第二章

使用matlab变成，基于FFT算法的电网中介谐波参数估计的程序(Matlab into use, the power grid based on the FFT algorithm intermediary harmonic parameter estimation procedure)

fixed point method of matlab

图像融合算法，快速准确，有独特的特征点连线(Image fusion algorithm, fast and accurate, with a unique feature point connection)

（2,1,3）卷积码Viterbi译码的matlab(Viterbi decoding algorithm matlab code)

这是一本经典的MatlAB书的习题代码，非常丰富，非常实用(This is a classic book of exercises code MatlAB, very rich, very practical)

一种新的针对卷积码的删余编码的编码方式，比较简洁明了，易于快速理解和掌握删余编码技术(A new code for convolution coding punctured encoding relatively concise, easy to understand and grasp quickly punctured coding technology)

matrix converter is having advantages when compared to traditional converters. the loss are less in this converter.

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 .)

通讯系统仿真（Matlab）Dynamic Resource Allocation（DCA） in JOINT system(Communication system simulation (Matlab) Dynamic Resource Allocation (DCA) in JOINT system)

This is a simple program to calculate the output of artificial neural network (ANN) using microcontroller ATMega 32. Assume that the neural architecture is : 2 hidden layers with 4 and 2 neurons respectively and 1 layer output with 1 neuron.This program explains the step how to compute the ANN in off-line mode.Means the weights and biases are already exist (microcontroller is not doing the learning steps). These parameters are produced using MATLAB. Therefore, we have already made the appropriate architecture using MATLAB. The tansig function is use in both of hidden layer. Finally, the neural output will be displayed on LCD port C.