DCAC

MatLab工具箱应用指南：控制工程篇 完整版(MatLab Application Toolbox Guide: Complete Control Engineering articles)

将matlab与vc++结合各自发挥特长，实现强大的界面控制的数字图像处理功能。(Matlab and vc++ respective strengths, powerful interface control digital image processing functions.)

说明：  逆合成孔径雷达成像代码，二维雷达成像，雷达回波数据去噪方法，SVD分解。(Inverse synthetic aperture radar imaging code, two-dimensional radar imaging, radar echo data denoising method, SVD decomposition.)

matlab draw examples

400赫兹矢量控制直流逆变电源MATLAB仿真(The 400 Hertz vector control DC power inverter MATLAB simulation)

采用积分分离 PID控制算法将塔顶轻组分含量Y控制在0.99 2.采用继电法整定PID参数 3.整定效果验证：当被控过程参数时变时，如滞后时间由4→8，开环增益由3.4→6时, 讨论PID控制的响应速度及鲁棒性问题，考察当系统参数发生改变时，上述PID参数是否选取合适。 (Using integral separation PID control algorithm to control the top of the tower light component content Y 0.99 2. Verify tuning PID parameters of the relay method. Tuning effect: the change When accused of process parameters, such as the lag time from 4 → 8 open loop gain from 3.4 → 6 discussed PID control of the response speed and robustness issues investigated when the system parameters are changed, the PID parameters is selected for suitable.)

说明：  快速随机树法，寻求无人机最优的路径规划，达到节省时间和能源的问题(Fast Random Tree Method for Unmanned Aerial Vehicle Path Planning)

This paper presents a novel algorithm of hiding secret messages by applying mathematical lemmas and information theory. The proposed algorithm can hide more information with acceptable embedded rate and achieves requirements of information hiding algorithm and simply information extracting. The main idea is applying modern algebra to solve the information hiding problem in multimedia. In order to prevent some attacks on stganography, hidden initial bits of image data pixels are chosen randomly. The extracted information are achieved without the reference of the original image and extracted keys. The experiment results show that our approach is effective and reliable.

图像配准技术，适合初学者，内部有解释说明，欢迎指正( Image registration techniques, suitable for beginners, internal explanation, please correct me)

说明：  主元分析方法（PCA）是一种基于多元统计分析的数据降维方法, 它利用过程变量间的相关关系, 建立正常工况下的主元模型, 通过检验新的数据样本相对于主元模型的背离程度, 从而发现异常和故障。 (Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process variables and establish the normal condition of the main element model, by testing samples of the new data relative to the principal component model departure from, and thus abnormal and failure.)