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Description: This function requires the Mirosoft Win32 Speech API (SAPI).
Examples:
Speak the text
tts( I can speak. )
List availble voices
tts( I can speak. , List )
Do not speak out, store the speech in a variable
w = tts( I can speak. ,[],-4,44100)
wavplay(w,44100) - This function requires the Mirosoft Win32 Speech API (SAPI).
Examples:
Speak the text
tts( I can speak. )
List availble voices
tts( I can speak. , List )
Do not speak out, store the speech in a variable
w = tts( I can speak. ,[],-4,44100)
wavplay(w,44100)
Platform: |
Size: 1024 |
Author: G眉ven |
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Description: 典型时间序列模型分析
设有ARMA(2,2)模型，
X（n）+0.3X(n-1)-0.2X(n-2)=W(n)+0.5W（n-1）-0.2W(n-2)
W(n)是零均值正态白噪声，方差为4
（1）用MATLAB模型产生X（n）的500观测点的样本函数，并会出波形；
（2）用你产生的500个观测点估计X（n）的均值和方差；
（3）画出理论的功率谱
（4）估计X（n）的相关函数和功率谱
-Analysis of typical time series model with ARMA (2,2) model, X (n)+0.3 X (n-1)-0.2X (n-2) = W (n)+0.5 W (n-1)-0.2 W (n-2) W (n) is zero mean normal white noise, variance of 4 (1) generated by MATLAB model X (n) of the 500 observation points, the sample function and the waveform (2) You generated an estimated 500 observation points X (n) the mean and variance (3) draw the theory of power spectrum (4) of the estimated X (n) of the correlation function and power spectrum
Platform: |
Size: 3072 |
Author: qiuxue |
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Description: The function implement the 1D sequency(Walsh) ordered fast Walsh-Hadamard transform which can be used in signal processing, pattern recongnition and Genetic alogorithms.
This algorithm uses a Cooley-Tukey type signal flow graph and is implemented in N log2 N additions and subtractions. Data sequence length must be an integer power of 2.
The inverse transform is the same as the forward transform except for the multiplication factor N. This can be easily achieved by deleting the last line i.e. x=inv(N)*x
Example:
x=[1 2 1 1]
W=FWHT(x) -The function implement the 1D sequency(Walsh) ordered fast Walsh-Hadamard transform which can be used in signal processing, pattern recongnition and Genetic alogorithms.
This algorithm uses a Cooley-Tukey type signal flow graph and is implemented in N log2 N additions and subtractions. Data sequence length must be an integer power of 2.
The inverse transform is the same as the forward transform except for the multiplication factor N. This can be easily achieved by deleting the last line i.e. x=inv(N)*x
Example:
x=[1 2 1 1]
W=FWHT(x)
Platform: |
Size: 2048 |
Author: ry |
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Description: Retinex matlab 源码,实现Retinex的一些功能-Retinex matlab source code to achieve some of the features of Retinex
Platform: |
Size: 1024 |
Author: yuhenry |
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Description: 坐标转换：已知n个点在a,b两坐标系中的坐标值，采用优化方法求转换关系（标准的7参数转换关系，x,y,z的移动，x,y,z的旋转，以及缩放系数）ps:附带空间旋转公式。input: points in A and B。 output：transfer relationship （u,v,w: shit of x,y,z。 a,b,g: rotate of x,y,z 。k：zoom）-Coordinate Transfer:A,B are tow coordinates. This program using optimal method to find the transfer relationship of A and B. input: points in A and B。 output：transfer relationship （u,v,w: shit of x,y,z。 a,b,g: rotate of x,y,z 。k：zoom）
Platform: |
Size: 2048 |
Author: 胡瑞飞 |
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Description: 求解TSP和MTSP的混合遗传算法_英文_-Abstract:M any app licat ions are invo lved w ith mult ip le salesmen each of w hom visits a subgroup
cit ies and returns the same start ing city. The to tal length of all subtours is required to be m ini2
mum. Th is is calledM ult ip le T raveling Salesmen P roblem (M TSP). There are various heurist ic
methods to obtain op t imal o r near2op t imal so lut ions fo r the TSP p roblem. But to the M ult ip le
T raveling Salesmen P roblem , there are no t much app roaches to so lveM TSP. In th is paper, a hy2
brid genet ic algo rithm to so lve TSP and M TSP is p resented. Th is algo rithm combines GA and
heurist ics. N umerical experiments show that the new algo rithm is very efficient and effect ive.
Key words: TSP op t im izat ion genet ic algo rithm 2op t
Platform: |
Size: 217088 |
Author: Notics |
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Description: 无线传感器网络LEACH层次型分簇算法仿真源码。 -Wireless sensor networks LEACH hierarchical clustering algorithm for simulation of source code.
Platform: |
Size: 3072 |
Author: denglu |
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Description: 关于Steerable filtering decomposition 的matlab程序,- STEERGAUSS Implements a steerable Gaussian filter. This m-file can be used to evaluate the first directional derivative of an image, using the method outlined in:
W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters", IEEE PAMI, 1991.
Platform: |
Size: 388096 |
Author: 张宇 |
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Description: mesh n/w in mobile communication
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Size: 1024 |
Author: naveen |
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Description: 激光主动照明的雷达方程，照明光源的辐射功率(W)，辐射距离(m)(一般值是1000m)。-Active laser radar equation lighting, illumination of the radiated power (W), radiation from the (m) (normal value is 1000m).
Platform: |
Size: 1024 |
Author: 胡先生 |
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Description: Function that returns a row vector of WGN with same length as input signal.
Platform: |
Size: 2048 |
Author: tdatSlave |
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Description: Won Y. Yang · Tae G. Chang · Ik H. Song · Yong S. Cho · Jun Heo · Won G. Jeon · Jeong W. Lee · Jae K. Kim
Signals and Systems
with MATLAB
Platform: |
Size: 7352320 |
Author: nclk |
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Description: 本代码为牛顿-SOR迭代法求解非线性方程组。其调用格式为[r,m]=mulNewtonSOR(F,x0,w,h,eps) 其中F:方程组，x0：方程组初始解，w：SOR迭代因子，h：数值积分常数，eps：根的精度，m：迭代步数。-The code for the Newton-SOR iteration method for solving nonlinear equations. Its call format [r, m] = mulNewtonSOR (F, x0, w, h, eps) where F: equations, x0: the initial solution of equations, w: SOR iterative factor, h: numerical integration constant, eps: the root of the accuracy, m: the number of iterative steps.
Platform: |
Size: 1024 |
Author: 锦夏 |
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Description: Block Matching Algorithms for Motion Estimation
This project contains the project report and source code by Aroh Barjatya for Digital Image Processing Class at Utah State University.
Following is a short description of the m files in this zip
motionsEstAnalysis.m Script to execute all Algorithms
motionEstES.m Exhaustive Search Algorithm
motionEstTSS.m Three Step Search Algorithm
motionEstNTSS.m New Three Step Search Algorithm
motionEstSESTSS.m Simple And Efficient Search Algorithm
motionEst4SS.m Four Step Search Algorithm
motionEstDS.m Diamond Search Algorithm
motionEstARPSm Adaptive Root Pattern Search Algorithm
costFuncMAD.m Mean Absolute Difference Function
minCost.m minimum cost among macro blocks
motionComp.m motion compensated image creator
imgPSNR.m finds image PSNR w.r.t. reference image
The test images can be found at http://cc.usu.edu/~arohb/caltrain.zip-Block Matching Algorithms for Motion Estimation
This project contains the project report and source code by Aroh Barjatya for Digital Image Processing Class at Utah State University.
Following is a short description of the m files in this zip
motionsEstAnalysis.m Script to execute all Algorithms
motionEstES.m Exhaustive Search Algorithm
motionEstTSS.m Three Step Search Algorithm
motionEstNTSS.m New Three Step Search Algorithm
motionEstSESTSS.m Simple And Efficient Search Algorithm
motionEst4SS.m Four Step Search Algorithm
motionEstDS.m Diamond Search Algorithm
motionEstARPSm Adaptive Root Pattern Search Algorithm
costFuncMAD.m Mean Absolute Difference Function
minCost.m minimum cost among macro blocks
motionComp.m motion compensated image creator
imgPSNR.m finds image PSNR w.r.t. reference image
The test images can be found at http://cc.usu.edu/~arohb/caltrain.zip
Platform: |
Size: 118784 |
Author: Yashil |
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Description: 用fleury算法计算一个欧拉图的穷举的方法 注意：w代表欧拉图矩阵 begin代表起点 只能是欧拉图！-Fleury algorithm using an exhaustive method of Euler diagram Note: w on behalf of the Euler diagram can only be a starting point matrix begin on behalf of the Euler diagram!
Platform: |
Size: 1024 |
Author: 梧桐雨 |
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Description: détection d objet
paramètrage
thresh = 500 Harris corner threshold
nonmaxrad = 3 Non-maximal suppression radius
dmax = 100
w = 11 Window size for correlation matching
Extraire les points de Harris sur chaque image
[cim] = harris( image2,1)
imagesc(cim)
[cim1] = harris(image1,1)
imagesc(cim1)
[r1,c1,v1] = find(image1)
[cim2] = harris(image2, 1)
imagesc(cim2)
[r2,c2,v2] = find(image2)
Apparier les points
[m1,m2] = matchbycorrelation(image1, [r1 c1 ], image2, [r2 c2 ], w, dmax)
Estimer la transformation dominante
-détection d objet
paramètrage
thresh = 500 Harris corner threshold
nonmaxrad = 3 Non-maximal suppression radius
dmax = 100
w = 11 Window size for correlation matching
Extraire les points de Harris sur chaque image
[cim] = harris( image2,1)
imagesc(cim)
[cim1] = harris(image1,1)
imagesc(cim1)
[r1,c1,v1] = find(image1)
[cim2] = harris(image2, 1)
imagesc(cim2)
[r2,c2,v2] = find(image2)
Apparier les points
[m1,m2] = matchbycorrelation(image1, [r1 c1 ], image2, [r2 c2 ], w, dmax)
Estimer la transformation dominante
Platform: |
Size: 1117184 |
Author: seb831 |
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Description: 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)
Platform: |
Size: 8192 |
Author: Mike Gao |
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Description: 用简单迭代法求非线性方程f(x)=0有根区间[a,b]中的一个根 maiter
用途：用定步长高斯就积公式求函数的积分 magsint-w
Platform: |
Size: 1024 |
Author: wangsizhao |
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Description: 使用Fisher 现行判别函数对给定的样本进行训练，对于两类，Fisher线性判别函数很好做，就是 w = Sw (m1 - m2), 其中：Sw为总类内离散度矩阵， m1, m2, 为两个模式类的均值-Fisher discriminant function using the current sample of a given training for two, Fisher linear discriminant function is well done, that is, w = Sw ' (m1- m2), where: Sw of the total within-class scatter matrices, m1, m2, the mean value for the two pattern class
Platform: |
Size: 1024 |
Author: shihao |
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Description: clear
num=[0,0,10]
den=[1,2,10]
p=roots(den)
[u,w]=solve( w^2=10 , 2*w*u=2 , u,w )
[y,x,t]=step(num,den)
plot(t,y)
[yss,n]=max(y)
finalvalue=dcgain(num,den)
percentovershoot=100*(yss-finalvalue)/finalvalue
timetopeak=t(n)
k=length(t)
while (y(k)>0.98*finalvalue)&(y(k)<1.02*finalvalue)
k=k-1
end
settlingtime=t(k)
p
w=vpa(w,4)
u=vpa(u,4)
yss
timetopeak
finalvalue
settlingtime
-clear num = [0,0,10] den = [1,2,10] p = roots (den) [u, w] = solve ( ' w ^ 2 = 10' , ' 2* w* u = 2 ' ,' u, w ' ) [y, x, t] = step (num, den) plot (t, y) [yss, n] = max (y) finalvalue = dcgain (num , den) percentovershoot = 100* (yss-finalvalue)/finalvalue timetopeak = t (n) k = length (t) while (y (k)> 0.98* finalvalue) & (y (k) < 1.02* finalvalue) k = k-1 end settlingtime = t (k) p w = vpa (w, 4) u = vpa (u, 4) yss timetopeak finalvalue settlingtime
Platform: |
Size: 2919424 |
Author: 董森 |
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