chap2-10

Numerical Techniques in Eletromagnetics 书中的所有用matlab编写的例程(All the examples procedures written by matlab in Numerical Techniques in Eletromagnetics)

对噪声及信号的功率谱估计,生物医学信号处理,matlab编程实现(Of noise and signal power spectrum estimation, biomedical signal processing, matlab Programming)

里面的文档详细阐述了LMS原理，包括常用的仿真方法，并附有使用LMS方法的自适应滤波的实例matlab仿真程序。(The document inside expounds the principles of the LMS in detail, including the simulation methods and a matlab simulation program of an adaptive filtering by using the LMS method.)

说明：  图像的几种锐化处理，包括sobel算子，roberts梯度法和laplace算子锐化。并且将四种图像放在一起形成对比。(Several image sharpening, including sobel operator, roberts gradient and laplace operator sharpening. And will contrast the four images together.)

说明：  1．什么是卡尔曼滤波器（What is the Kalman Filter?） 2．卡尔曼滤波器的介绍（Introduction to the Kalman Filter） 3．卡尔曼滤波器算法（The Kalman Filter Algorithm） 4．简单例子（A Simple Example） 5. 用matlab做的kalman滤波程序,已通过测试 还有下面一个 Matlab源程序，显示效果更好。 (1. What is a Kalman filter 2. Introduction to the Kalman Filter 3. The Kalman Filter Algorithm 4. A Simple Example 5. Kalman filter code with matlab, has been tested.Another Matlab code shows better.)

dijkstra算法matlab程序，很简单很好用(Dijkstra algorithm matlab, very simple works nicely )

关于食堂就餐顾客的排队问题的仿真程序，maltab编写(A simulation program on the queuing problem of dining room customers)

用matlab做方差分析的几个例程包括：单因素一元方差分析，双因素一元方差分析 非参数方差分析(ANOVA using matlab to do a few routines include: single-factor analysis of variance one yuan, two-factor analysis of variance one yuan non-parametric analysis of variance)

用于特征脸识别的MATLAB经典代码，本科毕业设计所做，调试成功，运行结果理想(Face Recognition MATLAB for feature classic code, doing graduate design, debugging success, the results are satisfactory)

偏最小二乘法是一种数学优化技术，它通过最小化误差的平方和找到一组数据的最佳函数匹配。 用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(Partial least squares method is a kind of mathematical optimization technique, it through the minimum error sum of squares to find a set of data the best function matching. With the Jane s method for some absolutely unknowable true value, and make the sum of error square to a minimum.)