tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2014-12-24T20:12:07-05:00226531100tag:www.mathworks.com,2005:FileInfo/488292014-12-21T07:05:23Z2014-12-25T01:01:42ZHermite Gaussian HologramsProduces computer generated Hermite Gaussian holograms for spatial light modulators<p>This is a GUI so you'll need both the .fig and .m files together in the same folder. Open the .m file and run to access the GUI. Have fun.
<br />This code was created in 2004 by Dr James Strohaber and is based upon his work in holography. The work also appears in his dissertation If you use this code, cite the author! This code produces computer generated holograms of the Hermite Gaussian modes of the paraxial wave equation. The holograms can be used on spatial light modulators (SLMs). The equation for the scalar amplitude of a Hermite Gaussian beam at z=0 with mode number n and m and beam waist w0 is HG(n,m)=(N_nm)*H_n(sqrt(2)x/w0)H_m(sqrt(2)y/w0)exp(-(r/w0)^2), where N_nm is a normalization constant Instructions: (1) goto "LCD Specifications" and enter the number of pixels and LCDsize of your SLM. This code assumes the pixel density in both the x and y direction are the same. (2) goto "Input Parameters" and enter the location you wish to same the holograms. (3) Enter the mode numbers, grating constant, modulation depth and waist of the HG beam you wish to create. To use this code as a function, comment all of the Input parameters and uncomment the function command.</p>James Strohaberhttp://www.mathworks.com/matlabcentral/fileexchange/authors/537472MATLAB 8.4 (R2014b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/488652014-12-24T02:58:21Z2014-12-24T20:17:31ZReversible Watermarking Algorithm Using Sorting and PredictionReversible Watermarking Algorithm Using Sorting and Prediction<p>This paper presents a reversible or lossless watermarking
<br />algorithm for images without using a location map in
<br />most cases. This algorithm employs prediction errors to embed
<br />data into an image. A sorting technique is used to record the
<br />prediction errors based on magnitude of its local variance. Using
<br />sorted prediction errors and, if needed, though rarely, a reduced
<br />size location map allows us to embed more data into the image
<br />with less distortion. The performance of the proposed reversible
<br />watermarking scheme is evaluated using different images and
<br />compared with four methods: those of Kamstra and Heijmans,
<br />Thodi and Rodriguez, and Lee et al. The results clearly indicate
<br />that the proposed scheme can embed more data with less
<br />distortion.</p>mongkolhttp://www.mathworks.com/matlabcentral/fileexchange/authors/461293MATLAB 7.9 (R2009b)MATLABCommunications System ToolboxMATLABCommunications System Toolboxfalsetag:www.mathworks.com,2005:FileInfo/480622014-10-09T08:46:09Z2014-12-24T19:25:34ZBuck ConverterIt is a step-down dc to dc converter.<p>It is a basic dc to dc converter or chopper known as Buck Converter. It used to step-down dc voltage.</p>Sanjay Lodwalhttp://www.mathworks.com/matlabcentral/fileexchange/authors/511013MATLAB 8.1 (R2013a)SimPowerSystemsSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/488712014-12-24T17:49:40Z2014-12-24T17:49:40ZFeedback Control of Dynamic Systems, 7th Edition, 2015Matlab files for Feedback Control of Dynamic Systems, 7th Edition, Pearson, 2015<p>Matlab files to create the figures in Feedback Control of Dynamic Systems, 7th Edition, Pearson, 2015, by G. F. Franklin, J. D. Powell, A. Emami-Naeini</p>Abbas Emami-Naeinihttp://www.mathworks.com/matlabcentral/fileexchange/authors/8710MATLAB 8.4 (R2014b)Control System ToolboxSimulinkMATLABSymbolic Math ToolboxSystem Identification ToolboxSymbolic Math ToolboxSystem Identification Toolboxfalsetag:www.mathworks.com,2005:FileInfo/487352014-12-13T18:29:16Z2014-12-24T17:20:59ZPolar decompositionPerform the polar decomposition of a regular square matrix<p>[R U V] = POLDECOMP(F) factorizes a non-singular square matrix F such that F=R*U and F=V*R, where U and V are symmetric positive definite matrices and R is a rotational matrix.</p>Zoltán Csátihttp://www.mathworks.com/matlabcentral/fileexchange/authors/225851MATLAB 7.12 (R2011a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/488702014-12-24T17:14:40Z2014-12-24T17:14:40ZFrobenius productCalculates the Frobenius inner product of two matrices<p>Calculates the Frobenius inner product of two matrices A and B. In mathematics, it is indicated as A:B. So I made a class that overloads the colon operator to be able to make this.</p>Zoltán Csátihttp://www.mathworks.com/matlabcentral/fileexchange/authors/225851MATLAB 7.12 (R2011a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/466372014-05-16T11:40:09Z2014-12-24T15:06:24ZA Simple Finite Volume Solver for MatlabA simple yet general purpose FVM solver for transient convection diffusion PDE<p>A simple Finite volume tool
<br />This code is the result of the efforts of a chemical/petroleum engineer to develop a simple tool to solve the general form of convection-diffusion equation:
<br />α∂ϕ/∂t+∇.(uϕ)+∇.(−D∇ϕ)+βϕ=γ
<br />on simple uniform mesh over 1D, 1D axisymmetric (radial), 2D, 2D axisymmetric (cylindrical), and 3D domains.
<br />The code accepts Dirichlet, Neumann, and Robin boundary conditions (which can be achieved by changing a, b, and c in the following equation) on a whole or part of a boundary:
<br />a∇ϕ.n+bϕ=c.
<br />It also accepts periodic boundary conditions.
<br />The main purpose of this code is to serve as a handy tool for those who try to play with mathematical models, solve the model numerically in 1D, compare it to analytical solutions, and extend their numerical code to 2D and 3D with the minimum number of modifications in the 1D code.
<br />The discretizaion schemes include
<br /> * central difference
<br /> * upwind scheme for convective terms
<br /> * TVD schemes for convective terms</p>
<p>A short html document is available to get you started. Simply type
<br />showdemo FVTdemo
<br />after downloading and extracting the code.</p>
<p>A few calculus functions (divergence, gradient, etc) and averaging techniques (arithmetic average, harmonic average, etc) are available, which can be helpful specially for solving nonlinear or coupled equations or implementing explicit schemes.</p>
<p>I have used the code to solve coupled nonlinear systems of PDE. You can find some of them in the Examples/advanced folder.</p>
<p>There are a few functions in the 'PhysicalProperties' folder for the calculation of the physical properties of fluids. Some of them are not mine, which is specified inside the file.</p>
<p>I'll try to update the documents regularly, in the github repository. Please give me your feedback/questions by writing a comment in my weblog: <<a href="http://fvt.simulkade.com/">http://fvt.simulkade.com/</a>>
<br />Special thanks: I vastly benefited from the ideas behind Fipy <<a href="http://www.ctcms.nist.gov/fipy/">http://www.ctcms.nist.gov/fipy/</a>>, a python-based finite volume solver.</p>
<p>To start the solver, download and extract the zip archive, open and run 'FVToolStartUp' function.
<br />To see the code in action, copy and paste the following in your Matlab command window:</p>
<p>clc; clear;
<br />L = 50; % domain length
<br />Nx = 20; % number of cells
<br />m = createMesh3D(Nx,Nx,Nx, L,L,L);
<br />BC = createBC(m); % all Neumann boundary condition structure
<br />BC.left.a(:) = 0; BC.left.b(:)=1; BC.left.c(:)=1; % Dirichlet for the left boundary
<br />BC.right.a(:) = 0; BC.right.b(:)=1; BC.right.c(:)=0; % Dirichlet for the right boundary
<br />D_val = 1; % value of the diffusion coefficient
<br />D = createCellVariable(m, D_val); % assign the diffusion coefficient to the cells
<br />D_face = harmonicMean(m, D); % calculate harmonic average of the diffusion coef on the cell faces
<br />Mdiff = diffusionTerm(m, D_face); % matrix of coefficients for the diffusion term
<br />[Mbc, RHSbc] = boundaryCondition(m, BC); % matix of coefficients and RHS vector for the BC
<br />M = Mdiff + Mbc; % matrix of cefficients for the PDE
<br />c = solvePDE(m,M, RHSbc); % send M and RHS to the solver
<br />visualizeCells(m, c); % visualize the results</p>
<p>You can find some animated results of this code in my youtube channel:
<br /><a href="https://www.youtube.com/user/processsimulation/videos">https://www.youtube.com/user/processsimulation/videos</a></p>Ehsanhttp://www.mathworks.com/matlabcentral/fileexchange/authors/383541MATLAB 8.3 (R2014a)MATLAB35710falsetag:www.mathworks.com,2005:FileInfo/488572014-12-23T11:04:38Z2014-12-24T15:06:12Zffts(X,V,Xq,method,window)Fast Fourier Transform ( FFT ) of scattered data<p>ffts, Performs the fast Fourier transform (FFT) on scatter data.
<br />
<br /> Yq = ffts(X,V,Xq)
<br />
<br /> or
<br />
<br /> Yq = ffts(X,V,Xq, method, window)
<br />
<br /> inputs,
<br /> X : Array with positions [m x 1]
<br /> V : Array with values [m x 1]
<br /> Xq : Node locations [ n x 1], with equally spaced points (see linspace)
<br /> (optional)
<br /> method : 1. 'grid', gridding (Default)
<br /> 2. 'fit' , b-spline fit
<br /> window : 1. 'bspline', bspline (default)
<br /> 2. 'kaiser', kaiser Bessel function
<br />
<br /> outputs,
<br /> Fq : The Fourier spectrum of the scatter data [ n x 1].
<br />
<br />
<br /> Gridding:
<br /> 1. The scattered values (V) at positions (X) are smoothed ( convolution )
<br /> by a kernel to a regular grid. With the grid a 2 times oversampled
<br /> version of Xq
<br /> 2. The data is multiplied with a set of density compensation weights.
<br /> Calculate as in step 1, but instead all values are set to 1. The
<br /> density compensation is 1 divided by this result.
<br /> 3. The values on the regular grid are converted to the fourier
<br /> domain using a FFT.
<br /> 4. Trim field of view, to size of Xq. This compensates the oversampling
<br /> of step 2. The sidelobes due to finite window size are now
<br /> clipped off.
<br /> 5. The fourier domain is multipled by an apodization correction
<br /> function. Which is the 1/Fourier Transform of the kernel of step 1
<br /> to remove the effect of the kernel.
<br />
<br /> B-spline fit:
<br /> 1. B-splines sampled on a regular grid are fitted to the values (V) at
<br /> positions (X), so they least squares approximate the data.
<br /> 2. At the regular grid (Xq), values are interpolated using the fitted
<br /> B-splines
<br /> 3. The FFT is done on the b-spline interpolated points</p>Dirk-Jan Kroonhttp://www.mathworks.com/matlabcentral/fileexchange/authors/29180MATLAB 8.0 (R2012b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/488692014-12-24T14:54:53Z2014-12-24T14:54:53ZHistogram EquilizationThis code is used for demonstration of Histogram Equilization<p>In this code an image is read and the histogram of this image is plotted. Then the process of histogram equalization is applied on original image and then transformed image and its histogram are plotted.</p>Akshay Bhosalehttp://www.mathworks.com/matlabcentral/fileexchange/authors/222499MATLAB 7.12 (R2011a)falsetag:www.mathworks.com,2005:FileInfo/488682014-12-24T14:52:59Z2014-12-24T14:52:59ZApply_hough(Img)hough transform function<p>Applying the hough transform function</p>Ahmedhttp://www.mathworks.com/matlabcentral/fileexchange/authors/423626MATLAB 8.2 (R2013b)false