boilerdigital

Histogram Equalization

可以提取信号的突变量，用于电力系统故障测距方面(Mutations in the signal can be extracted amount of respect for the power system fault location)

matlab中用矩阵迭代法求解问题的C程序(matrix iteration method)

线性预测分析是现代语音信号处理中最核心的技术之一，为现代语音信号处理的飞速发展立下了赫赫功劳，在语音分析、合成、编码、识别等方面都有着广泛的应用，至今仍是最有效的语音分析技术之一。(Linear prediction analysis is the most modern speech signal processing one of the core technology for the modern rapid development of voice signal processing and established illustrious contribution of the speech analysis, synthesis, coding, recognition and so on are widely used, still is the most effective voice analysis technologies.)

实验内容 信号与系统试验报告 自己的作业 1.设有两个信号： 要求x(t)和h(t)采样形成离散序列， 参数选择如下： 采样率Δ=4ms， 频率f=30Hz， a=2f2ln(M), M=2.5 在MATLAB下实现连续信号离散化， 并绘制出离散形式的信号波形。 (Experimental signal and system test the contents of the report of one)

《matlab在数学建模中的应用》卓金武版，从数学建模的角度介绍matlab的应用。书中内容完全是根据数学建模竞赛的需要而编排的，涵盖了绝大部分数学建模问题的matlab求解方法。源码为《Maltab在数学建模中的应用》的源程序和数据。 ("Matlab in mathematical modeling, " Zhuo Jinwu version, from the mathematical modeling point of view on the application of matlab. The book content is based entirely on the needs of mathematical modeling contest arranged, covering the majority of matlab mathematical modeling method for solving the problem. Source for the "Maltab in mathematical modeling, "the source code and data. )

采样频率 fs=10000 轴承外环故障信号 fid=fopen( bearingout.dat , r ) 故障 N=1024 xdata=fread(fid,N, int16 ) fclose(fid) xdata=(xdata-mean(xdata))/std(xdata,1) 时域波形 figure(1) plot(1:N,xdata) xlabel( 时间 t/n ) ylabel( 电压 V/v ) db10小波进行4层分解 一维小波分解 [c,l] = wavedec(xdata,4, db10 ) 重构第1～4层细节信号 d4 = wrcoef( d ,c,l, db10 ,4) d3 = wrcoef( d ,c,l, db10 ,3) d2 = wrcoef( d ,c,l, db10 ,2) d1 = wrcoef( d ,c,l, db10 ,1) ( Sampling frequency fs = 10000 bearing outer ring fault signal fid = fopen (' bearingout.dat' , ' r' ) failure N = 1024 xdata = fread (fid, N, ' int16' ) fclose (fid ) xdata = (xdata-mean (xdata))/std (xdata, 1) time-domain waveform figure (1) plot (1: N, xdata) xlabel (' Time t/n' ) ylabel ( ' voltage V/v' ) db10 wavelet decomposition 4 layer one-dimensional wavelet decomposition [c, l] = wavedec (xdata, 4, ' db10' ) 1 ~ 4 reconstructed detail signal d4 = wrcoef (' d' , c, l, ' db10' , 4) d3 = wrcoef (' d' , c, l, ' db10' , 3) d2 = wrcoef (' d' , c, l, ' db10' , 2) d1 = wrcoef (' d' , c, l, ' db10' , 1) )

k均值聚类，有聚类数目，聚类中心，实现有效聚类。(k-means clustering, there is the number of clusters, cluster centers achieve effective clustering.)

Matlab实现的动态聚类算法，最后以点阵图输出计算结果(Matlab realize the dynamic clustering algorithm, and finally to bitmap output results)

说明：  针对网络社团划分的EO算法（WEO算法），对于研究复杂网络很有帮助！(matlab Code for Complex Network Community)