tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2014-11-22T16:52:37-05:00224091100tag:www.mathworks.com,2005:FileInfo/485412014-11-22T18:41:33Z2014-11-22T18:41:33Zusmannazir/Gunshot-DetectionGUNSHOT detection using Baye's Rule<p>MATLAB program of Gunshot detection</p>Usmanhttp://www.mathworks.com/matlabcentral/fileexchange/authors/309311MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/485402014-11-22T17:41:56Z2014-11-22T17:41:56ZAdmittance Matrix Formation (Y bus formation)MATLAB Code for Y bus formation of given power system network<p>The code helps to determine bus admittance matrix or in other words Y bus matrix. Usually programmers exclude condition of reactance of generator connected to a bus. If solving by pen and paper, a reactor is connected between bus and ground. This inductive reactor has been included in code.</p>zameer abbashttp://www.mathworks.com/matlabcentral/fileexchange/authors/310286MATLAB 8.1 (R2013a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/470232014-06-21T11:40:25Z2014-11-22T07:01:02ZChebfunChebfun is an open-source package for numerical computation with functions to 15-digit accuracy<p>Chebfun is an open-source software system for numerical computing with functions. The mathematical basis is piecewise polynomial interpolation implemented with what we call “Chebyshev technology”. The foundations are described, with Chebfun examples, in the book Approximation Theory and Approximation Practice (L. N. Trefethen, SIAM 2013). Chebfun has extensive capabilities for dealing with linear and nonlinear differential and integral operators, and also includes continuous analogues of linear algebra notions like QR and singular value decomposition. The Chebfun2 extension works with functions of two variables defined on a rectangle in the x-y plane.</p>
<p>Most Chebfun commands are overloads of familiar MATLAB commands — for example sum(f) computes an integral, roots(f) finds zeros, and u = L\f solves a differential equation.</p>
<p>To get a sense of the breadth and power of Chebfun, a good place to start is by looking at our Examples (<a href="http://www.chebfun.org/examples/">http://www.chebfun.org/examples/</a>) or the introductory Guide (<a href="http://www.chebfun.org/docs/guide/">http://www.chebfun.org/docs/guide/</a>).</p>
<p>Please contact us with any questions/comments at <a href="mailto:help@chebfun.org">help@chebfun.org</a>.</p>Chebfun Teamhttp://www.mathworks.com/matlabcentral/fileexchange/authors/55471MATLAB 8.2 (R2013b)MATLAB23972falsetag:www.mathworks.com,2005:FileInfo/341992011-12-14T07:16:07Z2014-11-22T04:44:56ZPseudo B-Mode Ultrasound Image SimulatorSimulate pseudo B-mode ultrasonic images with customized tissue echogenicity maps<p>fcnPseudoBmodeUltrasoundSimulator generates a simulated Pseudo B-Mode Ultrasound image given the tissue acoustic echogenicity model for the structure to be imaged. The simulated image is of same image matrix size as the input echogenicity map. Linear transducer array architecture is assumed for image formation. Image formed with assumption of wave propagating vertically along the echogenicity map.
<br />This implementation is based on method proposed in </p>
<p>[1] Yongjian Yu, Acton, S.T., "Speckle reducing anisotropic diffusion," IEEE Trans. Image Processing, vol. 11, no. 11, pp. 1260-1270, Nov 2002. [<a href="http://dx.doi.org/10.1109/TIP.2002.804276">http://dx.doi.org/10.1109/TIP.2002.804276</a>]</p>
<p>[2] J. C. Bambre and R. J. Dickinson, "Ultrasonic B-scanning: A computer simulation", Phys. Med. Biol., vol. 25, no. 3, pp. 463–479, 1980. [<a href="http://dx.doi.org/10.1088/0031-9155/25/3/006">http://dx.doi.org/10.1088/0031-9155/25/3/006</a>]</p>Debdoot Sheethttp://www.mathworks.com/matlabcentral/fileexchange/authors/207426MATLAB 7.13 (R2011b)MATLABImage Processing ToolboxSignal Processing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/341722011-12-11T15:56:32Z2014-11-22T04:25:40ZBrightness Preserving Dynamic Fuzzy Histogram EqualizationBPDFHE employs fuzzy statistics of digital image to improve graylevel brightness preserved contrast <p>Brightness Preserving Dynamic Fuzzy Histogram Equalization(BPDFHE) proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. This technique, called uses fuzzy statistics of digital images for their representation and processing. Representation and processing of images in the fuzzy domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. Besides, the imprecision in gray levels is handled well by fuzzy statistics, fuzzy histogram, when computed with appropriate fuzzy membership function, does not have random fluctuations or missing intensity levels and is essentially smooth. This helps in obtaining its meaningful partitioning required for brightness preserving equalization.
<br />Details of the method are available in </p>
<p>D. Sheet, H. Garud, A. Suveer, J. Chatterjee and M. Mahadevappa, "Brightness Preserving Dynamic Fuzzy Histogram Equalization", IEEE Trans., Consumer Electronics, vol. 56, no. 4, pp. 2475 - 2480, Nov. 2010. [<a href="http://dx.doi.org/10.1109/TCE.2010.5681130">http://dx.doi.org/10.1109/TCE.2010.5681130</a>]</p>
<p>Implementation of the technique for grayscale images is outright. The function operates on non-sparce images of type uint8, uint16, int16, double and single.</p>
<p>Using BPDFHE for Color images can be done accordingly</p>
<p>rgbInputImage = imread('peppers.png');
<br />labInputImage = applycform(rgbInputImage,makecform('srgb2lab'));
<br />Lbpdfhe = fcnBPDFHE(labInputImage(:,:,1));
<br />labOutputImage = cat(3,Lbpdfhe,labInputImage(:,:,2),labInputImage(:,:,3));
<br />rgbOutputImage = applycform(labOutputImage,makecform('lab2srgb'));</p>
<p>Details of the technique for implementing BPDFHE on color images is detailed in </p>
<p>Garud, H. Sheet, D. Suveer, A. Karri, P.K. Ray, A.K. Mahadevappa, M. Chatterjee, J., "Brightness preserving contrast enhancement in digital pathology", Proc. ICIIP 2011. [<a href="http://dx.doi.org/10.1109/ICIIP.2011.6108964">http://dx.doi.org/10.1109/ICIIP.2011.6108964</a>]</p>Debdoot Sheethttp://www.mathworks.com/matlabcentral/fileexchange/authors/207426MATLAB 7.6 (R2008a)Image Processing ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/485362014-11-21T19:07:14Z2014-11-21T23:56:53ZBertiniLabToolbox for solving polynomial systems<p>BertiniLab is a MATLAB interface for Bertini, a general-purpose solver for systems of polynomial equations. Bertini can find isolated solutions and positive-dimensional solutions using homotopy continuation. The systems can be underdetermined, exactly determined or overdetermined. Bertini uses adaptive multiprecision and provides endgames to accurately compute singular roots. It also has efficient parameter continuation methods for families of systems that are related by one or more parameters.
<br />BertiniLab is a MATLAB interface that supports all features of Bertini version 1.4. The user can define a system of equations in several ways. They can write the equations as strings. They can create them as a Symbolic Toolbox array; if the user does not have this toolbox, a class POLYSYM is provided that emulates some of its features. The equations can also be generated using a MATLAB function, and matrix algebra is supported. The interface makes it easy to retrieve solutions, and BertiniLab can be tailored to specific applications by subclassing it.</p>Andrew Newellhttp://www.mathworks.com/matlabcentral/fileexchange/authors/73465MATLAB 8.0 (R2012b)MATLABBertini (Daniel J. Bates, Jonathan D. Hauenstein, Andrew J. Sommese, and Charles W. Wampler) can be obtained, free of charge, at https://bertini.nd.edu/.falsetag:www.mathworks.com,2005:FileInfo/485392014-11-21T22:25:30Z2014-11-21T22:25:30ZNewton_Raphson_LoadFlow_PQ2PVNewton Raphson LoadFlow Bus PQ to PV<p>Newton Raphson's method to solve power flow, where PQ bars become PV, and can be re PQ</p>Adilson Batistahttp://www.mathworks.com/matlabcentral/fileexchange/authors/470226MATLAB 8.1 (R2013a)falsetag:www.mathworks.com,2005:FileInfo/485382014-11-21T22:10:59Z2014-11-21T22:10:59ZA Matlab function For Randomly Partitioning Date into Training, Validation and Testing DataA Matlab function For Randomly Partitioning Date into Training, Validation and Testing Data<p>This function randomly partitions data into training, validation and testing data using Cross Validation. Partitioning data in this manner is commonly used for determining the performance of algorithms with free parameters. Training data is commonly used to train the system, the optimum value for the free parameters is determined using validation data. Finally the results of the algorithm are determined using testing data. Matlab has a cross validation but I find it hard to use so I wrote my own. An example determining the optimum kernel with Support vector machines is used. Contact me if you have questions or suggestions</p>Joseph Santarcangelohttp://www.mathworks.com/matlabcentral/fileexchange/authors/477227MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/484662014-11-21T22:02:40Z2014-11-21T22:02:40ZText RecognizerEasily perform Optical Character Recognition (OCR) on your images!<p>Automatically perform OCR on your image. Specify language, ROI(s), character sets, etc... All supported capabilities of OCR are facilitated.
<br />Options include automatic pre-processing to improve results.</p>Brett Shoelsonhttp://www.mathworks.com/matlabcentral/fileexchange/authors/911MATLAB 8.4 (R2014b)Computer Vision System Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484452014-11-21T22:01:36Z2014-11-21T22:01:36Zcolormap_whitejetJet colormap with white zeros<p>colormap_whitejet(), without any arguments, changes the colormap to a jet colormap with 0 as white, and changes the color axes limits from 0 to the current color axes maximum.
<br />
<br />map = colormap_whitejet(...) returns the colormap.
<br />
<br />... = colormap_whitejet(hax,N) uses the axes specified by "hax" instead of the current axes, and creates a colormap with N values. Note that both inputs are optional.
<br />
<br />Inputs (Optional):
<br />- hax - Handle to axes (default: current axes)
<br />- N - # of rows of desired colormap (default: 256)
<br />
<br />Outputs (Optional):
<br />- map - Resultant "white jet" colormap</p>Nade Sritanyaratanahttp://www.mathworks.com/matlabcentral/fileexchange/authors/491655MATLAB 8.3 (R2014a)false