RS

程序模拟了均匀光栅的反射谱特性、色散特性和时延特性(Program to simulate the reflection spectrum in uniform grating dispersion characteristics and delay characteristics)

一本在Matlab开发环境下，讲解面向对象的编程书籍。适合从事Matlab编程的朋友们。(a book that is suitable for the friends who want to research object oriented programming using Matlab.)

以227家公司的数据为基础进行kmeans clustering分组，用以预测50家未知公司的效益(Use 227 the companies data as the basis for kmeans clustering to predict the effectiveness of the 50 unknown companies)

无损预测编码的主函数和子函数，以及指数哥伦布编码的源代码(Lossless predictive coding main function and subfunction, and Exponential-Golomb coding of source code)

本程序实现了基带蒙特卡罗仿真,分析了基带误码性能。(This program simulates a Monte Carlo system to obtain a BER )

实现信号的DSB,FM，PM,FSK的调制与解调，同时包含加噪声处理。(Implement signal DSB, FM, PM, FSK modulation and demodulation, while including additional noise processing.)

溶液汽液平衡数据的计算，里面的包含的模型相信学化工的人一看就会明白。(VLE solution calculation, which contains the model believe that chemical science will understand at a glance.)

The most ofICI avoidance algorithms divide UEs into cell edge and cell centre users based on static thresholds such as path loss, SINR and geometry factors. However, in an actual communication environment, the UE distribution as well as the shape of a cell can be changed. Therefore selection of the cell edge bands is difficult and needs to be optimized according to the time variation environment. Reference [6] proposed an ICI avoidance algorithm that can change cell edge bands dynamically. For each active UE and every time gap (the update duration of CEU/CCU), the threshold is updated based on SINR in order to keep the ratio of number of CEUs and CCUs constant. Then, cell weight is calculated to follow the instant changing of network.

几篇关于空时编码与cpm调制相结合的文章，非常实用，适合初学者(Several articles on the space-time coding and modulation combination cpm article, very useful for beginners)

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) )