LDPC

说明：  IEEE14节点潮流计算程序。程序最初的数据模块可以修改成别的，如IEEE30节点和57节点的等等，矩阵取名用的是中文拼音(IEEE14 node flow calculation program)

潮流计算,计算简单的潮流分析程序,可以自己修改节点个数,节点类型(Flow calculation, calculation of simple trend analysis program can modify the number of nodes, node types)

通信工程中m序列编码，自相关，互相关计算,组合码的产生,GOLD码的产生(Communication Engineering in m-sequences encoding autocorrelation, cross-correlation calculations, produce a combined code, GOLD code generation)

强跟踪滤波程序，其中包括st-ukf和st-ekf(code for st-ekf and st-ukf)

说明：  分别使用库函数和自编函数实现AR模型谱估计 (Respectively, the use of library functions and the self-function AR model spectrum estimation)

对随机风速的仿真 可用于风电系统中发电的风速信号(Simulation of random wind speed can be used for wind power systems, power generation, wind speed signal)

可以进行直方图计算，也可以进行灰度运算，可以实现图像的变换操作(Histogram can be calculated, it can be gray-scale operation, the image transformation operation can be achieved)

This Matlab Code models a Rayleigh fading channel using a modified Jakes channel model. A modified Jakes model chooses slightly different spacings for the scatterers and scales their waveforms using Walsh–Hadamard sequences to ensure zero cross-correlation. alpha_n = frac{pi(n-0.5)}{2M} and eta_n = frac{pi n}{M}, results in the following model, usually termed the Dent model or the modified Jakes model: R(t,k) = sqrt{frac{2}{M}} sum_{n=1}^{M} A_k(n)left( cos{eta_n} + jsin{eta_n} ight)cos{left(2pi f_d t cos{alpha_n} + heta_{n} ight)}. The weighting functions A_k(n) are the kth Walsh–Hadamard sequence in n. Since these have zero cross-correlation by design, this model results in uncorrelated waveforms. The phases ,! heta_{n} are initialized randomly and have no effect on the correlation properties. Matlab fast Walsh-Hadamard transform function is used to efficiently generate samples using this model.

带通/带阻巴特沃斯滤波器设计，采用IIR滤波器设计思路，可以返回数字滤波器的二阶分割形式(Band-pass/band-reject Butterworth filter design, using IIR filter design ideas, you can return to the form of digital filter second-order segmentation)

训练错误率和交叉验证错误率相等，在样本比较大时，这个结果是可以预期的；训练错误率一般低于测试错误率，但是当样本数据比较少时，实验也出现了意外，样本多的那组测试错误率比样本少的训练错误率还要小；在本实验中，同组数据的交叉验证错误率比独立测试错误率高，这个反常现象是因为样本的原因所致，交叉验证的样本小，而独立测试时所用训练样本数目大，因而出现这种情况。分类线上，fisher准则是一条直线，而贝叶斯分类器实际上是一个类似椭圆的封闭曲线；很明显，贝叶斯分类器比fisher分类器要好。(Training error rate and cross- validation error rates are equal, the larger the sample , this result is to be expected training error rate is generally lower than the test error rate , but when comparing the sample data came from the experiment there was an accident, that more samples group test error rate less than the training sample error rate is smaller In this experiment, the same set of data cross-validation error rate than independent test error rate, this anomaly is because the sample of reasons , cross-validation sample is small,And independent testing large number of training samples used , resulting in this situation.Classification online , fisher criterion is a straight line , while the Bayesian classifier is actually a closed curve similar to elliptical It is clear that the Bayesian classifier is better than the fisher classifier .)