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三个小程序，对BURG法估计信号空间谱进行了仿真分析，比较基础，通俗易懂(Three small procedures, BURG method to estimate the signal space spectrum simulation analysis, comparison basis, easy to understand)

a fuzzy logic controller code for maximum power point tracking

基于双闭环矢量控制的永磁直驱风电系统的并网模型，输出功率和直流电压波形理想(Grid- connected model of permanent magnet direct drive wind power system based on double closed- loop vector control and Output power and DC voltage waveform are ideal)

nmf盲源分离，使用欠定的方法，可以用来分离两路语音信号，人声与其他音的分离(nmf_bss.The underdetermined method can be used to separate two voice signals, human voice and other voices.)

通过学习LDPC编译码原理，编写了LDPC码的编译码程序。通过此程序可以更好的实现LDPC码的仿真(in this passage we have write the program of edcoded and decoded of LDPC code.through it we can undersatand LDPC more.)

一种很容易理解的svm matlab工具箱，可用于分类，回归，并附很多示例。(A very easy to understand svm matlab toolbox, can be used for classification, regression, together with many examples.)

说明：  MATLAB实现的无源RC、LC滤波器设计系统。可设计LP、HP、BP、BS四种滤波器，附带设计结果(MATLAB realization of passive RC, LC filter design system. Can be designed LP, HP, BP, BS in four filters, with the design results)

说明：  NI采集和产生信号代码NI采集和产生信号代码(NI acquisition and signal generated code )

Wavelet denoising For using this code need to use signal toolbox and general toolbox in your matlab In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size 512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of having SNR=20dB by select the suitable value of variance for Gaussian noise formula. Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It means that, we should find the highest SNR value by finding the suitable value for threshold. Then we asked to do the same process but this time using soft thresholding. Finally for the last part of question one, we should compare the results of the obtained SNR with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft thresholding.

本实验语音库为免费的柏林情感语音库，其采样频率为16KHZ，16bit量化。该语音库共有500 句情感语音信号，分别由十名专业演员(5 男，5 女)在不同情感状态下（高兴、愤怒、平静、悲伤、害怕、厌烦、憎恨）朗读十句不同文本的德语组成。本实验选取其中的部分情感（高兴、愤怒、悲伤）加以识别。仿真实验环境为MATLAB7.0。 实验选取的情感特征为短时平均能量、短时平均幅度、基频和短时过零率。为了降低不同人在表达不同情感时的个人差异造成的影响，本文实验过程中将提取的情感特征进行归一化处理。归一化采取将同一个人的三种情感语音信号的情感特征放在一起归一化处理，并将归一化后的情感特征作为KNN分类器的训练样本和测试样本。 该实验使用了传统的KNN算法和改进算法两种识别方式，通过实验结果对比了各自的特点。 (This experiment YuYinKu for free Berlin YuYinKu emotion, the sampling frequency for 16 KHZ, 16 bit quantification. The YuYinKu 500 words of emotional speech signal, respectively by ten professional actors (5 male, five female) in different emotional state (happy, anger, sadness, fear, calm, bored, hate) read ten sentences in different text of the German. This experiment selection of part of the emotions (joy, anger, sadness) is recognized. The simulation experiment environment for MATLAB7.0. The selection of emotional characteristics for short-term average energy, short-term average amplitude, frequency, and the short zero rate. In order to reduce the different people in different emotional expression of the impact of individual differences, the experiment process will extract emotional characteristics is normalized. The normalized will take the same three kind of emotional speech signal emotional features normalized put together, and after the normalized the emotional characteristics)