LTEcommonsubblockinterleaver

matlab实例，给出了诸多matlab编程应用的例子，值得一看。(matlab example, matlab programming gives many examples of applications, worth a visit.)

signal generation using matlab

基于麦克风阵列的自适应波束形成算法.它是基于SMI自适应算法(Microphone array based adaptive beamforming algorithm which is based on the SMI adaptive algorithm)

code for edge detection

上海电气MATLAB仿真模型用户手册word文档说明的十分详细()

说明：  自己编写的使用ESPRIT算法进行信号频率估计的Matlab代码。(Matlab code of the ESPRIT algorithm for frequency estimation.)

光伏并网系统单相逆变器电压前馈电流控制，基于matlab 7.3 。(photovotaic grid-connected system single-phase inverter voltage feed forward current control,based on matlab 7.3)

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.)

16QAM的盲均衡算法-cma,它是研究盲均衡算法最基础的一种算法 (16QAM for blind equalization algorithm-cma, which is to study the most basic algorithm for blind equalization algorithm)

说明：  一个从甲苯中萃取笨酸的化工过程的模拟，并且可以改变萃取级数，供用户学习(A acid extracted from the toluene chemical process simulation, and extraction stages can be changed for the user to learn)