VC数字图像处理典型案例详解-光盘

matlab开发的一些关于基于GVF的图像分割算法，(matlab developed some of the GVF-based image segmentation algorithm,)

结合cmac 的pid控制原码 可仿真实现 结果有误差曲线 跟踪曲线 参数整定曲线等(Combination of CMAC)

在matlab环境下利用小波实现噪声的去除(Matlab environment in the realization of the use of wavelet noise removal)

说明：  飞控系统快速控制原型开发及半实物仿真系统方案研究(Rapid control prototyping flight control system development and simulation system Scheme)

说明：  中国标准数字地面电视中重要仿真代码 可实用化的(The practical matlab simulation code for the chinese standard DTV )

把肌电信号的特征值提取出来进行人工网络识别(EMGs eigenvalue extracted manual network identification)

1.CAmodelfusion.m 主要功能： （1）完成两个传感器各自对基于CA模型的目标状态的Kalman估计； （2）实现传感器的局部状态估计的SCC和CI融合算法的实现； （3）画出局部估计和两种融合估计的位置、速度、加速度的误差； （4）画出局部估计和两种融合估计的协方差椭圆。 2.CVmodelfusion.m 主要功能： 功能与CAmodelfusion.m基本相似，差异在于实现基于CV模型的估计和估计融合。 3.CovInter.m 主要功能： 寻找CI融合算法的融合估计的协方差阵的行列式最小的权值。 4.f_plot_ellipse_m.m 主要功能： 绘制四种估计的协方差椭圆 (1.CAmodelfusion.m main functions: (1) completion of two sensors each target state based on CA model of Kalman estimation (2) the realization of the sensor partial state estimation algorithm SCC and CI integration and (3) Draw Local estimated position and two fusion estimation, velocity and acceleration errors (4) Draw two fusion of local estimates and estimated covariance ellipse. 2.CVmodelfusion.m Main Features: a CAmodelfusion.m similar, the difference is that the estimated CV-based estimation and model integration. 3.CovInter.m main functions: Find fusion algorithm fusion CI estimated covariance matrix of the determinant minimum weight. 4.f_plot_ellipse_m.m main functions: Draw four kinds estimate covariance ellipse)

等几何分析MATLAB程序包,有着丰富的例子,其中fem包内为传统的拉格朗日插值有限元, iga包内为nurbs插值的等几何有限元(This is the README file for the Isogeometric Analysis (IGA) Matlab code. The code supports one, two and three dimensional linear elasticity problems. Extended IGA for hole, inclusion and crack modelling is also implemented.)

说明：  IBMde股票图形示例,含数据 有布林线,平均移动线(IBMde stock graphics samples, containing data Brin line, the average mobile lines)

y_compare= 1.0954 1.1832 1.2649 1.3416 1.4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.6125 1.6733 1.7321 var_y=2.1742e-010 精度再上一个层次(三阶代数精度) (y_compare = 1.0954 1.1832 1.2649 1.3416 1. 4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.61 25 1.6733 1.7321 var_y = 2.1742e-010 precision on a level again ( Third-order algebraic precision))