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Description: EM8511s中使用的mp4播放器，优秀的PMP方案。-EM8511s used in the then no time player, the PMP outstanding program.
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Size: 47104 |
Author: 浪心剑客 |
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Description: Oversampling Delta-Sigma Data Converters
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Size: 545792 |
Author: 张海 |
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Description: [自動控制] Routh-Hankel matrix數值計算工具，輸入System Function後，程式會計算Hankel singular values and vectors、Schmidt pairs等值。詳情請見英文描述。-<<singular values and vectors>>
[R_G,Sigma,U,V,Us,Vs]=HANKEL3(G) computes the Routh-Hankel matrix, the Hankel singular values and vectors, the Schmidt pairs of a given proper stable system G (created with either TF, ZPK, SS, or FRD).||
Output "R_G" is the Routh-Hankel matrix. ||
Output "Sigma" is a matrix whose elements on the diagonal are the Hankel singular values of the given system. ||
Output "U" is a matrix, the ith column of which is the left singular vector corresponding to the ith singular value. ||
Output "V" is a matrix, the ith column of which is the right singular vector corresponding to the ith singular value. ||
Output "Us" and "Vs" provides the Schmidt pairs. The ith elements of Us and Vs form the Schmidt pair corresponding the ith Hankel singular value.
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Author: Sam Chan |
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Description: 独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S + N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at a- Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S+ N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at all...
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Author: 王庆香 |
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Description: 小波图像去噪程序。th=sigma*sqrt(2*log10(L*T)) 计算阈值
ch=c(1,num(4-i,1):num(4-i,3)+step(4-i)-1) 对各高频系数进行阈值处理
chl=length(ch)
for j=1:chl
if abs(ch(j))>=th
ch(j)=sign(ch(j))*(abs(ch(j))-2*th/(1+exp(m*(ch(j)^2)))) 阈值处理函数
else
-Wavelet image denoising procedures. th = sigma* sqrt (2* log10 (L* T)) calculated threshold ch = c (1, num (4-i, 1): num (4-i, 3)+ step (4-i)-1) of the high-frequency coefficients thresholding chl = length (ch) for j = 1: chl if abs (ch (j))> = th ch (j) = sign (ch (j))* (abs (ch (j))-2* th/(1+ exp (m* (ch (j) ^ 2 )))) else threshold processing function
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Size: 3072 |
Author: 朱兰花 |
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Description: Function to plot uncertainty 3-sigma ellipse in matlab
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Size: 2048 |
Author: mbernardes |
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Description: constructive role of noise in signal detection from parallel arrays of quantizers.-A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers,in which all the quantizer outputs are added to produce the array output.s(t)=s1(t)with prior probability P0,or s(t)=s0(t)with prior probability P1=1-P0 input signal-noise mixture x(t)=s(t)+n(t) x(t) is observed by means of a parallel array of M threshold comparators. yi(t)=U[x(t)+wi(t)-theta],i=1,2...M.wi(t) is threshold noise.Y(t)=y1(t)+y2(t)+...yM(t), the array output Y(t) is meansured at T distinct times.the plot is fthe theoretical value of minimal detection error probability Per varied with sigma.
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Author: shasha |
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Description: 二叉树方法计算信用风险溢价
参数说明：
V：公司资产市场价值初值
r:市场无风险利率
sigma：企业资产市场价值波动率
T:期限
num：二叉树层数
dp：违约点，即债权价值
输出结果：
M:公司的风险溢价
-The binomial tree method to calculate the credit risk premium
Parameters:
V: the market value of assets initial value
r: market risk-free rate
sigma: Enterprise market value of assets volatility
T: Term
the num: binary tree layers
dp: default point, ie, the value of claims
Output:
M: The risk premium
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Author: shaopeng |
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Description: PCA人脸识别 的源代码 在matlaB 里面实现的-PCA MatlAB FaceRec.m eigface Reconstruct.m calc xmean,sigma and its eigen decomposition
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Size: 7168 |
Author: dm |
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Description: This the demo file for L1 adaptive controller with unknown parameter, unknown disturbance, unknown control effectiveness:
\dot x(t) = Ax(t) + b(\omega u(t) + \theta(t)^\top x(t) + \sigma(t))
DoSims.m: This script initializes the model parameters, run the simulation. You can split the parts you need and do the simulations according to your interest.
L1Model_sec2.mdl:the implementation of the system.
Refer to the following paper for background:
C. Cao, and N. Hovakimyan, “L1 adaptive controller for systems with unknown time-varying parameters and disturbances in the presence of non-zero trajectory initialization error”, International Journal of Control, Vol. 81, No. 7, 1147–1161, July 2008-This is the demo file for L1 adaptive controller with unknown parameter, unknown disturbance, unknown control effectiveness:
\dot x(t) = Ax(t) + b(\omega u(t) + \theta(t)^\top x(t) + \sigma(t))
DoSims.m: This script initializes the model parameters, run the simulation. You can split the parts you need and do the simulations according to your interest.
L1Model_sec2.mdl:the implementation of the system.
Refer to the following paper for background:
C. Cao, and N. Hovakimyan, “L1 adaptive controller for systems with unknown time-varying parameters and disturbances in the presence of non-zero trajectory initialization error”, International Journal of Control, Vol. 81, No. 7, 1147–1161, July 2008
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Author: mo |
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Description: 基于Matlab利用m文件编写并实现了3阶Mash结构的Sigma-Delta调制器-Sigma-Delta modulator based on Matlab m file written order Mash structure
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Author: Jeff |
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Description: 利用svd分解彩色图像后再重组，分解后可设定要重组的sigma值-SVD decomposition
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Size: 1024 |
Author: Ian |
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Description: 利用有穷确定自动机M=(K,Σ,f, S,Z）行为模拟程序算法，来对于任意给定的串，若属于该语言时，该过程经有限次计算后就会停止并回答“是”，若不属于，要么能停止并回答“不是”-Use poor to determine the automaton M = (K, sigma, F, S, Z) behavior simulation program algorithm, for any given string belongs to the language, the process is calculated by the finite number of times will stop and answer " Yes" , if it does not belong to either stop and answer is " No"
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Author: 英雄 |
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Description: 基于ukf目标追踪，包含sigma采样、ut变换、ukf的m文件和一个简单的追踪实例-Ukf-based target tracking, including sigma sampling, ut transform, ukf m-files and a simple trace instances
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Size: 2048 |
Author: liuxueou |
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Description: Matlab下的实时跟踪程序，内附demo程序，也可以选择手动选择跟踪区域，这种方式需要将demo中的一段代码前的注释去掉-Main file: Tracking_PLS.m
You can set the initial position of the target object with know parameters or select the target region manually in the first frame.
You can tune the \Sigma parameter for particle filtering to obtain better results.
You can also test the PLST1 method mentioned in our paper which is named "Tracking_PLST1.m".
Reference
---------
Qing Wang, Feng Chen, Wenli Xu, Ming-Hsuan Yang. Object Tracking via Partial Least Squares Analysis. IEEE Transactions on Image Processing. In press.
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Author: 唐璜 |
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Description: 根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及
C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是
Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。
基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。-According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.
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Author: 松 |
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Description: This MATLAB M-file implements a finite-difference time-domain solution of Maxwell s curl equations over a one-dimensional space lattice comprised of uniform grid cells.
To illustrate the algorithm, a 1-GHz sinusoidal wave propagating in a nonpermeable lossy medium (epsr=1.0, sigma=5.0e-3 S/m) is modeled. The simplified finite difference system for nonpermeable.
- This MATLAB M-file implements a finite-difference time-domain solution of Maxwell s curl equations over a one-dimensional space lattice comprised of uniform grid cells.
To illustrate the algorithm, a 1-GHz sinusoidal wave propagating in a nonpermeable lossy medium (epsr=1.0, sigma=5.0e-3 S/m) is modeled. The simplified finite difference system for nonpermeable.
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Size: 2048 |
Author: morteza |
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Description: 拉丁超立方抽样，调用方式如下：S=lhs(m,dist,mu,sigma,lowb,upb)
m: a scalar,the number of sample points
dist: A row with distribution type flags of basic random variables the
value of the flag can be 1 (for uniform distribution, 2(for normal distribution), 3(for lognormal)
and 4(for extreme type 1).
mu: A row vector comprising the mean value of basic random variables.
sigma: A row vector with its length equaligng to mu,including the standard
deviation of basic random variables.
lowb: a row vector with its elements are the lower bound of the sampling
interval
upb:a row vector with its elements are the upper bounds of the sampling
interval
dist,mu,sigma,lowb,upb must have the same length.
Output argument
S: sampling point matrix, of which each row is a sampling point.-code of Latin Hypercube Sampling
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Author: 邓志平 |
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Description: 这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。-This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.
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Author: 林真天 |
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Description: Sigma point Kalman fi lter for bearing only tracking
S. Sadhua,, S. Mondal
b
, M. Srinivasana
, T.K. Ghoshal
a
a
Department of Electrical Engineering, Jadavpur University, Kolkata-700032, India
b
Department of Mechanical Engineering, IIT Kharagpur 721302, India
Received 10 August 2005 received in revised form 14 December 2005 accepted 12 March 2006
Available online 6 April 2006-Sigma point Kalman fi lter for bearing only tracking
S. Sadhua,, S. Mondal
b
, M. Srinivasana
, T.K. Ghoshal
a
a
Department of Electrical Engineering, Jadavpur University, Kolkata-700032, India
b
Department of Mechanical Engineering, IIT Kharagpur 721302, India
Received 10 August 2005 received in revised form 14 December 2005 accepted 12 March 2006
Available online 6 April 2006
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Author: Gomaa Haroun |
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