tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2015-03-04T10:31:05-05:00232191100tag:www.mathworks.com,2005:FileInfo/498962015-03-02T19:33:30Z2015-03-04T14:01:25ZModulationA basic GUI for understanding how Amplitude and Frequency Modulation is done.<p>This is a very basic modulation example for a few certain signal types. The main goal is to understand how modulation is done and how it is demodulated.</p>Berat Atmacahttp://www.mathworks.com/matlabcentral/profile/authors/6087953-berat-atmacaMATLAB 8.3 (R2014a)Signal Processing ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/499212015-03-04T13:20:40Z2015-03-04T13:20:40ZNatural Point Motive (Optitrack) API interface to Matlab and Simulink 64bit by Or HirshfeldGet Motion capture data from optitrack cameras to matlab by Or Hirshfeld<p>My project is to interface between Natural Point Motive (Optitrack) to Simulink (Matlab 64bit) in Real-Time Windows Target, My testing OS is Win7 64bit but i belive it would work for other OS.
<br />by
<br />Or Hrishfeld
<br />אור הירשפלד</p>Or Hirshfeldhttp://www.mathworks.com/matlabcentral/profile/authors/5020917-or-hirshfeldMATLAB 8.4 (R2014b)MATLAB CompilerSimulinkMATLAB Coderoptitrack, Motive26449482794870049085falsetag:www.mathworks.com,2005:FileInfo/331702011-10-07T08:14:20Z2015-03-04T11:06:46ZMulti Class Support Vector MachineThis function removes out the limitation of MATLAB SVM function of two class and uses more classes.<p>This function can classify more than two classes which is limited in MATLAB SVM. This is primary work and does not include plotting function for SVM.
<br />This is version 3.0 of original function which removes some limitations of first & Second one.</p>Anand Mishrahttp://www.mathworks.com/matlabcentral/profile/authors/2945167-anand-mishraMATLAB 7.12 (R2011a)Bioinformatics ToolboxStatistics ToolboxMATLABBioinformatics Toolbox only.falsetag:www.mathworks.com,2005:FileInfo/486262014-12-02T15:23:10Z2015-03-04T10:21:25ZSEHR-ECHO v1.0: a Spatially Explicit Hydrologic Response model for ecohydrologic applicationsThe model simulates streamflow from precipitation and temperature data.<p>This model has been developed at the Laboratory of Ecohydrology of the Ecole Polytechnique Fédérale de Lausanne (<a href="http://www.epfl.ch">www.epfl.ch</a>) for the simulation of hydrological processes at the catchment scale. The corresponding paper is published in GMD <a href="http://www.geosci-model-dev.net/7/2733/2014/gmd-7-2733-2014.html">http://www.geosci-model-dev.net/7/2733/2014/gmd-7-2733-2014.html</a>.
<br />The key concept of the model is the formulation of water transport by geomorphologic travel time distributions through gravity-driven transitions among geomorphic states: the mobilization of water (and possibly dissolved solutes) is simulated at the subcatchment scale and the resulting responses are convolved with the travel paths distribution within the river network to obtain the hydrologic response at the catchment outlet. The model thus breaks down the complexity of the hydrologic response into an explicit geomorphological combination of dominant spatial patterns of precipitation input and of hydrologic process controls. Nonstationarity and nonlinearity effects are tackled through soil moisture dynamics in the active soil layer.
<br />The package comes with example data to test the model for an catchment with snow- and ice melt.</p>Bettina Schaeflihttp://www.mathworks.com/matlabcentral/profile/authors/4454610-bettina-schaefliMATLAB 7.11 (R2010b)Simulink Verification and ValidationStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/499202015-03-04T09:26:31Z2015-03-04T09:26:31ZAnt Lion Optimizer (ALO)ALO is a novel meta-heuristic algorithm for global optimization<p>The Ant Lion Optimizer (ALO) mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented.
<br />This is the source codes of the paper:</p>
<p>Seyedali Mirjalili, The Ant Lion Optimizer, Advances in Engineering Software, Volume 83, May 2015, Pages 80-98, ISSN 0965-9978, <a href="http://dx.doi.org/10.1016/j.advengsoft.2015.01.010">http://dx.doi.org/10.1016/j.advengsoft.2015.01.010</a>.
<br />(<a href="http://www.sciencedirect.com/science/article/pii/S0965997815000113">http://www.sciencedirect.com/science/article/pii/S0965997815000113</a>)</p>
<p>Download the paper for free until April 22, 2015 from : <a href="http://authors.elsevier.com/a/1Qe2l3Rf7adUmn">http://authors.elsevier.com/a/1Qe2l3Rf7adUmn</a></p>
<p>More information can be found in: <a href="http://www.alimirjalili.com/ALO.html">http://www.alimirjalili.com/ALO.html</a></p>Seyedali Mirjalilihttp://www.mathworks.com/matlabcentral/profile/authors/2943818-seyedali-mirjaliliMATLAB 7.13 (R2011b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/499192015-03-04T09:21:51Z2015-03-04T09:21:51ZAnt Lion Optimizer toolboxA toolbox for the Ant Lion Optimizer (ALO) algorithm<p>This is a simple toolbox with a use-friendly graphical interface, which is very suitable for those without high programming skills.
<br />The parameters of the ALO algorithm can be easily defined in the toolbox.
<br />The default name of the objective function is CostFunction. If you have a look at the CostFunction.m file, you may notice that the cost function gets the variables in a vector ([x1 x2 ... xn]) and returns the objective value. You can either write you objective function in this file or create a new file and pass its name to the toolbox. Remember to follow the same structure for input and output if you decided to go for the second option.
<br />The lower bounds and upper bounds of variables should also be written as lb1,lb2,...,lbn and ub1,ub2,...,ubn. If all of the variables have equal lower and/or upper bounds you can just define lb and ub as two single number numbers: lb, ub.</p>
<p>Just run the ALO_toolbox.m file and enjoy!</p>
<p>This is the source codes of the paper:</p>
<p>Seyedali Mirjalili, The Ant Lion Optimizer, Advances in Engineering Software, Volume 83, May 2015, Pages 80-98, ISSN 0965-9978, <a href="http://dx.doi.org/10.1016/j.advengsoft.2015.01.010">http://dx.doi.org/10.1016/j.advengsoft.2015.01.010</a>.
<br />(<a href="http://www.sciencedirect.com/science/article/pii/S0965997815000113">http://www.sciencedirect.com/science/article/pii/S0965997815000113</a>)</p>
<p>Download the paper for free until April 22, 2015 from : <a href="http://authors.elsevier.com/a/1Qe2l3Rf7adUmn">http://authors.elsevier.com/a/1Qe2l3Rf7adUmn</a></p>
<p>More information can be found in: <a href="http://www.alimirjalili.com/ALO.html">http://www.alimirjalili.com/ALO.html</a></p>Seyedali Mirjalilihttp://www.mathworks.com/matlabcentral/profile/authors/2943818-seyedali-mirjaliliMATLAB 7.13 (R2011b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/382252012-09-18T20:08:42Z2015-03-04T09:20:49ZEnsemble methodsEnsemble methods include four strategies which can obtain samples of ensemble individual learners <p>There are four ensemble strategies--- random selecting samples, Bagging strategy, Random subspace method, Rotation forest method. They are ensemble methods which can obtain the samples of individual learner. Bagging method is a classical ensemble strategy proposed by Leo Breiman in Ref [L.Breiman. Bagging Predictors. Machine learning, vol.24(2), pp.123-140, 1996.]. Random subspace method is proposed by Tin Kam Ho in Ref [Ho T.K.. The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on pattern analysis and Machine Intelligence, vol.20(8), pp.832-844, 1998.]. Rotation forest method is better than bagging, random subspace, adaboost methods and so on, which is proposed by Juan J. Rodriguez and Ludmila I. Kuncheva in Ref [J.J. Rodriguez, L.I. Kuncheva. Rotation Forest: A New Classifier Ensemble Method. IEEE transactions on Pattern Analysis and Machine Intelligence, vol.28(10), pp.1619-1630, October, 2006.]. </p>Mao Shashahttp://www.mathworks.com/matlabcentral/profile/authors/2762932-mao-shashaMATLAB 7.4 (R2007a)falsetag:www.mathworks.com,2005:FileInfo/496032015-02-10T08:23:02Z2015-03-04T08:58:24ZQFWEC ensemble methodAn ensemble method that employs quadradic form to seek an optimal weight vector of classifiers<p>'weighted classifier ensemble based on quadratic form' : this method weights each classifier in an ensemble by maximising three different quadratic forms. This method seeks the optimal weight vector of classifiers directly by minimising the ensemble error and avoid efficiently the difficulty derived of balancing diversity and accuracy. Based on two constraints, we approximate the normal ensemble error in an an objective function. By introducing an initial weight vector of classifiers, the minimization of the objective function is equivalent to the maximization of quadratic forms. In fact, when the value of the quadratic form is larger, the obtained ensemble error corresponding to it is smaller, and also smaller than the ensemble error corresponding to the initial weight vector. Particularly, in QFWEC method, the initial weight vector can be given by user, and it can also given by a kind of ensemble methods. It means this method can improve the performance of other ensemble methods, when the weight vector obtained by other ensemble methods is used as the initial weight vector of the QFWEC method. <a href="http://www.sciencedirect.com/science/article/pii/S0031320314004348">http://www.sciencedirect.com/science/article/pii/S0031320314004348</a>.</p>Mao Shashahttp://www.mathworks.com/matlabcentral/profile/authors/2762932-mao-shashaMATLAB 7.14 (R2012a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/474812014-08-07T22:21:48Z2015-03-04T06:04:33Zamjams/spca_amSparse Principal Component Analysis using Alternating Maximization<p>This is a Sparse Principal Component Analysis (SPCA) Toolbox which Performs 8 formulations of the SPCA algorithm introduced by Richtarik et. al
<br /><a href="http://arxiv.org/abs/1212.4137">http://arxiv.org/abs/1212.4137</a> (For theoretical background)</p>
<p>Details of the toolbox can be found in the ReadMe entry of the github link provided</p>Ahmad Alsahafhttp://www.mathworks.com/matlabcentral/profile/authors/5507005-ahmad-alsahafMATLAB 8.1 (R2013a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/497732015-02-20T13:19:54Z2015-03-04T06:03:07ZCubic Splines Made EasyFits a set of cubic splines to given data, and returns an inline function.<p>Fits a set of cubic splines to given data, and returns an inline function.</p>Chris McCombhttp://www.mathworks.com/matlabcentral/profile/authors/3994685-chris-mccombMATLAB 8.4 (R2014b)false