Aiming at the problems of over-smoothing, singular structure residual noise, contrast loss and stair effect of common denoising methods, an image denoising model with adaptive non-local data fidelity and bilateral total variation regularization was proposed, which provides an adaptive non-local regularization energy function and the corresponding variation framework. Firstly, the data fidelity term was obtained by non-local means filter with adaptive weighting method. Secondly, the bilateral total variation regularization was introduced in this framework, and a regularization factor was used to balance the data fidelity term and the regularization term. At last, the optimal solutions for different noise statistics were obtained by minimizing the energy function, so as to achieve the purpose of reducing residual noise and correcting excessive smoothing. The theoretical analysis and simulation results on simulated noise images and real noise images show that the proposed image denoising model can deal with different statistical noise in image, and the Peak-Signal-to-Noise Ratio (PSNR) of it can be increased by up to 0.6 dB when compared with the adaptive non-local means filter; when compared with the total variation regularization algorithm, the subjective visual effect of the proposed model was improved obviously and the details of image texture and edges was protected very well when denoising, and the PSNR was increased by up to 10 dB, the Multi-Scale Structural Similarity index (MS-SSIM) was increased by 0.3. Therefore, the proposed denoising model can theoretically better deal with the noise and the high frequency detail information of the image, and has good practical application value in the fields of video and image resolution enhancement.

In the dynamic spectrum access network, how to utilize the spectrum resource efficiently is a vital problem to be solved. Presently, the main study focuses on the scenario that spectrum providers lease spectrum to users for spectrum access. Considering the issue that the user's actual payment capacity and Quality of Service (QoS) demands were different, a joint budget and power control scheme was proposed, the interactions among spectrum providers and users were investigated as a two-stage Stackelberg game model. Based on the budget and QoS demands, providers dynamically adjusted spectrum leasing prices, and users adjusted their demands according to the prices. Finally, the existence and uniqueness of Nash Equilibrium (NE) of maximum profit of users and providers was proved. Simulation results show that the sum of budget of users is the upper bound of the whole network payoff, users and providers can get the optimal profit and achieve the Nash Equilibrium (NE).

In view of maintenance difficulties and high cost in large-scale development of Wireless Local Access Network (WLAN), the Control and Provisioning of Wireless Access Points (CAPWAP) protocol that applied to communication between Access Controller (AC) and Wireless Terminator Point (WTP) was researched and implemented. In Linux environment, main features were realized, such as state machine management, and WTP centralized configuration. A platform of WLAN centralized management system based on local Medium Access Control (MAC) framework was built up. Wireshark capture tool, Chariot and Iperf were used to test the platform. The capture test results verify the feasibility of the framework, and the results of throughput and User Datagram Protocol (UDP) test also show that network performance is efficient and stable.

Any video camera equipment has certain temporal resolution, so it will cause motion blur and motion aliasing in captured video sequence. Spatial deblurring and temporal interpolation are usually adopted to solve this problem, but these methods can not solve it completely in origin. A temporal super-resolution reconstruction method based on Maximum A Posterior (MAP) probability estimation for single-video was proposed in this paper. The conditional probability model was determined in this method by reconstruction constraint, and then prior information model was established by combining temporal self-similarity in video itself. From these two models, estimation of maximum posteriori was obtained, namely reconstructed a high temporal resolution video through a single low temporal resolution video, so as to effectively remove motion blur for too long exposure time and motion aliasing for inadequate camera frame-rate. Through theoretical analysis and experiments, the validity of the proposed method is proved to be effective and efficient.

A hybrid control method based on state feedback and adaptive neural network was proposed, which considered the taking off and landing control problem under unknown mass of the Unmanned Aerial Vehicle (UAV). A state feedback controller was designed to realize the horizontal position and heading control. The accurate control of height was archived considering the vehicle's unknown load through the Radial Basis Function (RBF) neural network. The simulation analysis and experiments illustrate that the proposed control method can effectively realize the accurate control of height, and can be able to online estimate aircraft quality parameters.

At present, the existing general no-reference image quality assessment methods are used for special purpose. Furthermore, the distortion type of the image is unknown in practical application. Most of no-reference image quality assessment methods are based on the geometrical description feature of image, but this type of method relies on the boundary of image too much. To solve this problem, a no-reference image quality assessment method was proposed based on the gradient similarity decomposition of image, which was named Decomposition of Gradient Similarity (DGS). The proposed method extracted the gradient feature and decomposed the correlation of gradient as the main structure information of the image. The experimental results show that the proposed DGS model is better than Peak Signal-to-Noise Ratio (PSNR) (or Mean Square Error (MSE)) model, which is more sensitive to Human Visual System (HVS) characteristics and more consistent to the subjective evaluation value.

Most of the existing image orientation estimation algorithms are very sensitive to noise. Therefore, this paper presented a new local orientation estimation method of image, which was based on Principal Component Analysis (PCA) and multi-scale gradient pyramid decomposition. The PCA was applied to search for the Maximum Likelihood (ML) estimation of the local orientation of block in image. Through presenting the local orientation estimation both of simulated images and real images, the experimental results show that the proposed algorithm has excellent robustness against noise, its speed is very fast and the accuracy is very high.

Most security problems of software result from the buffer overflow, therefore, in order to decrease security bug of software, a detection method of primary code buffer overflow based on color Petri Net was put forward. Its correctness and simpleness were proved by simulation using the CPNTools.This added a new method for the buffer overflow detection of software.