tag:la.mathworks.com,2005:/matlabcentral/fileexchange/feed?q=citation_type%3AdoiMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - citation_type:doiUser-contributed code library2020-12-03T21:08:45-05:00382160276592020-11-12T14:42:48Z2020-11-12T14:42:48ZPIVlab - particle image velocimetry (PIV) tool with GUIEasy to use, GUI based tool to analyze, validate, postprocess, visualize and simulate (micro) PIV data.http://pivlab.blogspot.com/<p>PIVlab is a time-resolved (micro) particle image velocimetry (PIV, LSPIV) software that is updated regularly with software fixes and new features. It does not only calculate the velocity distribution within particle image pairs, but can also be used to derive, display and export multiple parameters of the flow pattern. A user-friendly graphical user interface (GUI) makes PIV analyses and data post-processing fast and efficient.Video Explanation of the tool:<a href="https://www.youtube.com/watch?v=Sp3Ounq07QcExample">https://www.youtube.com/watch?v=Sp3Ounq07QcExample</a> analyses & videos can be found on the PIVlab website:<a href="http://PIVlab.blogspot.comPlease">http://PIVlab.blogspot.comPlease</a> ask your questions in the PIVlab forum:<a href="http://pivlab.blogspot.de/p/forum.htmlHOW">http://pivlab.blogspot.de/p/forum.htmlHOW</a> TO INSTALL:Download the file 'PIVlab.mltbx', and run it on your computer. It will automatically add the PIVlab toolbox and app in your Matlab installation. Alternatively (my preferred method, but also the only way if your Matlab release is older than R2015b [8.6]), download the zip file from GitHub by going here: <a href="https://github.com/Shrediquette/PIVlab/releases">https://github.com/Shrediquette/PIVlab/releases</a> , click 'Source code (zip)' of the latest release and extract the contents to a new folder in your Matlab work directory. Then run PIVlab_GUI.m. Main features:* completely GUI based PIV tool* multi-pass, multi grid window deformation technique* ensemble correlation e.g. for micro-PIV* import bmp/ tiff/ jpeg image pairs/ series* image sequencing styles A-B, C-D, ... or A-B, B-C, ...* individual image masking and region of interest (ROI) selection* image pre-processing (contrast enhancement, high-pass, intensity capping)* two different sub-pixel estimators* multiple vector validation methods* magnitude/ vorticity/ divergence/ shear / ...* data smoothing, vector field high-pass* multiple color maps* streamlines* extensive data extraction tools/ integration via poly lines/ circles/ area* statistics (histograms, scatter plot, mean & stdev)* precise particle image pair generation with user-defined parameters and several flow simulations (synthetic PIV image generator)* data export (Matlab, ASCII, movie file, image, Paraview, Tecplot...)* main features accessible via command line scriptingWe would like to acknowledge Uri Shavit, Roi Gurka & Alex Liberzon for sharing their code for 3-point Gaussian sub-pixel estimation. Thanks to Nima Bigdely Shamlo for allowing me to include the LIC function. Thanks to Raffel et al. for writing the book "Particle Image Velocimetry, A Practical Guide", which was a very good help.</p>William Thielickehttps://www.mathworks.com/matlabcentral/profile/1377529-william-thielicke738782020-10-02T20:23:39Z2020-10-02T20:23:39ZHybrid AC/DC & DC microgrid test system simulationIn this model, a Microgrid test system based on the 14-busbar IEEE distribution system is proposed.<p>This AC/DC HMG has two AC voltage distribution levels (the primary level is 13,8 kV and the secondary level is 220 V) and one DC distribution level (300V). The AC MG operates at a frequency of 60 Hz.This test system simulation includes:• One diesel generator,• Two photovoltaic (PV) systems,• Two battery energy storage system,• Various linear and non-linear loads.Additionally, the DC microgrid model is extracted from the original model.</p>L. Ortizhttps://www.mathworks.com/matlabcentral/profile/8189619-l-ortiz704992020-11-26T03:56:18Z2020-11-26T03:56:18ZAugmented Lagrangian Digital Image Correlation and Tracking2D-AL-DIC(Augmented Lagrangian DIC) is a fast, parallel-computing DIC algorithm which also considers global kinematic compatibility. <p>AL-DIC is a fast, parallel-computing DIC algorithm, which combines advantages of the Local Subset DIC (fast, compute in parallel) and the FE-based Global DIC (guarantee kinematic compatibility). For full details, and to use this code, please cite our paper:Yang, J. and Bhattacharya, K. Exp.Mech. (2019) 59: 187. <a href="https://doi.org/10.1007/s11340-018-00457-0.or">https://doi.org/10.1007/s11340-018-00457-0.or</a> request full text at:<a href="https://www.researchgate.net/publication/329456141_Augmented_Lagrangian_Digital_Image_CorrelationCode">https://www.researchgate.net/publication/329456141_Augmented_Lagrangian_Digital_Image_CorrelationCode</a> manual is available at: <a href="https://www.researchgate.net/publication/344796296_Augmented_Lagrangian_Digital_Image_Correlation_AL-DIC_Code_Manual_v33%">https://www.researchgate.net/publication/344796296_Augmented_Lagrangian_Digital_Image_Correlation_AL-DIC_Code_Manual_v33%</a> =========================================Here I highlight some advantages of AL-DIC algorithm:•It’s a fast algorithm using distributed parallel computing.•Global kinematic compatibility is added as a global constraint in the form of augmented Lagrangian, and solved using Alternating Direction Method of Multipliers scheme.•Both displacement fields and affine deformation gradients are correlated at the same time.•No need of much manual experience about choosing displacement smoothing filters.•It works well with compressed DIC images and adaptive mesh. See our paper: Yang, J. & Bhattacharya, K. Exp Mech (2019). <a href="https://doi.org/10.1007/s11340-018-00459-y;•Both">https://doi.org/10.1007/s11340-018-00459-y;•Both</a> accumulative and incremental DIC modes are implemented to deal with image sequences, which is especially quite useful for very large deformations.•ALDIC application example -- uniaxial compression experiment:<a href="https://github.com/jyang526843/2D_ALDIC_v3/blob/master/Example_aldic_foam_compression_strain_eyy.gif%">https://github.com/jyang526843/2D_ALDIC_v3/blob/master/Example_aldic_foam_compression_strain_eyy.gif%</a> =========================================ALDIC Matlab code demo:(Youtube) <a href="https://www.youtube.com/watch?v=JctudMfO-7w(Bilibili)">https://www.youtube.com/watch?v=JctudMfO-7w(Bilibili)</a> <a href="https://www.bilibili.com/video/BV1hf4y1i7bK/I">https://www.bilibili.com/video/BV1hf4y1i7bK/I</a> also attach my EASF webinar to introduce AL-DIC/DVC algorithm and review other DIC/DVC methods:(Youtube) <a href="https://www.youtube.com/watch?v=-t61WrVagZ4(Bilibili)">https://www.youtube.com/watch?v=-t61WrVagZ4(Bilibili)</a> <a href="https://www.bilibili.com/video/BV1ff4y1B71L/">https://www.bilibili.com/video/BV1ff4y1B71L/</a> % =========================================Finite-element-based Global DIC code is also available at:<a href="https://www.mathworks.com/matlabcentral/fileexchange/82873-2d-finite-element-global-digital-image-correlation-fe-dicBesides">https://www.mathworks.com/matlabcentral/fileexchange/82873-2d-finite-element-global-digital-image-correlation-fe-dicBesides</a> 2D-DIC, our new code "ALDVC" (augmented Lagrangian Digital Volume Correlation) to track deformations in volumetric images is also available: <a href="https://www.mathworks.com/matlabcentral/fileexchange/77019-augmented-lagrangian-digital-volume-correlation-aldvc%">https://www.mathworks.com/matlabcentral/fileexchange/77019-augmented-lagrangian-digital-volume-correlation-aldvc%</a> =========================================Contact & support: I appreciate your comments and ratings to help me further improve this code. If you have other questions, feel free to email me: <a href="mailto:aldicdvc@gmail.com">aldicdvc@gmail.com</a> </p>Jin Yanghttps://www.mathworks.com/matlabcentral/profile/14701582-jin-yang703732020-05-03T11:20:50Z2020-05-03T11:20:50ZSpatial Math ToolboxSpatial math primitives for MATLAB<p>This Toolbox contains functions and classes to represent orientation and pose in 2D and 3D (SO(2), SE(2), SO(3), SE(3)) as orthogonal and homogeneous transformation matrices, quaternions, twists, triple angles, and matrix exponentials. The Toolbox also provides functions for manipulating these datatypes, converting between them, composing them, transforming points and graphically displaying them.Much of this Toolbox functionality was previously in the Robotics Toolbox for MATLAB.</p>Peter Corkehttps://www.mathworks.com/matlabcentral/profile/44145-peter-corke685462020-08-19T01:54:19Z2020-08-19T01:54:19Zcrameri perceptually uniform scientific colormapsPerceptually uniform scientific colormaps from Fabio Crameri.<p>When color is a numerical axis, it should not be distorted. This function is similar to the cmocean (Thyng et al., 2016) function also found on File Exchange, but this one's for Fabio Crameri's colormaps (Crameri 2018a,b). </p>Chad Greenehttps://www.mathworks.com/matlabcentral/profile/1062128-chad-greene503902020-02-20T19:20:45Z2020-02-20T19:20:45ZbordersSimply plot national and US state boundaries, with or without Matlab's Mapping Toolbox.<p>* * * The functions on this page are no longer being updated. They should still work as shown in the examples here, but I am only actively maintaining the versions of these functions which are in the Climate Data Toolbox for MATLAB, which can be found here <a href="https://www.mathworks.com/matlabcentral/fileexchange/70338">https://www.mathworks.com/matlabcentral/fileexchange/70338</a>. * * *This submission contains functions to plot the outlines and names of National borders and US states. Matlab's Mapping Toolbox is NOT required. There are two functions for plotting: borders and bordersm, and they both work the same way, except that bordersm is for use with maps created using Matlab's Mapping Toolbox. Similarly, labelborders and labelbordersm place text labels within the boundaries of countries or states. </p>Chad Greenehttps://www.mathworks.com/matlabcentral/profile/1062128-chad-greene544652020-05-31T20:08:24Z2020-05-31T20:08:24Zgramm (complete data visualization toolbox, ggplot2/R-like)Quickly create publication-quality plots: automatic colors & subplots, stats, violin/box plots, etc.<p>Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab.USE CASES AND EXAMPLE SCREENSHOTS ON THE GITHUB README: <a href="https://github.com/piermorel/grammFor">https://github.com/piermorel/grammFor</a> quick help use the cheat sheet: <a href="https://github.com/piermorel/gramm/raw/master/gramm%20cheat%20sheet.pdfCITE">https://github.com/piermorel/gramm/raw/master/gramm%20cheat%20sheet.pdfCITE</a> GRAMM:Morel, (2018). Gramm: grammar of graphics plotting in Matlab. Journal of Open Source Software, 3(23), 568, <a href="https://doi.org/10.21105/joss.00568WORKFLOW:The">https://doi.org/10.21105/joss.00568WORKFLOW:The</a> typical workflow to generate a figure with gramm is the following (the example figures in the vignette are generated using 6 lines of code):- In a first step, provide gramm with the relevant data for the figure: X and Y variables, but also grouping variables that will determine color, subplot rows/columns, etc.- In the next steps, add graphical layers to your figure: raw data layers (directly plot data as points, lines...) or statistical layers (plot fits, histograms, densities, summaries with confidence intervals...). One instruction is enough to add each layer, and all layers offer many customization options.- In the last step, gramm draws the figure, and takes care of all the annoying parts: no need to loop over colors or subplots, colors and legends are generated automatically, axes limits are taken care of, etc.FEATURES:- Accepts X,Y and Z data as arrays, matrices or cells of arrays- Accepts grouping data as arrays or cellstr. Gramm works best with table-like data: separate variables/fields/columns for the variables of interest, with each variable having as many elements as observations.- Multiple ways of separating data by groups: - Colors, lightness, point markers, line styles, and point/line size ('color', 'lightness', 'marker', 'linestyle', 'size') - Subplots by row and/or columns, or wrapping columns (facet_grid() and facet_wrap()). Multiple options for consistent axis limits across facets, rows, columns, etc. (using 'scale' and 'space').- Multiple ways of directly plotting the data: - scatter plots (geom_point()) and jittered scatter plot (geom_jitter()) - lines (geom_line()) - confidence intervals (geom_interval()) - bars plots (geom_bar()) - raster plots (geom_raster()) - point counts (point_count())- Multiple ways of plotting statistical visualizations of the data: - y data summarized by x values (uniques or binned) with confidence intervals (stat_summary()) - histograms and density plots of x values (stat_bin() and stat_density()) - histograms of x-y differences (stat_cornerhist()) - box and whisker plots (stat_boxplot()) - violin plots (stat_violin()) - quantile-quantile plots (stat_qq()) of x data distribution against theoretical distribution or y data distribution. - spline-smoothed y data with optional confidence interval (stat_smooth()) - 2D binning with contour or heatmap output (stat_bin2d()) - GLM fits (stat_glm(), requires statistics toolbox) - Custom fits with user-provided anonymous function (stat_fit(), requires curve fitting toolbox) - Ellipses of confidence (stat_ellipse())- Subplots are created without too much empty space in between (and resize properly !)- Polar coordinates (set_polar())- 'z' input data in gramm() creates 3D plots when using geom_point() or geom_line()- Color data can also be displayed as a continous variable, not as a grouping factor (set_continuous_color())- X and Y axes can be flipped to get horizontal statistics visualizations (coord_flip())- Color generation can be customized in the LCH color space, or can use alternative/custom colormaps (set_color_options())- Marker shapes and sizes can be customized with set_point_options()- Line styles and width can be customized with set_line_options()- Text elements aspect can be customized with set_text_options()- Confidence intervals as shaded areas, error bars or thin lines- Set the width and dodging of graphical elements in geom_ functions, stat_bin(), stat_summary(), and stat_boxplot(), with 'width' and 'dodge' arguments- The member structure results contains the results of computations from stat_ plots as well as graphic handles for all plotted elements- Global title (set_title)- Multiple gramm plots can be combined in the same figure by creating a matrix of gramm objects and calling the draw() method on the whole matrix. An overarching title can be added by calling set_title on the whole matrix.- Different groupings can be used for different stat_ and geom_ layers with the update() method- Matlab axes properties are acessible through the method axe_property- Custom legend labels with set_names- Plot reference elements on the plots with geom_abline, geom_vline, geom_hline, and geom_polygon- Date ticks with set_datetick- Draw in a specific figure or uipanel/uitab with set_parent()</p>Pierre Morelhttps://www.mathworks.com/matlabcentral/profile/902225-pierre-morel767632020-06-10T04:27:16Z2020-06-10T04:27:16ZChimp Optimization Algorithmthis code is related to the following paper:https://www.sciencedirect.com/science/article/abs/pii/S0957417420301639<p>This article proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this article, a mathematical model of diverse intelligence and sexual motivation is proposed. Four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, the four main steps of hunting, driving, blocking, and attacking, are implemented. Afterward, the algorithm is tested on 30 well-known benchmark functions, and the results are compared to four newly proposed meta-heuristic algorithms in term of convergence speed, the probability of getting stuck in local minimums, and the accuracy of obtained results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms. </p>M. Khishehttps://www.mathworks.com/matlabcentral/profile/3705586-m-khishe527872020-11-03T11:35:41Z2020-11-03T11:35:41ZMarcusVollmer/HRVA user friendly application for screening and manipulation of ECG data and the analysis of heart rate variability.<p># HRVTool v1.07## Methods for analyzing Heart Rate VariabilityThe present functions are originally made for Matlab R2016b. Errors may occur using older releases (at least R2014b required). Additional toolboxes are not required to run the basic analysis. The Image Processing Toolbox is recommended and required to use the 'picker'-functionality.Importing ECGs out of PDFs requires Matlab start as administrator and the installation of Inkscape (or for Linux: PDFminer and pdf2svg).**HRV.m** is a Matlab class containing function for analyzing HRV.**HRVTool.m** contains the code to start the GUI (Graphical User Interface) on Matlab.**HRVTool.mlappinstall** is the app package which can be installed with Matlab.Please run HRVTool.m to start the GUI or click on the icon in the App menu of Matlab.The user interface has been tested on Windows 10, Linux Ubuntu 18.04 and Mac OS 10.15.6.### Supported file types- [x] HRM - Polar files- [x] MAT - Matlab files, structures or workspace variables containing waveforms or RR intervals (in ms)- [x] TXT - text files containing waveforms or RR intervals (in ms)- [x] ECG - PhysioNet files (PhysioNet wfdb toolbox required)- [x] WAV - Hexoskin files- [x] EDF - European Data Format- [x] ACQ - BIOPAC data (Source code of Jimmy Shen)- [x] ISHNE - Holter Standard Format (ECG and annotation data)- [x] MIBF - Machine Independent Beat file (GE Marquette holter format)- [x] PDF - ECG-PDFs from Apple Watch and AliveCor devices (Kardia and aliveecg)Other formats are possible to integrate. Please address your wishes to <a href="mailto:marcus.vollmer@uni-greifswald.deSupporting">marcus.vollmer@uni-greifswald.deSupporting</a> files to load BIOPAC ACQ data (load_acq.m, acq2mat.m) are licensed by Jimmy Shen given the copyright notice LICENSE_ACQ.Copyright (c) 2009, Jimmy ShenAll other supported files and functions are licensed under the terms of the MIT License (MIT) given in LICENSE and LICENSE_ICONSCopyright (c) 2015-2020 Marcus Vollmer27 October 2020</p>Marcus Vollmerhttps://www.mathworks.com/matlabcentral/profile/3430999-marcus-vollmer501242020-12-01T08:30:13Z2020-12-01T08:30:13ZTopoToolboxA MATLAB program for the analysis of digital elevation models<p>TopoToolbox provides a set of Matlab functions that support the analysis of relief and flow pathways in digital elevation models. The major aim of TopoToolbox is to offer helpful analytical GIS utilities in a non-GIS environment in order to support the simultaneous application of GIS-specific and other quantitative methods.TopoToolbox enables calculation of standard terrain attributes such as- slope- curvature- aspect- local topography- ...flow related terrain attributes such as- drainage basin delineation- flow accumulation- flow distance- ...stream network analysis such as- stream order- slope-area plots- chiplotsMoreover, TopoToolbox contains several tools to modify stream networks in an automated way and derive swath profiles, among other tools. The algorithms are fast and can thus be used in spatially distributed, dynamic modelling approaches in hydrology, glaciology and geomorphology. See <a href="http://topotoolbox.wordpress.com">http://topotoolbox.wordpress.com</a> for examples and instructions.</p>Wolfgang Schwangharthttps://www.mathworks.com/matlabcentral/profile/870595-wolfgang-schwanghart571532020-11-17T15:07:14Z2020-11-17T15:07:14ZAutomated Frequency Domain Decomposition (AFDD)Modal parameters identification from ambient vibrations measurement using the FDD<p>The automated Frequency Domain Decomposition presented here is inspired by the Frequency Domain Decomposition (FDD) introduced by [1, 2]. The goal is to identify the mode shapes, eigenfrequencies and modal damping ratios from acceleration records obtained during structural health monitoring of civil engineering structures subjected to ambient noise. In this submission, an automated procedure is implemented in addition to the manual one proposed by [3]. For the automated procedure, I am using the peak picking function “pickpeaks” developed by [4] and available in [5], which was much more efficient than the Matlab function "findpeaks" for this purpose. I am, therefore, indebted to [3-5] for their previous works. The modal damping ratios are determined for each mode by using [6]. The acceleration data comes from a time-domain simulation of a clamped-free beam response to white noise excitation. The target modal properties from the beam come from [7].The submission contains: - The function AFDD - A Matlab livescript file Documentation.mlx - acceleration data beamData.m (4 Mb) - The function pickpeaks.m [5]Any comment, suggestion and question are welcome.References[1] Brincker, R.; Zhang, L.; Andersen, P. (2001). "Modal identification of output-only systems using frequency domain decomposition". Smart Materials and Structures 10 (3): 441. doi:10.1088/0964-1726/10/3/303.[2] Brincker, R., Zhang, L., & Andersen, P. (2000, February). Modal identification from ambient responses using frequency domain decomposition. In Proc. of the 18*‘International Modal Analysis Conference(IMAC), San Antonio, Texas.[3] <a href="https://se.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd-[4]">https://se.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd-[4]</a> Antoine Liutkus. Scale-Space Peak Picking. [Research Report] Inria Nancy - Grand Est (Villers-lès-Nancy, France). 2015. .[5] <a href="https://se.mathworks.com/matlabcentral/fileexchange/42927-pickpeaks-v-select-display-[6]">https://se.mathworks.com/matlabcentral/fileexchange/42927-pickpeaks-v-select-display-[6]</a> <a href="https://se.mathworks.com/matlabcentral/fileexchange/55557-modal-parameters-identification-from-ambient-vibrations--sdof-[7]">https://se.mathworks.com/matlabcentral/fileexchange/55557-modal-parameters-identification-from-ambient-vibrations--sdof-[7]</a> <a href="https://se.mathworks.com/matlabcentral/fileexchange/52075-eigen-value-calculation-of-a-continuous-beam--transverse-vibrations-">https://se.mathworks.com/matlabcentral/fileexchange/52075-eigen-value-calculation-of-a-continuous-beam--transverse-vibrations-</a></p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet500412020-05-13T07:54:39Z2020-05-13T07:54:39ZWind field simulation (the user-friendly version)Simulation of spatially correlated wind velocity time-histories<p>For a more robust and time-efficient Matlab implementation, see <a href="https://se.mathworks.com/matlabcentral/fileexchange/68632-wind-field-simulation-the-fast-version.SummaryA">https://se.mathworks.com/matlabcentral/fileexchange/68632-wind-field-simulation-the-fast-version.SummaryA</a> method to simulate spatially correlated turbulent wind histories is implemented following [1,2]. Two possible vertical wind profiles and two possible wind spectra are implemented. The user is free to implement new ones. The wind co-coherence is a simple exponential decay as done by Davenport [3]. If the wind field is simulated in a grid, the function windSim.m should be used (cf. Examples 1 and 2). For a more complex geometry, such as a radial grid, the function windSim.m has an optional parameter to include two inputs (cf. Example3.mlx): The first one contains the wind properties, and the second one contains the coordinates of the nodes where wind histories are simulated (cf. Example 3).ContentThe submission contains: - 1 input file INPUT.txt for Example1.m - 1 input file INPUT_MAST.txt for Example2.m - 2 input files windData.txt and circle.txt for Example3.m - The function windSim.m - 3 examples files Example1.m, Example2.m, Example3.m - The function coherence.m that computes the co-coherence. Notes: - Simulating the wind field in a high number of points with a high sampling frequency may take a lot of time. - This code aims to be highly customizable A more straightforward version of the present submission has been used to simulate the turbulent wind load on a floating suspension bridge [4].References[1] Shinozuka, M., Monte Carlo solution of structural dynamics, Computers and Structures, Vol. 2, 1972, pp. 855 – 874[2] Deodatis, G., Simulation of ergodic multivariate stochastic processes, Journal of Engineering Mechanics, ASCE, Vol. 122 No. 8, 1996, pp. 778 – 787.[3] Davenport, A. G. (1961), The spectrum of horizontal gustiness near the ground in high winds. Q.J.R. Meteorol. Soc., 87: 194–211[4] Wang, J., Cheynet, E., Snæbjörnsson, J. Þ., & Jakobsen, J. B. (2018). Coupled aerodynamic and hydrodynamic response of a long span bridge suspended from floating towers. Journal of Wind Engineering and Industrial Aerodynamics, 177, 19-31.</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet686322020-11-09T08:24:57Z2020-11-09T08:24:57ZWind field simulation (the fast version)A three-variate turbulent wind field (u,v and w components) is simulated in three-dimensions. <p>The present submission deals with the simulation of turbulent wind field (u,v,w, components) in 3-D (two dimensions for space and one for the time). The computational efficiency of the simulation relies on Ref. [1], which leads to a significantly shorter simulation time than the function windSim, also available on fileExchange. However, only the case of a regular 2D vertical grid normal to the flow is here considered.The submission contains:- An example file Example1 that illustrates simply how the output variables look like.- An example file Example2, which is more complete, and which simulates a 3-D turbulent wind field on a 7x7 grid.- An example file Example3, which illustrates the implementation of the quad-coherence to generate a turbulent wind field.- A data file exampleData.mat used in Example1.- The function windSimFast.m, which is used to generate the turbulent wind field. A similar implementation of windSimFast.m was used in ref. [2].- The function getSamplingpara.m, which computes the time and frequency vectors.- The function KaimalModel.m, which generates the one-point auto and cross-spectral densities of the velocity fluctuations, following the Kaimal model [3]. I have corrected the cross-spectrum density formula used by Kaimal et al. so that the simulated friction velocity is equal to the target one. - The function coherence used to estimate the root-mean-square coherence, the co-coherence and the quad-coherence.References: [1] Shinozuka, M., & Deodatis, G. (1991). Simulation of stochastic processes by spectral representation. Applied Mechanics Reviews, 44(4), 191-204. [2] Wang, J., Cheynet, E., Snæbjörnsson, J. Þ., & Jakobsen, J. B. (2018). Coupled aerodynamic and hydrodynamic response of a long span bridge suspended from floating towers. Journal of Wind Engineering and Industrial Aerodynamics, 177, 19-31. [3] Davenport, A. G. (1961). The spectrum of horizontal gustiness near the ground in high winds. Quarterly Journal of the Royal Meteorological Society, 87(372), 194-211.Any comment, suggestion or question is welcomed.</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet513202020-11-29T20:35:22Z2020-11-29T20:35:22ZPanel Data Toolbox for MATLABA Panel Data Toolbox for MATLAB<p>Panel Data Toolbox v2.0 is a new package for MATLAB that includes functions to estimate the main econometric methods of panel data analysis. The package covers the standard fixed, between and random effects methods, that are extended to allow for instrumental variables, as well as spatial panel data specifications.Journal paper describing the functionality of the toolbox can be found here:<a href="https://ideas.repec.org/a/jss/jstsof/v076i06.htmlCopyright">https://ideas.repec.org/a/jss/jstsof/v076i06.htmlCopyright</a> 2013-2019 Inmaculada C. Álvarez, Javier Barbero, José L. Zofío</p>Javier Barbero Jiménezhttps://www.mathworks.com/matlabcentral/profile/2264023-javier-barbero-jimenez789612020-09-10T14:16:01Z2020-09-10T14:16:01ZJellyfish Search Optimizer (JS)A Novel Metaheuristic Optimizer Inspired By Behavior of Jellyfish in Ocean<p>This study develops a novel metaheuristic algorithm that is inspired by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. The new algorithm is successfully tested on benchmark functions and optimization problems. Notably, JS has only two control parameters, which are population size and number of iterations. Therefore, JS is very simple to use, and potentially an excellent metaheuristic algorithm for solving optimization problems.</p>nhat truonghttps://www.mathworks.com/matlabcentral/profile/9827317-nhat-truong786012020-07-26T15:07:59Z2020-07-26T15:07:59ZBinary Optimization Using Hybrid GWO for Feature SelectionThis is the Matlab Code for BGWOPSO<p>MATLAB code for BGWOPSO: Binary Optimization Using Hybrid Grey Wolf Optimization for Feature SelectionPaper Reference - Al-Tashi, Q., Kadir, S. J. A., Rais, H. M., Mirjalili, S., & Alhussian, H. (2019). Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496-39508.Link for algorithm details: Paperhttps://ieeexplore.ieee.org/abstract/document/8672550Running the codeSet all the required parametersrun file demo.mAbstract:A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.</p>Qasem Al-Tashihttps://www.mathworks.com/matlabcentral/profile/11296281-qasem-al-tashi665842020-10-25T05:55:00Z2020-10-25T05:55:00ZReal Time Object Detection using Deep Learning.Object Detection using Deep Learning tool.<p>The smart phone is used as webcam device. We can use it by installing IP Webcam app. Make sure that the Laptop and your smart phone must me connected to the same network using Wifi.A specific solution for Android:Install the free IP Webcam app. (Make sure you read the corresponding permissions and understand any security issues therein)Open the app, set the desired resolution (will impact the speed!)Scroll to the bottom and tap on 'Start Server'</p>ABHILASH SINGHhttps://www.mathworks.com/matlabcentral/profile/5569287-abhilash-singh634022020-07-13T12:30:03Z2020-07-13T12:30:03ZMicroscopy Image Browser 2 (MIB2)MIB2 is an update package for segmentation of multi-dimensional (2D-4D) microscopy datasets<p>With MIB2 you can analyse, segment and visualize various multidimensional datasets from both light and electron microscopy. MIB2 is completely rewritten to follow MVC architecture and brings additional stability among many new features. See more further details and tutorials on MIB website: <a href="http://mib.helsinki.fiI">http://mib.helsinki.fiI</a> would like to acknowledge Matlab File Exchange user community and especially the authors whose functions were utilized during development of the program: <a href="http://mib.helsinki.fi/acknowledgements.htmlThe">http://mib.helsinki.fi/acknowledgements.htmlThe</a> MIB version 1 is available from here <a href="http://se.mathworks.com/matlabcentral/fileexchange/56481-microscopy-image-browser--mib-">http://se.mathworks.com/matlabcentral/fileexchange/56481-microscopy-image-browser--mib-</a> and recommended for Matlab version: R2011a - 2014a</p>Ilya Belevichhttps://www.mathworks.com/matlabcentral/profile/1366808-ilya-belevich690302020-04-28T16:49:23Z2020-04-28T16:49:23ZOperational modal analysis with automated SSI-COV algorithmThe modal parameters of a line-like structure are automatically identified using an SSI-COV algorithm applied to ambient vibration data<p>The function SSICOV.m aims to automatically identify the eigenfrequencies, mode shapes and damping ratios of a line-like structure using ambient vibrations only. The covariance-driven stochastic subspace identification method (SSI-COV) is used in combination with a clustering algorithm to automatically analyse the stabilization diagrams.The algorithm has been applied for ambient vibration monitoring of the Lysefjord Bridge [2] and was compared to the frequency domain decomposition technique [3]. Finally, the algorithm was found accurate enough to visualise the evolution of the bridge eigenfrequencies with the temperature [4]. A similar algorithm was proposed earlier by Magalhaes et al. [1].The submission file contains:- A data file BridgeData.mat- A Matlab Live Script Example1.mlx that illustrates the application of the algorithm.- The function SSICOV which is the automated SSI-COV algorithm.- The function plotStabDiag.m, which plot the stabilization diagram.Any question, suggestion or comment is welcomed.References[1] Magalhaes, F., Cunha, A., & Caetano, E. (2009). Online automatic identification of the modal parameters of a long span arch bridge. Mechanical Systems and Signal Processing, 23(2), 316-329.[2] Cheynet, E., Jakobsen, J. B., & Snæbjörnsson, J. (2016).Buffeting response of a suspension bridge in complex terrain. Engineering Structures, 128, 474-487.[3] Cheynet, E., Jakobsen, J. B., & Snæbjörnsson, J. (2017).Damping estimation of large wind-sensitive structures.Procedia Engineering, 199, 2047-2053.[4] Cheynet, E., Snæbjörnsson, J., & Jakobsen, J. B. (2017).Temperature Effects on the Modal Properties of a Suspension Bridge.In Dynamics of Civil Structures, Volume 2 (pp. 87-93). Springer.</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet722472020-09-25T04:16:51Z2020-09-25T04:16:51ZColebrook-White EquationComputes the friction factor in pipes for given values of the Reynolds number (Re) and the relative roughness coefficient (epsilon).<p>MATLAB code to compute the friction factor in pipes for given values of the Reynolds number (Re) and the relative roughness coefficient (epsilon).Syntax: f = colebrook(Re,epsilon)Example 1: Single Re, single epsilon Re = 1e5; epsilon = 1e-4; f = colebrook(Re,epsilon)Example 2: Multiple Re, single epsilon Re = 5000:1000:100000; epsilon = 1e-4; f = colebrook(Re,epsilon); plot(Re,f)Example 3: Single Re, multiple epsilon Re = 1e5; epsilon = linspace(1e-4,1e-1,100); f = colebrook(Re,epsilon); plot(epsilon,f)Example 4: Multiple Re, multiple epsilon Re = logspace(4,8,100); epsilon = linspace(1e-4,1e-1,100); [RE,EPSILON] = meshgrid(Re,epsilon); F = colebrook(RE,EPSILON); surf(RE,EPSILON,F)References: [1] Colebrook, C. F., & White, C. M. (1937). Experiments with fluid friction in roughened pipes. Proceedings of the Royal Society of London. Series A - Mathematical and Physical Sciences, 161(906), 367-381. [2] Colebrook, C. (1939). Turbulent Flow in Pipes, with Particular Reference to the Transition Region between the Smooth and Rough Pipe Laws. Journal of the Institution of Civil Engineers, 11(4), 133-156.</p>Ildeberto de los Santos Ruizhttps://www.mathworks.com/matlabcentral/profile/5153703-ildeberto-de-los-santos-ruiz766192020-06-23T15:31:31Z2020-06-23T15:31:31ZSlime Mould Algorithm (SMA): A Method for OptimizationA new stochastic optimizer slime mould algorithm (SMA): https://www.sciencedirect.com/science/article/pii/S0167739X19320941<p>In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based on the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics using an extensive set of benchmarks to verify its efficiency. Moreover, four classical engineering problems are utilized to estimate the efficacy of the algorithm in optimizing constrained problems. The results demonstrate that the proposed SMA benefits from competitive, often outstanding performance on different search landscapes. The source codes of SMA are publicly available at <a href="http://www.alimirjalili.com/SMA.html">http://www.alimirjalili.com/SMA.html</a> and <a href="https://tinyurl.com/Slime-mould-algorithm.Main">https://tinyurl.com/Slime-mould-algorithm.Main</a> paper: Slime mould algorithm: A new method for stochastic optimizationShimin Li Huiling Chen Mingjing Wang Ali Asghar Heidari Seyedali Mirjalili Future Generation Computer Systems Volume 111, October 2020, Pages 300-323More information, source code, and related supplementary materials such as Latex files and Visio files for figures of the original paper can be found in:(a) <a href="https://www.researchgate.net/profile/Ali_Asghar_Heidari(b)">https://www.researchgate.net/profile/Ali_Asghar_Heidari(b)</a> <a href="http://alimirjalili.com/SMA.html(c)">http://alimirjalili.com/SMA.html(c)</a> <a href="https://github.com/aliasghar68/Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-e-Mail">https://github.com/aliasghar68/Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-e-Mail</a>: <a href="mailto:aliasghar68@gmail.com">aliasghar68@gmail.com</a>, <a href="mailto:as_heidari@ut.ac.ir">as_heidari@ut.ac.ir</a>(singapore) <a href="mailto:aliasgha@comp.nus.edu.sg">aliasgha@comp.nus.edu.sg</a>, <a href="mailto:t0917038@u.nus.eduHomepage">t0917038@u.nus.eduHomepage</a>: <a href="https://www.researchgate.net/profile/Ali_Asghar_Heidari">https://www.researchgate.net/profile/Ali_Asghar_Heidari</a></p>aliasgharheidari.comhttps://www.mathworks.com/matlabcentral/profile/3996469-aliasgharheidari-com784922020-07-25T12:46:12Z2020-07-25T12:46:12ZHeap-Based Optimizer (HBO)A novel meta-heuristic inspired by Corporate Rank Hierarchy for global optimization<p>In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The code is also available at <a href="https://github.com/qamar-askari/HBO.This">https://github.com/qamar-askari/HBO.This</a> is the source code of "Askari Q, Saeed M, Younas I. Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Systems with Applications. 2020 Jul 18" <a href="https://doi.org/10.1016/j.eswa.2020.113702.My">https://doi.org/10.1016/j.eswa.2020.113702.My</a> Homepage: <a href="http://qamaraskari.com/The">http://qamaraskari.com/The</a> link to download the paper without subscription is available at the homepage. The Latex sources and MATLAB implementation of my algorithms and benchmark functions are also available at my homepage. I'm open to collaborate if you are interested in to work on my algorithms and enhance them or hybridize them with existing techniques or apply them to solve real-world applications. My research interests and current projects are also available at my homepage.</p>Qamar Askarihttps://www.mathworks.com/matlabcentral/profile/8668353-qamar-askari555572020-05-14T14:52:23Z2020-05-14T14:52:23ZDamping ratio estimation from ambient vibrations (SDOF)The modal damping ratio of a Single-Degree-of-Freedom (SDOF) System is estimated using ambient vibrations data<p>If the free-decay response (FDR) of a Single Degree-of-Freedom (SDOF) system is not directly available, it is possible to use ambient vibrations data yo estimate the modal damping ratio. Here, the Random Decrement Technique (RDT) [1], as well as the Natural Excitation Technique (NExT) [2], are used. First, the response of a SDOF to white noise is simulated in the time domain using [3]. Then the IRF is computed using the RDT or NExT. Finally, and an exponential decay is fitted to the envelop of the IRF to obtain the modal damping ratio.The present submission contains:- a function RDT.,m that implements to Random Decrement Technique (RDT)- a function NExT that implements the Natural Excitation Technique (NExT)- a function expoFit that determine the modal damping ratio by fitting an exponential decay to the envelope of the IRF.- a function CentDiff used to simulate the response to a white noise load of a SDOF in the time domain.- An example file Example.mAny question, comment or suggestion is welcomed.References[1] Ibrahim, S. R. (1977). Random decrement technique for modal identification of structures. Journal of Spacecraft and Rockets, 14(11), 696-700.[2] James III, O. H., & Came, T. G. (1995). The natural excitation technique (next) for modal parameter extraction from operating structures.[3] <a href="http://www.mathworks.com/matlabcentral/fileexchange/53854-harmonic-excitation-of-a-sdof">http://www.mathworks.com/matlabcentral/fileexchange/53854-harmonic-excitation-of-a-sdof</a></p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet721082020-04-22T21:35:23Z2020-04-22T21:35:23ZOpenCossanOpenCossan is an open and free toolbox for uncertainty quantification and management.<p>OpenCossan is a Matlab-based toolbox for uncertainty quantification and management. The implemented framework includes third-party software integration (e.g. ANSYS), efficient numeric algorithms (e.g. Line Sampling) and parallelization for high performance computing. OpenCossan functionalities can be summarized in:Uncertainty QuantificationSimulation-based Reliability AnalysisSensitivity AnalysisMeta-ModellingStochastic Finite Elements AnalysisReliability-Based Optimization</p>Edoardo Patellihttps://www.mathworks.com/matlabcentral/profile/2112693-edoardo-patelli714842020-10-22T08:48:21Z2020-10-22T08:48:21ZBinary Grey Wolf Optimization for Feature SelectionDemonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.<p>This toolbox offers two types of binary grey wolf optimization (BGWO) methods The "Main" script demos the examples of how BGWO solves the feature selection problem using benchmark data-set. **********************************************************************************************************************************Detail of BGWO can be found in the following papers:[1] Too, J., Abdullah, A.R., Mohd Saad, N., Mohd Ali, N. and Tee, W., 2018. A new competitive binary Grey Wolf Optimizer to solve the feature selection problem in EMG signals classification. Computers, 7(4), p.58.DOI: <a href="https://doi.org/10.3390/computers7040058[2]">https://doi.org/10.3390/computers7040058[2]</a> Too, J. and Abdullah, A.R., 2020. Opposition based competitive grey wolf optimizer for EMG feature selection. Evolutionary Intelligence.DOI: <a href="https://doi.org/10.1007/s12065-020-00441-5">https://doi.org/10.1007/s12065-020-00441-5</a></p>Jingwei Toohttps://www.mathworks.com/matlabcentral/profile/12879262-jingwei-too633242020-02-06T22:47:22Z2020-02-06T22:47:22ZArctic Mapping ToolsOr: Antarctic Mapping Tools for Matlab, Greenland Edition<p>This toolbox contains some Antarctic Mapping Tools functions, adapted for the northern hemisphere. Check the Examples tab above for an overview of Arctic Mapping Tools. Each function here mimics a similarly-named, but much-better-documented function in Antarctic Mapping Tools. Be sure to check the Examples in the Antarctic version here: <a href="https://www.mathworks.com/matlabcentral/fileexchange/47638">https://www.mathworks.com/matlabcentral/fileexchange/47638</a></p>Chad Greenehttps://www.mathworks.com/matlabcentral/profile/1062128-chad-greene745772020-07-22T08:50:32Z2020-07-22T08:50:32ZPolitical Optimizer (PO)A novel socio-inspired meta-heuristic for global optimization. <p>PO is the mathematical mapping of all the major phases of politics such as constituency allocation, party switching, election campaign, inter-party election, and parliamentary affairs. PO assigns each solution a dual role by logically dividing the population into political parties and constituencies. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behavior of the politicians from the previous election. This is the source code of "Askari Q, Younas I, Saeed M. Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems. 2020 Mar 5" <a href="https://doi.org/10.1016/j.knosys.2020.105709">https://doi.org/10.1016/j.knosys.2020.105709</a>. My Homepage: <a href="http://qamaraskari.com/">http://qamaraskari.com/</a> The Latex sources and MATLAB implementation of my algorithms and benchmark functions are also available at my homepage. I'm open to collaborate if you are interested in to work on my algorithms and enhance them or hybridize them with existing techniques or apply them to solve real-world applications. My research interests and current projects are also available at my homepage.</p>Qamar Askarihttps://www.mathworks.com/matlabcentral/profile/8668353-qamar-askari825852020-11-10T06:44:24Z2020-11-10T06:44:24ZPre-trained 3D ResNet-18Pre-trained Neural Network Toolbox Model for 3D ResNet-18 Network<p>To transfer the learnable parameters from pre-trained 2D ResNet-18 (ImageNet) to 3D one, we duplicated 2D filters (copying them repeatedly) through the third dimension. This is possible since a video or a 3D image can be converted into a sequence of image slices. In the training process, we expect that the 3D ResNet-18 learns patterns in each frame. This model has 34 million learnable parameters. simply, call "resnet18TL3Dfunction()" function.</p>Amir Ebrahimihttps://www.mathworks.com/matlabcentral/profile/12814523-amir-ebrahimi741512020-07-26T22:13:47Z2020-07-26T22:13:47ZVehicle Following Model with 3D AnimationThis model contains two vehicle models. The following vehicle uses different controllers to follow the leading vehicle.<p>This model contains two vehicle models. The following vehicle uses different controllers to follow the leading vehicle. The purpose of this model is to tune the MPC controller for the following vehicle so that it keeps a safe distance. The visualization is done by 3D Animations at this point but we improve the model every week, we will include different vehicle following algorithms and visualizations such as gaming engines. How to build this model is explained in this playlist and it is updated whenever another video is uploaded:<a href="https://www.youtube.com/playlist?list=PLNNL3443z4lHTmBFrrur6aYhJnwvITqWM">https://www.youtube.com/playlist?list=PLNNL3443z4lHTmBFrrur6aYhJnwvITqWM</a></p>Mustafa Saraogluhttps://www.mathworks.com/matlabcentral/profile/9124022-mustafa-saraoglu784442020-07-26T11:04:38Z2020-07-26T11:04:38ZMOBATSimMOBATSim (Model-based Autonomous Traffic Simulation Framework) is a simulation framework based on MATLAB Simulink that allows the user to as<p>MOBATSim: a Model-based Autonomous Traffic Simulation Framework for Safety Assessment, is a framework developed in the Faculty of Electrical and Computer Engineering, Technische Universität Dresden. The main developer, Mustafa Saraoğlu, is working on the safety assessment of autonomous vehicle components and functions as his Ph.D. topic in the Model-based Analysis Group from the Institute of Automation.Related MathWorks Online Article: <a href="https://www.mathworks.com/company/newsletters/articles/developing-an-autonomous-traffic-simulation-framework-for-functional-safety-testing.htmlAutomated">https://www.mathworks.com/company/newsletters/articles/developing-an-autonomous-traffic-simulation-framework-for-functional-safety-testing.htmlAutomated</a> driving systems tend to be more important and sophisticated in the nearest future. The functional safety assessment for these systems becomes an urgent necessity for the transition to full autonomy. Testing these functions consisting of decision and control algorithms with a lot of variables and parameters in a unified manner is a daunting task. Threat assessment has to be made for vehicles to actively avoid hazardous situations. This requires the analysis of complex operational profiles such as routing, intersection management and collision prediction in an environment where multiple vehicles are in different positions, and traveling at different speeds. There is a need for a comprehensive traffic simulation framework which models not only the functionality of the vehicles but also the interactions between them.As a solution, we offer a new simulation framework, MOBATSim, which is completely based on MATLAB Simulink. It allows the user to customize decision and control algorithms for the modeled autonomous vehicles and analyze their effects on the overall safety of the high-level urban traffic environment. The user can define safety goals, derive functional safety requirements, describe driving scenarios, and verify if these goals are met for certain autonomous vehicle functions that are tested. The simulation-based fault injection is used to perform the tests in the presence of various types of random or predetermined faults of low-level vehicle components such as sensors and communication modules. The definitions of safety and controllability requirements of autonomous vehicle functions are derived from the safety standard ISO 26262. Automatically generated reports allow the user to save time and costs in the early design phase through comprehensive testing and it can also prove a useful framework to overcome the challenges of the adoption of ISO 26262. The data can be logged during the simulations and later be used by Simulink 3D Animation for visual investigations.</p>Mustafa Saraogluhttps://www.mathworks.com/matlabcentral/profile/9124022-mustafa-saraoglu806952020-10-05T12:14:45Z2020-10-05T12:14:45ZHybrid Elephant Herding Optimization and TOPSIS approachEHO and TOPSIS approach for multiobjective/MCDM DER/DG integration in distribution networks<p>This folder provides the Matlab codes of metaheuristic (EHO) and TOPSIS approach for solving the multiobjective optimal DG integration problems of distribution networks. The objective functions considered here are the minimization of power loss and node voltage deviation while maximizing the voltage stability index of the distribution system. It also includes the backwards-forward load flow method to solve the power flow equations.</p>NAND KISHOR MEENAhttps://www.mathworks.com/matlabcentral/profile/4904581-nand-kishor-meena698582020-10-17T15:33:31Z2020-10-17T15:33:31ZFOPID-tunerFractional order proportional integral derivative controller tuner<p>FOPID tunerThis project is based on FOPD tuner: <a href="https://github.com/cnpcshangbo/FOPD-tuner/tree/optimization-method/controller-analysis-with-Simulink/optimizationUsageRun">https://github.com/cnpcshangbo/FOPD-tuner/tree/optimization-method/controller-analysis-with-Simulink/optimizationUsageRun</a> "run_patternsearch_npm"</p>Bohttps://www.mathworks.com/matlabcentral/profile/2991582-bo803442020-09-20T12:50:50Z2020-09-20T12:50:50ZDTC Scheme for a 4-Switch Inverter-Fed Induction MotorDTC Scheme for a Four-Switch Inverter-Fed Induction Motor Emulating the Six-Switch Inverter Operation<p>DTC Scheme for a Four-Switch Inverter-Fed Induction Motor Emulating the Six-Switch Inverter Operation</p>T Vijay Munihttps://www.mathworks.com/matlabcentral/profile/4397299-t-vijay-muni747502020-03-29T19:40:38Z2020-03-29T19:40:38ZMulti-objective Flower Pollination Algorithm (MOFPA)This is the demo code for multi-objective flower pollination algorithm. <p>MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function of D=30 (dimensions), which can be extended to solve other multi-objective optimization problems. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub). X.-S. Yang, M. Karamanoglu, X.-S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, vol. 46, no. 9, 1222-1237 (2014). </p>XS Yanghttps://www.mathworks.com/matlabcentral/profile/3659939-xs-yang744732020-03-10T04:51:03Z2020-03-10T04:51:03ZAdaptive Gaussian Notch Filter for Removing Periodic NoiseMatlab Implementation of the paper "Adaptive Gaussian Notch Filter for Removing Periodic Noise from Digital Images" <p>Matlab Implementation of the paper Varghese, Justin, et al. "Adaptive Gaussian Notch Filter for Removing Periodic Noise from Digital Images" IET Image Processing, Institution of Engineering and Technology (IET), Mar. 2020<a href="https://doi.org/10.1049/iet-ipr.2018.5707Please">https://doi.org/10.1049/iet-ipr.2018.5707Please</a> Cite our paper where ever you use it</p>Justin Varghesehttps://www.mathworks.com/matlabcentral/profile/17446231-justin-varghese750842020-05-01T09:50:30Z2020-05-01T09:50:30ZDigital-Image-WatermarkingDigital Image Watermarking Method Based on Hybrid DWT-HD-SVD Technique: Attacks, PSNR, SSIM, NC<p>The main goal of this project is to provide a basic watermark toolbox for researchers to evaluate watermarking methods under various attacks. To run the simulation, please open the main.m file.You can also download the source code from GitHub: <a href="https://github.com/Saeid-jhn/Digital-Image-WatermarkingThe">https://github.com/Saeid-jhn/Digital-Image-WatermarkingThe</a> simulation is based on the IEEE Access journal, "An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain". The following methods are used:Discrete Wavelet Transformation (DWT)Hessenberg Decomposition (HD)Singular Value decomposition (SVD)First, in the embedding process, the host image is modified to embed the watermark image. Different attacks are applied to evaluate the robustness and invisibility of the proposed method by considering peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Finally, the watermark image is extracted, and the robustness is evaluated considering normalized correlation (NC).Getting started:* In order to make a copy of the repo, please fork it in GitHub; otherwise simply Clone or Download it to your local device.* Run the main.m file in MATLAB. It is recommended to run it section by section; otherwise, you would need to wait for the whole code to run.* The initial sections merely, run code to illustrate the proposed watermarking method.* The midsections, plots NC, PSNR, SSIM values for each alpha (Fig. 5, 6, and 7 paper).* The last sections evaluate the invisibility and robustness of the watermarked image and extracted watermark logo, for different watermark image size under various attacks (fig. 8, 9, and 10 paper).* Finally, the NC value of different attack parameters are evaluated for each attack (fig. 11 paper).Support:If you find this MATLAB code helpful, please star and fork it in GitHub or rank it in MathWorks.</p>Saeid Jahandarhttps://www.mathworks.com/matlabcentral/profile/13800799-saeid-jahandar715152020-10-22T13:53:33Z2020-10-22T13:53:33ZBinary Differential Evolution for Feature SelectionThe binary version of Differential Evolution (DE), named as Binary Differential Evolution (BDE) is applied for feature selection tasks.<p>This toolbox offers Binary Differential Evolution (BDE) method The "Main" script illustrates the example of how BDE can solve the feature selection problem using benchmark data-set.**********************************************************************************************************************************Detail of BDE can be found in the following papers: [1] Too, J., Abdullah, A.R. and Mohd Saad, N., 2019. Hybrid Binary Particle Swarm Optimization Differential Evolution Based Feature Selection for EMG Signals Classification. Axioms, 8(3), p.79.DOI: <a href="https://doi.org/10.3390/axioms8030079[2]">https://doi.org/10.3390/axioms8030079[2]</a> Too, J., Abdullah, A.R., Mohd Saad, N. and Tee, W., 2019. EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Computation, 7(1), p.12.DOI: <a href="https://doi.org/10.3390/computation7010012">https://doi.org/10.3390/computation7010012</a></p>Jingwei Toohttps://www.mathworks.com/matlabcentral/profile/12879262-jingwei-too747522020-03-29T19:39:20Z2020-03-29T19:39:20ZMultiobjective Cuckoo Search (MOCS)This demo shows how the multiobjective cuckoo search works.<p>The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).</p>XS Yanghttps://www.mathworks.com/matlabcentral/profile/3659939-xs-yang760032020-10-10T22:49:22Z2020-10-10T22:49:22ZVariational Mode Extraction (VME.m)This is a modified code of VME method (Ver. 2) which is a useful decomposition algorithm to extract a specific mode from the signal.<p>The VME is a robust method when there is no need to decompose the whole signal. Indeed, if the aim is to achieve a particular mode from the signal VME is the best choice (just by knowing an approximation of the frequency band of the specific mode of interest). Indeed, VME assumes that signal is composed of two parts: F(t)=Ud(t)+Fr(t); in which F(t) refers to input signal, Ud(t) is the desired mode, and Fr(t) indicates the residual signal.</p>Mojtaba Nazarihttps://www.mathworks.com/matlabcentral/profile/16833607-mojtaba-nazari768542020-11-09T14:49:08Z2020-11-09T14:49:08ZOne-point random process generationMinimalist Matlab implementation of a random process generation in one point using the spectral method<p>A stationary Gaussian random process is generated using the spectral method. This means that the function requires only two inputs: the target power spectral density (PSD) and the associated frequency vector.The present submission contains: - The function randomProcess.m, which generates the (random) time series associated with a target PSD - An example file Documentation.mlx, which illustrates the generation of the random process using the case of atmospheric turbulence- The function getSamplingPara.m, which computes the target frequency vector and the associated time vector.Any question, suggestion or comment is welcome.</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet579562020-04-25T23:18:34Z2020-04-25T23:18:34ZTauFactorApp for analysis of image based geometry data in terms of tortuosity factors, volume fractions, surface areas and triple phase boundaries.<p>Please contact the author at <a href="mailto:sjc08@ic.ac.uk">sjc08@ic.ac.uk</a> for any questions or if you wish to cite this application in a academic study.Journal paper available here: <a href="http://www.sciencedirect.com/science/article/pii/S2352711016300280User">http://www.sciencedirect.com/science/article/pii/S2352711016300280User</a> manual available here: <a href="https://sourceforge.net/projects/taufactor/files/?source=navbarTauFactor">https://sourceforge.net/projects/taufactor/files/?source=navbarTauFactor</a> is a MatLab application for efficiently calculating the tortuosity factor, as well as volume fractions, surface areas and triple phase boundary densities, from image based microstructural data. The tortuosity factor quantifies the apparent decrease in diffusive transport resulting from convolutions of the flow paths through porous media. TauFactor calculates this value using an over relaxed finite-different approach. This tool provides a fast computational platform applicable to the big datasets (up to 4x10^9 voxels) typical of modern tomography, without requiring high computational power.</p>Samuel Cooperhttps://www.mathworks.com/matlabcentral/profile/4961663-samuel-cooper522762020-05-08T21:05:53Z2020-05-08T21:05:53ZMode shapes extraction by time domain decomposition (TDD)The modal parameters of a line-like structure are estimated in the time domain using displacement records only.<p>The Time domain decomposition (TDD) [1] is an output-only method to extract mode shapes of a structure. Here, the modal damping ratios and modal displacements are in addition extracted using the functions presented in [6]. The TDD is similar to a more popular technique called Frequency-domain method (FDD) that was introduced by [2,3]. A good example of the FDD already exists on the Matlab File Exchange [4]. In a previous version, the present submission contained a function for the FDD. This function has been modified and moved to a new submission [5].This script contains:- The function TDD.m: function to apply the TDD method.- An example file Example1.m- Acceleration data beamData.m (4 Mb) Comments, suggestions for improvements and questions are welcome. All the credits for the theory go to [1] and [2].References [1] Byeong Hwa Kim, Norris Stubbs, Taehyo Park, A new method to extract modal parameters using output-only responses, Journal of Sound and Vibration, Volume 282, Issues 1–2, 6 April 2005, Pages 215-230, ISSN 0022-460X, <a href="http://dx.doi.org/10.1016/j.jsv.2004.02.026.[2]">http://dx.doi.org/10.1016/j.jsv.2004.02.026.[2]</a> Brincker, R.; Zhang, L.; Andersen, P. (2001). "Modal identification of output-only systems using frequency domain decomposition". Smart Materials and Structures 10 (3): 441. doi:10.1088/0964-1726/10/3/303.[3] BRINCKER, Rune, ZHANG, Lingmi, et ANDERSEN, P. Modal identification from ambient responses using frequency domain decomposition. In: Proc. of the 18*‘International Modal Analysis Conference (IMAC), San Antonio, Texas. 2000 [4] <a href="http://www.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd-[5]">http://www.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd-[5]</a> <a href="https://se.mathworks.com/matlabcentral/fileexchange/57153-automated-frequency-domain-decomposition--afdd-[6]">https://se.mathworks.com/matlabcentral/fileexchange/57153-automated-frequency-domain-decomposition--afdd-[6]</a> <a href="https://se.mathworks.com/matlabcentral/fileexchange/55557-modal-parameters-identification-from-ambient-vibrations--sdof">https://se.mathworks.com/matlabcentral/fileexchange/55557-modal-parameters-identification-from-ambient-vibrations--sdof</a></p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet735412020-07-15T06:20:18Z2020-07-15T06:20:18ZOTT: Optical Tweezers ToolboxToolbox for simulating optical tweezers<p>The optical tweezers toolbox can be used to calculate optical forces and torques of particles using the T-matrix formalism in a vector spherical wave basis. The toolbox includes codes for calculating T-matrices, beams described by vector spherical wave functions, functions for calculating forces and torques, simple codes for simulating dynamics and examples.</p>Isaac Lentonhttps://www.mathworks.com/matlabcentral/profile/12835902-isaac-lenton720142020-03-23T08:59:58Z2020-03-23T08:59:58ZaPC Matlab Toolbox: Data-driven Arbitrary Polynomial ChaosaPC Matlab Toolbox constructs the Data-driven Arbitrary Polynomial Chaos Expansion:Uncertainty quantification/Global sensitivity analysis<p>Polynomial chaos expansion (PCE) introduced by Norbert Wiener in 1938. PCE can be seen, intuitively, as a mathematically optimal way to construct and obtain a model response surface in the form of a high-dimensional polynomial in uncertain model parameters. Recently the polynomial chaos expansion received a generalization towards the arbitrary polynomial chaos expansion (aPC: Oladyshkin S. and Nowak W., 2012), which is a so-called data-driven generalization of the PCE. Like all polynomial chaos expansion techniques, aPC approximates the dependence of simulation model output on model parameters by expansion in an orthogonal polynomial basis. The aPC generalizes chaos expansion techniques towards arbitrary distributions with arbitrary probability measures, which can be either discrete, continuous, or discretized continuous and can be specified either analytically (as probability density/cumulative distribution functions), numerically as histogram or as raw data sets. The aPC at finite expansion order only demands the existence of a finite number of moments and does not require the complete knowledge or even existence of a probability density function. This avoids the necessity to assign parametric probability distributions that are not sufficiently supported by limited available data. Alternatively, it allows modellers to choose freely of technical constraints the shapes of their statistical assumptions. Investigations indicate that the aPC shows an exponential convergence rate and converges faster than classical polynomial chaos expansion techniques. The aPC Matlab Toolbox have been developed in the year 2010 for scientific purpose and now it is available for the Matlab community. AUTHOR: Sergey OladyshkinAFFILIATION: Stuttgart Research Centre for Simulation Technology, Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Pfaffenwaldring 5a, 70569 StuttgartCONTACT INFORMATION: E-mail: <a href="mailto:Sergey.Oladyshkin@iws.uni-stuttgart.dePhone">Sergey.Oladyshkin@iws.uni-stuttgart.dePhone</a>: +49-711-685-60116Fax: +49-711-685-51073Website: <a href="http://www.iws.uni-stuttgart.de">http://www.iws.uni-stuttgart.de</a></p>Sergey Oladyshkinhttps://www.mathworks.com/matlabcentral/profile/9146260-sergey-oladyshkin746632020-11-23T22:52:37Z2020-11-23T22:52:37ZGeneralized chi-square distributionCompute the statistics, pdf, cdf, inverse cdf and random numbers of the generalized chi-square distribution.<p>The generalized chi-square variable is a quadratic form of a normal variable, or equivalently, a linear sum of independent non-central chi-square variables and a normal variable. Try the Getting Started guide for a quick demo of all the functions.</p>Abhranil Dashttps://www.mathworks.com/matlabcentral/profile/6667082-abhranil-das520752020-05-08T20:10:12Z2020-05-08T20:10:12Zeigen-value calculation of continuous beamsCompute the eigenfrequencies and mode shapes of a continuous beam with different boundary conditions<p>SummaryThe eigenfrequencies and mode shapes of a simple beam are calculated based on [1]. During the calculation procedure, It is assumed that: *There is no structural coupling between the different degrees of freedom of the beam *The beam is homogeneous *The beam is un-damped * there are free oscillationsFour boundaries conditions are included: *pinned-pinned *clamped-free *clamped-clamped *clamped-pinnedTwo Geometries are available: *rectangular beam *cylinderContent: eigenModes.m: a function used to compute the eigenfrequencies and modes shapes of a continuous beam with different boundaries conditions. Example.m is an application of this function.References[1] Engineering vibration, Daniel J. Inman (3rd edition), near page 500</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet816582020-11-11T15:58:13Z2020-11-11T15:58:13ZGAP - Generalized Adaptive Polynomial Window FunctionJ. F. Justo and W. Beccaro, "Generalized Adaptive Polynomial Window Function," in IEEE Access, vol. 8, pp. 187584-187589, 2020.<p>IntroductionDiscrete Fourier Transform (DFT) is a powerful tool to perform Fourier analysis in discrete data, with several applications, such as astronomy, chemistry, acoustics, geophysics, and digital processing.The use of window functions affects the analysis in the frequency domain, sometimes introducing unwanted artifacts, such as signal leakage, scalloping loss, and intensity of sidelobes.We propose a generalized functional form to describe windows combined with an optimization method to improve their spectral properties.We present a generalized window function as a non-linear polynomial expansion in which all the current windows could be mimic with the appropriate expansion coefficients.This functional form is very flexible, which allows searching for sets of expansion coefficients that provide superior properties, considering a reference figure of merit associated to the property to be improved.This procedure paves the way for optimization and adaptive methods, such as machine learning and genetic algorithms, to adapt window functions to certain data sets and specific applications.This method to obtain windows is quite general, allowing the use of several optimization methods, such as global optimization (genetic algorithms and simulated annealing) or local optimization (Newton and gradient-based methods) techniques, or even machine learning.Any new window obtained by optimization procedures represents an improvement of the properties in the frequency domain, when compared to that initial window function guess.This method allows to improve several spectral properties simultaneously.</p>Wesley Beccarohttps://www.mathworks.com/matlabcentral/profile/19930242-wesley-beccaro742452020-11-26T14:20:51Z2020-11-26T14:20:51ZElasticMatrix ToolboxThe ElasticMatrix software uses the partial-wave method to model elastic wave propagation in multilayered anisotropic media. <p>Simulating the propagation of elastic waves in multi-layered media has many applications. A common approach is to use matrix methods where the elastic wave-field within each material layer is represented by a sum of partial waves along with boundary conditions imposed at each interface. While these methods are well-known, coding the required matrix formation, inversion, and analysis for general multi-layered systems is non-trivial and can take a long time. Here, a new open-source toolbox called ElasticMatrix is introduced which solves the problem of acoustic and elastic wave propagation in multi-layered media for isotropic and transverse-isotropic materials where the wave propagation occurs in a material plane of symmetry. The toolbox is implemented in MATLAB using an object oriented programming framework and is designed to be easy to use and extend. Methods are provided for calculating and plotting dispersion curves (Lamb waves and other guided waves), displacement and stress fields, reflection and transmission coefficients, and slowness profiles (from the Christoffel Equation).Known issues:- For leaky structures the Lamb modes can trace incorrectly. If this occurs, try the .calculateDispersionCurveCoarse method.</p>Danny Ramasawmyhttps://www.mathworks.com/matlabcentral/profile/7043210-danny-ramasawmy714062020-02-15T07:06:30Z2020-02-15T07:06:30ZTrajectory ToolboxA Matlab/Simulink Tool for the Automatic Design of Time-Optimal Trajectory Generators under Kinematic and Frequency Constraints<p>The toolbox is composed by a Simulink model library containing the (two) basic elements for the design of FIR (Finite Impulse Response) filters based trajectory generators in both the continuous- and the discrete-time domain and a Matlab function which automatically builds the block-scheme of the generator providing the minimum-time polynomial multi-segment trajectory compliant with the given kinematic constraints (max velocity, acceleration, jerk, etc.) and the angular frequencies to be cancelled for complete vibration suppression in resonant plants.A cascade of FIR (Finite Impulse Response) filters can be used for planning minimum-time multi-segment polynomial trajectories, i.e. trajectories composed of several polynomial segments,under constraints of velocity, acceleration, etc. The same filters can be used for shaping the frequencycontent of the trajectory with the purpose of suppressing residual vibrations at specific frequencies. Tothis end, it is necessary to select the (minimum) number of filters of the chain which are necessary toimpose the desired specifications and compute the characteristic parameter of each filter according to the algorithms reported in Biagiotti, Luigi, and Claudio Melchiorri. “Trajectory Generation via {FIR} Filters: A Procedure for Time-Optimization under Kinematic and Frequency Constraints.” Control Engineering Practice, vol. 87, Elsevier {BV}, June 2019, pp. 43–58. The function BuildTrajectoryGenerator(KinematicConstraints,AngularFrequencies,SamplingPeriod) contained in the toolbox performs this operation on the basis of the given kinematic constraints and of the angular frequencies of the trajectory spectrum to be cancelled. Then, the function builds the Simulink model of the trajectory planner that, along the position trajectory, provides its derivatives up to the order n (being n the order of the trajectory) If the sampling period is specified the trajectory planner is built as a discrete-time filter. Otherwise, a continuous-time model is provided.See the help of the function it BuildTrajectoryGenerator for additional information about its usage andsome examples.</p>Luigi Biagiottihttps://www.mathworks.com/matlabcentral/profile/3785771-luigi-biagiotti596222020-02-07T10:53:42Z2020-02-07T10:53:42ZatomAtomistic Topology Operations in MATLAB (atom), is a MATLAB library for manipulation of molecular systems<p>Atomistic Topology Operations in Matlab, scripts for manipulation of molecular dynamics or monte carlo simulation systems.% Note that version 2.0x comes with an extensive html-documentation for all the >100 functions, which can be used interactively from Matlab's own browser. % The purpose of the atom library is to automate and enable efficient construction/manipulation and analysis of complex and multicomponent molecular systems, and generate topological information with bonds and angles etc. It is especially useful for building inorganic/geochemical systems, since bond distances can be compared to the ideal semi-empirical bond distances computed with the Bond Valence Sum Method, or just simply just compared to Shannon's revised radii. Or one could plot a theoretical X-ray profile with the xrd_atom() function.% For lists of all available functions by category, see inside these files:List_all_functions.mList_build_functions.mList_export_functions.mList_general_functions.mList_import_functions.mList_forcefield_functions.m % The atom scripts can read and write basic .pdb|.xyz|.gro|.mol2 structure files as well as write basic .itp and .psf topology files with bonds and angles across the PBC. The can also manipulate/transform the structures in various ways making use of the Matlab struct variable and indexing. The atom scripts can be used to build and plot multicomponent systems, by adding molecules, ions and SPC/TIP3P/TIP4P water molecules or other solvents (ie solvating an existing molecule/slab) into a simulation box, and remove molecular overlap. For plotting one can call vmd(atom,Box_dim) if the VMD software is also installed and the PATH2VMD() function is properly set. Else the very quick-and-dirty plot_atom(atom,Box_dim) or the slower show_atom(atom,Box_dim) can be used. Most functions takes PBC into account, which allows for generation of topologies of molecules with bonds, angles, dihedrals across the PBC. There is also some support for triclinic support using the tilt vectors xy, xz, yz, as well as for generating powder X-ray diffractograms using the function xrd_atom(). Michael Holmboe <a href="mailto:michael.holmboe@umu.se">michael.holmboe@umu.se</a> Chemistry departmentUmeå University, Sweden % Where to start? Perhaps the html-documentation with some basic examples? % % Some typical commands...%% To read a structure file into matlab (check the variable explorer) atom=import_atom(filename) % filename could be a .pdb | .xyz | .gro file % or... atom=import_atom_pdb(filenamepdb)atom=import_atom_gro(filenamegro) atom=import_atom_xyz(filenamexyz)% Note that you get a lot more info then just the atom struct variable, like the box dimension variable Box_dim % To write a atom struct to a new topology or structure filewrite_atom_lmp(atom,Box_dim,filename,1.2,1.2,'clayff') % supports bonds, angles, simple dihedralswrite_atom_psf(atom,Box_dim,filename,1.2,1.2,'clayff') % note only bonds and angleswrite_atom_itp(atom,Box_dim,filename,1.2,1.2,'clayff','spce') % Gromacs topology file, note only bonds and angleswrite_atom_pdb(atom,Box_dim,filename)write_atom_cif(atom,Box_dim,filename)write_atom_gro(atom,Box_dim,filename) write_atom_xyz(atom,Box_dim,filename)% Adding water to a box % - This function SOLvates a certain region defined by limits with a water % structure with density. r (and r-0.5 for H) is the closest distance of solvent atoms% to the (optional) solute atomsSOL_atom = solvate_atom(limits,density,r,maxsol) % limits can be [10] | [10 20 30] | [10 20 30 40 50 60]SOL_atom = solvate_atom(limits,density,r,maxsol,solute_atom,'tip4p') % spc | tip3p | tip4p | tip5p % One can filter the atom struct with respect to molid, resname, atomtype, index, coordinates and so on. This allows manipulation of an atom struct on the atomic, molecule and molecular type level. This also allows us to use 'dynamic indexes' of groups of atom.{molid/resname/type/index/} when analyzing a trajectory for instance. Some basic examples: index=ismember([atom.type],[{'Al' 'Alt' 'Mgo'}]) % gives a binary (1/0) logical array index=strcmp([atom.type],'Al') % try also strncmp or strncmpi? index=find(strncmpi([atom.type],'al',2) % Will find the indexes of 'Al' 'Alt? new_atom=atom(index) % This creates a new_atom struct with the filtered/selected atomtypes positive_z_atom=atom([atom.z]>0) % finds all atoms with a positve z-coordinatefirst100_atom=atom([atom.index]<101) % finds the first 100 atoms in the atom struct first100_v2_atom=atom(1:100) % also finds the first 100 atoms in the atom struct % Merging two different atom structs % - This function returns the second atom set with non-overlapping atomsnew_atom = merge_atom(atom1,Box1,atom2,Box2,type,Atom_label,r) % Calculating bonds or the distance matrix/es atom = bond_angle_dihedral_atom(atom,Box_dim,1.2,2.2)dist_matrix = dist_matrix_atom(atom,Box_dim) % another cell lists version also exist. </p>Michael Holmboehttps://www.mathworks.com/matlabcentral/profile/6584852-michael-holmboe519702020-06-12T14:25:33Z2020-06-12T14:25:33ZBuffeting response of a suspension bridge (frequency domain)The dynamic response of a suspension bridge to wind turbulence is computed in the frequency domain.<p>The estimation of the displacement response of a large civil engineering structure to wind turbulence is based on the buffeting theory [1, 2, 5]. Ref. [5] contains the theoretical background I have used for the function dynaRespFD3. In the present script, the structure in question is a suspension bridge modelled using the theory of continuous beams [3]. The buffeting response is computed in the frequency domain using the quasi-steady theory. Modal coupling was assumed negligible, which is generally well verified for most of the wind velocities recorded in full scale [4]. The present script is a simplified version of the one used in [6]. The present script computes the lateral, vertical and torsional displacement response. A multi-modes approach is used. Some knowledge in the field of random vibration analysis and wind loading on structures are advised for proper use of this script. The present submission contains • dynaRespFD.m : Function that calculates the displacement response spectrum of the bridge• A function VonKarmanSpectrum.m to generate the power spectral density of the velocity fluctuations based on von Karman model.• Two example files Example_1.m and Example_2.m• Two .mat files bridgeModalProperties.mat and DynamicDispl.mat that are used in the 2 examples.Any question, comment or suggestion to improve the submission is welcomed.References [1] Davenport, A.G., The response of slender line-like structures to a gusty wind, Proceedings of the Institution of Civil Engineers, Vol. 23, 1962, pp. 389 – 408. [2] Scanlan, R. H. (1978). The action of flexible bridges under wind, II: Buffeting theory. Journal of Sound and vibration, 60(2), 201-211.[3] <a href="http://www.mathworks.com/matlabcentral/fileexchange/51815-suspension-bridge--eigen-frequency-and-mode-shapes-benchmark-solutions">http://www.mathworks.com/matlabcentral/fileexchange/51815-suspension-bridge--eigen-frequency-and-mode-shapes-benchmark-solutions</a> [4] Thorbek, L. T., & Hansen, S. O. (1998). Coupled buffeting response of suspension bridges. Journal of Wind Engineering and Industrial Aerodynamics, 74, 839-847.[5] Hjorth-Hansen, E. (1993). Fluctuating drag, lift and overturning moment for a line-like structure predicted (primarily) from static, mean loads. Wind Engineering, Lecture note no, 2.[6] Cheynet, E., Jakobsen, J. B., & Snæbjörnsson, J. (2016). Buffeting response of a suspension bridge in complex terrain. Engineering Structures, 128, 474-487. <a href="http://dx.doi.org/10.1016/j.engstruct.2016.09.060">http://dx.doi.org/10.1016/j.engstruct.2016.09.060</a></p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet778642020-08-04T15:46:42Z2020-08-04T15:46:42ZNeuroRobot ToolboxNeuroRobots for Education!<p>A neurorobot is a robot controlled by a computer simulation of a biological brain. At Backyard Brains we use brain-shaped neurorobots with camera, microphone, speaker, distance sensor, wheels and WiFi to teach computational neuroscience in high schools. The NeuroRobot Toolbox visualizes our neurorobots’ visual and auditory input, and brain activity, and allows you to design and simulate new brains, and deliver dopamine-like reward signals.</p>Christopher Harrishttps://www.mathworks.com/matlabcentral/profile/11998194-christopher-harris722482020-10-03T05:19:52Z2020-10-03T05:19:52ZMoody chartPlots the Moody chart.<p>MATLAB code to plot the Moody chart, showing the relationship between the friction factor and the Reynolds number, for different roughness coefficients in a pipe.</p>Ildeberto de los Santos Ruizhttps://www.mathworks.com/matlabcentral/profile/5153703-ildeberto-de-los-santos-ruiz518152020-05-08T20:25:30Z2020-05-08T20:25:30ZCalculation of the modal parameters of a suspension bridgeThe eigenfrequencies and modes shapes of a suspension bridge are calculated using a continuum model<p>The calculation of the eigenfrequencies and mode shapes of a suspension bridge using the present Matlab code is based on the theory of continuous beam and the theory of shallow cables. The mode shapes are obtained using Galerkin's method where a series expansion is used. The method was first applied by Sigbjörnsson & Hjorth-Hansen [1]. E. Strømmen [2] expanded their works to the vertical and torsional motion.The bridge is represented as a horizontal streamlined beam, where the z-axis is the vertical axis, the y-axis is the along-beam axis and the x-axis is the cross-beam axis. The three motions of interests (lateral, vertical, and torsional) and both symmetric and asymmetric modes are computed.Content: - eigenBridge is a function that computes the mode shapes and eigenfrequencies of the suspension bridge - Documentation.mlx: is an example of the application of this functionReferences:[1] Sigbjönsson, R., Hjorth-Hansen, E.: Along wind response of suspension bridges with special reference to stiffening by horizontal cables. Engineering Structures 3, 27-37 (1981)[2] Structural Dynamics, Einar N Strømmen, Springer International Publishing, 2013. ISBN: 3319018019, 9783319018010 Characteristics of the single-span suspension bridge</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet609592020-10-31T19:33:50Z2020-10-31T19:33:50ZEvolutionary Power Spectral Density (EPSD)Compute the Evolutionary Power Spectral Density (EPSD) as an alternative to the spectrogram<p>The Evolutionary Power Spectral Density (EPSD) [1] is compared to the well-known spectrogram implemented in Matlab. The EPSD produces a smoother signal, especially if the amount of data point is low. In the following, I am using the example from [2] where the spectrogram applies well. For other application, e.g. civil engineering, the spectrogram method may provide a too low frequency or time resolution. The use of the EPSD is therefore more common in this field, see e.g. [3] for an application of the EPSD to compute the bridge response to non-stationary wind load.The submission contains:•An example file•The function EPSD.mAny comment, question or suggestion to improve the submission is warmly welcomed. Shiyu Zhao is gratefully acknowledged for the suggestion regarding the definition of the lower-boundary of the frequency vector.References[1] Priestley, M. B. (1965). Evolutionary spectra and non-stationary processes. Journal of the Royal Statistical Society. Series B (Methodological), 204-237.[2] <a href="http://www.mathworks.com/help/signal/ref/spectrogram.html[3]">http://www.mathworks.com/help/signal/ref/spectrogram.html[3]</a> Hu, L., Xu, Y. L., & Huang, W. F. (2013). Typhoon-induced non-stationary buffeting response of long-span bridges in complex terrain. Engineering Structures, 57, 406-415.</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet714692020-12-02T08:21:20Z2020-12-02T08:21:20ZMTEX2GmshMatlab toolbox for generating meshes from EBSD data<p>In order to evaluate the thermo-mechanical behaviour of crystalline materials (such as metals or ceramics) at microscopic scale, one usually perform numerical simulation at grain scale using the Finite Element Method. In order to proceed, one must first create a mesh which is representative of the real material.The microstructure of crystalline materials is usually made from Electron Backscattered Diffraction (EBSD) technique. Thus, this toolbox is designed to generate meshes from EBSD in a robust and accurate way.</p>Dorian Depriesterhttps://www.mathworks.com/matlabcentral/profile/14320211-dorian-depriester742722020-05-14T15:40:58Z2020-05-14T15:40:58ZConverting acceleration to displacements records The Discrete Fourier transform is compared to the double integration technique when transforming acceleration to displacement records<p>The present submission introduces a simple function ASD.m that is inspired by [1] but includes also the possibility to use the double integration technique instead of the Discrete Fourier Transform (DFT) when transforming acceleration records to displacement records. The simple right-hand difference technique is also implemented as an alternative to the DFT for transforming displacement data to acceleration data.The function also includes the possibility to compute the velocity histories from the acceleration of displacement records.The submission contains three files:- The function ASD.m, which is an acronym for Acceleration-Speed-Displacement.- Two data file data_bridge.mat and data_beam.mat that contains the computed vertical acceleration, velocity and displacement response from a suspension bridge and a cantilever beam, respectively. The data set is created using [2] - Two example files Example1.mlx and Example2.mlx, that illustrates how the function ASD.m can be called.The is the second version of the submission, Several typos may still be present as well as bugs. Any suggestion, comment or question is welcomed. Credits for the present submission should also go to ref. [1] for the function iomega.References: [1] <a href="https://www.mathworks.com/matlabcentral/answers/21700-finding-the-velocity-from-displacement#answer_33902[2]">https://www.mathworks.com/matlabcentral/answers/21700-finding-the-velocity-from-displacement#answer_33902[2]</a> <a href="https://www.mathworks.com/matlabcentral/fileexchange/66016-response-of-a-line-like-structure-to-a-random-load">https://www.mathworks.com/matlabcentral/fileexchange/66016-response-of-a-line-like-structure-to-a-random-load</a></p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet743612020-02-26T13:40:15Z2020-02-26T13:40:15ZAEFA-C for Constrained OptimizationThis code is designed for constrained optimization problems.<p>Nature-inspired optimization algorithms have attracted significant attention from researchers during the past decades due to their applicability to solving the challenging optimization problems, efficiently. Many intelligent systems require an excellent constrained optimization scheme to act as an artificially intelligent system. Artificial electric field algorithm (AEFA) is an intelligently designed artificial system that deals with the purpose of function optimization. AEFA works on the principle of Coulombs’ law of electrostatic force and Newtons’ law of motion. The present article extends the AEFA algorithm for constrained optimization problems by introducing the new velocity and position bound strategies. These bounds lead the particle to interact with each other within the domain of the problem, and they are allowed to learn from the problem space individually. They also help to make a better balance between exploration and exploitation by controlling the position update of the particles. The challenging IEEE CEC 2017 constrained benchmark set of 28 problems, and five multidimensional non-linear structural design optimization problems are solved using AEFA-C, which tests the effectiveness and the efficiency of the proposed scheme. The comparative study of AEFA-C is performed with nine state-of-art algorithms, including some IEEE CEC 2017 competitors. The comparative study, statistical analysis, and the findings suggest that the proposed AEFA-C is an efficient constrained optimizer.</p>Anupam Yadavhttps://www.mathworks.com/matlabcentral/profile/2870277-anupam-yadav687632020-10-23T15:23:57Z2020-10-23T15:23:57ZModelling and Analysis of Polarization Noise in VCSELThis file contains the Matlab implementation of the research paper "Modelling and analysis of polarization noise in VCSEL "<p>This file contains the complete Matlab implementation of the following research papers. You are welcome to explore and come up with a new add-on.Please find the publication:<a href="https://rdcu.be/532whttps://www.researchgate.net/publication/327510546_Modelling_and_analysis_of_polarization_noise_in_vertical_cavity_surface_emitting_LASERsfor">https://rdcu.be/532whttps://www.researchgate.net/publication/327510546_Modelling_and_analysis_of_polarization_noise_in_vertical_cavity_surface_emitting_LASERsfor</a> citation:<a href="https://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=bibtex&flavour=citationhttps://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=refman&flavour=citationhttps://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=endnote&flavour=citation">https://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=bibtex&flavour=citationhttps://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=refman&flavour=citationhttps://citation-needed.springer.com/v2/references/10.1007/s41939-018-0033-9?format=endnote&flavour=citation</a></p>ABHILASH SINGHhttps://www.mathworks.com/matlabcentral/profile/5569287-abhilash-singh543682020-04-28T17:12:45Z2020-04-28T17:12:45ZMathematical modelling of an outbreak of zombie infectionHow long will humanity survive to zombiism ?<p>In 2009, Munz et al. [1] have written a pedagogical paper that helps to understand how to solve systems of coupled differential equations. it dealt with the simulation of zombie infection among the human population. I have re-written the Matlab code they propose in their paper. The present code is made of: - one example file - one zombies.m function that implements (partly) the model proposed by Munz et al. - one erad.m function that is based on the "impulsive eradication" model. There are no guarantees that the code I have written is reliable. If any doubt, please refers to the original one. [1] Munz, Philip, et al. "When zombies attack!: mathematical modelling of an outbreak of zombie infection." Infectious Disease Modelling Research Progress 4 (2009): 133-150</p>E. Cheynethttps://www.mathworks.com/matlabcentral/profile/4608373-e-cheynet