LMSprogram

说明：  FSK在Rayleigh 衰落下的matlab误码率仿真，包括理论计算和仿真结果(FSK in Rayleigh Fading matlab error rate simulation, including theoretical calculations and simulation results)

关于单载波频域均衡系统，判决反馈均衡的仿真。(On single-carrier frequency domain equalization using decision feedback equalization simulation material, and I hope to help you.)

该法是经过改进的基于象素匹配的方法并且还含有图片,对进行景象匹配的朋友有所帮助(The Act are improved pixel-based matching method and also contains a picture of the scene matching to help a friend)

matlab教程,来自于罗建军老师,正在学matlab的不妨下载来看看(matlab tutorials, the teacher from the Luo Jianjun)

说明：  趋势EA，不加仓，不网格，每单都带止损止盈，加载周期30分(Trend EA, no warehousing, no grid, stop-loss and stop-profit for each single, loading cycle 30)

1.分别在两个图中画出δ(n)和δ(n-2)，其中-10≤n≤10，画出U（n）其中-10≤n≤10。 2.分别在两个图中画出x1(n)=cos(2π×20×0.01×n)，0≤n≤10和x2(n)=(0.8)n，1≤n≤6。 3.由上一题，计算y(n)=x1(n)+x2(n)，并画图。(1. Draw the figure at two δ (n) and δ (n-2), where-10 ≤ n ≤ 10, draw the U (n) where-10 ≤ n ≤ 10. 2. Respectively in the two Draw the figure x1 (n) = cos (2π × 20 × 0.01 × n), 0 ≤ n ≤ 10 and x2 (n) = (0.8) n, 1 ≤ n ≤ 6. 3. from the previous question, calculate y (n) = x1 (n)+ x2 (n), and drawing.)

ldpc的编码和译码方法，译码我验证过了，都是对的，包括wbf和bf译码方法，编码方法包括四种不同的编码方法(LDPC encoding and decoding method, decoding I verified, all is right, including any WBF and bf decoding method, the encoding methods include four different coding methods )

图像高斯金字塔，生成高斯金字塔，依次进行图像下采样( Image gaussian pyramid,To generate the gaussian pyramid, under the image sampling in turn )

快速计算rcwa一维，投射场 衍射场分布(Rapid calculation rcwa one-dimensional projection field diffraction field distribution)

试者工作特征曲线 （receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。(PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots. )