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[Algorithm] SINDDS DL : 0: sin数据生成，可以生成DDS的ROM文件-sin data generation, the generation DDS ROM documents
Update : 2025-02-19 Size : 183kb Publisher : lxq
[matlab] ForcedPendulum DL : 0: This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega + omega/Q + sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) -This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega+ omega/Q+ sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986)
Update : 2025-02-19 Size : 8kb Publisher : Mike Gao
[Algorithm] ExpandingRods DL : 0: 题意:已知一个圆的弦长l0及这条弦所在的弧长l1,求弦的中心点到弧的中心点的距离思想:这是一个列方程然后利用二分法解方程的题目,令该疑弧所对的圆心角为anlg, 半径为r,根据题意有两个方程:l1=anlg*r l0=2*r*sin(anlg/2) 两个方程两个未知数, 通过化简有:2*l1*sin(anlg/2)-anlg*l0=0 因为角度的值是从0到2*pi,题目中讲到过弧的长度不可能大于弦的两倍,所以角度不可能取到2*pi,但是有可能为0,把零特殊考虑, 再从0到2*pi间二分找解就不会出错了!但是要注意精度问题,取七位小数才能得出正确解.-Italian title: Known l0 chord of a circle and arc length of this string where l1, seeking the center of the arc chord distance of the center of thought: This is a solution out equations and then use the dichotomy of the title equation, so that The arc of the doubt on the central angle for the anlg, radius r, according to the meaning of problems with two equations: l1 = anlg* r l0 = 2* r* sin (anlg/2) two equations two unknowns, by simplification are: 2* l1* sin (anlg/2)-anlg* l0 = 0 because the value of the angle is from 0 to 2* pi, the title referred to had not greater than the arc length of the string twice, So can not get to the point of 2* pi, but there may be 0, to zero special consideration, and then from 0 to 2* pi to find solution between the two points will not be wrong! but to pay attention to accuracy problems, were taken to seven decimal the correct solution.
Update : 2025-02-19 Size : 1kb Publisher : yangxiuyi
[matlab] atan2sc DL : 0: sin and cosine of atan(y/x) [S,C,R,T]=(Y,X)
Update : 2025-02-19 Size : 1kb Publisher : vyvas
[matlab] chap11 DL : 0: syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )-syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' )
Update : 2025-02-19 Size : 1kb Publisher : 景国彬
[Special Effects] chap9 DL : 0: syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )-syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' )
Update : 2025-02-19 Size : 21kb Publisher : 景国彬
[Special Effects] jianmo DL : 0: syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )-syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' )
Update : 2025-02-19 Size : 64kb Publisher : 景国彬
[TreeView] circle DL : 0: function [] = circleagain(a,b,c,r) pixel = 0.1 theta1 = 0 theta2 = 360*pi/180 pix = pixel/r theta = theta1:pix:theta2 global x y z x = a + r*cos(theta) y = b + r*sin(theta) z = ones(1,length(x))*c x=round(x*10)/10 y=round(y*10)/10 z=round(z*10)/10 plot3(x,y,z, c )
Update : 2025-02-19 Size : 370kb Publisher : boom
[matlab] matlab DL : 0: 文件1：复数的表达与计算；文件2：用matlab计算∛ (-8)，并用图形表示；文件3：用符号计算研究方程sin(3)uz^2+vz+3w-a5=0的解；文件4：求阿基米德螺线r=a*θ,(a>0)在θ=0到φ间的曲线长度函数，并求a=1,φ=2п间的曲线长度；文件五：著名的Givens旋转G=[■(cos t&-sin t@sin t&cos t)]对矩阵A=[■(√3/2&1/2@1/2&√3/2)]的旋转作用。-five programs about matlab
Update : 2025-02-19 Size : 2kb Publisher : 潘登
[matlab] Discret-Wavelet-Transform-got-Daub2-by-Santhana-R DL : 0: y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level It returns the matrix of all decompositions and the final approximations. Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes. For other wavelets, the filter values alone can be changed or WFILTERS can be used. The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author. Example: clear all t=[0:0.0003:8*pi] x=sin(5000*t)+sin(1000*t) x=x(1:2^16) level=5 Fs=1/0.003 d2wavelet(x,Fs,level) Thanks for Downloading. Don t forget to rate or comment. -y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level It returns the matrix of all decompositions and the final approximations. Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes. For other wavelets, the filter values alone can be changed or WFILTERS can be used. The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author. Example: clear all t=[0:0.0003:8*pi] x=sin(5000*t)+sin(1000*t) x=x(1:2^16) level=5 Fs=1/0.003 d2wavelet(x,Fs,level) Thanks for Downloading. Don t forget to rate or comment.
Update : 2025-02-19 Size : 2kb Publisher : 无界
[Windows Mobile] circle.m DL : 0: function circle(cx, cy, r, linetype) N = 150 x = zeros(1,N+1) y = zeros(1,N+1) for n=1:N+1 x(n) = cx + r*cos(2*pi*n/N) y(n) = cy + r*sin(2*pi*n/N) end hold on plot(x, y, linetype) hold off -function circle(cx, cy, r, linetype) N = 150 x = zeros(1,N+1) y = zeros(1,N+1) for n=1:N+1 x(n) = cx + r*cos(2*pi*n/N) y(n) = cy + r*sin(2*pi*n/N) end hold on plot(x, y, linetype) hold off
Update : 2025-02-19 Size : 1kb Publisher : tristancohn
[EditBox] 11 DL : 0: empirical formula with kaiser clc clear all fs=1000 fc=250 df=50 r=0.001 f=fc/fs dw=2*pi*(df/fs) a=-20*log(r) n=floor(((a-8)/(2.285*dw))+1) if a>50 b=0.1102*(a-8.7) elseif a>=21 && a<=50 b=0.5842*((a-21)^0.4)+0.07886*(a-21) elseif a<21 b=0.0 end w=kaiser(n,b) for i=1:n if i~=(n-1)/2 hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f) elseif i==(n-1)/2 hd(i)=2*f end end for j=1:n h(j)=w(j)*hd(j) end subplot(3,1,1), plot(w) subplot(3,1,2), plot(h) subplot(3,1,3), plot(h,n) - empirical formula with kaiser clc clear all fs=1000 fc=250 df=50 r=0.001 f=fc/fs dw=2*pi*(df/fs) a=-20*log(r) n=floor(((a-8)/(2.285*dw))+1) if a>50 b=0.1102*(a-8.7) elseif a>=21 && a<=50 b=0.5842*((a-21)^0.4)+0.07886*(a-21) elseif a<21 b=0.0 end w=kaiser(n,b) for i=1:n if i~=(n-1)/2 hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f) elseif i==(n-1)/2 hd(i)=2*f end end for j=1:n h(j)=w(j)*hd(j) end subplot(3,1,1), plot(w) subplot(3,1,2), plot(h) subplot(3,1,3), plot(h,n)
Update : 2025-02-19 Size : 80kb Publisher : rezwan
[Dialog_Window] EEE-212-lab-sheet DL : 0: empirical formula with kaiser clc clear all fs=1000 fc=250 df=50 r=0.001 f=fc/fs dw=2*pi*(df/fs) a=-20*log(r) n=floor(((a-8)/(2.285*dw))+1) if a>50 b=0.1102*(a-8.7) elseif a>=21 && a<=50 b=0.5842*((a-21)^0.4)+0.07886*(a-21) elseif a<21 b=0.0 end w=kaiser(n,b) for i=1:n if i~=(n-1)/2 hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f) elseif i==(n-1)/2 hd(i)=2*f end end for j=1:n h(j)=w(j)*hd(j) end subplot(3,1,1), plot(w) subplot(3,1,2), plot(h) subplot(3,1,3), plot(h,n) - empirical formula with kaiser clc clear all fs=1000 fc=250 df=50 r=0.001 f=fc/fs dw=2*pi*(df/fs) a=-20*log(r) n=floor(((a-8)/(2.285*dw))+1) if a>50 b=0.1102*(a-8.7) elseif a>=21 && a<=50 b=0.5842*((a-21)^0.4)+0.07886*(a-21) elseif a<21 b=0.0 end w=kaiser(n,b) for i=1:n if i~=(n-1)/2 hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f) elseif i==(n-1)/2 hd(i)=2*f end end for j=1:n h(j)=w(j)*hd(j) end subplot(3,1,1), plot(w) subplot(3,1,2), plot(h) subplot(3,1,3), plot(h,n)
Update : 2025-02-19 Size : 529kb Publisher : rezwan
[matlab] Modeling-Rayleigh-fading-channel-based-on-modifie DL : 0: This Matlab Code models a Rayleigh fading channel using a modified Jakes channel model. A modified Jakes model chooses slightly different spacings for the scatterers and scales their waveforms using Walsh–Hadamard sequences to ensure zero cross-correlation. \alpha_n = \frac{\pi(n-0.5)}{2M} and \beta_n = \frac{\pi n}{M}, results in the following model, usually termed the Dent model or the modified Jakes model: R(t,k) = \sqrt{\frac{2}{M}} \sum_{n=1}^{M} A_k(n)\left( \cos{\beta_n} + j\sin{\beta_n} \right)\cos{\left(2\pi f_d t \cos{\alpha_n} + \theta_{n}\right)}. The weighting functions A_k(n) are the kth Walsh–Hadamard sequence in n. Since these have zero cross-correlation by design, this model results in uncorrelated waveforms. The phases \,\!\theta_{n} are initialized randomly and have no effect on the correlation properties. Matlab fast Walsh-Hadamard transform function is used to efficiently generate samples using this model.-This Matlab Code models a Rayleigh fading channel using a modified Jakes channel model. A modified Jakes model chooses slightly different spacings for the scatterers and scales their waveforms using Walsh–Hadamard sequences to ensure zero cross-correlation. \alpha_n = \frac{\pi(n-0.5)}{2M} and \beta_n = \frac{\pi n}{M}, results in the following model, usually termed the Dent model or the modified Jakes model: R(t,k) = \sqrt{\frac{2}{M}} \sum_{n=1}^{M} A_k(n)\left( \cos{\beta_n} + j\sin{\beta_n} \right)\cos{\left(2\pi f_d t \cos{\alpha_n} + \theta_{n}\right)}. The weighting functions A_k(n) are the kth Walsh–Hadamard sequence in n. Since these have zero cross-correlation by design, this model results in uncorrelated waveforms. The phases \,\!\theta_{n} are initialized randomly and have no effect on the correlation properties. Matlab fast Walsh-Hadamard transform function is used to efficiently generate samples using this model.
Update : 2025-02-19 Size : 2kb Publisher : Manzar Hussain
[Software Engineering] mins DL : 0: optimazion of R=((θ_1*sin(20*θ_2)+ θ_2*sin(20*θ_1))^2)*cosh(θ_1*sin(10*θ_1))+(( θ_1*cos(10*θ_2)- θ_2*sin(10*θ_1))^2)*cosh(θ_2*cos(20*θ_2)))) with powel
Update : 2025-02-19 Size : 3kb Publisher : ashkan irannezhad
[Graph program] jiancezhixian DL : 0: 为了能有效解决H ough 变换的计算量大的问题，文中提出了一种基于直线局部结构特征的H ough 变换改进的直线检测算法。该算法根据F reem an 准则分析了直线上基元的特征信息，通过图像上邻近的同类基元的倾斜角约束基元上像素点的极角范围，减少每个点的计算次数，在保持精度的同时，提高直线检测的速度，在有噪声的情况下，该算法相对标准H ough 变换算法可以提高到6 到7 倍。 -In o rder to effectively solve the pro blem of decreasing com putation in the process of using H ough T ransform (w r ) to extract str aigh t line ．an im p ro v ed H T algo ri th m m etho d is p res en ted b ased O n-th e loc al ch aracteri stics o f the straig h t lin e ．A cc ord ing to F ree m a n C ri te ria ，th e im p rov ed m eth o d an a lyzes e lem en tary line seg m e n ts s tru ctu re o f th e stra ig h t lin e ，an d d efine s th e sco p e of t3o lar an g le o f th e points on elem entary line segm ents by com putin g the tilt ang le of the straight line d eterm ined by the tw o adjacent an d cong eneri c elem en — tary sin e seg m en ts ，an d th en d ec rea se s th e c om p uting tim es o f ev ery po in t ．P o in ting to th e im a g e co n tain ing n o ise ，th e tes ting da ta sh o w s th a t v elo c ity o f th e im p ro v ed a lg o rithm is 6- 7 tim es as fas t aS th e stan d ~ d o n e ，w h ile th e prec ision is k e p t．
Update : 2025-02-19 Size : 16.07mb Publisher : zheng mingzhi
[Other] music DL : 0: MUSIC 算法MATLAB仿真源代码 clc clear all format long %将数据显示为长整型科学计数 N=200;%快拍数 doa=[20 60]/180*pi; %信号到达角 w=[pi/4 pi/3]';%信号频率 M=10;%阵元数 P=length(w); %信号个数 lambda=150;%波长 d=lambda/2;%阵元间距 snr=20;%信噪比 B=zeros(P,M); %创建一个P行M列的0矩阵 for k=1:P B(k,:)=exp(-j*2*pi*d*sin(doa(k))/lambda*[0:M-1]); %矩阵赋值 end B=B'; xx=2*exp(j*(w*[1:N])); %仿真信号 x=B*xx; x=x+awgn(x,snr);%加入高斯白噪声 R=x*x'; %数据协方差矩阵 [U,V]=eig(R); %求R的特征值和特征向量 UU=U(:,1:M-P); %估计噪声子空间 theta=-90:0.5:90; %%谱峰搜索 for ii=1:length(theta) AA=zeros(1,length(M)); for jj=0:M-1 AA(1+jj)=exp(-j*2*jj*pi*d*sin(theta(ii)/180*pi)/lambda); end WW=AA*UU*UU'*AA'; Pmusic(ii)=abs(1/ WW); end Pmusic=10*log10(Pmusic/max(Pmusic)); %空间谱函数 plot(theta,Pmusic,'-k') xlabel('角度 \theta/degree') ylabel('谱函数P(\theta) /dB') title('MUSIC算法的DOA估计谱') grid on(MUSIC algorithm MATLAB simulation source code)
Update : 2025-02-19 Size : 15kb Publisher : 冠华