* * * The functions on this page are no longer being updated.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 https://www.mathworks.com/matlabcentral/fileexchange/70338. * * *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.

The SIR model has been developed in the past years to simulate the spread of a virus over time. The script includes a brief introduction, in which the model is presented, and the code to run the simulation of the epidemic over time. Two cases are analysed: one without immunity loss, where recovered individuals don't get infected again, and one with immunity loss.

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 http://topotoolbox.wordpress.com for examples and instructions.

This example shows how to do a simple analysis of an electrocardiogram (ECG) signal and heart rate calculation. The signal is a measure of electrical activity of the heart over time. The analysis includes removing trends and finding the max peaks in the R-wave. These calculations are performed using Live Editor Tasks to visually explore the effects of choosing different parameters.

## SummaryThe 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 is inspired by the one used by Magalhaes et al. [1]. It 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].## contentThe submission file contains:- A data file BridgeData.mat- A Matlab Live Script Example1.mlx that illustrates the application of the algorithm.- A Matlab Live Script Example1_noToolbox.mlx that reproduce Example1 but using the function SSICOV_noToolbox.- The function SSICOV which is the automated SSI-COV algorithm.- The function SSICOV_noToolbox which is the automated SSI-COV algorithm but does not use the Statistics and Machine Learning Toolbox. The Linkage algorithm is replaced by the function "PHA_Clustering" by [5] and the function "cluster" is replaced by the function "Cluster2", which is derived from [6].- 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.[5] Yonggang (2021). Fast hierarchical clustering method - PHA (https://www.mathworks.com/matlabcentral/fileexchange/46134-fast-hierarchical-clustering-method-pha), MATLAB Central File Exchange. Retrieved February 4, 2021. [6] Eric Ogier (2021). Hierarchical clustering (https://www.mathworks.com/matlabcentral/fileexchange/56844-hierarchical-clustering), MATLAB Central File Exchange. Retrieved February 4, 2021.

The goal of this case study is to explore storm events in various locations in the United States and analyze the frequency and damage costs associated with different types of events. A machine learning model is used to predict the damage costs, based on historical data from 1980 - 2020. The calculations are then performed in an app, which can be shared as a web application.This example also highlights techniques for cleaning data in various forms (numeric, text, categorical, dates and times) and working with large data sets which do not fit into memory.The example is used in the "Data Science with MATLAB" webinar series.

This example models a triplex pump with a predictive maintenance algorithm that can detect which parts of the pump are failing simply by monitoring the pump output pressure. The Simscape model of the pump can be configured to model degraded behavior due to seal leakage, blocked inlets, bearing wear, and broken motor windings. MATLAB code shows how to accelerate testing by reusing results from previous simulations. The model can be used to generate training data for the machine learning algorithm and can be used to test the deployed algorithm. MATLAB Live Scripts show you how to develop the algorithm. Mechanical, hydraulic, and electrical parameters are all defined in MATLAB which lets you easily resize the pump. The pump housing is imported from CAD. Please read the README.md file to get started.Use the "Download from GitHub" button above to get files compatible with the latest release of MATLAB.Use the links below to get files compatible with earlier releases of MATLAB.For R2020a: https://github.com/mathworks/Simscape-Triplex-Pump/archive/20.1.2.1.zipFor R2019b: https://github.com/mathworks/Simscape-Triplex-Pump/archive/19.2.2.0.zipFor R2019a: https://github.com/mathworks/Simscape-Triplex-Pump/archive/19.1.1.3.zipFor R2018b: https://github.com/mathworks/Simscape-Triplex-Pump/archive/18.2.1.2.zipFor R2018a: https://github.com/mathworks/Simscape-Triplex-Pump/archive/18.1.1.1.zipFor R2017b: https://github.com/mathworks/Simscape-Triplex-Pump/archive/17.2.1.0.zip See how to model a fluid actuation system in Simscape (7 min): https://www.mathworks.com/videos/modeling-a-hydraulic-actuation-system-68833.html To see an overview of this multibody modeling in Simscape Multibody, watch this video (1.5 min): https://www.mathworks.com/videos/simmechanics-introduction-69809.html Read the e-book “Predictive Maintenance with MATLAB”https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/p/93060v00_Predictive_Maintenance_e-book_v04.pdf Find other Simscape examples by searching posts for the keyword "physical modeling" http://www.mathworks.com/matlabcentral/fileexchange/?term=%22physical+modeling%22 Learn more about MathWorks Simscape Products: http://www.mathworks.com/physical-modeling/

Voltage and frequency grid codes, such as IEEE 1547-2018, dictate how distributed generation, such as utility-scale solar and wind, must remain connected during grid fault events. These examples show how you can evaluate grid code compliance in MATLAB against historical measured data from PMUs. Additionally, Simulink blocks provide a path to evaluate grid code compliance of simulated systems and protection logic.For more information and a video walk-through, these examples are used in the "Renewable Grid Integration Studies with Simscape Electrical" Webinar to evaluate grid code compliance of renewable and distributed resources: https://www.mathworks.com/videos/renewable-grid-integration-studies-with-simscape-electrical-1543529438031.html

Overview : This example demonstrates how to register a new face, label new face, extract features and recognise the face in real time.It is a very interesting topic. However, in this example, we are not particular in the accuracy, instead of that, i'm demonstrating the workflow. In order to increase the accuracy, we might need to extract more features for machine learning. There are many examples available online, some links are provided below, you may take a look at their example.Example 1: How to activate your webcamhttps://www.mathworks.com/videos/webcam-support-89504.htmlExample 2 : Face Detection and Tracking Using Live Video Acquisitionhttps://www.mathworks.com/help/vision/examples/face-detection-and-tracking-using-live-video-acquisition.htmlExample 3: Code for Face Recognition with MATLAB Webinarvideo : https://www.mathworks.com/videos/face-recognition-with-matlab-100902.htmlExample 4 : Detecting Faces in Imagehttps://blogs.mathworks.com/pick/2014/03/14/detecting-faces-in-images/Example 5 : Facial Recognition using Kekre transformhttps://www.mathworks.com/matlabcentral/fileexchange/32284-face-recognition-system-using-kekre-transformExample 6 : Real-time Face Recognhttps://www.mathworks.com/matlabcentral/fileexchange/46674-real-time-face-recognition-and-detection-systemExample 7 : Creating a Cloud Based People Counter Using MATLAB (ThingSpeak)https://www.mathworks.com/matlabcentral/fileexchange/58435-creating-a-cloud-based-people-counter-using-matlabHighlights : Registration of New Face in Webcam through Image Acquisition ToolboxLabel of New Face captured by WebcamExtract Features for Machine LearningMachine Learning and Prediction Performs Real-Time Facial RecognitionProduct Focus :MATLABImage Acquisition ToolboxImage Processing ToolboxComputer Vision System ToolboxMachine Learning and Statistic ToolboxWritten at 1 October 2018

For a more robust and time-efficient Matlab implementation, see https://se.mathworks.com/matlabcentral/fileexchange/68632-wind-field-simulation-the-fast-version.SummaryA 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- The function getSamplingPara.m which get the time and frequency vectors - 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.

This contribution includes a single MATLAB function ('harmonicY') that computes spherical harmonics of any degree and order, evaluated at arbitrary inclination, azimuth and radius. Capabilities include the computation of surface/solid, complex/real and normalized/unnormalized spherical harmonics.Documentation is provided in the form of a live script with examples, as well as an HTML page for convenience and/or compatibility.

regularizeNd Fits a nD lookup table with smoothness to scattered data. Constraints are possible. regularizeNd answers the question what is the best possible lookup table that the scattered data input x and output y in the least squares sense with smoothing? regularizeNd is meant to calculate a smooth lookup table given n-D scattered data. regularizeNd supports extrapolation from a scattered data set. The calculated lookup table, yGrid, is meant to be used with griddedInterpolant class with the conservative memory form. Call griddedInterpolant like xGrid = cell array of grid vectors smoothness = smoothness value or vector yGrid = regularizeNd(xData, yData, xGrid, smoothness); F = griddedInterpolant(xGrid, yGrid). Desirable properties of regularizeNd: -Calculates a relationship between the input x and the output y without definition of the functional form of x to y. -Often the fit is superior to polynomial type fitting without the wiggles. -Extrapolation is possible from a scattered data set. -After creating the lookup table yGrid and using it with griddedInterpolant, as the query point moves away from the scattered data, the relationship between the input x and output y becomes more linear because of the smoothness equations and no nearby fidelity equations. The linear relationship is a good choice when the relationship between x and y is unknown in extrapolation. -regularizeNd can handle 1D, 2D, nD input data to 1D output data. RegularizeData3D and gridfit can only handle 2D input and 1D out (total 3D). -regularizeNd can handle setting the smoothness to 0 in any/some axis/dimension. This means no smoothing is applied in a particular axis/dimension and the data is just a least squares fit of a lookup table in that axis/dimension. Note this is not recommended and often can lead to an ill-conditioned fitting problem. However, I have found it useful so I left this as an option. - Constraints are possible with the function regularizeNdMatrices. See the example.The source code is locate here: https://github.com/jasonnicholson/regularizeNdFor an introduction on how regularization of a lookup table works, start here: https://mathformeremortals.wordpress.com/2013/01/29/introduction-to-regularizing-with-2d-data-part-1-of-3/Acknowledgement Special thanks to Peter Goldstein, author of RegularizeData3D, for his coaching and help through writing regularizeNd.

Project Bonsai enables engineers to add intelligent control to their Simulink models and deploy trained brains (AI agents) on real world physical systems by using machine teaching. Learn more here: https://blogs.microsoft.com/ai/machine-teaching/Engineers can connect their existing simulation models with Project Bonsai by using our Simulink Toolbox. We’ve created example models that are showing how to replace a traditional control system with a trained AI controller. CartpoleSimulink model of an inverted pendulum and one of the classic examples for reinforcement learning. Teach a brain to balance a pole attached to frictionless cart.Project MoabProject Moab is a small balancing robot useful for demonstrating machine teaching on a physical device for Engineers. You can teach a brain how to balance variety of balls by using the Simulink model as training environment and then deploy the trained brain to the physical device. More information on Moab and availability here: https://aka.ms/moab. Follow these steps to experience the service firsthand.1)Follow the instructions to sign-up for a Bonsai account: https://docs.microsoft.com/en-us/bonsai/guides/account-setup2)Download and install the toolbox https://aka.ms/as/bonsai-toolbox 3)Decide which example you would like to use4)Follow the instructions outlined in the readme.md that is part of example5)Start training a Bonsai brainProject Bonsai documentation: https://aka.ms/bonsai-docs Support and feedback: http://aka.ms/as/forums

FOPID tunerThis project is based on FOPD tuner: https://github.com/cnpcshangbo/FOPD-tuner/tree/optimization-method/controller-analysis-with-Simulink/optimizationUsageRun "run_patternsearch_npm"

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, http://dx.doi.org/10.1016/j.jsv.2004.02.026.[2] 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] http://www.mathworks.com/matlabcentral/fileexchange/50988-frequency-domain-decomposition--fdd-[5] https://se.mathworks.com/matlabcentral/fileexchange/57153-automated-frequency-domain-decomposition--afdd-[6] https://se.mathworks.com/matlabcentral/fileexchange/55557-modal-parameters-identification-from-ambient-vibrations--sdof

The toolbox contains two functions:(a) getMarketDataViaYahoo() % INPUT: % symbol - is a ticker symbol i.e. 'AMD', 'BTC-USD' % startdate - the market data will be requested from this data % enddate - the market data will be requested till this date % interval - the market data will be returned in this intervals % supported intervals are '1d', '5d', '1wk', '1mo', '3mo' % % OUTPUT: % data - is a retrieved dataset returned as a tabledata = getMarketDataViaYahoo('AMD', '1-Jan-2018', datetime('today'), '5d'); % Downloads AMD share historic price(b) getMarketDataViaQuandl() % INPUT: % set_name - is a dataset name e.g. 'WIKI/AAPL' % startdate - the market data will be requested from this data % enddate - the market data will be requested till this date % collapse - the market data will be returned in this intervals % supported intervals are 'daily', 'weekly', 'monthly', 'quarterly', 'annual' % key - user's api key % % OUTPUT: % data - is a retrieved dataset returned as a tableopec_orb_raw = getMarketDataViaQuandl('OPEC/ORB', '1-Jan-2018', date(), 'weekly'); % Downloads historic OPEC basket price from QuandlFor a complete list of free datasets provided by Quandl check https://www.quandl.com/search?filters=%5B%22Free%22%5DExamples:(a) Yahoo Finance disp('Request historical YTD Bitcoin price and plot Close, High and Low');initDate = '1-Jan-2018';symbol = 'BTC-USD';btcusd = getMarketDataViaYahoo(symbol, initDate);btcusdts = timeseries([btcusd.Close, btcusd.High, btcusd.Low], datestr(btcusd(:,1).Date));btcusdts.DataInfo.Units = 'USD';btcusdts.Name = symbol;btcusdts.TimeInfo.Format = "dd-mm-yyyy";plot(btcusdts);legend({'Close', 'High', 'Low'});(b) Quandldataset = 'LBMA/GOLD';initDate = '1-Jan-2018';lbma_gold_raw = getMarketDataViaQuandl(dataset, initDate, date(), 'daily');lbma_gold_ts = timeseries(lbma_gold_raw.("EURO(AM)"), datestr(lbma_gold_raw.Date));lbma_gold_ts.DataInfo.Units = 'USD';lbma_gold_ts.Name = dataset;lbma_gold_ts.TimeInfo.Format = "dd-mm-yyyy";figure, plot(lbma_gold_ts);

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.

Deep learning object detection technology is sometimes used for people detection in images.People detection are very common in Automated driving system. In addition, it is also used at factories and workplaces. They check the behavior of worker or if workers wear necessary equipment in hazardous areas.his file is a sample code that trains an object detection model (Yolo v2) with labeled video data to detect a man wearing or not wearing helmets.[Keyward] image processing・Computer vision・Deep Learning・Machine Learning・CNN・Yolo v2・Object detection

安全性を担保しつつ不要なメンテナンス作業を減らすというバランスの良さから、予知保全という方法が注目を集めています。ただ、データを精度よく故障予測や異常検出に結びつけるには、観測対象への理解だけでなく統計的なノウハウも必要不可欠です。このサンプルではMATLABの機械学習機能を使い、機器の交換時期を見積もる処理を行います。ターボファンエンジンのデータを用いて、データのインポート、前処理、ラベリング、特徴量の抽出、そして学習・評価を実施します。このファイルはWebセミナー「MATLABを使った予知保全・故障予測」で使用したファイルです。事前に prepareData.m を実行しデモに必要なデータを準備してください。- Case1: 故障データ無し UnsupervisedWebinarLive_JP.mlx- Case2: 故障データ有り ClassificationWebinarLive_JP.mlxビデオ：https://jp.mathworks.com/videos/predictive-maintenance-with-matlab-a-prognostics-case-study-121138.html同じデータセットを使用した例題としては他に以下のものもあります。深層学習を使用した sequence-to-sequence 回帰（Deep Learning Toolbox を使用）https://jp.mathworks.com/help/deeplearning/ug/sequence-to-sequence-regression-using-deep-learning.html類似度ベースの残存耐用期間推定（Predictive Maintenance Toolboxを使用）https://jp.mathworks.com/help/predmaint/ug/similarity-based-remaining-useful-life-estimation.htmlまた、教師無し学習の例としてこちらの File Exchange のコードでは他の手法も試しております。https://jp.mathworks.com/matlabcentral/fileexchange/86323-matlabThis zip file includes the demo files for the webinar "Predictive Maintenance with MATLAB" (in Japanese)Please run prepareData.m to download and preprocess the data set.- Case1 No data from failures: UnsupervisedWebinarLive_JP.mlx- Case2 Have failure data: ClassificationWebinarLive_JP.mlx

The position of the sun is an afterthought for most, unless it is blinding you on your commute home. However, the position of the sun remains incredibly important, even in the modern age. While solar navigation through sextants was common until GPS came along, architects/engineers still track the sun for building shading and solar panel optimization. This script dives into the "analemma" which is an interesting phenomenon associated with solar observations. Try testing different latitudes to see how the sun's trajectory changes and what the analemma looks like!

This example shows how to forecast time series data using Machine Learning.To forecast the values of future time steps of a sequence, you can train a regression Machine Learning, where the responses are the training sequences with values shifted by one time step. That is, at each time step of the input sequence, the Machine Learning learns to predict the value of the next time step.This example uses the data set covid-19 patients of India. The example trains a Machine Learning Model to forecast the number of covid-19 cases given the number of cases in previous days. I used data till 31st March 2020.

elfun18 is a collection of Matlab functions that enable the computation of wide set of Elliptic integrals, Jacobi's elliptic functions and Jacobi's theta functions for real arguments. The set has two levels: higher level functions with matrix arguments and low level functions with scalar arguments. Each function is available either with the modulus k or parameter m as argument. In later case the function name begin with m. Incomplete elliptic integrals are given in Jacobi form, Legendre form and Jacobi's second form (Epsilon function and Lambda functions). List of functions:Elliptic integrals: - Bulirsch's elliptic integrals: cel, cel1, cel2, cel3, el1, el2, el3 - Carlson's elliptic integrals: rc, rd, rf, rg, rj - Complete elliptic integrals: B, C, D, K, E, Pi - Complementary complete elliptic integrals: K', E', Pi' - Jacobi form of elliptic integrals: B, D, E, F, Pi - Legendre form of elliptic integrals: B, D, E, F, Pi - Jacobi second form of elliptic integrals: Epsilon, Zeta ( periodic part of Eps) Lambda ( elip. int. of 3rd kind), Omega function ( periodic part of Lambda) Jacobian elliptic functions - am, cd, cn, cs, dc, dn, ds, nc, nd, ns, sc, sd, sn Inverse Jacobian elliptic functions - invam, invcd, invcn, invcs, invdc, invdn, invds, invnc, invnd, invns, invsc, invsd, invsn Jacobi Theta Functions - theta1, thet12, theta3, theta4, nome, modulusNeville theta functions -nthetac, nthetad, nthetan, nthetas Misc. functions - agm ( arithmetic geometric mean), cl (lemniscate cos), sl, (lemniscate sin), invcl (inverse lemniscate cos), invsl (inverse lemniscate sin), Lambda0 (Heuman's function) gd (Gudermannian function), invgd (inverse Gudermannian function)

eBook: 機械学習をマスターする: MATLAB ステップ・バイ・ステップ ガイドhttps://jp.mathworks.com/campaigns/offers/mastering-machine-learning-with-matlab.htmlで紹介するデモを再現するためのスクリプトです。このデモでは、組み込み機械学習アプリ開発のための一連のワークフロー、具体的にはデータの読み込み、特徴抽出、各種アルゴリズムの検討、モデルのチューニング、そしてプロトタイプ配布までを紹介します。特にここでは、危険な心臓病のリスクがある患者を診察する医療業務で応用可能で、熟練臨床医への依存軽減につながる、心音の"正常"と"異常"を分類するアルゴリズムを開発します。このアプリケーションの開発では、以下の手順に従います。1. データの読み込みと探索2. データを前処理して特徴抽出3. 予測モデルを開発4. モデルの最適化5. 学習済みモデルのCコード生成English version is available here:https://jp.mathworks.com/matlabcentral/fileexchange/65286-heart-sound-classifier

The computation of the one-point spectra, co-spectra and coherence of turbulent wind is conducted using the uniform-shear model from Mann (1994) [1]. The goal is to describe the spatial structure of stationary homogeneous turbulence under a neutral atmospheric stratification using only 3 adjustable parameters.The present submission contains:- The function MannTurb.m that computes the sheared spectral tensor- The function MannCoherence that computes the wind co-coherence- A LiveScript for the example file- A data file GreatBeltSpectra.mat that contains the wind spectra from the Great belt bridge experiment for comparison with the computed spectra.Any question, comment or suggestion is warmly welcomed.References[1] Mann, J. (1994). The spatial structure of neutral atmospheric surface-layer turbulence. Journal of fluid mechanics, 273, 141-168.

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] http://www.mathworks.com/matlabcentral/fileexchange/51815-suspension-bridge--eigen-frequency-and-mode-shapes-benchmark-solutions [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. http://dx.doi.org/10.1016/j.engstruct.2016.09.060

Matlab Programming for beginners and intermediate learnerswww.pirc.co.inceo@pirc.co.in

On shipping inspection for chemical materials, clothing, and food materials, etc, it is necessary to detect defects and impurities in normal products. However, it is difficult to collect enough abormal images to use for deep learning.This demo shows how to detect and localize anomalies using CAE.This method using only normal images for training may allow you to detect abnormalities that have never been seen before. By customizing SegNet model, you can easily get the network structure for this task.[Japanese]このデモでは正常な画像に紛れる異常をディープラーニングを用いて検出ならびに位置の特定を行えます。正常な画像のみ使ってモデルを学習させるこの方法では,これまで見たことがない異常に対しても検出できる可能性があります。簡単にモデル構造を得るためにSegNetモデルをカスタムして利用しています。[Keyward] 画像処理・画像分類・ディープラーニング・DeepLearning・IPCVデモ ・SegNet ・異常検出・外観検査・セマンティックセグメンテーション・オートエンコーダー・畳み込み

Matlab Programming for Beginners and Intermediate learners www.pirc.co.in PDF : https://pircdotcodotin.files.wordpress.com/2020/05/matcodepirc.pdf

This submission contains functions implementing the fractional linear prediction (FLP) models to estimate the one-dimensional signal. Two versions of FLP are implemented. The first approach to FLP is using the whole history of the signal ("full" memory) - function flp_f.m. The second approach to FLP uses the "restricted" memory (restricted to two, three or four previous samples) - function flp_r.m. The methods, models and applications implemented in the form of the proposed functions were presented in the works [1], [2]. The MATLAB implementation of the discretization of the fractional order derivative can be found in [3]. [1] Tomas Skovranek, Vladimir Despotovic: Signal prediction using fractional derivative models. In: Handbook of Fractional Calculus with Applications, Volume 8: Applications in Engineering, Life and Social Sciences, Part B, Pages 179-205. De Gruyter, 2019.https://doi.org/10.1515/9783110571929-007[2] Vladimir Despotovic, Tomas Skovranek, Zoran Peric: One-parameter fractional linear prediction, Computers & Electrical Engineering, vol. 69, July 2018, Pages 158-170 (Included in Special Issue on Signal Processing, March 2018).https://doi.org/10.1016/j.compeleceng.2018.05.020[3] Igor Podlubny, Tomas Skovranek, Blas M. Vinagre Jara: Matrix approach to discretization of ODEs and PDEs of arbitrary real order, MathWorks, Inc., Matlab Central File Exchage, 2008 (Updated 04 Mar 2016). https://www.mathworks.com/matlabcentral/fileexchange/22071

Examples of how to make models more accessible. Specifically, how to use the MathWorks Live Editor with Simulink models and Simscape models. This will provide a more interactive way for users to interact with Simulink models without needing to know how to use Simulink. Has examples with and without Simscape so Simscape license is not necessary.

This code creates a private leaderboard for Cody players. To use it, you need the Cody IDs of everyone you want to track on the board.

This tutorial will guide you through the steps necessary to implement a MATLAB algorithm in FPGA hardware, including: * Create a streaming version of the algorithm using Simulink * Implement the hardware architecture * Convert the design to fixed-point * Generate and synthesize the HDL code

WARNING - This version of Widgets Toolbox is intended to support forward compatibility of *existing* apps only. If you are building new apps in MATLAB R2020b or later, please instead use the new "Widgets Toolbox - MATLAB App Building Components":https://www.mathworks.com/matlabcentral/fileexchange/83328-widgets-toolbox-matlab-app-building-componentshttps://github.com/mathworks/widgets-toolbox-uifiguresStarting in MATLAB R2020b, most existing apps that depend on Widgets Toolbox (uiw.* package) can be migrated from traditional figure windows (Java-based) into modern UI Figures (uifigure). There are known limitations and incompatibilities associated with migrating existing Widgets Toolbox content into uifigure. For this, please see the release notes at the end of the Getting Started Guide and begin testing migration of your apps into uifigure. It is recommended you install this as a toolbox to automatically set up the proper MATLAB and Java paths. If you install this manually, please review the installation directions in the Getting Started guide.Need help upgrading a business-critical app? MathWorks Consulting can help: https://www.mathworks.com/services/consulting/proven-solutions/software-upgrade-service.html

Click on the Examples Tab ^^^ for detailed descriptions of AMT functions. This toolbox is for importing, analyzing, and displaying Antarctica-related data. AMT is designed to provide a standard framework to allow easy pairing of multiple different types of data sets (surface elevation, ice velocity, grounding line, etc). For a quick overview, check the Examples tab on this page and click "AMT Getting Started". To find data-specific plugins for this toolbox, search the File Exchange site for "AMT".Note to users: AMT was originally written to be used with Matlab's Mapping Toolbox. However, Matlab's Mapping Toolbox is sometimes inefficient and difficult to work with. And depending on Matlab's Mapping Toolbox makes it harder to share codes. So I've been moving more toward plotting mostly in polar stereographic meters. There is a suite of functions ending in "ps" that make this easy. If AMT is useful for you, please cite our paper!

LaserPulse is a simple Matlab toolbox for nonlinear and ultrafast optics. It can be used to simulate propagation through optical media, and pulse compression techniques (e.g. FROG, G-MIIPS).More information can be found here: http://albeco.github.io/LaserPulse/A concise overview and example code can be found here: http://albeco.github.io/LaserPulse/manual/laserpulse_overview.html

polarPcolor aims to represent a pseudocolour plot in polar coordinates, with a radial grid to allow clear visualization of the data. It is well suited for Plan Position Indicator (PPI) scan for radar or lidar for example [1]. A similar function is available in ref. [2], which propose a visualization in 3D.References:[1] Cheynet, E., Jakobsen, J. B., Snæbjörnsson, J., Reuder, J., Kumer, V., & Svardal, B. (2017). Assessing the potential of a commercial pulsed lidar for wind characterisation at a bridge site. Journal of Wind Engineering and Industrial Aerodynamics, 161, 17-26. http://dx.doi.org/10.1016/j.jweia.2016.12.002[2] http://www.mathworks.com/matlabcentral/fileexchange/13200-3d-polar-plot

Code files for MATLAB and Simulink Robotics Arena - Deep Learning for Object Detection video series. This series comprises of the following 4 videos:1. Data Pre-processing for Deep Learning - https://www.mathworks.com/videos/data-preprocessing-for-deep-learning-1578031296056.html2. Design and Train a YOLOv2 Network in MATLAB - https://www.mathworks.com/videos/design-and-train-a-yolov2-network-in-matlab-1578033233204.html3. Import Pre-trained Deep Learning Networks into MATLAB - https://www.mathworks.com/videos/import-pretrained-deep-learning-networks-into-matlab-1578034627950.html4. Deploy YOLOv2 to an NVIDIA Jetson - https://www.mathworks.com/videos/deploy-yolov2-to-an-nvidia-jetson-1578035533852.htmlRefer to the GitHub page for more information and links: https://github.com/mathworks-robotics/deep-learning-for-object-detection-yolov2For any questions, email us at roboticsarena@mathworks.com.

This submission includes 2 code examples of analyzing and visualizing earthquake data: 1) Creating an interactive map, reading the data from the web, and 2) Using datastore to process a large historical earthquake dataset, enabling results to be compared with a published model.These files are companions to the MATLAB for Analyzing and Visualizing Geospatial Data webinar delivered on February 24, 2015. They include links to the data. To watch the video of Loren running these code examples in MATLAB, view the IRIS-sponsored webinar: http://bit.ly/1B58VT4Additional resources for learning more about analyzing and visualizing geospatial data with MATLAB:*New features in MATLAB R2014b: http://www.mathworks.com/products/matlab/whatsnew.html, including: *New graphics system *Date-time objects and computation *datastore, for handling big tabular data *webread, for accessing and interacting with external map data (with Mapping Toolbox): http://www.mathworks.com/products/mapping/ *Parallel for-loops and GPU processing (with Parallel Computing Toolbox): http://www.mathworks.com/products/parallel-computing/*Seismology-related community-developed code and resources (subset) *GISMO Suite: http://geoscience-community-codes.github.io/GISMO/ *irisfetch: http://ds.iris.edu/ds/nodes/dmc/software/downloads/irisfetch.m/ Also see this blog post for more information: http://blogs.mathworks.com/loren/2015/03/03/direct-access-to-seismological-data-using-matlab/ *Teaching Computation in the Geosciences with MATLAB: http://serc.carleton.edu/NAGTWorkshops/data_models/toolsheets/MATLAB.html*MATLAB and add-ons product information *What’s New in MATLAB Release 2014b? (with videos) http://www.mathworks.com/products/matlab/whatsnew.html *Parallel Computing Toolbox product page http://www.mathworks.com/products/parallel-computing/ *Speeding up MATLAB Applications (webinar) http://www.mathworks.com/videos/speeding-up-matlab-applications-81729.html *Mapping Toolbox product page http://www.mathworks.com/products/mapping/ *Intro to Geospatial Computing with MATLAB (webinar) http://www.mathworks.com/videos/introduction-to-geospatial-mapping-analysis-using-matlab-86566.html *Signal Processing Toolbox product page http://www.mathworks.com/products/signal/ *Signal Processing with MATLAB (webinar) http://www.mathworks.com/videos/signal-processing-with-matlab-88866.html

This toolbox provides tools to create a sandbox for developing custom MATLAB toolbox. It uses a convention enforcing best practices in order to help streamline and standardise your toolbox development and packaging process.http://blogs.mathworks.com/developer/2017/01/13/matlab-toolbox-best-practices/This version is for MATLAB release R2019a onwards.

What´s the minimum distance between two SuperEllipsoids? What's the maximum overlap between two SuperEllipsoids?A proximity query contact detection between convex superellipsoids surfaces using its implicit equations.The contact detection of two superellipsoids is formulated as a convex nonlinear constrained optimization problem that is solved using fmincon function, with an Interior Point method. The objective function to be minimized is the distance between both surfaces. The design constraints are the implicit superquadrics surfaces equations and operations between its normal vectors and the distance itself (several constraint sets can be selected). The contact points or the points that minimize the distance between the surfaces are the design variables.The visualization of the surfaces, the initial estimate and the optimization solution for the minimum distance is provided.References:- Portal, Ricardo. Sousa, Luís. Dias, João. "Contact Detection between Convex Superquadric Surfaces". The Archive of Mechanical Engineering. Versita, Warsaw. LVII(2), pp. 165-186, DOI 10.2478/v10180-010-0009-8. 2010.- Chakraborty, N., J. Peng, et al. (2008). "Proximity Queries Between Convex Objects: An Interior Point Approach for Implicit Surfaces." IEEE Transactions on Robotics: 211-220.

Example files for the MATLAB and Simulink Robotics Arena videos and blog posts on walking robots.Refer to the GitHub page for more information and links, as well as to download older releases of this submission: https://github.com/mathworks-robotics/msra-walking-robotFor any questions, email us at roboticsarena@mathworks.com.

A live script that describes how finite difference methods works solving heat equations.

A live script that describes how finite difference methods works.

The Star Wars API (SWAPI) provides open data to explore and analyze the universe: --You can read this data into MATLAB with the function swapiread. The function imports the data by record or by collection and converts the data to a MATLAB-friendly format for analysis.

Functions for computing and visualizing a particular type of Penrose tiling. Penrose tilings are non-periodic and self-similar. The particular tiling, called P3, is constructed from a pair of rhombuses, a thin one and a thick one.

Modeling operations often perturb a model's layout. Layout readjustment is usually needed, and represents a tedious activity if performed manually. Although achieving a proper layout of a Simulink model is deemed very important, there does not exist a comprehensive commercial automatic layout tool for Simulink models. The Auto Layout Tool resizes models' blocks based on number of inputs and outputs, and organizes the signal lines such that the number of crossings is minimized. Auto Layout Tool can leverage three different layout approaches: 1) "Graphviz", a third-party open source tool for drawing graphs; 2) Matlab’s built-in "GraphPlot" layout capability; 3) an in-house "DepthBased" method. Approaches 1) and 3) can be utilized on any version of Matlab/Simulink, while approach 2) only works on R2015b+.• For installation instructions and instructions on how to use the tool, see Auto-Layout/doc/AutoLayout_UserGuide.pdf.• This tool relies on our Simulink Utility. Please download it here: https://github.com/McSCert/Simulink-Utility.For more about the capabilities of the tool and how it can be used in model-based development with Simulink, see the following two papers:[1] Vera Pantelic, Steven Postma, Mark Lawford, Alexandre Korobkine, Bennett Mackenzie, Jeff Ong, Marc Bender, "A Toolset for Simulink: Improving Software Engineering Practices in Development with Simulink," In Proceedings of 3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2015), SCITEPRESS, 2015, 50-61. DOI: https://doi.org/10.5220/0005236100500061 (Best Paper Award)[2] Vera Pantelic, Steven Postma, Mark Lawford, Monika Jaskolka, Bennett Mackenzie, Alexandre Korobkine, Marc Bender, Jeff Ong, Gordon Marks, Alan Wassyng, “Software engineering practices and Simulink: bridging the gap,” International Journal on Software Tools for Technology Transfer (STTT), 2017, 95–117. DOI: https://doi.org/10.1007/s10009-017-0450-9

Deep Learning is powerful approach to segment complex medical image. This example shows how to create, train and evaluate a V-Net network to perform 3-D lung tumor segmentation from 3-D medical images. The steps to train the network include:・Download and preprocess the training data.・Create a randomPatchExtractionDatastore that feeds training data to the network. ・Define the layers of the V-Net network.・Specify training options.・Train the network using the trainNetwork function.After training the V-Net network, the example performs semantic segmentation. The example evaluates the predicted segmentation by a visual comparison to the ground truth segmentation and by measuring the Dice similarity coefficient between the predicted and ground truth segmentation.[Japanese] 医用画像処理において、Deep Learningは非常に強力なアプローチの一つです。 本デモでは、3-D医用画像(ボリュームデータ)からの肺腫瘍のセマンティックセグメンテーション例をご紹介します。利用するネットワークはV-Netで、V-Netの作成、学習と評価までの流れでご紹介します。V-Netを学習させるまでの手順は以下の通りとなります。・学習用データのダウンロードと前処理・randomPatchExtractionDatastoreの作成 ・V-Netの定義・学習オプションの指定・trainNetwork関数によるV-Netの学習V-Netを学習した後、予め分割しておいたテストデータに対してセマンティックセグメンテーションを行い、結果の評価を行います。結果の可視化と、Dice類似係数による定量評価を行います。[Keyward] 画像処理・セグメンテーション・3次元・3-D・ディープラーニング・DeepLearning・デモ・IPCVデモ ・ニューラルネットワーク・医用画像

The function pixelgrid superimposes a grid of pixel edges on an image. The purpose is to easily visualize pixel extents when zooming in closely on an image. The grid is drawing using lines with contrasting colors so that it is visible regardless of the colors of the underlying pixels.

Live Script document is possible to export into several formats (PDF, Word, HTML, LaTeX). Actually the form of automatic LaTeX export does not match the Matlab-language syntax highlighting, and it does not solve problems by special characters displaying (e.g. special letters or symbols in comments or in text strings). The improvement is in modification of matlab.sty:- adding required package matlab-prettifier (package in LaTeX distributions)- changing lst-style into Matlab-editor- adding special characters in lstset from following link: https://stackoverflow.com/questions/1116266/listings-in-latex-with-utf-8-or-at-least-german-umlauts In matlabINF.sty there are special Czech characters support, but it is possible to use the same technique for supporting other languages as well (see the link above).

Check the Examples tab above for function contents, syntax, and examples ^ ^. Bedmap2 is a 1 km resolution dataset of Antarctic surface, ice thickness, and bed topography. Details about Bedmap2 can be found here:https://www.bas.ac.uk/project/bedmap-2/. This set of functions is a plugin for Antarctic Mapping Tools (Greene et al., 2017). References Fretwell, P., et al. "Bedmap2: improved ice bed, surface and thickness datasets for Antarctica." The Cryosphere 7.1 (2013). http://dx.doi.org/10.5194/tc-7-375-2013Chad A. Greene, David E. Gwyther, and Donald D. Blankenship. Antarctic Mapping Tools for Matlab. Computers & Geosciences. 104 (2017) pp. 151-157 http://dx.doi.org/10.1016/j.cageo.2016.08.003

This toolbox contains the MATLAB code for the technical article "Creating Specialized Charts with MATLAB Object-Oriented Programming". These examples were developed by Ken Deeley, David Sampson, Michele Facchinelli, Davide Fantin and Bruno Rodriguez Esteban at MathWorks. This File Exchange entry contains all code and examples used in the article.A chart provides a task-specific application programming interface (API) for creating custom visualizations. Designing and implementing a chart not only provides a convenient API for end users, but simultaneously removes the need for the user to manipulate low-level graphics objects.The code comprises several examples of custom MATLAB charts, together with a catalog app for browsing the available charts.You can inspect the source code used for each chart, explore the features and functionality of each chart, and run Live Script examples demonstrating the use of each chart. The app features several diverse examples of custom charts, together with documentation and resources to help you get started with developing your own charts.

We can estimate the time of sunrise and sunset for any location on the earth if we know the latitude, and longitude for that location. To estimate sunrise and sunset for any day of the year we need to calculate two values for a given date and place -- the equation of time and the hour angle.This live script illustrates the technique used to estimate sunrise and sunset times.

In this example we will sovle a maze using Q-Learning (Reinforcement Learning)(Check Example Tab or Q_Learn_Maze.mlx)

This contribution contains an algorithm to calculate the orbit of a satellite according to the third law of Kepler and Newton's law of gravitation. The program loads input data from a file of type two-line elements (TLE). An input TLE file: https://www.dropbox.com/s/lezxmsxnn6vai5n/TLE_skCUBE.txt?dl=0 TLE file type, information about satellite skCUBE is described in the following article: Szabó, P., Gombíková, K., Ferencová, M. and Košuda, M.:"Keplerian Orbit and Satellite skCUBE", 2019 New Trends in Aviation Development (NTAD), pp. 174-179, IEEE, https://ieeexplore.ieee.org/document/8875530 This program is a part of a diploma thesis. Author and title of thesis: Katarína Gombíková: MATLAB for flight engineers (2020).

This toolbox provides utilities for robot simulation and algorithm development. This includes:- 2D kinematic models for robot geometries such as differential drive, three, and four-wheeled vehicles, including forward and inverse kinematics- Configurable lidar, object, and robot detector simulators - Visualization of robotic vehicles and sensors in occupancy maps- MATLAB and Simulink examples and documentation

One method to compute the friction velocity knowing the along-wind and vertical turbulence components relies on the ogive function. For turbulence modelling, it is useful to ensure that the simulation duration is long enough to reduce the random error and to include all turbulent ranges. The present submission implements a simple ogive function model based on the semi-empirical model proposed by Kaimal et al. [1]. In the example file, it is fitted in the least square sense to ogive function computed with simulated data. The output of the fitting is an estimate of the friction velocity. The submission contains:•The function ogiveFun.m that computes the ogive from the cospectral estimate.•The function fitOgive.m that fits the analytical ogive model to the one estimated with the function ogiveFun.m.•A data file data.m containing time series of u and w•An interactive example file Example.mlxAny question, suggestion of comment is welcomed.Reference(s):[1] Kaimal, J. C., Wyngaard, J. C. J., Izumi, Y., & Coté, O. R. (1972). Spectral characteristics of surface‐layer turbulence. Quarterly Journal of the Royal Meteorological Society, 98(417), 563-589.

This package contains 5 MATLAB live scripts showing how to use the Live Editor and the Symbolic Toolbox to illustrate calculus concepts.The lectures ordering is, by design, conventional, starting with functions and then defining limits, which in turn allow explaining continuity, derivatives and ending with integrals. Of course, according to the instructor's preference, other arrangements of topics are possible, such as leading directly with difference equations, defining derivatives with infinitesimals (as in nonstandard analysis), or introducing from the very beginning vectors and multivariable calculus.Other examples and ideas might be found in the Teaching Calculus with MATLAB page https://www.mathworks.com/academia/courseware/teaching-calculus-with-matlab.html.

The function defines a customized colobar given the positions and the colors that are going to generate the gradients. A Live Script example is also provided to understand the following parameters:- positions: Vector of positions, from 0 to 1. Note that the first position must be 0, and the last one must be 1.- colors: Colors to place in each position. This parameter can be specified as a RGB matrix (n_colors x 3), or as a cell vector, containing HTML values. For instance: {'#ffffff','#ff0000','#000000'} is equivalent to [1 1 1; 1 0 0; 0 0 0].*Update*: The function customcolormap_preset provides 8 new cool presets in order to save time configuring your own!. The presets are 'pasteljet', 'red-yellow-blue', 'red-yellow-green', 'red-white-blue', 'orange-white-purple', 'purple-white-green', 'pink-white-green', 'brown-white-pool'.Example of use:J = customcolormap([0 0.5 1], {'#ffffff','#ff0000','#000000'});colorbar; colormap(J); axis off;

This package includes MATLAB and Simulink files that allow users to communicate with and control the sensors and actuators used in the Arduino Engineering Kit, most of which are connected through the MKR Motor Carrier. This includes: • DC motor – control up to 4 DC motors simultaneously • Servo motor – control up to 8 servo motors simultaneously • Encoder – read up to 2 encoders simultaneously• Tachometer – read rotational speed from the hall sensor on the motorcycle’s inertia wheel• BNO055 IMU sensor – read from the accelerometer, magnetometer, and gyroscope• Ultrasonic sensor – measure the distance to an object• LiPo Battery – read the battery voltage Examples are included to demonstrate how to use the MATLAB functions and Simulink blocks included in this package. Learn more about the Arduino Engineering Kit at www.mathworks.com/arduino-kit Important notes: 1) After installing this toolbox, type the following command in MATLAB to open the ReadMe>> edit ArduinoKitHardwareSupportReadMe.txt2) Be sure to follow the steps in this file, as it provides instructions on downloading the Arduino library for the MKR Motor Carrier. This library is required for some of the functionality to work.

Generate JavaScript using MATLAB Coder Add-On in combination with the Emscripten compiler converts your MATLAB functions into high-performance, client-side JavaScript/WebAssembly apps and libraries. Generated code can be compiled, embedded, and run in any modern browser; including, Google Chrome, Mozilla FireFox, Microsoft Edge, and Apple Safari. The generated code can also be run in standalone JavaScript engines, such as NodeJS.