tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2015-01-26T13:51:54-05:00228851100tag:www.mathworks.com,2005:FileInfo/491702015-01-26T15:06:35Z2015-01-26T15:06:35ZMultiple plot line colorsExtend default "recycle every 6 colors" plot behavior to N different colors<p>By default MATLAB "recycles" every 6 plot line colors. Replace default "recycle every 6 lines" behavior of MATLAB graphics functions (e.g. plot) by N different colors usin this simple script.</p>Igalhttp://www.mathworks.com/matlabcentral/profile/authors/5926764-igalMATLAB 8.3 (R2014a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/413962013-04-19T18:56:47Z2015-01-26T14:49:00ZnonrigidICPNon rigid registration of surfaces<p>The function aligns, and non-rigidly deforms a source/template mesh to a second target mesh.
<br />nonrigidICP is the principal file to be used and requires both vertices and faces of the meshes as input</p>Manuhttp://www.mathworks.com/matlabcentral/profile/authors/4165925-manuMATLAB 7.10 (R2010a)falsetag:www.mathworks.com,2005:FileInfo/491282015-01-23T14:35:06Z2015-01-26T14:44:35Z2 Dimensional Optimization using a Continuous Genetic AlgorithmFunction Optimization using a Continuous Genetic Algorithm<p>This submission includes a GUI for the optimization of any 2 dimensional function using a Continuous Genetic Algorithm. Read the included README file and Example Functions file for further details and instructions.</p>Hashem Rizkhttp://www.mathworks.com/matlabcentral/profile/authors/3223721-hashem-rizkMATLAB 8.1 (R2013a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/491692015-01-26T13:45:21Z2015-01-26T13:45:21ZTriangular Taylor Hood finite elementsSolve unsteady incompressible flows and related problems.<p>This toolbox solves PDE problems with mixed P2/P1 (Taylor Hood) finite elements. The capabilities of the toolbox are demonstrated with an unsteady thermally driven flow in a tall cavity, as described in <<a href="http://dx.doi.org/10.1002/fld.395">http://dx.doi.org/10.1002/fld.395</a> Christon et al. (2002)>. An introductory example of a simple Poisson problem is also available.</p>Sebastian Ullmannhttp://www.mathworks.com/matlabcentral/profile/authors/2589438-sebastian-ullmannMATLAB 8.1 (R2013a)Symbolic Math ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/491682015-01-26T13:39:15Z2015-01-26T13:39:15ZMIMO OFDM LSE CHAN ESTIMATIONIn this code we consider the least square error channel estimation for a MIMO OFDM system.<p>%% Help
<br />% In this code we consider the least square error channel estimation for a
<br />% MIMO OFDM system. The user have access to the design parameters of
<br />% the MIMO OFDM system and the channel state information. The L-tap
<br />% Rayleigh fading channel is considered between any pair of the transmit and
<br /> % receive antenna. The mean squared error of the LSE channel obtained
<br /> % by the simulation result is compared with theory.
<br /> %
<br /> % source paper : "Optimal Training Design for MIMO OFDM Systems in Mobile Wireless Channels"
<br /> %
<br /> % Author : Hamid Ramezani
<br /> % Author's contact : <a href="http://ens.ewi.tudelft.nl/~ramezani/">http://ens.ewi.tudelft.nl/~ramezani/</a>
<br /> % Matlab Vession : 7.13.0.564 (R2011b)
<br /> %
<br /> %======================================================================
<br /> % Inputs
<br /> %======================================================================
<br /> % Input parameters are (if not set the defalt value will be set)
<br /> % ofdm.Nb = 1e2; % number of blocks
<br /> % ofdm.Nt = 2; % number of transmit antennas
<br /> % ofdm.Nr = 4; % number of receive antennas
<br /> % ofdm.K = 128; % number of subcarriers
<br /> % ofdm.G = 1/4; % Guard interval percentage
<br /> % ofdm.Mod = 4; % QPSK Modulation
<br /> % ofdm.PSpace = 1; % subcarrier space between two pilots
<br /> % channel parameters
<br /> % chan.SNR_dB = 15; % signal to noise ratio
<br /> % chan.L = 6; % number of taps in each transmit-receive antenna
<br /> % control parameters
<br /> % ofdm.ifDemodulateData = 1; % (1,0) if 1, the code demodulates the transmitted via LS data and calculates the BER
<br /> % ofdm.ifDisplayResults = 1; % (1,0) if 1, display the results in the command window
<br /> %======================================================================
<br /> % Outputs
<br /> %======================================================================
<br /> % The main outputs are listed below
<br /> % chan.MSE_Theory % Minimum squared error of LSE channel estimation in theory
<br /> % chan.MSE_Simulation % Minimum squared error of LSE channel estimation in simulations
<br /> % ofdm.BER % Bit Error Rate if ofdm.ifDemodulateData = 1</p>Hamid Ramezanihttp://www.mathworks.com/matlabcentral/profile/authors/452959-hamid-ramezaniMATLAB 7.13 (R2011b)falsetag:www.mathworks.com,2005:FileInfo/491672015-01-26T11:39:43Z2015-01-26T11:39:43ZDiscrete single phase power phase and power factoraccurate single phase phase difference between voltage and current measurement<p>A simple block that can measure the single phase power phase accurately.You just have to connect the voltage and current from the circuit across the block and it will display the phase difference between voltage and current. This block can be used as a utility block and can be used as a subsytem in any of the simulink model. the block has been design for discrete Simulation type. in order to use it in other domains some modifications are needed.</p>Hossein Hafezihttp://www.mathworks.com/matlabcentral/profile/authors/5033135-hossein-hafeziMATLAB 8.1 (R2013a)falsetag:www.mathworks.com,2005:FileInfo/491202015-01-22T20:39:03Z2015-01-26T10:20:23ZX-13 Toolbox for Seasonal FilteringMatlab toolbox providing access to X-13 seasonal adjustemnt programs of the US Census Bureau.<p>The X-13 Toolbox for Matlab is a shell for interacting with the programs of the US Census Bureau, known as X-13, that perform seasonal filtering. The X-13 programs are the "industry standard" and are widely used by many statistical agencies and researchers. The toolbox ought therefore to be useful for statisticians or economists who use Matlab, and who lacked access to the standard seasonal adjustment method until now.
<br />
<br />The toolbox contains a short documentation in a PDF. Maybe the easiest way to get started is to study the three demo files that are provided. The X-13 program has a plethora of specifications one can fiddle around with. The best source to learn this is the original US Census Bureau documentation. Their website also has working papers devoted to this topic (see <a href="https://www.census.gov/srd/www/x13as/">https://www.census.gov/srd/www/x13as/</a>).
<br />
<br />The original X-13 program can only be used with monthly or quarterly data. The X-13 toolbox therefore also provides a much simpler seasonal filter based on moving averages, but that works with timeseries of arbitrary frequency.
<br />
<br />Note: The toolbox requires freely available executables from the US Census Bureau in order to run. It attempts to download these executables automatically for you whenever you need one that is not on your harddrive. Of course, that works only if you are online, and it is limited to Windows computers. Versions of these programs for other operating systems are available from the Census website, however, and can easily be installed manually.
<br />
<br />Please comment below if you find this software useful.</p>Yvan Lengwilerhttp://www.mathworks.com/matlabcentral/profile/authors/1003439-yvan-lengwilerMATLAB 8.3 (R2014a)MATLABRequires software from the US Census Bureau. The toolbox will attempt to download this software automatically whenever needed. Tested only on a Windows computer.falsetag:www.mathworks.com,2005:FileInfo/491662015-01-26T09:44:19Z2015-01-26T09:48:35Zbond pricingcompute the yield curve and price zero coupon bond<p>Compute the yield curve based on two SDE models, i.e. CIR and Vasicek. The parameters are estimated applying Kalman Filter.</p>monohttp://www.mathworks.com/matlabcentral/profile/authors/2223546-monoMATLAB 7.14 (R2012a)Optimization ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/491652015-01-26T08:03:08Z2015-01-26T08:03:08Zsub2ind_mexA very fast sub2ind routine in mex ( C )<p>This function operates in the same manner as the 'sub2ind' method. However, this method is quite faster. It is very similar to the sub2ind.m code.
<br />Example :
<br />>> a = [ 50 50 50 ];
<br />>> b = [ 1 30 49 ];
<br />>> x = sub2ind(a,b,b,b)</p>
<p>x =</p>
<p> 1 73980 122449</p>
<p>>> xx = sub2ind_mex(a,b,b,b)</p>
<p>xx =</p>
<p> 1 73980 122449</p>
<p>I would like to acknowledge "Marco" from the following thread. He began this bit of code a while ago and posted it here. I have simply taken it and improved its performance a bit. Thank you Marco, I do hope you don't mind what I have done here.
<br /><a href="http://www.mathworks.com/matlabcentral/newsreader/view_thread/262036">http://www.mathworks.com/matlabcentral/newsreader/view_thread/262036</a></p>Christopher Harrishttp://www.mathworks.com/matlabcentral/profile/authors/4077745-christopher-harrisMATLAB 8.1 (R2013a)falsetag:www.mathworks.com,2005:FileInfo/491642015-01-26T07:39:53Z2015-01-26T07:39:53ZPopulation dynamics toolboxImplementation of some evolutionary dynamics from game theory for multiple populations.<p>PDToolbox contains a set of functions to implement evolutionary dynamics with multiple populations. This toolbox is designed to facilitate the implementation of any game with different evolutionary dynamics or revision protocols. In particular, our attempt is to make an efficient implementation of the algorithms to compute the dynamical evolution of the society. Also, the toolbox counts with some functions to plot the state of the system and the evolution of each strategy.</p>Carlos Barretohttp://www.mathworks.com/matlabcentral/profile/authors/1299478-carlos-barretoMATLAB 7.6 (R2008a)Communications System ToolboxStatistics ToolboxMATLABfalse