Theredarea

小数据量法求Lyapunov指数_龙格库塔(Small amount of data Lyapunov index _ method Runge-Kutta)

power flow algorithm for Transmission Systems

很实用的一个OFDM仿真程序，OFDM采用BPSK、QPSK、16QAM、64QAM4种调制方式。按照星座图，将每个子信道上的数据，映射到星座图点的复数表示，转换为同相Ich和正交分量Qch。(A very practical OFDM simulation program, OFDM the BPSK, QPSK, 16 qam, 64 qam4 kind of modulation method. According to the constellation diagram, each will be the data on the channel, the plural of constellation is mapped to a point, said into in-phase Ich and quadrature Qch. )

这是一个fir滤波器的仿真模型，可以提供为进一步的仿真提供基础(This is a fir filter simulation model, it can provide to further provide the basis for the simulation)

说明：  后联平滑滤波器的卡尔曼滤波器的使用，这个代码很不错的哦(After the joint smoothing filter is the use of the Kalman filter, this code is very good Oh)

用matlab实现简单的卡尔曼滤波功能，(Using matlab to achieve a simple Kalman filtering)

说明：  在matlab环境下编写的快速傅里叶变换程序，改变换程序简单有效(Matlab environment in the preparation of the fast Fourier transform procedure to change the procedure for simple and effective)

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) )

Multiple input multiple output ofdm

摘 要：本论文主要研究了语音识别的基本原理,对语音识别系统的构成进行分析处理,其中包括预处理、特征参数提取、建立模块库、识别匹配几大部分。预处理又包括语音采样、预加重、加窗（汉明窗）、端点检测；特征提取的参数是梅尔频率倒谱系数MFCC。 该语音系统采用的是动态时间伸缩算法(DTW)，研究对象是特定人的语音识别，并在MATLAB平台上实现。为了进行后续研究，首先使用电脑中的录音系统录制了阿拉伯数字0—9的语音文件，并转化成 “.wav”格式的文件。(Abstract: This thesis mainly studied the basic principle of speech recognition, to analyze the composition of the speech recognition system, including the preprocessing, feature extraction, to set up the module library, identify several most matches. Pretreatment, including speech sampling, pre-emphasis, add window (hamming window), endpoint detection Feature extraction of MFCC MEL frequency cepstrum coefficient. The voice system USES a dynamic time scale (DTW) algorithm, the research object is the speaker-dependent speech recognition, and realized in MATLAB platform.To carry out the follow-up study, the first to use the recording in a computer system to record the audio files of Arabic Numbers 0-9, and translated into . Wav format file.)