Considering the isolated cluster problem caused by traditional software modularization methods, a new metric named Improved Modularization Quality (IMQ) was proposed and used as the fitness function of an evolutionary algorithm to eliminate isolated clusters effectively. A mathematical programming model with the goal of maximizing IMQ was developed to represent software modularization problem. In addition, an Improved Genetic Algorithm (IGA) with competition and selection mechanism similarity was designed to solve this model. Firstly, a heuristic strategy based on edge contraction was used to generate high-quality solutions. Then the solutions were implanted as seeds into the initial population. At last, the proposed IGA was employed to further improve solution quality. Comparison experimental results prove that IMQ can effectively reduce the number of isolated clusters, and IGA has stronger robustness and ability of finding better solutions than Improved Hill Climbing Algorithm (IHC) and GA based on Group Number Encoding (GNE).

There are over-fitting phenomenon and prone omissions when moving window adaptive Kernel Principal Component Analysis (KPCA) is utilized to deal with sensitive parameters or slow degradation problem. In order to solve the problem, a step dynamic auto-regression KPCA was proposed. Firstly, the initial model was established step by step drawing on dynamic data matrix. Then, the exponentially weighting rule was introduced to process real-time data and update the model based on the moving window adaptive KPCA. Finally, the algorithm complexity was analyzed and specific steps were given. The simulation data was utilized to analyze the impact of decomposition coefficient and weighting factor. The results show that, compared with the moving window adaptive KPCA, the proposed algorithm efficiency was improved by nearly 90% and the number of false positives was almost 0 in the case of appropriate parameter selection; and it could also control the adaptive ability to solve a variety of dynamic problems by adjusting the value of weighting factor. The algorithm was applied to the experimental data analysis of compressor surge and bearing fault, the result verified its ability to deal with the problem of sensitive parameter and slow degradation.

Since the dark channel prior algorithm has dull restoration effect and too long processing time, a fast dehazing algorithm for single image based on relative transmittance estimation was proposed. On the basis of the analysis of relationship between field depth under haze condition and minimum image of color channel (RGB) images, a preliminary transmittance was estimated through the relative amount of field depth, and then it was adjusted with an improved mean filter. At last, the clear image could be recovered by the atmospheric scattering model and the brightness was enhanced to improve its visual effects. The estimation of transmittance in this paper is simple and effective, the restored images are clear and natural, and have high detail visibility and scenery layering. The experimental results show that the proposed algorithm has great improvement in image dehazing quality and computational time, which is propitious to achieve real-time application.

Concerning that the parameter estimation in defogging algorithms based on image restoration is easy to cause the loss of scene information, a new defogging algorithm for single image was proposed. On the basis of the dark channel prior method, the atmospheric scattering model was analyzed and then the influence to dark channel image caused by fog distribution was summarized, which is the basis for adding fog to the outdoor images. The transmittance was estimated through the field depth relationship between the fog added reference image and the outdoor image to defogging. The algorithm used physical model and multiple images to complete the estimation of relevant parameters and had a better result in retaining scene information. The experimental results show that the proposed algorithm is more effective than the comparison algorithms, and its processing speed is also improved significantly.

According to the idea of manually drawing a train diagram of the express and slow, the different proportions modularization thought of express and slow trains was proposed. Under the condition to satisfy the requirements of trains,the transitions and adjustments between different modules were added to meet the entrance/exit tracks of depot, so that the automatically generated diagram could meet the needs of designers. It can draw different full-day operation plans of different lines in particular proportions. Besides it can convert train diagram into CAD scripts and realize automatic drawing in CAD software. Finally, taking line 18 of Chengdu Metro as an example, the proposed method is implemented and its feasibility is proved.

A task scheduling strategy based on the dynamically adaptive ant colony algorithm was proposed for the first time to solve the drawbacks like slow convergence and easily falling into local optimal that have long existed in the ant colony algorithm. Chaos disruption was introduced when selecting the resource node, the pheromone evaporation factors were adjusted adaptively based on nodes pheromone and the pheromone were updated dynamically according to the solutions performance. When the number of tasks was greater than 150, compared with the dynamically adaptive ant colony algorithm and ant colony algorithm, time efficiency could be maximally improved up to 319% and resource load was 0.51.The simulation results prove that the proposed algorithm is suitable for improving convergence rate and the global searching ability.

An echo cancellation algorithm based on additional signal was proposed to eliminate the spatial co-channel interference of digital repeater in mobile communication network. Firstly, the paper studied the formation and characteristics of the echo, and established the model of true echo channel parameter matrix. Secondly, the single frequency sinusoidal signal was attached to the frequency spectrum hole of the base station, and the channel parameters of the estimated-echo matrix were estimated by making use of the convolution relationship of the cross-correlation function of the mixed-signal received by repeater, the transponder signal and the additional signal. Finally, the estimated-echo of the transponder signal was subtracted from the received mixed signal, which was achieved by the estimated-echo channel, in order to eliminate the echo interference. The simulation results show that the relative error of the attenuation coefficient of main path is 1.8493×10-5 in the frequency selective fading channel model of COST 207 standard. The estimated-echo can track the actual-echo better, so as to avoid self-excited phenomenon effectively.

In order to realize the CAN bus communication with PC and remote monitoring, a design method of the embedded Ethernet-CAN communication transform card based on FPGA was proposed. The design chose the embedded soft processors Nios Ⅱ in FPGA as the main control chip, MCP2515 as the CAN bus controller and 88E1111 as the Ethernet PHY chip. A system hardware model was built with the SOPC (System-On-a-Programmable-Chip) technology, and the CAN controller, Ethernet initialization and the Ethernet-CAN conversion process were completed in the Nios Ⅱ IDE （Integrated Development Environment）.The experimental results show that the design completely meets the requirements of the Ethernet and CAN bus communication.

Signal decomposition is a process that obtains information from signals and it is a foundational and key technique for many fields such as pattern recognition, intelligent system and machinery fault diagnosis. It is very important to study non-stationary signal decomposition which always includes lots of information that can reflect the changing of the system and widely exists. After improving the sparsity of signal representation, the engineering background of feature extraction for non-stationary signal was studied in this paper, the characteristics, mechanisms, development history and current and future challenges of five types of methods were analyzed in depth, the models of these methods were compared, together with the state-of-the-art of feature extraction models in signal processing and analysis and some successful applications available were systematically reviewed. Finally, several main problems and a few deficiencies were pointed out, and future research directions were anticipated.

In order to improve the accuracy and stability of the players in the movement of RoboCup3D simulation platform, a kind of real-time resolution of inverse kinematics for humanoid NAO model was proposed. Firstly, the lower limb topology of NAO model was analyzed, and its forward kinematics model was established. Secondly, the equations of every joint angle in all lower limbs were derived by real-time inverse kinematics analytic method. Finally, the algorithm was realized by coding. The experimental results validate the numerical stability and feasibility of online execution of the method, and the overall competitive level of the RoboCup3D simulation soccer team has been enhanced.

With the features of convenience and flexibility,Ad hoc network becomes one of the most valuable next-generation networks.However,it has many secure defects because of its lack of inherent infrastructure.The possible secure defects of Ad hoc network were analyzed,and an improved routing protocol with encryption and authentication for Ad hoc network was brought forth,which can prevent both active and passive attacks.The security and efficiency of this protocol were also evaluated.