tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - product:"Simulink" product:"Simulink Verification and Validation"User-contributed code library2015-01-30T12:45:54-05:00301100tag:www.mathworks.com,2005:FileInfo/493822015-01-29T08:27:49Z2015-01-29T08:27:49ZPICT: Particle Image Characterization ToolQuantitative image analysis tool for characterizing structural properties of nanoparticle clusters<p>This quantitative image analysis tool is used to characterize key structural properties of nanoparticle clusters from experimental images of nanocomposites. This analysis can be carried out on images captured at intermittent times during assembly to monitor the time evolution of nanoparticle clusters in a highly automated manner. The software outputs averages and distributions in the size, radius of gyration, fractal dimension, backbone length, end-to-end distance, anisotropic ratio, and aspect ratio of nanoparticle clusters as a function of time along with bootstrapped error bounds for all calculated properties. The polydispersity in nanoparticles and biases in the sampling of nanoparticles clusters are accounted for through the use of probabilistic weights.</p>Gaurav Aryahttp://www.mathworks.com/matlabcentral/profile/authors/6117309-gaurav-aryaMATLAB 8.0 (R2012b)Curve Fitting ToolboxImage Processing ToolboxSimulink Verification and ValidationStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/415152013-04-29T13:35:22Z2015-01-17T04:24:44ZProcessNetwork Version 1.4 SoftwareFunctions for the delineation of Dynamical Process Networks using Information Theory<p>ProcessNetwork v1.4, January 16th 2015
<br />Attribution and Licensing</p>
<p>The ProcessNetwork software was written between 2005 and 2008 at the University of Illinois at Urbana-Champaign, in the Department of Civil Engineering, funded 2006-2008 by a NASA fellowship #NNX06AF71H , and then continued development has occurred at Arizona State University, funded 2013-2015 by a grant from the NSF’s Macrosystems Biology program BIO-1241960. Dr. Benjamin L. Ruddell is the primary author of the software, but many collaborators have contributed to its ongoing development. The work is that of the author, and its accuracy and implications are not necessarily supported by the funding and employing organizations. The software copyright is held by Dr. Ruddell, but is shared for fair use with acknowledgement and citation of the author’s contribution under a Creative Commons Public License. Those using the software are requested to cite the software using the following three citations, and to communicate with the author regarding modifications, extensions, and applications of the software.</p>
<p>Citation for the software:</p>
<p>Ruddell, B.L. (2008), ProcessNetwork Software, version (INSERT VERSION HERE), accessed at (INSERT URL OR SOURCE HERE) on (INSERT DATE HERE).</p>
<p>Citations for the methods employed in the software:</p>
<p>Ruddell, B. L.*, and P. Kumar (2009a), Ecohydrologic process networks: 1. Identification, Water Resour. Res., 45, W03419, doi:10.1029/2008WR007279.</p>
<p>Ruddell, B. L.*, and P. Kumar (2009b), Ecohydrologic process networks: 2. Analysis and characterization, Water Resour. Res., 45, W03420, doi:10.1029/2008WR007280.</p>
<p>Ruddell, B.L.*, N. Oberg, P. Kumar, and M. Garcia (2010), Using Information-Theoretic Statistics in MATLAB to Understand How Ecosystems Affect Regional Climates, MATLAB Digest Academic Edition, February 2010, <a href="http://www.mathworks.com/academia">www.mathworks.com/academia</a>.</p>
<p>Acknowledgements:</p>
<p>Dr. Praveen Kumar, for advising and co-authorship of publications at the University of Illinois
<br />Dr. Ricky Robertson, for important histogram algorithm advice at the University of Illinois
<br />Numerous students and colleagues who provided help with MATLAB coding
<br />Cove Sturdevant and Dennis Baldocchi of the University of California, Berkeley for bug identification
<br />Minseok Kang and Rong Yu for contributions to code versions 1.2 through 1.4
<br />Introduction</p>
<p>The purpose of this software is to delineate Dynamical Process Networks based on observations of information flow between discrete vector variables using information theory, including the Transfer Entropy statistic. The software also computes Shannon Entropy, Mutual Information, Relative Entropy, and many other information theoretic statistics. The software is valid for all kinds of data, but the typical application is to Dynamical information flow and Shannon Entropy, and the typical dataset a multivariate timeseries. The software is written for the MATLAB® scientific computing environment. This basic version of the software does not contain any visualization, plotting, or GUI features, but some of these features may be available separately. The basic version of the software contains a small set of preprocessing functions and filters, but others may also be useful.</p>Ben Ruddellhttp://www.mathworks.com/matlabcentral/profile/authors/1660525-ben-ruddellMATLAB 8.4 (R2014b)Simulink Verification and ValidationStatistics ToolboxMATLABSupports multiple-core and parallel toolbox calculation of the Matlab 2014b release, but this is not required.falsetag:www.mathworks.com,2005:FileInfo/200032008-05-21T14:39:32Z2015-01-14T19:12:28ZPanelLike subplot, but easier, and WYSIWYG export to file. Also fixes dashed/dotted lines in export.<p>Panel is an alternative to Matlab's "subplot", providing easier control over layout (particularly, easy elimination of whitespace). It also fixes dashed/dotted lines during export to image files (both vector and bitmap formats).
<br />If you find the layouts generated by subplot() have too much space and not enough axis, try Panel. If you find it tedious to construct subplot layouts that are more complex than a simple grid, try Panel. If you always resort to other software to prepare your final figures for publication because you can't get the appearance you want from Matlab, try Panel.</p>
<p>Panel was designed to produce output for print publications directly from Matlab. Layouts are, by default, in physical units (mm, by default), and exporting to a graphics file targets print columns directly. However, it performs equally well if the end goal is digital display, providing easy control over use of screen real estate.</p>
<p>Panel incorporates features suggested by several Matlab Central users, as well as some code - see the documentation for details.</p>
<p>Questions? Please see the <a href="http://www.mathworks.co.uk/matlabcentral/fileexchange/20003-panel/content/docs/faq.html">http://www.mathworks.co.uk/matlabcentral/fileexchange/20003-panel/content/docs/faq.html</a> first.</p>
<p>NB: Release 2.11 is required for all functions to work correctly at and beyond Matlab R2014b.</p>Ben Mitchhttp://www.mathworks.com/matlabcentral/profile/authors/869322-ben-mitchMATLAB 7.10 (R2010a)Control System ToolboxSimulink Verification and ValidationMATLABRequires a version of Matlab that supports the "classdef" syntax. I understand that means R2008a or higher.falsetag:www.mathworks.com,2005:FileInfo/333812011-10-19T23:19:21Z2015-01-03T05:52:50ZJSONlab: a toolbox to encode/decode JSON files in MATLAB/OctaveJSONlab is an open-source JSON/UBJSON encoder and decoder (parser) for MATLAB and Octave.<p>** JSONLAB v1.0 (Optimus - Final) is released on 01/02/2015.**
<br />JSONlab is a component of the "iso2mesh" toolbox (<a href="http://iso2mesh.sf.net">http://iso2mesh.sf.net</a>). For the latest information regarding JSONlab, please visit its homepage at <a href="http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab">http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab</a></p>
<p>JSON (JavaScript Object Notation) is a highly portable, human-readable and "fat-free" text format to represent complex and hierarchical data. It is as powerful as XML, but less verbose. JSON format is widely used for data-exchange in applications, and is essential for the wild success of Ajax and Web2.0.</p>
<p>UBJSON (Universal Binary JSON) is a binary JSON format, specifically optimized for compact file size and better performance while keeping the semantics as simple as the text-based JSON format. Using the UBJSON format allows to wrap complex binary data in a flexible and extensible structure, making it possible to process complex and large dataset without accuracy loss due to text conversions.</p>
<p>We envision that both JSON and its binary version will serve as part of the mainstream data-exchange formats for scientific research in the future. It will provide the flexibility and generality achieved by other popular general-purpose file specifications, such as HDF5, with significantly reduced complexity and enhanced performance.</p>
<p>JSONlab is a free and open-source implementation of a JSON/UBJSON encoder and a decoder in the native MATLAB language. It can be used to convert a MATLAB data structure (array, struct, cell, struct array and cell array) into JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB data structure. JSONlab supports both MATLAB and GNU Octave (a free MATLAB clone).</p>
<p>JSONlab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
<br />and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
<br />two equivallent functions -- loadubjson and saveubjson for the binary
<br />JSON. The savejson, loadubjson and saveubjson functions were written by
<br />Qianqian Fang, while the loadjson.m script was derived from the previous works by the following people:</p>
<p>- Nedialko Krouchev: <a href="http://www.mathworks.com/matlabcentral/fileexchange/25713">http://www.mathworks.com/matlabcentral/fileexchange/25713</a>
<br /> date: 2009/11/02
<br />- FranÃ§ois Glineur: <a href="http://www.mathworks.com/matlabcentral/fileexchange/23393">http://www.mathworks.com/matlabcentral/fileexchange/23393</a>
<br /> date: 2009/03/22
<br />- Joel Feenstra: <a href="http://www.mathworks.com/matlabcentral/fileexchange/20565">http://www.mathworks.com/matlabcentral/fileexchange/20565</a>
<br /> date: 2008/07/03</p>
<p>Please find detailed online help at <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab/Doc">http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab/Doc</a></p>Qianqian Fanghttp://www.mathworks.com/matlabcentral/profile/authors/1583198-qianqian-fangMATLAB 7.4 (R2007a)Communications System ToolboxSimulink Verification and ValidationMATLABJSONlab is platform independent.falsetag:www.mathworks.com,2005:FileInfo/482382014-10-24T05:42:51Z2014-12-22T17:34:36ZNonstationary Extreme Value Analysis (NEVA) ToolboxNonstationary Extreme Value Analysis (NEVA)<p>Nonstationary Extreme Value Analysis (NEVA) Software Package, Version 2.0
<br />By: Linyin Cheng, PhD, University of California, Irvine
<br />Release: 09/14/2014
<br />Source Code: Matlab
<br />The Nonstationary Extreme Value Analysis (NEVA) software package has been developed to facilitate extreme value analysis under both stationary and nonstationary assumptions. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.
<br />NEVA includes two components:
<br />(1) The Generalized Extreme Value (GEV) distribution for analysis of annual maxima (block maxima).
<br />(2) The Generalized Pareto Distribution (GPD) for analysis of extremes above a certain threshold (i.e., peak-over-threshold (POT) approach).
<br />Both NEVA GEV and NEVA GPD can be used for stationary (time-independent) and nonstationary (transient) extreme value analysis.
<br />Reference Publication:</p>
<p>Cheng L., AghaKouchak A., Gilleland E., Katz R.W., 2014, Non-stationary Extreme Value Analysis in a Changing Climate , Climatic Change, doi: 10.1007/s10584-014-1254-5.
<br />Download Reference Paper: <a href="http://amir.eng.uci.edu/publications/14_NEVA_CC.pdf">http://amir.eng.uci.edu/publications/14_NEVA_CC.pdf</a></p>
<p>The toolbox includes a sample observation and simulation data sets. Run NEVA.m to see sample outputs. </p>
<p>Additional information:</p>
<p><a href="http://amir.eng.uci.edu/neva.php">http://amir.eng.uci.edu/neva.php</a></p>HRLhttp://www.mathworks.com/matlabcentral/profile/authors/2424579-hrlMATLAB 7.13 (R2011b)Simulink Verification and ValidationStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/486262014-12-02T15:23:10Z2014-12-18T11:42:40ZSEHR-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/487522014-12-16T00:20:31Z2014-12-16T00:31:50ZProject 3D into 2D image coordinates using a camera modelProject 3D points using a camera model with lens distortion parameters<p>Code for finding the location of 3D points in a camera's image coordinates. Takes into account the cameras transformation matrix, camera matrix and distortion coefficients.</p>Zachary Taylorhttp://www.mathworks.com/matlabcentral/profile/authors/3383185-zachary-taylorMATLAB 8.1 (R2013a)Aerospace ToolboxNeural Network ToolboxSimulink Verification and ValidationMATLABfalsetag:www.mathworks.com,2005:FileInfo/190992008-03-07T09:08:17Z2014-12-15T08:09:01ZTidal fitting toolboxfit tidal components to an observed series of sea level and use them for prediction<p>* Fitting of tidemodels to e.g. sea level data.
<br />* Predictions of tide based on tide model.
<br />Uses familar syntax from polyfit/polyval: tidalfit/tidalval</p>
<p> % TIDALFIT: Fits a tidal model to data
<br />
<br /> tidalfit uses the HAMELS (ordinary least squares)
<br /> technique to fit tidal components to the detrended data. Additionally it
<br /> can also do robust fitting.
<br />
<br /> Please include an acknowledgement to Aslak Grinsted if you use this code.
<br />
<br /> USAGE: tidal=tidalfit(data[,parameter,value])
<br />
<br /> INPUT:
<br /> ------
<br /> data: A two column vector.
<br /> \ - first column should be a serial date number (See help datenum)
<br /> \ - second column should be the y-values (i.e. sea level)
<br /> \ (missing values and nans are OK.)
<br />
<br /> OPTIONAL PARAMETERS:
<br /> --------------------
<br /> Components: cell-array of strings with names of the which
<br /> \ components should be included in the fit? (ALL is default)
<br /> \ Note: The routine will only attempt to fit components
<br /> \ that have period<data_timespan/4 and period>dt*2.
<br /> FittingMethod: 'OLS' for ordinary least squares or 'ROBUST' for robustfitting.
<br /> \ (default=OLS)
<br /> RobustFitOptions: cell of options for robustfit. (See help robustfit.)
<br /> \ only used if FittingMethod='ROBUST'. (default={})
<br /> DetrendData: should the data be detrended prior to fitting? (default=true)
<br />
<br /> Note: optional parameters can be specified using abbreviations. e.g. RFO for RobustFitOptions.
<br />
<br /> OUTPUT:
<br /> -------
<br /> If no output arguments are specified the routine will display the results
<br /> visually.
<br />
<br /> tidal: A struct-array containing the fitted model parameters.
<br /> \ .name: name of tidal component (see e.g. <a href="http://www.mhl.nsw.gov.au/www/tide_glossary.htmlx">http://www.mhl.nsw.gov.au/www/tide_glossary.htmlx</a>)
<br /> \ .period:period of tidal component in days
<br /> \ .speed: frequency of tidal component in degrees per solar hour
<br /> \ .amp: amplitude of fitted component
<br /> \ .phase: phase of fitted component
<br />
<br /> Components that are not included in the fit will have NaN in .amp and .phase.
<br />
<br />
<br />
<br /> EXAMPLE:
<br /> data=datenum(1971,1,1):datenum(2008,1,1);
<br /> data=[data;randn(size(data))]';
<br /> tidal=tidalfit(data,'fm','robust');
<br /> future=[datenum(2008,1,1):datenum(2009,1,1)'];
<br /> plot(future,tidalval(tidal,future));</p>Aslak Grinstedhttp://www.mathworks.com/matlabcentral/profile/authors/870202-aslak-grinstedMATLAB 7 (R14)Simulink Verification and ValidationStatistics ToolboxMATLAB4450falsetag:www.mathworks.com,2005:FileInfo/485852014-11-27T08:43:11Z2014-12-01T07:46:37ZWeighted Iterative Truncated Mean FilterWITM filters<p>The codes for a rich class of filters named weighted ITM (WITM) filters are provided here. By iteratively truncating the extreme samples, the output of the WITM filter converges to the weighted median. Proper stopping criterion makes the WITM filters own merits of both the weighted mean and median filters and hence outperforms the both in some applications. Three structures are designed to enable the WITM filters being low-, band- and high-pass filters. Properties of these filters are presented and analyzed.
<br />Demo codes includes:
<br />1) low-pass WITM filters,
<br />2) band-pass WITM filters,
<br />3) high-pass WITM filters
<br />4) WITM filters for image denoising.
<br />Some further demo codes can be found in the codes for the fast ITM filters <a href="https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48583-fast-iterative-truncated-arithmetic-mean-filter--fitm-filter-</a>
<br />and ITTM filters
<br /><a href="https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-">https://www.mathworks.com/matlabcentral/fileexchange/48584-iterative-trimmed-and-truncated-mean-algorithm-filter--ittm-filter-</a></p>
<p>You may need to compile the c files before using them. Please try the function WITM_compile.</p>
<p>More inforation can be found from the webpage of Prof. Jiang:
<br /><a href="http://www.ntu.edu.sg/home/exdjiang/default.htm">http://www.ntu.edu.sg/home/exdjiang/default.htm</a></p>
<p>and my homepage:
<br /><a href="https://sites.google.com/site/miaozhenwei/">https://sites.google.com/site/miaozhenwei/</a></p>
<p>Reference paper:
<br />Z. W. Miao and X. D. Jiang, "Weighted Iterative Truncated Mean Filter," IEEE Transactions on Signal Processing, Vol. 61, no. 16, pp. 4149-4160, August, 2013.</p>
<p>Related papers:
<br />Z. W. Miao and X. D. Jiang, "Additive and Exclusive Noise Suppression byIterative Trimmed and Truncated Mean Algorithm," Signal Processing, vol. 99, pp. 147-158, June, 2014.
<br />Z. W. Miao and X. D. Jiang, "Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter" IEEE Transactions on Circuits and Systems-II, vol. 59, no. 11, pp. 810-814, November 2012.
<br /> X.D. Jiang, "Iterative Truncated Arithmetic Mean Filter And Its Properties," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1537-1547, April 2012.</p>Miao Zhenweihttp://www.mathworks.com/matlabcentral/profile/authors/5725746-miao-zhenweiMATLAB 7.2 (R2006a)Control System ToolboxImage Processing ToolboxRobust Control ToolboxSignal Processing ToolboxSimulink Verification and ValidationStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/293032010-11-07T11:34:50Z2014-11-27T20:39:26ZDynamic Copula Toolbox 3.0Functions to estimate copula GARCH and copula Vine models.<p>Updates from version 2.0:
<br />1. The marginal GARCH models are estimated from the toolbox functions (without the use of the econometrics/GARCH toolbox of MATLAB).
<br />2. Hansen's Skew t distribution for the margins is supported.
<br />3. Asymptotic standard errors are computed (Godambe info. matrix)</p>Manthos Vogiatzoglouhttp://www.mathworks.com/matlabcentral/profile/authors/785095-manthos-vogiatzoglouMATLAB 7.7 (R2008b)Econometrics ToolboxOptimization ToolboxSimulink Verification and ValidationStatistics ToolboxMATLABfalse