Spectral-Clustering

FH Spread Spectrum System simulation in MATLAB

直流电动机控制的matlab实现，有完整的simulink图(DC motor control matlab achieved complete simulink map)

Pseudo-Random Generator(Autokorelation function of PN secvence)

这是SMITH预估器的相关程序，适合搞过程控制的同学学习，希望对大家有用(This is SMITH predictor procedures for students engaged in learning process control, want to be useful)

北斗信号模拟生成，包括CA码生成和北斗信号生成正确性验证(Compass analog signal generation, including the CA code generation and signal generation Beidou correctness verification)

计算光纤陀螺的非线性度，并且绘制出不同速率对应的非线性度曲线。(Calculation of the non-linearity of the optical fiber gyro, and draw out the different rates corresponding to the non-linearity curve.)

过零无功功率补偿的研究及其matlab仿真(Zero-crossing compensation)

MATLAB小波分析的所有章节源码，本书从新号处理的角度阐述小波分析的基本原理及其应用。书籍适用于从事信号处理、图像处理等方面工作的工程技术人员。(MATLAB wavelet analysis all sections of the source code, this book from the perspective of the new, deal with the basic principle of wavelet analysis and its application. Book is suitable for the work in the aspects such as signal processing, image processing, engineering and technical personnel.)

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.)

说明：  GSM的仿真源码,比较详细,分为模块和全信道两部分(Simulation GSM source, more detailed, divided into modules and the entire channel in two parts)