NOTE: Starting in MATLAB R2021a, the project files have been integrated within the MATLAB & Simulink Support Package for Arduino.For more details please visit this link: https://www.mathworks.com/help/supportpkg/arduinoio/ug/arduino-aek.htmlThis package includes the MATLAB and Simulink files used to program the three projects in the Arduino Engineering Kit Rev 2:• A drawing robot that takes a reference drawing and duplicates it on a whiteboard• A webcam controlled rover that navigates between reference points and moves objects with its forklift• A self-balancing motorcycle that maneuvers itself on varying terrain and remains upright using a flywheel for balanceThese files along with the associated online learning materials included with the Rev 2 kit guide users through the process of programming each project, teaching important engineering concepts along the way in areas such as controls, system modeling, image processing, and robotics.Learn more about the Arduino Engineering Kit at: www.mathworks.com/arduino-kitThe official link for Arduino Engineering Kit Rev 2: https://store-usa.arduino.cc/products/arduino-engineering-kit-rev2Important note:1. You can either download the toolbox file or the zip file.2. If you choose to download the toolbox file, type the following command in MATLAB to move to the appropriate folder after installing the toolbox:>> cd(arduinokit.kitRoot)3. If you choose to download the zip file, add the contents of the unpackaged folder to the MATLAB path. Right-click on the folder in MATLAB and select "Add to Path" -> "Selected Folders and Subfolders".

Matlab Web Apps used in KTEF25 Reaction Enginenering at Lund UniversitySee also Matlab Apps for Apps to be installed directly into Matlab

Financial stock market prediction of some companies like google and apple Any doubts pls contact emaal- josemebin@gmail.com

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.

Note: This version is for MATLAB release R2018b only. For MATLAB R2018a please use this file here: https://www.mathworks.com/matlabcentral/fileexchange/66568-arduino_engineering_kit_hardware_support For MATLAB R2019a and later, please use this file here: https://www.mathworks.com/matlabcentral/fileexchange/70554-arduino_engineering_kit_hardware_support_19aThis 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 voltageExamples 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-kitImportant notes: 1) After installing this toolbox, type the following command in MATLAB to open the ReadMe >> edit ArduinoKitHardwareSupportReadMe.txt 2) 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.

After signing up for key from Alphavantage, enter this as variable Key, after which queries can be called for individual ticker symbols. Refer to website documentation for proper input pairs. As of latest upload date, all 'functions' appear to download correctly and format nicely into tabular data. The 'sector' query was particularly devilish to format. Version 2016b requires voiding of web secure certificate (which is done here) in order to fetch data using webread.

Reinforcement Learning For Financial Trading ?How to use Reinforcement learning for financial trading using Simulated Stock Data using MATLAB.SetupTo run:Open RL_trading_demo.prjOpen workflow.mlxRun workflow.mlxEnvironment and Reward can be found in: myStepFunction.mOverview:The goal of the Reinforcement Learning agent is simple. Learn how to trade the financial markets without ever losing money. Note, this is different from learn how to trade the market and make the most money possible.The aim of this example was to show:1. What reinforcement learning is2. How it can be applied to trading the financial markets3. Leave a starting point for financial professionals to use and enhance using their own domain expertise.The example use an environment consisting of 3 stocks, $20000 cash & 15 years of historical data.Stocks are:Simulated via Geometric Brownian Motion orHistorical Market data (source: AlphaVantage: www.alphavantage.co)Copyright 2020 The MathWorks, Inc.

We present methods for calculating the risk neutral density for several financial models. We consider:Black, Displaced Diffusion, CEV, SABR, Heston, Bates, Hull-White, Heston-Hull-White, VG, NIG, CGMY, VGGOU, VGCIR, NIGCIR, NIGGOU.For models where no analytic representation of the density is available we either use approximation formulae or methods based on fourier transform.We study the effects of changing the model parameters. This illustrates the topics from chapters 2 and 3 of the book Financial Modelling - Theory, Implementation and Practice.We provide scripts for testin each model and plot the densities.

Files used in the Webinar "Developing a Financial Market Index Tracker using MATLAB OOP and Genetic Algorithms" The zip file contains the data and files used to develop an application to track a market index using Genetic algorithm. The initial algorithm is then wrapped up in MATLAB OOP to create an applcation, which can then be tested against the data. Also included aresimple demo files of both genetic algorithms and MATLAB OOP.

The function SIP2NUM converts a string with an SI prefix (aka metric prefix, or engineering prefix) into a numeric value. For example the string '1 k' is converted to 1000. The bonus function BIP2NUM converts from Binary-prefixed string to numeric, for example the value '1 Ki' is converted to 1024.After testing many submissions on MATLAB FEX (see Acknowledgements) and not finding a single one that converted all values correctly, I wrote my own functions. And then exhaustively tested them to confirm that they actually give the correct output.This submission:Automatically detects the prefix, or it may be restricted to either a name or symbol.Detects coefficients including +/- sign, decimal digits, and exponent E-notation.Detects zero or more coefficients in the string.Returns the parts of the input string that are split by the detected coefficients and prefixes.Returns the number of significant figures detected in the coefficients.Includes the prefixes added in November 2022: ronna, quetta, ronto, and quecto.Reverse Conversionhttp://www.mathworks.com/matlabcentral/fileexchange/33174-number-to-scientific-prefixSI Prefix Examples>> sip2num('10 k') % OR sip2num('10.0 kilo') OR sip2num('10000') OR sip2num('1e4')ans = 10000>> [num,spl] = sip2num("Power: 200 megawatt")num = 200000000spl = ["Power: ","watt"]>> [num,spl,sgf] = sip2num("from -3.6 MV to +1.24kV potential difference.")num = [-3600000,1240]spl = ["from ","V to ","V potential difference."]sgf = [2,3]>> [num,spl] = sip2num("100 meter","meter") % Try it without the second option.num = 100spl = ["","meter"]>> sip2num(num2sip(9e12)) % 9 tera == 9e12 == 9*1000^4ans = 9000000000000Binary Prefix Examples>> bip2num('10 Ki') % OR bip2num('10.0 kibi') OR bip2num('10240') OR bip2num('1.024e4')ans = 10240>> [num,spl] = bip2num("Memory: 200 mebibyte")num = 209715200spl = ["Memory: ","byte"]>> [num,spl,sgf] = bip2num("From -3.6 MiB to +1.24KiB data allowance.")num = [-3774873.6,1269.76]spl = ["From ","B to ","B data allowance."]sgf = [2,3]>> [num,spl] = bip2num("100 Pixel","Pixel") % Try it without the second option.num = 100spl = ["","Pixel"]>> bip2num(num2bip(pow2(9,40))) % 9 tebi == pow2(9,40) == 9*1024^4ans = 9895604649984SI Prefixes (Bureau International des Poids et Mesures) Magnitude | Symbol | Name 1000^-10 | q | quecto 1000^-9 | r | ronto 1000^-8 | y | yocto 1000^-7 | z | zepto 1000^-6 | a | atto 1000^-5 | f | femto 1000^-4 | p | pico 1000^-3 | n | nano 1000^-2 | µ | micro 1000^-1 | m | milli 1000^0 | | 1000^+1 | k | kilo 1000^+2 | M | mega 1000^+3 | G | giga 1000^+4 | T | tera 1000^+5 | P | peta 1000^+6 | E | exa 1000^+7 | Z | zetta 1000^+8 | Y | yotta 1000^+9 | R | ronna 1000^+10 | Q | quettaBinary Prefixes (IEC 60027-2 A.2 and ISO/IEC 80000-13:2008) Magnitude | Symbol | Name 1024^1 | Ki | kibi 1024^2 | Mi | mebi 1024^3 | Gi | gibi 1024^4 | Ti | tebi 1024^5 | Pi | pebi 1024^6 | Ei | exbi 1024^7 | Zi | zebi 1024^8 | Yi | yobiNotesThese functions have been extensively tested against many edge cases, with particular attention to ensuring the correct handling of exponential notation. Compared to similar submissions available on MATLAB File Exchange, these functions correctly:parse negative strings (try '-1').parse E-notation values (try '1e0', '1e0 k', '1e30').

A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Optimize Portfolios with Financial Toolbox." The scripts demonstrate features of the Portfolio object and follows with case studies that demonstrate how to customize the tools for different tasks, including Sharpe/information ratio optimization and 130/30 portfolios. A readme.txt. file in the .zip folder describes how to use the scripts.

The function NUM2SIP converts a numeric scalar into a string with an SI prefix (aka metric prefix, or engineering prefix). For example the value 1000 is converted to '1 k'. The bonus function NUM2BIP converts from numeric to binary-prefixed string, for example the value 1024 is converted to '1 Ki'. After testing many submissions on MATLAB FEX (see Acknowledgements) and not finding a single one that converted all values correctly and that supported the correct SI spacing, I wrote my own functions. And then exhaustively tested them to confirm that they actually give the correct output.These functions are particularly useful to help create publications following SI standards, or where control over significant digits and trailing zeros is required, or for including numeric values in figures.This submission:Always includes the space character (required by the SI standard).Automatically selects the most suitable prefix.Rounds to the requested significant figures (default==5).Prefix may be selected as either the full name ('kilo') or the symbol ('k').Trailing decimal zeros of the coefficient may be included or removed.Rounds up to the next prefix when significant figures require, e.g. '1 M', not '1000 k'.Also returns the numeric coefficient and the prefix separately.Includes the prefixes added in November 2022: ronna, quetta, ronto, and quecto.Reverse Conversionhttp://www.mathworks.com/matlabcentral/fileexchange/53886-scientific-prefix-to-numberSI Prefix Examples>> num2sip(10000) % OR num2sip(1e4)ans = '10 k'>> num2sip(10000,4,true)ans = '10 kilo'>> num2sip(10000,4,false,true)ans = '10.00 k'>> num2sip(999,3)ans = '999 '>> num2sip(999,2)ans = '1 k'>> num2sip(0.5e6)ans = '500 k'>> num2sip(0.5e6,[],'M')ans = '0.5 M'>> sprintf('Power: %swatt', num2sip(200e6,[],true))ans = 'Power: 200 megawatt'>> sprintf('Clock frequency is %sHz.', num2sip(1234567890,3))ans = 'Clock frequency is 1.23 GHz.'>> num2sip(sip2num('9 T')) % 9 tera == 9e12 == 9*1000^4ans = '9 T'Binary Prefix Examples>> num2bip(10240) % OR num2bip(1.024e4) OR num2bip(pow2(10,10)) OR num2bip(10*2^10)ans = '10 Ki'>> num2bip(10240,4,true)ans = '10 kibi'>> num2bip(10240,4,false,true)ans = '10.00 Ki'>> num2bip(1023,3)ans = '1020 '>> num2bip(1023,2)ans = '1 Ki'>> num2bip(pow2(19))ans = '512 Ki'>> num2bip(pow2(19),[],'Mi')ans = '0.5 Mi'>> sprintf('Memory: %sbyte', num2bip(pow2(200,20),[],true))ans = 'Memory: 200 mebibyte'>> sprintf('Data saved in %sB.', num2bip(1234567890,3))ans = 'Data saved in 1.15 GiB.'>> num2bip(bip2num('9 Ti')) % 9 tebi == pow2(9,40) == 9*1024^4ans = '9 Ti'SI Prefixes (Bureau International des Poids et Mesures) Magnitude | Symbol | Name 1000^-10 | q | quecto 1000^-9 | r | ronto 1000^-8 | y | yocto 1000^-7 | z | zepto 1000^-6 | a | atto 1000^-5 | f | femto 1000^-4 | p | pico 1000^-3 | n | nano 1000^-2 | µ | micro 1000^-1 | m | milli 1000^0 | | 1000^+1 | k | kilo 1000^+2 | M | mega 1000^+3 | G | giga 1000^+4 | T | tera 1000^+5 | P | peta 1000^+6 | E | exa 1000^+7 | Z | zetta 1000^+8 | Y | yotta 1000^+9 | R | ronna 1000^+10 | Q | quettaBinary Prefixes (IEC 60027-2 A.2 and ISO/IEC 80000-13:2008) Magnitude | Symbol | Name 1024^1 | Ki | kibi 1024^2 | Mi | mebi 1024^3 | Gi | gibi 1024^4 | Ti | tebi 1024^5 | Pi | pebi 1024^6 | Ei | exbi 1024^7 | Zi | zebi 1024^8 | Yi | yobiNotesThese functions have been extensively tested against many edge cases, with particular attention to ensuring the correct rounding for all choices of significant figures. Compared to similar submissions available on MATLAB File Exchange, these functions correctly:include the space character following the coefficient, even if there is no prefix (try value 1).round to the requested significant figures (try 0.999 or 999e3, with 1 or 2 sigfig).return a coefficient without a prefix for zero and values outside the prefix range (try values 0, 7, Inf, 1E99).return a coefficient without exponent notation when the significant digits are less than three (try 1e5 to 1 sigfig).

Ten demos, most of which are shown at The MathWork's financial modeling seminars. All of the demos are in their own folders, which contain the code, and a ReadMe file that explains what the demos do and gives directions on how to run it. The ReadMe also mentions which Toolboxes are needed for each demo. To run all of the demos, you'll need the Toolboxes listed in the required list. However, not all of the demos require all of the Toolboxes.MLTutorial:Creates an array in MATLAB and shows indexing ability and examples of matrix mathDFDBportOpt:GUI that inputs data from database or Yahoo(Datafeed) and finds the efficient frontierBLSVIS:Plots a 3d visualization of option sensitivities ? Delta and GammaGarchFXdemo:GARCH demo showing time-series, simulation, optimization, and graphics abilities of MATLAB.OpriceAnimation:Animates option prices, gamma, and volatility in 3D as time to maturity changesXlderiv:Illustrates how to price an fixed-income instrument portfolio using the Heath-Jarrow-Morton and Black-Derman-Toy interest rate modelsSpotCurveFit:Computes and compares spot and forward curves calculated from bootstrapping and spline fitting methodsOptVar:Calculates the Value at Risk (VaR) of a portfolio of equity options using the delta-gammamethod.PortVaRmc:Calculates the Value at Risk (VaR) of a portfolio of equities using Monte Carlo simulationPortVaRreturns:Calculates the Value at Risk (VaR) of a portfolio of equities using historical return data

Optimization algorithms are commonly used in the financial industry with examples including Markowitz portfolio optimization, Asset-Liability management, credit-risk management, volatility surface estimation etc. Many optimization problems involve nonlinear objective functions and constraints. These problems can be computationally expensive, especially with numerically estimated gradients. We have seen many cases where optimizations were sped up by incorporating pre-computed analytical derivatives. In the Wilmott Magazine May 2011 article, we illustrate how optimization problems can be sped up using this approach with MATLAB® and Symbolic Math Toolbox™.A copy of the article is included in the submission

Read Stock price from Yahoo and plot ZigZag Wave, Leading and lagging moving averages chart. Further more, plot a Signal (SF) if a new decision was made to change the arm direction.

Welcome!The following material is aimed at engineering students who want to acquiredifferent skills through the use of the Arduino Engineering Kit -threechallenging engineering projects- and the MATLAB, Simulink and Stateflowapplications. The available material has been designed in collaboration betweenMondragón University and MathWorks.Table of ContentsResourcesAcademic MaterialMiscellaneous MaterialAEK Motorcycle DevelopmentAEK Rover DevelopmentAEK Drawing Robot DevelopmentRESOURCESAcademic MaterialThis repository contains educational material with explanations of basic physicsconcepts and practical exercises to test the newly acquired skills.Also, we have also put at your disposal a Gitbook page. This Gitbook will consistof an explanation of the collaboration done between Mondragon Unibertsitatea andMATHWORKS, where the Arduino Engineering Kit, onwards AEK, was used in order tomakes changes in the lectures imparted in the aforemencioned university:ViewMiscellaneous MaterialIn this repository you can find different resources, such as videos of thedifferent models in operation.AEK Motorcycle DevelopmentIn this repository you can find a series of scripts and models that will helpyou understand how the motorcycle works.Two models are delivered, the first of them seeks to achieve the balance ofthe motorcycle and the second tries to maintain the balance while it moves on the plane.AEK Rover DevelopmentIn this repository you can find a series of scripts and models that will helpyou understand how the rover works. This includes the scripts needed to calibrateand locate the rover.Of course, you will find a fully functional model to command the rover.AEK Drawing Robot DevelopmentIn this repository you can find a series of scripts that will allow you to startthe drawing robot, and as you complete each task you will learn new concepts.Mondragon University in collaboration with MathWorkshttps://www.mondragon.edu/en/home

NOTE: This package contains the project files for Arduino Engineering Kit Rev 1. - For Arduino Engineering Kit Rev 2 project files, use this link: https://www.mathworks.com/matlabcentral/fileexchange/80419-arduino_engineering_kit_project_files_rev_2This package includes the MATLAB and Simulink files used to program the three projects in the Arduino Engineering Kit Rev 1. • A drawing robot that takes a reference drawing and duplicates it on a whiteboard • A mobile rover that navigates between reference points and moves objects with its forklift • A self-balancing motorcycle that maneuvers itself on varying terrain and remains upright using a flywheel for balance These files along with the associated online learning materials included with the kit guide users through the process of programming each project, teaching important engineering concepts along the way in areas such as controls, system modeling, image processing, and robotics. Learn more about the Arduino Engineering Kit at www.mathworks.com/arduino-kitImportant note: After installing this toolbox, type the following command in MATLAB to move to the appropriate folder. >> cd(arduinokit.kitRoot)

Vehicle Dynamics Blockset™ comes installed with prebuilt 3D scenes in which to simulate and visualize the vehicles modeled in Simulink®. These 3D scenes are visualized using the Unreal Engine® from Epic Games®. By using the Unreal® Editor, you can customize these scenes or simulate within the scenes from your own custom project.By using the Unreal Editor and the Vehicle Dynamics Blockset Interface for Unreal Engine Projects support package, you can customize these scenes. You can also use the Unreal Editor and the support package to simulate within scenes from your own custom project.With custom scenes, you can co-simulate in both Simulink and the Unreal Editor so that you can modify your scenes between simulation runs. To customize scenes, you should be familiar with creating and modifying scenes in the Unreal Editor.For information about installing and using the support package, see Customize 3D Scenes for Vehicle Dynamics Simulations (https://www.mathworks.com/help/vdynblks/ug/customize-3d-scenes-for-vehicle-dynamics-simulations.html).

Matlab & Its Engineering Application - Presentation Its very useful for Matlab beginners

This zip file contains the Presentation (PDF) and M-files that were demonstrated in the MathWorks Webinar: Using Genetic Algorithms in Financial Applications delivered on Dec 11 2007.The purpose of the webinar was to highlight how Genetic Algorithms may be used to supplement portfolio optimization problems. The Genetic Algorithm contains custom evolution algorithms that were built specifically for this webinar. They allow the user to explore subsets of fixed size from a larger universe of stocks to search for a minimum variance portfolio with a given return. This is related to what is known as portfolio "cardinality constraints" or "mean variance spanning". This will also be useful for anyone interested in solving mixed integer proglems in MATLAB.Please see the included ReadMe.doc for a description of the contents.

This support package allows you to customize scenes in the Unreal® Editor and use them in Simulink®.You will be able to simulate in custom scenes simultaneously from both the Unreal® Editor and Simulink®. By using this co-simulation framework, you can add vehicles and sensors to a Simulink model and then run this simulation in your custom scene.

Social Network Search (SNS) is a novel metaheuristic optimization algorithm, and its socrce code for solving mixed continuous/discrete engineering optimization problems is presented here. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including Imitation, Conversation, Disputation, and Innovation are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views.Related papers:Hadi Bayzidi, Siamak Talatahari, Meysam Saraee, Charles-Philippe Lamarche, "Social Network Search for Solving Engineering Optimization Problems", Computational Intelligence and Neuroscience, vol. 2021, Article ID 8548639, 32 pages, 2021. https://doi.org/10.1155/2021/8548639S. Talatahari, H. Bayzidi and M. Saraee, "Social Network Search for Global Optimization," in IEEE Access, vol. 9, pp. 92815-92863, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3091495 .In this source code, the sns algorithm is employed for solving the following benchmark problems:1 - Speed reducer design2 - Tension/compression spring design3 - Pressure vessel design4 - Three-bar truss design problem5 - Design of gear train6 - Cantilever beam7 - Minimize I-beam vertical deflection8 - Tubular column design9 - Piston lever10 - Corrugated bulkhead design11 - Car side impact design12 - Design of welded beam design13 - A reinforced concrete beam design

This is material from the bookFinancial Modelling: Theory, Implementation and Practice with Matlab source from Joerg Kienitz and Daniel Wetterau, WILEY, September 2012Pricing Call Options for advanced financial models using FFT and the Carr-Madan or the Lewis Method. We cover:Diffusion:Bachelier, Black-Scholes, CEV, Displaced Diffusion, Hull-WhiteStochastic Volatility:Heston, SABR, Displaced Diffusion Heston, Heston-Hull-WhiteJump-Diffusion:Merton, Bates, Bates-Hull-WhiteLevy:Variance Gamma, Normal Inverse GaussianLevy+Stochastic Volatility:Gamma Ornstein-Uhlenbeck and CIR clock

This function eigendecomposes a correlation matrix of financial time series and filters out the Market Mode Component and Noise Component, leaving only the components of the correlation matrix that correspond to mesoscopic structure in the set of original time series. The function is intended to be used in conjunction with a community detection algorithm (such as the Louvain method) to allow for community detecion on time series based networks

The engineering optimization algorithms have been presented drastically recently, here is a simple method for constrained engineering optimization for both static and dynamic systems. The simplicity of the method and random solution for reaching the optimal answer are the advantages of this method. The formalism and details of the method could be followed in the following publication: Nekoo, Saeed Rafee, José Ángel Acosta, and Anibal Ollero. "A Search Algorithm for Constrained Engineering Optimization and Tuning the Gains of Controllers." Expert Systems with Applications, vol. 206: 117866, 2022. The codes have been named based on the sections of the above report. Some excel files and a more complete version is available as the supplementary material of the paper on the journal website.https://doi.org/10.1016/j.eswa.2022.117866

In the modern digital era, knowledge is expanding very quickly. Knowledge is becoming difficult to retain despite the vast amounts of information within software. How can the engineering and science organizations adapt to minimize the loss of knowledge moving forward? This content stems from a demo called "Engineering Design and Documentation with MATLAB" discussing how MATLAB's tools provide a streamline solution to the growing issue of knowledge "discontinuities". The analysis topic performed has two sections: 1) Wing Loading Profile2) FEA Stress AnalysisWing Loading:Develops and documents an analytical model of the loads on the wing of a small passenger aircraft. Using the MATLAB Live Editor we can incorporate math equations, descriptive text, and images into our calculations to clearly document our work. Completed live scripts can be published in PDF or HTML.The following components are calculated analytically using the Symbolic Math Toolbox to produce the total load profile. Units will be carried along our calculations to ensure dimensional consistency and allow for simple unit conversions. The list of units supported in MATLAB can be found here: List of units supported with MATLAB Symbolic Math Toolbox. Components: -Aerodynamic Lift-Weight of Wing-Weight of FuelThe total load is then calculated by combining the three components above. Values for the specific aircraft parameters are plugged in to provide results for the specific aircraft. Finally, the analytical representation is converted to a MATLAB function that can be leveraged for any downstream projects for this particular aircraft wing design. FEA Stress Analysis of 3D Aircraft WingThis Live Script performs a stress analysis of an aircraft wing and visualizes the results. It relies on a 3D CAD model of an aircraft wing, the analytically-derived wing load profile found in the Wing Loading Live Script (TotalWingLoad.m), and the Partial Differential Equations (PDE) Toolbox. The general approach for this analysis is outlined below: Import CAD Model of the wingMesh the wing model with tetrahedral elementsPerform unit conversion from feet to meters Specify the material properties of the wingDefine boundary conditions (Leverages results from previous live script via the function TotalWingLoad). Visualize ResultsFinally, a high level PDF report is generated using MATLAB Report Generator. NOTE: This type of automatic report generation is a game-changer for individuals that currently manually copying and pasting analyses results into presentations and reports (only to do it all over when the analysis results change).

LINKAGE MECHANISM (MECHANICAL ENGINEERING) The command xxx = mnism(r1,r3,l,r4,r5) example: xxx = mnism(3,5,6,6,8) -----------(1)gives two figures. The first shows 7 displacement curves and the second gives description. File f4bar.fig -----------(2)gives details. r1,r2,r3,l - decide the mobility. The computed r2 value is given on the title line of the diagram. For given link lengths, point E, is related to the points B & C. Point B supplies two variables and these are controled by ?a? ? the crank angle. ?a? is the variable . This is included in the file Tang.m ------------(3) Curve C is processed algebraically -(Affaine variety & Groebner basis) and the results are in the files X3fun.m ------------(4) X4fun.m ------------(5) A similar fate occurs for the point E. The files X5fun.m ------------(6) X6fun.m ------------(7) do the processing. Quadratic functions do the descriptions. Thiey are procesed in the file Qfun.m ------------(8) Four curves satisfy point E ? locus. Intermixing of the coodinate points is resolve by the file Organize.m -----------(9) The animation file is animism.m --------------------------(10) An example xxx = animism(3,5,6,7,8,1,10) 6th argument selects a particular configuration among {1,2,3,4}, and the 7th is the number of frames. The first 5 arguments are similar to the mnism.m. The dynamic analysis is included in the file Dymism.m -------------------------------(11) An example [xxx1, xxx2] = dymism(3,5,6,7,8,1,1500,60) 6th is argument selects the particular configuration. 7th argument is the rpm of the crank. 8th argument is a particular angular position of the crank xxx1 is (361X 13) matrix and it gives all details for any crank location. It is also stored in link_age.dat file. Xxx2 is the same as xxx1 but it is for particular crank position. velo.m --------(12) cvelo.m --------(13) normalize.m ---(14)are subroutines for (11) zzz = allcurve(3,5,6,7,8,choice,a,n) gives atlas of curves , a is an nX2 matrix of morphism between a rigt triangle and the coupler plate. Subroutines files are Allcurve.m ------(15) Acurve.m --------(16) Map3t.m---------(17)

A set of hyperlinked maths notebooks which allow Y1 and Y2 engineering students to visualize and experiment with difficult maths concepts. The aim is to allow students to do lots of examples, in an interactive fashion, without focussing on the derivation. The notebooks encourage the students to visualize and reflect on the the answer, rather than the detail of the derivations. As such it can also be used in lectures.To run the notebooks, simply unzip mathexplorer.zip in a folder, start Matlab and double click on the MathExplorer.mn file. This has all the other files linked in.The green variables at the top are for students to alter and re-evaluate the notebook. A set of exercises are at the end of each file.

Simulink documentation is short on examples that are (a) simple and (b) do not come from the engineering domain. We present one that meets both criteria and so may aid another non-engineer curious about Simulink.The models presented in this submission are motivated by a topical, if hardly important :), question. Chartered Financial Analyst (CFA) is an esteemed professional certification in the field of finance. The CFA program includes three examinations, or levels; to qualify for the CFA charter, a candidate must complete Levels I-III, one examination per year, possibly skipping years and retaking failed examinations. A CFA charterholder may be proud of completing the program on first attempt, i.e. without retaking any examination. What is the actual weight of such an accomplishment, as suggested by the fraction of CFA charterholders with a 'first-time pass' (FTP)? The statistic is not reported, and relevant data is limited to pass rates for Levels I-III: 40, 40 and 50 per cent respectively, taken to be constant through time.We focus on drop-out behavior of candidates as the parameter of interest: intuitively, if everyone who fails an examination abandons the program, any charterholder must be an FTP, whereas hordes of repeat test takers ought to make a first-time pass rare. We make several additional assumptions, notably, that (a) candidates taking an examination for the first time and those retaking it have the same chances of passing and dropping out if failed; (b) a candidate failing an exam re-takes it at first opportunity, i.e. next year; (c) the drop-out rate is constant for Levels I-III. (We may fix it at 50%, for example). Please make a guess, and proceed to the files. Do let me know if you spot an error, or scope for better design!

Matlab Apps used in KTEF25 Reaction Enginenering at Lund University.See also Web Apps for use with Matlab Web Apps Server

Written for advanced undergraduate students, this book emphasizes the practical application of control systems engineering to the analysis and design of feedback systems.

UAV Toolbox Interface for Unreal Engine® Project allows you to co-simulate autonomous flight algorithms with Simulink® in a 3D environment using custom scenes created in Unreal Editor. This simulation environment uses the Unreal Engine by Epic Games®. You will be able to use the custom scenes to simultaneously simulate in both Unreal Engine and Simulink. By using this co-simulation framework, you can add vehicles and sensors to a Simulink model then simulate and visualize in your custom scene.

Converts an input numerical value into an engineering-formatted string (as a character vector), in either scientific format with multiples-of-three exponent, or using SI prefixes e.g. k, M, n, p etc. It can also process the tick labels of graphs and colorbars. Please see here for an extensive list of num2eng examples.What makes this submission unique amongst the several num2eng and num2si functions on the file exchange is that it: • Supports scalar, vector, and matrix inputs• Can process axes and colorbar tick labels, including keeping the tick labels up-to-date if the ticks change (e.g. due to limit change, figure resize, etc.)• Supports complex number inputs• Properly handles edge-cases such as rounding, empty inputs, Inf, NaN, non-numerical inputs etc.• Uses either SI prefixes or engineering-formatted scientific notation• Optionally uses the Greek lower-case mu (Unicode U+03BC) as the SI prefix for numbers with magnitude in the range [1e-6, 1e-3)• Optionally uses the true minus character (Unicode U+2212) instead of hyphen-minus (U+002D) for negative numbers• Optionally uses the infinity symbol (Unicode U+221E) for infinite inputs• Optionally pads output strings using left or right justification• Optionally inserts trailing zeros to pad output string to the specified number of significant figures• Optionally outputs as a unified character vector for vector and 2D array inputs, rather than the default cell array of character vectors• Optionally prevents use of exponent or SI prefix for numbers with magnitude in the range [0.1, 1) (i.e., tenths)• When not using SI prefixes:-- Optionally pads the exponent with zeros to a specified width-- Optionally only shows a sign character in the exponent for negative exponents-- Optionally forces the exponent to always be included, even if it is zero-- Offers the choice of lower-case e, capital E, or small capital E (Unicode U+1D07) for the exponent• Optionally forces the mantissa to lie in the range [0.001, 1), rather than the more usual [1, 1000). One reason to use this option is to unambiguously convey accuracy via the number of significant figures in the output.• Optionally uses a comma, instead of a point, as the decimal separator• Allows the user to specify the units, to be appended to the end of the string (with additional control over whether this word is automatically pluralised or not)The function has two alternative call syntaxes:1. string = num2eng(number,[useSI],[useMu],[spaceAfterNum],[sigFigs],[fullName],[useMinus],[useInf],[trailZeros],[pad],[cellOutput]), where input variables in square brackets are optional - i.e. The function accepts anywhere from 1 to 11 inputs, inclusive.2. string = num2eng(number,optionsStruct), where the control options are passed in a structure. This syntax offers more options than syntax one.Using an options structure instead of individual option inputs:When num2eng was originally developed, the individual option-input approach was selected in order to make function hints as helpful as possible. However, as the number of options has grown, the function call has become unwieldy, especially if you only want to set one of the later options and leave the others at the default value. It’s also difficult to see at a glance what options are being used in a call to num2eng, when reading code that you’ve written earlier. This is where using syntax 2 comes in. You can pass num2eng an options structure as the second input. This structure can have anywhere from one to 21 fields, named as per the options listed above, with the additional options: noExp4Tenths, expWidth, expSign, expForce, unit, noPlural, capE, smallCapE, fracMant, useComma, axes.

Retrieves historical stock data for the ticker symbols in Asset cell array (ticker symbol and yahoo stock exchange symbol), between the dates specified by Date1 (beginning) and Date2 (end) in the Matlab datenums format. The program returns the stock data in xls at '/Data' folder, giving the Date, Open, High, Low, Close, Volume, and Adjusted Close price adjusted for dividends and splits.For PERSONAL, INFORMATIONAL use ONLY.-------------------------------------------------------------------------------------------------------------------------------------Example:Asset = { 'AAPL',''; 'ANA','MC'; 'BKIA','MC'; 'CDR','MC'; 'ENG','MC'; 'GLD',''; 'IAG','MC'; 'LYXIB','MC'; 'MT','AS'; 'OHL','MC'; 'ITX','MC'; 'SAN','MC'; 'TEF','MC' };Asset = table(Asset(:,1), Asset(:,2), 'VariableNames', {'Symbol', 'SE'});Date1 = '26-Jan-2017';Date2 = '27-Jan-2018';interval = '1d';downloadStocksData(Asset,Date1,Date2,interval)

In the age of computerized trading, financial services companies and independent traders must quickly develop and deploy dynamic technical trading systems. The technical trader's toolbox includes three forms of our new trading system based on dynamic technical analysis and a set of functions for graphical presentation, performance calculation and optimization of trading systems. The risk of trading systems is determined by applying Probability of Ruin Analysis. MATLAB's vast built-in mathematical and financial functionality along with the fact that is both an interpreted and compiled programming language make this technical trader's toolbox easily extendable by adding new complicated systems with minimum programming effort.The theoretical work has been published in Financial Engineering News Journal May/June 2003.Instructions setup:1.Load Matlab 2.Double click FintradeTool.mat from directory TechTradeTool3.In the directory TechTradeTool you should enter Run in the command window 4. Sample data are provided in sub-directory data

Written for advanced undergraduate students, this book emphasizes the practical application of control systems engineering to the analysis and design of feedback systems.For a full book description and ordering information, please visit http://www.mathworks.com/support/books/book1647.jsp.

This is a toolbox of educational software for engineering students and professionals who are analyzing and designing static systems and dynamic systems. The software is used in ENGINEERING STATICS & DYNAMICS, PRENTICE HALL, INC., L. SILVERBERG (TO APPEAR)

This book provides a practical treatment of the study of control systems.For a full book description and ordering information, please refer to http://www.mathworks.com/support/books/book1420.jsp.

SHape Analyser for Particle Engineering What SHAPE does • Architectural features • File tree • Simple example • Credits • BYOS • Acknowledging SHAPEWhat SHAPE doesSHAPE implements morphological characterisation of three-dimensional particles from imaging data, such as point clouds, surface and tetrahedral meshes or segmented voxelated images (derived using Computed Tomography). Characterisation of morphology is performed for all three aspects of shape, namely form, roundness and surface texture (roughness). The code also supports shape simplification, using edge-collapse techniques, to reduce the number of triangular faces of each particle to user-defined fidelity levels. The particle shapes can be exported to several formats, compatible with various FEA and DEM solvers.Architectural featuresSHAPE is built using an object-oriented architecture, where each particle has the following set of attributes:-Particle % e.g. 1, 2, 3, etc. -Particle_type % e.g. Original, Convex_hull, Face_No_100, Face_No_50, etc. -Mesh % Surface_mesh, Tetrahedral_mesh, Voxelated_image, Surface_texture -Auxiliary_geometries % AABB, OBB, Fitted_ellipsoid, Minimal_bounding_sphere, Maximal_inscribed_sphere -Geometrical_features % Volume, Centroid, Surface_area, Current_inertia_tensor, Principal_inertia_tensor, Principal_orientations -Morphological_features % Form, Roundness, RoughnessFile treeSHAPELICENSEREADME.mdREADME.txtclasses (Definition of objects)examplesfiguresfunctionslib (External dependencies)Simple exampleThis example demonstrates different ways to define Particle objects and characterise their morphology.addpath(genpath('functions'));% Load in-house functionsaddpath(genpath('lib'));% Load external functions (dependencies)addpath(genpath('classes'));% Load object-oriented architecture% Define particle from Point Cloudp1=Particle(P,[],[],[],options); % P (Nv x 3): List of Vertices; options (struct): options for shape characterisation and/or simplification% Define particle from Surface/Tetrahedral Mesh and Texture profilep2=Particle(P,F,[],Texture,options); % P (Nv x 3): List of Vertices; F (Nf x 3) or (Nf x 4): List of Faces/Elements; Texture (Nx x Ny): Planar roughness profile% Define particle from voxelated (volumetric) imagep3=Particle([],[],Vox,[],options); % Vox.img (Nx x Ny x Nz): Segmented voxelated (3-D) image of particle geometry;New users are advised to start from running the available examples, to get familiarised with the syntax and functionalities of SHAPE.CreditsSHAPE uses several external functions available within the Matlab FEX community. We want to acknowledge the work of the following contributions, for making our lives easier:Qianqian Fang - Iso2MeshLuigi Giaccari - Surface Reconstruction From Scattered Points CloudJohaness Korsawe - Minimal Bounding BoxPau Micó - stlToolsYury Petrov - Ellipsoid fitAnton Semechko - Exact minimum bounding spheres and circlesThese external dependencies are added within the source code of SHAPE, to provide an out-of-the-box implementation. The licensing terms of each external dependency can be found inside the lib folder.BYOS (Bring Your Own Scripts)!If you enjoy using SHAPE and you are interested in shape characterisation, you are welcome to ask for the implementation of new morphological descriptors and features or even better contribute and share your implementations. SHAPE was created out of our excitement and curiosity around the characterisation of irregular particle morphologies and we share this tool hoping that members of the community will find it useful. So, feel free to expand the code, propose improvements and report issues.Acknowledging SHAPEAngelidakis, V., Nadimi, S. and Utili, S., 2021. SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data. Computer Physics Communications 265, p.107983.Download BibTeX entry2021 © Vasileios Angelidakis, Sadegh Nadimi, Stefano Utili. Newcastle University, UK

Companion Software

[VINDX FRACDIM V_SIGMA COVERSIZE] = varIndx(LOW,HIGH) LOW is a column vector of the lowest price in the bar (second, minute, etc). HIGH is a column vector of the highest price in the bar. VINDX is the variation index of the data. FRACDIM is the fractal dimension of the data. V_SIGMA is the variation with interval size sigma. SIGMA is the actual interval size. Units of sigma is index. A minimum of 128 datapoints are recommended for best results. Implements method outlined in this paper: http://spkurdyumov.narod.ru/Dubovikov1.pdfThe variation index method is much more efficient than box counting.

A few popular metaheuristic algorithms are included, such as the particle swarm optimization, firefly algorithm, harmony search and others.

Instructions:1. Give the symbol of the stock.2. Give today's date in the specific format (months-days-year).3. 'GET DATA' button fetches the data from Yahoo server. 4. Choose the number of days you want to examine.5. Pick the fast and slow averages used by the functions (remember fast has to be smaller than slow).6. Press the 'RESULTS' button to obtain the plots: Bollinger, simple moving average, square root weighted moving average, linear moving average, square weighted moving average and exponential moving average.7. You can update the results using the days/fast/slow options and re-press 'RESULTS'.Enjoy,Leonidas Bleris

Design a low-pass filter for fabrication using microstrip lines. The specificationsinclude a cutoff frequency of 4 GHz, an impedance of 50 ohm, and a third-order3 dB equal-ripple passband response.

To run the GUI we need to unzip the content of archive in the current Matlab Directory. Run M-file and chose to display Time Evolution versus Probability Density for fixed year and banc.

Lynch_Chaps.zip contains the MATLAB files, Simulink model files and a Tutorial Introduction to MATLAB for the new book, "Applications of Chaos and Nonlinear Dynamics in Engineering - Vol. 1". The book is published by Springer and edited by S.Banerjee, M.Mitra and L.Rondoni.

DescriptionThis is a recreation of the code used for Case 3 in the ENG2120 coursework, but re-applied using MATLAB App Designer. It demonstrates graph manipulation from a user control (by clearing all previous plots, or adding the next plot to the same graph, etc...) , as well as being a good introduction to object orientated coding in MATLAB. This was created as a learning tool for working in MATLAB app designer in future projects.What it modelsThis app shows the concentration of an arbitrary chemical (called A), in a simple first order conversion reaction of A -> B, over time. The reaction occurs in a Continous Stirred Tank Reactor (CSTR) with a constant volume of liquid in the tank (hence inlet flow-rate = outlet flow-rate). The user can vary four parameters regarding the CSTR, including: Concentration of A in the feed stream, Initial concentration of A in the CSTR, Flow rate of the inlet and outlet streams, and the volume of liquid in the tank.

Dissertation can be found by the following link http://papers.ssrn.com/abstract=2521545.Programme calculates realised volatility and applies HAR specification to produce one day ahead volatility forecast.Special thanks to Dr. Alev Atak, Dr. Fulvio Corsi, Oleg Komarov.

GETNEXTCOMBINATION returns the next K-subset of an N-set. It implements the "Revolving Door" (NEXKSB) algorithm found in "Combinatorial Algorithms", 2nd Ed., by Albert Nijenhuis and Herbert S. Wilf. It is a recursive alternative to the NCHOOSEK command that is extremely fast (O(1)), at the expense of slightly more user interaction. (You do get something that NCHOOSEK does not give you forthis extra effort: see input "m".) Apart from its efficiency, it is awesome because each new K-subset differs from the last by a single element and the last combination generated differs from the first combination by a single element.

microwae engineering

The Arduino Engineering Kit offers 3 projects : Drawing Robot, Rover and a Motorcycle. In this submission, we show how to use an Android phone to control the rover. For more details about the kit itself, please visit this page -https://www.mathworks.com/campaigns/products/arduino-kit.htmlHere is a video of the final project in action -https://www.youtube.com/watch?v=jUGCIfsYLo4

A tool for random number generation on a distribution, such as a triangular distribution or PERT distribution, is very convenient for making estimates for physical values in the real world. Scientists and engineers often make estimates or assumptions, but they are also able to bracket the estimate based on experience or physics. For example, in estimating the mass of a small object, a normal distribution centered at my best guess could result in negative mass, which is known to be impossible. A triangular distribution allows quick and simple characterization of values as "probably M, but definitely not less than A or more than B."The formulations here have some features that make it easy to integrate this capturing of uncertainty into scripts.Example: Based purely on guesstimates that include a best guess and intuitive upper/lower limits, how much do a dime, nickel, and quarter weigh together?% First, create a function based on randt to conveniently define and generate "uncertain variables."uvar = @(x) randt(x,[1e5,1]); dime = uvar([1 1.5 3.5]); %Pretty light, but not less than a gram..."nickel = uvar([3 5 6]);quarter = uvar([5 8 10]); total = dime + nickel + quarter; histogram(total,'Normalization','pdf');

From the book of the same name by Steven T. Karris

n an electric power system, a fault is any abnormal electric current. For example, a short circuit is a fault in which current bypasses the normal load. An open-circuit fault occurs if a circuit is interrupted by some failure. In three-phase systems, a fault may involve one or more phases and ground, or may occur only between phases. In a "ground fault" or "earth fault", charge flows into the earth.

This file contains malab code to resilve the post maximization of a financial friction model as estimated in Chang and Fernandez (2013) on the sources of zggregate fluctuations in emerging economies. But I put the disaggregated data for Tunisian country from 1867:Q1 to 2013:Q4.

The exploratory factor analysis class helps you quickly derive, analyze, and apply underlying latent variables in your dataset to help you build your predictive model or analyze trends in data. The class can handle both array and table data.The following class will also be integrated into my modern GUI project, Link: https://www.mathworks.com/matlabcentral/fileexchange/65073-modern-gui-toolbox-iiihttps://www.youtube.com/watch?v=_rf9LHcA89g&feature=youtu.be

GRN CONSTRUCTIONS

Structural Engineering of Microwave Antennas

Exploring Risk Contagion Using Graph Theory and Markov ChainsRecent financial crises and periods of market volatility have heightened awareness of risk contagion and systemic risk among financial analysts. As a result, financial professionals are often tasked with constructing and analyzing models that will yield insight into the potential impact of risk on investments, portfolios, and business operations.Several authors have described the use of advanced mathematical and statistical techniques for quantifying the dependent relationships between investments, foreign exchange rates, industrial sectors, or geographical regions. Bridging the gap between formal methods and a working code implementation is a key challenge for analysts.This code, along with the corresponding technical article shows how MATLAB® can be used to analyze aspects of risk contagion using various mathematical tools. Topics covered include:Data aggregation, preprocessing, and risk benchmarkingQuantifying dependent relationships between financial variablesVisualizing the resulting network of dependencies together with proximity informationAnalyzing periods of risk contagion using hidden Markov modelsInstallation and Getting StartedThe examples are provided in a MATLAB project.Double-click on the project archive (Contagion.mlproj) to extract it using MATLAB.With MATLAB open, navigate to the newly-created project folder and double-click on the project file (Contagion.prj) to open the project.The example file is the live script RiskContagion.mlx within the project.MathWorks® Product RequirementsThis example was updated using MATLAB release R2022b.MATLAB®Statistics and Machine Learning Toolbox™Parallel Computing Toolbox™ (Optional)LicenseThe license for this entry is available in the license.txt file in this GitHub repository.Copyright 2016-2023 The MathWorks, Inc.Community SupportMATLAB Central

'Microwave Engineering' by David Pozar is a cornerstone literature reference http://www2.electron.frba.utn.edu.ar/~jcecconi/Bibliografia/Ocultos/Libros/Microwave_Engineering_David_M_Pozar_4ed_Wiley_2012.pdf yet the hand written anonymous solutions manual doesn't have a single line of MATLAB.https://www.scribd.com/doc/176505749/Microwave-engineering-pozar-4th-Ed-solutions-manualthis is part of the upgraded collection of examples and solved exercises with MATLAB and KEYSIGHT ADS.chapter 05 exercise 03 single oc stub and transmission line match