UKF

LMS_Newton算法，应用于自适应滤波(LMS_Newton algorithm used in adaptive filtering)

利用matlab的gui设计的带有输入和输出功能的界面图形，可视性很强，自己独立编写，原始数据和图形，学习非常值得(Uses the Matlab GUI design to have the input and output function the interface graph, the visibility is very strong, writes independently, the original data and the graph, the study is very worthwhile.)

The random walker algorithm was introduced in the paper: Leo Grady and Gareth Funka-Lea, "Multi-Label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials", in Proceedings of the 8th ECCV04, Workshop on Computer Vision Approaches to Medical Image Analysis and Mathematical Methods in Biomedical Image Analysis, p. 230-245, May 15th, 2004, Prague, Czech Republic, Springer-Verlag. Available at: http://cns.bu.edu/~lgrady/grady2004multilabel.pdf

matlab的FDATOOL工具设计的50Hz陷波器，可用于心电图中工频干扰的消除(50 hz notch filter designed by FDATOOL tool in Matlab which can be used for eliminationing the power-interference of ECG)

code for flat fadding

matlab绘制蝙蝠侠的经典logo的代码，放在目录下即可运行，从matlab论坛下载的(matlab draw Batman' s classic logo of code to run on the directory, download matlab Forum)

OFDM ls和LMMSE的信道估计。比较详细，可以运行结果。(The OFDM ls and LMMSE the channel estimation. In more detail, you can run the results.)

calculated interaction curve of the concrete column

做过模糊PID的朋友很多遇到过这种问题，就是仿真启动不起来，停留在e-7等地方或者仿真很慢，自次上次有个朋友说改变步长能解决这个问题，最后的曲线能达到满足要求的时候，我让他找个例子他就没有后文，以前我做了很多这样的模糊PID，但靠改变步长并没有解决问题，仿真曲线直接发散出去了，我自己这两天也总在思考这个问题，通过不断的调试总结，明白了不少，给遇到这个问题的朋友讲解一下解决办法吧，以一个例子详解：(Done a lot of friends of the fuzzy PID encountered this problem, simulation start up, stay in the e-7 and other local or simulation is very slow, have a friend who said to change the step size to solve this problem since the times the last, the finalcurve to achieve to meet the requirements when I asked him to find examples of his later, I have done a lot of fuzzy PID, but by changing the step does not solve the problem, the simulation curve directly divergence out, Itwo days always thinking about this problem, summarized by the debug and understand a lot of friends to experience this problem to explain the solution, an example Detailed:)

离散时间信号的变换域分析,求信号的单边Z变换,求Z反变换(Transform domain analysis of discrete time signal, unilateral Z transform for the signal, and inverse Z transform )