人体运动建模仿真

对行人的运动规律进行介绍，建立模型，进行仿真，可用于人体微动特性的分析，超级有用

时频Gabor变换

function [tfr,dgr,gam]=tfrgabor(sig,N,q,h,trace) %TFRGABOR Gabor representation of a signal. % [TFR,DGR,GAM]=TFRGABOR(SIG,N,Q,H,TRACE) computes the Gabor % representation of signal X, for a given synthesis window H, on a % rectangular grid of size (N,M) in the time-frequency plane. M and N % must be such that % N1 = M * N / Q % where N1=length(X) and Q is an integer corresponding to the % degree of oversampling. % % SIG : signal to be analyzed (length(SIG)=N1). % N : number of Gabor coefficients in time (N1 must be a multiple % of N) (default : divider(N1)). % Q : degree of oversampling ; must be a divider of N % (default : Q=divider(N)). % H : synthesis window, which was originally chosen as a Gaussian % window by Gabor. Length(H) should be as closed as possible % from N, and must be >=N (default : Gauss(N+1)). % H must be of unit energy, and CENTERED. % TRACE : if nonzero, the progression of the algorithm is shown % (default : 0). % TFR : Square modulus of the Gabor coefficients. When % called without output arguments, TFRGABOR runs TFRQVIEW. % DGR : Gabor coefficients (complex values). % GAM : biorthogonal (dual frame) window associated to H.

非平稳信号分析工具

function [tfr,rtfr,hat] = tfrrspwv(x,t,N,g,h,trace); %TFRSPWV Reassigned smoothed pseudo Wigner-Ville distribution. % [TFR,RTFR,HAT] = TFRRSPWV(X,T,N,G,H,TRACE) % computes the smoothed pseudo Wigner-Ville distribution and its % reassigned version. % % X : analysed signal. % T : the time instant(s) (default : 1:length(X)). % N : number of frequency bins (default : length(X)). % G : time smoothing window, G(0) being forced to 1. % (default : Hamming(N/10)). % H : frequency smoothing window, H(0) being forced to 1 % (default : Hamming(N/4)). % TRACE : if nonzero, the progression of the algorithm is shown %

Winger_Viller变换

非平稳信号分析工具，通过Winger_Viller 变换，将一维时域信号变换到时频域二维信号。

椭圆积分程序

#include<iostream> #include<cmath> using namespace std; double pi=3.14159; int main() { int i,j; double K,E,k=0.5; double M=0,N=0; for (i=1;i<101;i++) { double M0,M1=1; double N0,N1=1; for (j=1;j<i+1;j++) { //M0=((2*j-1)/(2*j))^2*(k^(2*i)); M0=pow(double((2*j-1)/(2*j)),2)*pow(k,2*i); N0=M0/(2*i-1); M1=M1*M0; N1=N1*N0; } M=M+M1; N=N+N1; } K=pi/2*(1+M); E=pi/2*(1-N); cout<<K<<endl; cout<<E<<endl; return 0; }

矩阵保存的C++程序

#include <iostream> using namespace std; int main() { int i,j; float val[100][50]; for (i=0;i<100;i++) { for (j=0;j<50;j++) { val[i][j]=rand()/10000; } } FILE *pfile; pfile=fopen("RBFLaplace2D.txt","w+"); for (i=0;i<100;i++) { for (j=0;j<50;j++) fprintf(pfile,"%8.5e\t",val[i][j]); fprintf(pfile,"\n"); } fclose(pfile); return 0; }

辛普生算法

#include<iostream> #include<cmath> using namespace std; int main() { int i; double pi=3.1415926; double h=pi/100/2; double x,y0; double y=0; for (i=0;i<100;i++) { x=i*h; y0=sin(x)+4*sin(x+h/2.0)+sin(x+h); y=y0+y; } y=y*h/6.0; cout<<y<<endl; return 0; }

模糊函数程序

function [naf,tau,xi]=ambifunb(x,tau,N,trace); %AMBIFUNB Narrow-band ambiguity function. % [NAF,TAU,XI]=AMBIFUNB(X,TAU,N,TRACE) computes the narrow-band % ambiguity function of a signal X, or the cross-ambiguity % function between two signals.

产生时域信号

通过设定长度，最大值和最小值，在时间域产生信号

短时傅里叶逆变换

短时傅里叶逆变换与短时傅里叶变换互为逆变换，之间可以互相转换，将一维信号转换为二维时间频域信号，便于时频分析。