tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2014-12-18T17:35:44-05:00226041100tag:www.mathworks.com,2005:FileInfo/487682014-12-16T23:17:16Z2014-12-18T22:24:35ZSimple example and generic function for kmeans clusteringSimple generic function and example to perform kmeans clustering<p>Simple generic function that takes two vectors and performs kmeans clustering. Has very basic example code to call kmeans clustering algorithm and display plots. Based on code from the mathworks website and matlab documentation.</p>Soumya Banerjeehttp://www.mathworks.com/matlabcentral/fileexchange/authors/192000MATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/487942014-12-18T22:06:10Z2014-12-18T22:06:10Zflujo de carga con gauss seidel sistemas electricos de potenciasumario<p>analisis de un sistema electrico de potencia fijo</p>saulhttp://www.mathworks.com/matlabcentral/fileexchange/authors/536982MATLAB 7.8 (R2009a)falsetag:www.mathworks.com,2005:FileInfo/487932014-12-18T21:31:41Z2014-12-18T21:31:41ZPicket-Fence Effect of the DFTPicket-fence effect of the DFT.<p>Interactive app illustrating the picket-fence effect of the DFT through the use of a length-10 discrete-time pulse, its discrete-time Fourier transform (DTFT) and its discrete Fourier transform (DFT) computed after optional zero-padding. The DFT of the (possibly zero-padded) discrete-time pulse signal is superimposed with its DTFT, showing the relationship between the two. Requires R2014b or newer.
<br /><a href="http://www.signalsandsystems.org">http://www.signalsandsystems.org</a></p>Oktay Alkinhttp://www.mathworks.com/matlabcentral/fileexchange/authors/138222MATLAB 8.4 (R2014b)MATLAB47982falsetag:www.mathworks.com,2005:FileInfo/487922014-12-18T21:08:09Z2014-12-18T21:16:40ZgiqrInterquartile range of a grouped sample.<p>In some scientific works, once the data have been gathered from a population of interest, it is often difficult to get a sense of what the data indicate when they are presented in an unorganized fashion.
<br />Assembling the raw data into a meaningful form, such as a frequency distribution, makes the data easier to understand and interpret. It is in the context of frequency distributions that the importance of conveying in a succinct way numerical information contained in the data is encountered.
<br />So, grouped data is data that has been organized into groups known as classes. The raw dataset can be organized by constructing a table showing the frequency distribution of the variable (whose values are given in the raw dataset). Such a frequency table is often referred to as grouped data.</p>
<p>Here, we developed a m-code to calculate the interquartile range of a grouped data.</p>
<p>One can input the returns or modified vectors n and xout containing the frequency counts and the bin locations of the hist m-function, in a column form matrix.</p>
<p>Interquartile range calculation uses the formula,</p>
<p> GIQR = GP75 - GP25 (= GQ3 - GQ1)</p>
<p>where:
<br />GP75 = grouped percentile 75
<br />GP25 = grouped percentile 25 </p>
<p>--In orden to run it you must first download the m-file gprctile at:
<br /><a href="http://www.mathworks.com/matlabcentral/fileexchange/38228-gprctile">http://www.mathworks.com/matlabcentral/fileexchange/38228-gprctile</a></p>
<p>Syntax: function y = giqr(x)
<br />
<br />Input:
<br />x - data matrix (Size of matrix must be n-by-2; absolut frequency=
<br /> column 1, class mark=column 2) </p>
<p>Output:
<br />y - interquartile range of the values in x</p>Antonio Trujillo-Ortizhttp://www.mathworks.com/matlabcentral/fileexchange/authors/7089MATLAB 7.10 (R2010a)falsetag:www.mathworks.com,2005:FileInfo/477502014-09-04T21:47:47Z2014-12-18T20:46:50ZSpace-Time Adaptive Processing for Airborne Radar by J.Ward (Tech. Report 1015)Reproduction of J.Ward's Technical Report 1015 figures.<p>This submission contains a set of scripts that reproduce the figures included in the seminal J.Ward's technical report on airborne STAP. This may be valuable to any STAP newcomer because it demonstrates the fundamental STAP concepts. It provides the path from mathematical formulas to crisp performance results. Specifically, all figures from the following chapters have been reproduced:
<br />Chapter 1: Airborne Array Radar Signal Environment.
<br />Chapter 2: Space-Time Processing Fundamentals.
<br />Chapter 5: Element-Space STAP.
<br />Chapter 6: Beamspace STAP. </p>
<p>Also, the performance of the basic Sample Matrix Inversion (SMI) algorithm is demonstrated by use of relative metrics.</p>Ilias Konsoulashttp://www.mathworks.com/matlabcentral/fileexchange/authors/128396MATLAB 7.13 (R2011b)falsetag:www.mathworks.com,2005:FileInfo/487912014-12-18T19:36:47Z2014-12-18T19:36:47ZIterative learning motion controlThe very basic repetitive compensator with forgetting is implemented in a position control system.<p>This model illustrates the repetitive control concept. A servo drive system is selected as the case study. It should be noted that the very basic repetitive compensator introduces integration in the pass to pass direction. Such an approach is not robust. You just cannot do that in a physical control system. To observe possible consequences of doing that please set the forgetting factor gamma=0. The most simple, yet not the most effective, way to robustify the scheme is to use 0<gamma<1. This obviously replaces the pure integrator in the k-direction with a first-order lag element. The system is now more robust, however, its tracking performance has been compromised. More elaborate solutions involve frequency dependent forgetting, i.e. filtering of control signal. More on this will be available soon from Michal Malkowski -- check him out at Matlab Central in late December 2014. This submission does not contain any groundbreaking findings but we hope that some models co-authored by Michal and me will, so stay tuned :)</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)SimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/487902014-12-18T19:16:14Z2014-12-18T19:32:50ZdicomHounsfiledHounsfiled Units of a dicom image<p>This function calculate the Hounsfiled Units of a dicom medical image.</p>Abdelmoumen Bacettihttp://www.mathworks.com/matlabcentral/fileexchange/authors/74696MATLAB 7.14 (R2012a)falsetag:www.mathworks.com,2005:FileInfo/401342013-02-01T19:58:50Z2014-12-18T16:59:26ZPicoScope® 2000 Series - MATLAB® Generic Instrument DriverMATLAB Instrument Driver for use with PicoScope 2000 Series oscilloscopes (Beta Release)<p>The MATLAB Generic Instrument Driver allows a user to acquire block and streaming data from the PicoScope 2000 Series Oscilloscopes and control in-built signal generator functionality. The data could be processed in MATLAB using functions from Toolboxes such as the Signal Processing Toolbox.
<br />The driver has been created using Instrument Control Toolbox v3.2 and updated using Instrument Control Toolbox v3.6.</p>
<p>The PS2000_Generic_IC_Driver_Beta_Release zip file includes the following:</p>
<p>- The MATLAB Generic Instrument Driver
<br />- scripts that demonstrate how to call various functions in order to capture data in block and streaming modes, as well as using the signal generator.</p>
<p>Please note that the streaming mode capture example will return the data on completion of data acquisition.</p>
<p>The driver can be used with the Test and Measurement Tool.</p>
<p>The driver will work with the following PicoScope models:</p>
<p>PicoScope 2104 & 2105
<br />PicoScope 2204A & 2205A</p>
<p>The driver should also support the following products which are now obsolete:</p>
<p>PicoScope 2202
<br />PicoScope 2203, 2204 & 2205</p>
<p>Please note that the driver will not work with the PicoScope 2205 MSO, 2206, 2206A, 2207, 2207A, 2208 & 2208 devices.</p>
<p>For MATLAB on Microsoft Windows operating systems, the Instrument driver requires the dynamic link library (DLL) files provided as part of the PicoScope 2000 Software Development Kit (SDK) in order to connect to and operate the oscilloscope. This can be downloaded for free from <a href="http://www.picotech.com/software.html">http://www.picotech.com/software.html</a></p>
<p>The mex -setup command may need to be run on your PC in order to select a C compiler. For MATLAB 64-bit on Microsoft Windows you will also need to install the Windows 7.1 SDK:</p>
<p><a href="http://uk.mathworks.com/matlabcentral/answers/101105-how-do-i-install-microsoft-windows-sdk-7-1">http://uk.mathworks.com/matlabcentral/answers/101105-how-do-i-install-microsoft-windows-sdk-7-1</a></p>
<p>If you have previously installed the PicoScope 6 software, available free from our website, the USB driver for the oscilloscopes will already be installed on your machine. If not, you can use the USB driver installer included in the SDK.</p>
<p>For 64-bit versions of MATLAB on Linux operating systems, please install the libps2000 and libpswrappers packages following the instructions available from:</p>
<p><a href="http://www.picotech.com/linux.html">http://www.picotech.com/linux.html</a></p>
<p>The main PicoScope 2000 Series Programmer’s Guide in the SDK should also be referred to.</p>
<p>For further information on MATLAB and the Instrument Control Toolbox, please visit:</p>
<p><a href="http://www.mathworks.co.uk/products/instrument/">http://www.mathworks.co.uk/products/instrument/</a></p>
<p>To view Pico Technology's Hardware Support page, please visit:</p>
<p><a href="http://www.mathworks.co.uk/hardware-support/picoscope.html">http://www.mathworks.co.uk/hardware-support/picoscope.html</a></p>
<p>Please rate this Instrument Driver package and send any feedback or report bugs to <a href="mailto:support@picotech.com">support@picotech.com</a></p>Pico Technologyhttp://www.mathworks.com/matlabcentral/fileexchange/authors/292982MATLAB 8.4 (R2014b)Instrument Control ToolboxMATLABMATLAB (32-bit/64-bit version for Microsoft Windows, 64-bit for Linux OS). Windows XP, Vista, 7 or 8* Operating System. * Not Windows RT Windows 7.1 SDK for 64-bit version of MATLAB for Microsoft Windows.26143falsetag:www.mathworks.com,2005:FileInfo/296382010-12-06T15:49:12Z2014-12-18T16:39:31Zcontourfcmap: filled contour plot with precise colormapCreates a filled contour plot, with more precise control over colors than contourf.<p>Create a filled contour plot in Matlab, with better color-to-value clarity. See full description, along with examples, at <a href="https://github.com/kakearney/contourfcmap-pkg">https://github.com/kakearney/contourfcmap-pkg</a></p>Kelly Kearneyhttp://www.mathworks.com/matlabcentral/fileexchange/authors/287218MATLAB 7.10 (R2010a)falsetag:www.mathworks.com,2005:FileInfo/487702014-12-17T05:46:58Z2014-12-18T15:24:59ZMarginal distributions of a bivariate functionThis function computes the marginal distributions of each variable in a bivariate function.<p>function [fx, fy, MeanVar] = marginaldist(f,x,y,distributionType)</p>
<p>f is a bivariate function, which can be a normalized or unnormalized distribution function. x and y are the two independent variables of f. The variable values can be taken as either row or column vectors. fx and fy are the marginal distributions of x and y, respectively.
<br />distributionType defines whether the marginal distributions have to be computed on continuous or discrete domain. Default is continuous. The strings that can be assigned to distributionType as an input may include:
<br />(for continuous) 'Continuous','continuous','Con', or 'con'
<br />and (for discrete) 'Discrete','discrete','Dis', or 'dis'</p>
<p>MeanVar is optional. This is a vector output, whose elements are mean of fx, variance of fx, mean of fy, and variance of fy, respectively.</p>
<p>Define function f in a separate m-file. In the example below, we take a two-dimensional Gaussian function as our test function, whose m-file is saved as testFunction.m</p>
<p>function f = testFunction(x,y)
<br />sx = 2; sy = 0.5; mx = 2; my = -1; % s stands for variance, and m for mean
<br />f = 1/(2*pi*sx*sy)*exp(-(x-mx).^2/(2*sx^2)-(y-my).^2/(2*sy^2));</p>
<p>Example 1
<br />x = -10:0.1:10; y = -10:0.1:10;
<br />[fx, fy, MV] = marginaldist(@testFunction,x,y,'continuous');
<br />subplot(211), plot(x,fx), xlabel('x'), ylabel('f_x(x)')
<br />title('Marginal distribution of x','FontSize',12,'Color','r')
<br />subplot(212), plot(y,fy), xlabel('y'), ylabel('f_y(y)')
<br />title('Marginal distribution of y','FontSize',12,'Color','r')</p>
<p>Example 2
<br />x = -10:10; y = -10:10;
<br />[fx, fy] = marginaldist(@testFunction,x,y,'discrete');
<br />subplot(211), plot(x,fx), xlabel('x'), ylabel('f_x(x)')
<br />title('Marginal distribution of x','FontSize',12,'Color','r')
<br />subplot(212), plot(y,fy), xlabel('y'), ylabel('f_y(y)')
<br />title('Marginal distribution of y','FontSize',12,'Color','r')</p>Shoaibur Rahmanhttp://www.mathworks.com/matlabcentral/fileexchange/authors/533225MATLAB 8.4 (R2014b)MATLABfalse