matlabppt

这是一个简单的语音基音检测方法，通过对语音信号进行滤波、分帧、求自相关函数，可得到浊音的基音周期，从而实现基频检测。(This is a simple voice pitch detection, voice signals by filtering, sub-frame, find the autocorrelation function, voiced the pitch period available to achieve the fundamental frequency testing.)

guassian low pass filter

matlab编程函数，很详细，很有用(Matlab programming function matlab matlab programming function Matlab programming function programming function matlab programming function)

2D周期性圆柱组成的，lens的几何设计和delay的计算，使用各项同性圆柱(2D periodic cylinder’s delay)

说明：  利用互相关信息法求混沌序列的相空间重构时延，绝对可用，并附上lorenz序列，以供验证。(Inter-related Information Act, seeking a chaotic sequence of phase space reconstruction delay is absolutely available, along with lorenz sequence for verification.)

自写较为完善的STBC空时分组编码的MATLAB源代码，包括G2(Alamouti),G3(码率1/2),G4(码率1/2),H3(码率3/4),H4(码率3/4)的五种正交编码矩阵，调制方式有BPSK、QPSK、8PSK、16QAM，得出了仿真值的误比特率BER曲线，连带给出了所有调制方式和编码矩阵的理论误比特率BER曲线。(This is a matlab code for stbc,including five kinds of coding matrix: G2(Alamouti),G3(rate 1/2),G4(rate 1/2),H3(rate 3/4),H4(rate 3/4) and many kinds of modulation,such as BPSK,QPSK,8PSK,16QAM etc.I present all the simulative BER figures and theory BER figure.enjoy:))

MATLAB视频教程3帮你轻松入门学习MATLAB编程(MATLAB Video Tutorial 3 Introduction to help you easily learn MATLAB programming)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

非线性系统鲁棒无模型学习自适应控制论文，内容见附件(Nonlinear systems model-free learning adaptive control study, the content of Annex)

以单相桥式分支模块组成的整流电路仿真模型(Single phase bridge rectifier modules branch circuit simulation model)