic spectrum access was proposed for the problem that how to coordinate multiple user access multiple idle channels. Firstly, concerning the channel sensing error of the cognitive user being existed in the practical network, the Whittle index policy which can deal with sensing error effectively was derived. In this policy, the users achieved one belief value for every channel based on the historical experience accumulation and chose the channel, which was needed to sense and access, by considering the immediate and future rewards based on the belief values. Secondly, this paper used the multi-bid auction algorithm to deal with the collision among secondary users when they selected the channels to improve the spectrum utilization. The simulation results demonstrate that, in the same environment, the cognitive users with the proposed mechanism have higher throughtput than the mechanism without dealing with sensing error or without multi-bid.

To solve the problem of transmission in wireless networks, a transmission and scheduling scheme based on W-learning algorithm in wireless networks was proposed in this paper. Building the system model based on Markov Decision Progress (MDP), with the help of W-learning algorithm, the goal of using this scheme was to transmit intelligently, namely, the package loss under the premise of energy saving by choosing which one to transmit and the transmit mode legitimately was reduced. The curse of dimensionality was overcome by state aggregate method, and the number of actions was reduced by action set reduction scheme. The storage space compression ratio of successive approximation was 41%; the storage space compression ratio of W-learning algorithm was 43%. Finally, the simulation results were given to evaluate the performances of the scheme, which showed that the proposed scheme can transport data as much as possible on the basis of energy saving, the state aggregation method and the action set reduction scheme can simplify the calculation with little influence on the performance of algorithms.

Concerning the linear collusion attack problem in Combined Public Key (CPK) cryptosystem, on the basis of the nature of the linear collusion attack and according to the principle of key generation, a new equation set was constructed. Through the linear transformation to the coefficient matrix of the equation set, the rank of the equations can be solved, and it is less than the number of seeds of private key seed matrix. At the same time, the analysis of the private key's structure shows that the rank of the augmented matrix is not equal to the rank of coefficient matrix. Thus both sides above prove that the attacker never get the unique solution to the private key seed matrix even if he get all the private keys. Therefore, it demonstrates that there does not exist the threat of linear collusion attack in the CPK cryptosystem.

According to the characteristics of primary users’ service, this paper applied mixed Poisson distribution to primary users’ service modeling, evaluating the probability density of non-occupied duration of channel by using Expectation-Maximization (EM) algorithm to estimate the parameters of mixed Poisson distribution model and putting forward a multi-channel allocation algorithm based on channel estimation. The simulation results indicate that the proposed algorithm has excellent performance on conflict rate and throughput. Meanwhile, the proposed algorithm has lower implementation complexity for practical use.