human pose estimation with deep learningthis demo shows how to train and test a human pose estimation using deep neural network. in r2019b, deep learning toolbox(tm) supports low-level apis to customize training loops and it enables us to train flexible deep neural networks. gpu coder(tm) also enables us to deploy the trained model to an nvidia(r) jetson(tm) devices. once deployed, the human pose estimator will be running as a standalone.this repository includes a pretrained network using the coco dataset, which was collected by the coco consortium (cocodataset.org).required toolboxespose estimation toolbox requires the following products:matlab(r) r2019b or later (due to the r2019b new feature)deep learning toolbox(tm)image processing toolbox(tm)computer vision toolbox(tm)parallel computing toolbox(tm) (required for using gpu computation)to deploy the model to a nvidia jetson or drive platforms, you'll also need the following products.matlab coder(tm)gpu cder(tm)gpu coder interface for deep learning libraries support package (addon package for gpu coder)gpu coder support package for nvidia gpus (addon package for gpu coder)installationdownload the latest release of this repository. to install, open the .mltbx file in matlab.getting startedopen the project file to add paths to related folders if you cloned the github respotory.open simple-pose-estimation.prjload a pose estimator model.detector = posenet.poseestimator;first, read a test image, then crop a person and resize it to fit to the network input.i = imread('visionteam1.jpg');bbox = [182 74 303 404];iin = imresize(imcrop(i,bbox),detector.inputsize(1:2));then, perform the pose estimation on the image. to visualise the results we can superimpose the detected keypoints on the original image.keypoints = detectpose(detector,iin);j = detector.visualizekeypoints(iin,keypoints);imshow(j);click here for a complete example.examplesestimate human pose for multiple person using pretrained networktrain deep neural network for human pose estimationhuman pose estimation with webcam images using deep learningdeploy simple pose estimation on nvidia(r) jetson(tm) using gpu coder(tm)about the modelthe network architecture is based on xiao's pose estimation network[1] which combines upsampling and convolutional parameters into transposed convolutional layers in a much simpler way, without using skip layer connections. we also use coco dataset[2] which is one of the well known large public human pose dataset.[1] xiao, bin, haiping wu, and yichen wei. “simple baselines for human pose estimation and tracking.” proceedings of the european conference on computer vision (eccv). 2018.[2] lin, t., et al. "microsoft coco: common objects in context. arxiv 2014." arxiv preprint arxiv:1405.0312.凯发官网入口首页 copyright 2020 the mathworks, inc.
related paper:joint opposite selection (jos): a premiere joint of selective leading opposition and dynamic opposite enhanced harris’ hawks optimization for solving single-objective problems available at ( as :arini, f. y., chiewchanwattana, s., soomlek, c., & sunat, k. (2022). joint opposite selection (jos): a premiere joint of selective leading opposition and dynamic opposite enhanced harris’ hawks optimization for solving single-objective problems. expert systems with applications, 188. journal above can be download in other proposed algorithms (gwo-jos, mfo-jos, soa-jos, and woa-jos) still on submission process.noted: the code set on cec 2017 (download: also include another competition package on cec 2014. for the use of cec 2014, only change the name of 'cec17_func' to 'cec14_func'.
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competitive sota algorithms and classic test problems benchmark suite this benchmark suite consists of the most commonly used test problems for testing and validating the performance of metaheuristic search algorithms. in this benchmark suite, there are thirty test problems whose problem size can be changed dynamically. this benchmark suite was used to develop sota algorithms in the literature and to compare their performance. here are a few sota algorithms that demonstrate competitive search performance on classic single-objective optimization problems suites, ieee cec benchmark problems suites, and real-world engineering optimization problems: links for source codes of the most up-to-date and competitive sota algorithms in the literature:kahraman, h. t., katı, m., aras, s., & taşci, d. a. (2023). development of the natural survivor method (nsm) for designing an updating mechanism in metaheuristic search algorithms. engineering applications of artificial intelligence, 122, 106121., s., kahraman, h. t., & kati, m. (2023). economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm. engineering applications of artificial intelligence, 117, 105501., h., duman, s., guvenc, u., & kahraman, h. t. (2023). improved adaptive gaining-sharing knowledge algorithm with fdb-based guiding mechanism for optimization of optimal reactive power flow problem. electrical engineering, 1-40., hamdi tolga; aras, sefa; gedikli, eyüp. fitness-distance balance (fdb): a new selection method for meta-heuristic search algorithms. knowledge-based systems, 2020, 190: 105169. guvenc, u., duman, s., kahraman, h. t., aras, s., & katı, m. (2021). fitness–distance balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. applied soft computing, 108, 107421. duman, s., kahraman, h. t., guvenc, u., & aras, s. (2021). development of a lévy flight and fdb-based coyote optimization algorithm for global optimization and real-world acopf problems. soft computing, 25(8), 6577-6617. aras, s., gedikli, e., & kahraman, h. t. (2021). a novel stochastic fractal search algorithm with fitness-distance balance for global numerical optimization. swarm and evolutionary computation, 61, 100821. duman, s., kahraman, h. t., sonmez, y., guvenc, u., kati, m., & aras, s. (2022). a powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. engineering applications of artificial intelligence, 111, 104763., y., duman, s., kahraman, h. t., kati, m., aras, s., & guvenc, u. (2022). fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem. journal of experimental & theoretical artificial intelligence, 1-40. kahraman, h. t., bakir, h., duman, s., katı, m., aras, s., & guvenc, u. (2022). dynamic fdb selection method and its application: modeling and optimizing of directional overcurrent relays coordination. applied intelligence, 52(5), 4873-4908.
this code is used to compare the pal2v activation function against log-sigmoid, tanh, relu (retifier linear) and l-relu (leaky relu).all neural networks are created considering 3 neurons in the hidden layer and 1 neuron at output layer. all neurons with the same activation function.the neural network has 2 inputs and 1 output.the weights and learning rate are the same for all neural networks.
m-exdexmatlab distribution of the exdex r package (estimation of the extremal index): code has only been minimally tested (but results mirror that of the r package) - use with cautionat present, only the functions for the extremal index calculation have been completed, in the future i will look to translating the rest of the package, such as the diagnostic plots. for full details about the theory, functions, and inputs, please refer to the original package.syntax is exdex.function(), the currently working functions are:theta = exdex.spm(data, b, bias_adjust, constrain, varn, which_dj, nv);theta = exdex.kgaps(data, u, k, inc_cens, nv);theta = exdex.dgaps(data, u, d, inc_cens, nv);theta = exdex.iwls(data, u, maxit, nv);% 'nv' is name value argument 'disp' to specify whether to display output to command window or not.% default is to display, specify 'disp', 'n' to not display.all credit and thanks to authors of the original r package.
a novel meta-heuristic algorithm named sinh cosh optimizer (scho) is proposed, which is based on the mathematical inspiration of the characteristics of sinh and cosh. it includes the two different phases of exploration and exploitation, bounded search strategy, and switching mechanism.jianfu bai, yifei li, mingpo zheng, samir khatir, brahim benaisa, laith abualigah, magd abdel wahab."a sinh cosh optimizer", 2023.
load excel data created with bdh in excel. it can efficiently load hundreds of megabytes of excel data.; tbbg = loadlargebloombergexceldatafile('bbghistory.xlsx',4,4,10)tbbg = 22×2 timetable date aapl us equity ibm us equity ___________ ______________ _____________ 08-may-2023 173.5 123.4 09-may-2023 171.77 122.83 10-may-2023 173.56 123.69 11-may-2023 173.75 122.56 12-may-2023 172.81 124.52 15-may-2023 172.31 125.05 16-may-2023 172.31 125.15 17-may-2023 172.93 127.43 18-may-2023 175.29 127.88 19-may-2023 175.4 129 22-may-2023 174.44 129.25 23-may-2023 171.8 129.94 24-may-2023 172.08 127.4 25-may-2023 173.23 128.5 26-may-2023 175.67 130.66 30-may-2023 177.55 131.25 31-may-2023 177.5 130.35 01-jun-2023 180.34 131.6 02-jun-2023 181.2 134.23 05-jun-2023 179.83 134.46 06-jun-2023 179.46 134.51 07-jun-2023 180.42 134.32
pixhawk sil connector for simulinksimulink c s-function for software-in-the-loop simulation with pixhawk.requirementsmatlab & simulink (matlab r2022a or earlier)mingw-w64 or msvc c/c compilerqgroundcontrolpx4-autopilot source code (the latest stable release) subsystem for linux (wsl 2) (contains the asio c and mavlink c libraries)build instructionsinstall matlab-supported compilerhttps://mathworks.com/support/requirements/supported-compilers.html.download the "pixhawk_sil_connector.cpp" and "make.m" files and the "includes.zip" archive.unzip the "includes.zip archive".run "make.m" to create a "pixhawk_sil_connector.mexw64" (windows), "pixhawk_sil_connector.mexa64" (linux), "pixhawk_sil_connector.mexmaci64" (macos) file.note: if you are using a compiler other than msvc (e.g. mingw64) you need to add the -lws2_32 flag to the "mex" command in the "make.m" file.use instructions (simulink model running in windows, px4 autopilot running in wsl 2)download and install qgroundcontrol for windows a new "comm link" in qgroundcontrol via the "application settings" page. the type of the link must be udp, thed port 18570, and the server address must be the ip address of the wls 2 instance. you can use the "ip addr" command to find the ip of the wsl 2 instance. note that the ip of the wsl isntance will change every time you relaunch the instance.open and run "pixhawk_sil_connector_example.slx".build the px4-autopilot source code in wsl 2 using the following commands: git clone --recursive px4-autopilotgit checkout v1.13.x #(px4 version)git submodule sync --recursivegit submodule update --init --recursiveexport px4_sim_host_addr=x.x.x.x #(the ip of the computer running the simulink model)make px4_sitl none_iris of the pixhawk sil connector examplepixhawk sil connector examplepixhawk sil connector example sensorsadditional information available at:
simulink-plane-to-3d-animationsimulink® 3d animation interface for airplanesthis block allows the use of aeronautics conventions to express orientation and position of an object in a virtual world.specifically, the object's orientation can be expressed by means of euler angles and its position by means of x,y,and h cartesian coordinates.this block was created in october 2002, since then there is probably a built-in block somewhere that accomplishes the same task.
(more information is available at ; in case of questions or requests, open an issue at the github repository of prima)prima is a package for solving general nonlinear optimization problems without requiring derivatives. it is the successor of pdfo.prima provides a matlab functions prima, which can automatically identify the type of your problem and then solve it by one of powell's methods, namely cobyla, uobyqa, newuoa, bobyqa, and lincoa.the prima function is designed to be compatible with the fmincon function available in the optimization toolbox of matlab. you can call prima in the same way as calling fmincon:x = prima(fun, x0)x = prima(fun, x0, a, b)x = prima(fun, x0, a, b, aeq, beq)x = prima(fun, x0, a, b, aeq, beq, lb, ub)x = prima(fun, x0, a, b, aeq, beq, lb, ub, nonlcon)x = prima(fun, x0, a, b, aeq, beq, lb, ub, nonlcon, options)x = prima(problem) % problem is a structure defining the optimization problem[x, fval] = prima(___)[x, fval, exitflag, output] = prima(___)in addition, prima can be called in some flexible ways that are not supported by fmincon. if your problem can be solved by fmincon without specifying the derivatives, then it can probably be better solved by prima; if your problem cannot be solved by fmincon, then try prima.for the installation of prima, see .if you need help with the setup of mex, see .the "p" in the name stands for powell, and "rima" is an acronym for "reference implementation with modernization and amelioration".prima is dedicated to the late professor m. j. d. powell frs (1936--2015).
nestore package readme matlab package able to forecast strong aftershocks starting from the first hours after the mainshockssoftware descriptionnestore is a matlab package capable to estimate, duringongoing of an aftershock sequence following a damagingearthquake, the likelihood of the occurrence of another strongearthquake.the code is based on the seismicity characteristics and uses amachine learning approach to provide forecasting for theongoing seismic sequence.starting from an input catalogue, the packageprovides an identification of clusters by a window basedmethod (cluster identification module)trains the algorithm by machine learning on cluster'sfeatures (training module)tests the algorithm performances (testing module)classifies in near-real-time new clusters (near-real-timeclassification module)for further details see the paper:nestorev1.0: a matlab package to identify patterns for strongfollowing earthquake forecastings. gentili, p. brondi, r. di giovambattistainstallationdownload nestorev1.0.zip and extract in a folder you prefer(e.g. nestore_folder)or clone nestore repository on yourcomputer; no other action is required.nestore has been tested on matlab r2018a and later versions.usageto run nestore code start matlab, move in your sub-directorynestorev1.0/user(e.g. nestore_folder/nestorev1.0/user); in thematlab command line, type the corresponding run you need (e.g.run_training); examples are provided. see folder_struc.txtand fileinput_format.txt for further details.software supportthis package is the first online version of nestore, so anysuggestions or bug reporting are welcome.please contact sgentili@ogs.itcreditsplease use the following citation for any use of this software:stefania gentili, piero brondi, rita di giovambattista;nestorev1.0: a matlab package for strong forthcoming earthquake forecasting.seismological research letters 2023; doi: , s., and r. di giovambattista (2017). pattern recognitionapproach to the subsequent event of damaging earthquakes initaly, phys. earth planet. in. 266, 1–17.gentili, s., and r. di giovambattista (2020). forecasting strong aftershocksin earthquake clusters from northeastern italy and westernslovenia, phys. earth planet. in. 303, doi: 10.1016/j.pepi.2020.106483.gentili, s., and r. di giovambattista (2022). forecasting strong subsequentearthquakes in california clusters by machine learning,phys. earth planet. in. 327, doi: 10.1016/j.pepi.2022.106879.gentili, s., e. anyfadi, p. brondi, and f. vallianatos (2023).forecasting strong subsequent earthquakes in greece usingnestore machine learning algorithm, egu general assembly2023, vienna, austria, 23–28 april 2023.acknowledgmentsthe nestore software improvement for making it more robust andfor distributing to the scientific community has been fundedby a grant from the italian ministry of foreign affairs andinternational cooperation.licensegnu general public license as published by the free softwarefoundation; version 3 of the license or any later version.
aboutthis reference application demonstrates how to perform model-in-the-loop (mil) and hardware-in-the-loop (hil) simulation of a grid-connected/islanded microgrid. the model in this example comprises a medium voltage (mv) microgrid with a battery energy storage system, a photovoltaic solar park (pv), loads, as well as an energy management system (ems) supervisory logic integrating peak shaving control. the microgrid can operate both autonomously (in islanded mode) or in synchronization with the main grid.learn how tomodel and simulate grid-connected and islanded microgrids with renewables and utility-scale energy storage systemsdesign and test ems supervisory logic and peak shaving controls for various operational scenarios to ensure compliance with grid codesseamlessly transition from mil to hil within simulink without changing your modelconfigure a profinet fieldbus interface between the microgrid digital twin and the ems controller hardwareleverage hil simulation to validate ems hardware using a digital twin of the microgridgetting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationwatch demohttps://youtu.be/90vt2oddjpm?t=1405learn moremicrogrids and renewables protocol support for microgrid hilhil testing for microgridsmicrogrid system development and analysis
aboutthis reference application demonstrates how to perform model-in-the-loop (mil) and hardware-in-the-loop (hil) testing of an energy magement system (ems) design for a more electric aircraft (mea). the plant model used in this example is based on mathworks' energy management systems for a hybrid electric source example. the plant model represents a fuel cell hybrid power system that is based on the emergency flight profile of a bombardier aircraft. the system comprises the following components:a 12.5 kw (peak), 30-60 v proton exchange membrane (pem) fuel cell power module (fcpm), with nominal power of 10 kw.a 48 v, 40 ah, li-ion battery system.a 291.6 v, 15.6 f, supercapacitor system (six 48.6v cells in series)a 12.5 kw fuel cell dc/dc boost converter, with regulated output voltage and input current limitation.two dc/dc converters for discharging (4 kw boost converter) and charging (1.2 kw buck converter) the battery system.a 15 kva, 270 v dc in, 200 v ac, 400 hz inverter system.a 3 phase ac load with variable apparent power and power factor, to emulate the mea emergency load profile.a 15 kw protecting resistor to avoid overcharging the supercapacitor and battery systems.in addition to the mea emergency power system, the example includes ems controls, which distribute power among the energy sources. ems strategy is based on a classical pi control approach.learn how tomodel and simulate an emergency power system of a more electric aircraft (mea) composed of fuel cells, lithium-ion batteries and supercapacitorsseamlessly transition from mil to hil within simulink without changing your modelinterface the mea digital twin with the ems controller using arinc 429leverage hil simulation to validate ems controls in emergency landing scenariossystem configurationgetting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationwatch demohttps://youtu.be/u1ka3rq-spk?t=834learn morereal-time testing for vtol and conventional aircraft developmentelectric aircraft modeling and simulationmore electric aircraftaerospace protocols
[model-experiment interactive correlation toolbox]the mexico toolbox is a programme capable of modal model correlation for validation. it implements a series of methods (mac, comac, modal complexity plots, natural frequency plot and diagram, additionally invented mode complexity indexes and the modal validity score metric) to provide a user with information about the accuracy of the theoretical model based on comparing modal properties with the experimental results. moreover, the toolbox has a feature of extracting the data for particular nodes from fea based on the geometrical correlation. the toolbox requires:• results of modal testing (mode shapes and natural frequencies) in the ‘.mat’ format• coordinates of points excited in the modal testing• ‘.inp’ file containing the fea meshing data• ‘.dat’ file with printed displacements for each dofto use the toolbox, follow the below steps:1. copy the results from modal testing to the toolbox folder. the results should contain ‘ms’ and ‘fn’ files with mode shapes and natural frequencies. name the results file as exp_mp_results 2. open the insert_geometry.m file in matlab and put the coordinates of points extracted in the modal testing. complete the following lines with node labels and their connectivity. after inserting those data, run the script to create the exp_geometry.mat file3. open mexico.mlx4. substitute lines 5 and 6 with paths for to the ‘.inp’ and ‘.dat’ files from the fea5. (optional) if your fe model meshing type is different than c3d10, open the mesh.m function and substitute matrix ‘face’ with element connection numbering suitable for the mesh type used6. run the mexico script (line 1)7. in the geometric correlation window align the experimental geometry with the fe meshing by using the manipulation console in the bottom right-hand side8. once the nodes are matched (you can check that with the node showing buttons), push the get nodes button. this will print the number of 20 nodes to the workspace, which will be extracted from the ‘.dat’ file9. push the correlate button. after a few seconds, a model validity score window will pop up telling you the estimated quality of the model. after clicking see figures, you can see 11 figures correlating numerical and experimental natural frequencies, and mode shapes and presenting the complexity of experimental mode shapes
detecting gases in environment using mq2 gas sensor remotely, generating an alarming sound through laptop/pc. in order to perform the project, mq2 gas sensor data is captured using arduino in laptop and simulteneously uploaded
preamblethere are already several decent tools for the analysis of composite layups available on the matlab file exchange, so why add another?layup analysis tool is a comprehensive package which offers abd matrix output, stress calculation, failure assessment and layup design optimisation within a convenient and simple-to-use file submission:definition of n-ply layups with user-defined fibre orientationssupport for user-defined orthotropic elastic material properties per plysupport for variable thickness pliessupport for n evenly-distributed section points per plycomputation of the a, b and d matrices, and their inversecomputation of the equivalent extensional and bending moduli (for symmetric layups)stress and strain calculation in x-y and ply coordinates, based on applied forces and momentspretty matlab figures of through-thickness stresses and strainsinclusion of thermal and hydroscopic loadsuser-defined output locations: face (top, middle, bottom), envelope or section point listevaluation of stress and strain-based static failure criteria (maximum stress, tsai-hill, tsai-wu, azzi-tsai-hill and maximum strain)evaluation of stress-based damage initiation criteria based on hashin's theorystacking sequence optimiser: find the stacking sequence which minimises a user-selected criterion based on an objective function (maximum or mean). the optimiser algorithm takes advantage of the parallel computing toolboxwith all of the above functionality in one place, i hope this submission adds value to the current offering!summary[varargout] = abd.main(varargin) analyses a user-defined composite layup.to get started with a calculation, simply run the helper function:user_definitionsthe methodology used by layup analysis tool follows classical lamination theory (clt) and assumes that the laminate obeys the kirchhoff hypothesis:transverse shear is negligible (plane stress in each ply)displacements u and v (in the plane of the lamina) are assumed to be linear functions of the thickness coordinate z (no warping)filesmain analysis function (user can run this function): abd/main.mhelper function containing all of the required user definitions (user is recommended to run this script):user_definitions.mvalidation cases to show that layup analysis tool produces reliable output:validation/validate_abaqus.mvalidation/validate_joyce.mabaqus input file for validate_abaqus_uniaxial.m:validation/validate_abaqus_uniaxial.inpinternal functions for calculation (user should only run these functions for debugging purposes): abd/internal_.mdocumentationfor detailed instructions on the required inputs for layup analysis tool, please consult the help text located inside abd.main:help abd.mainsupportfor questions, comments or suggestions, please contact the author: help.qft@gmail.com.acknowledgementstheory referenceh. n. r. wagner, python code (github link)y. jack weitsman et al., coefficient of hydroscopic expansion (sciencedirect link)abbott aerospace canada ltf, definition of laminate plate element behaviour (link)resourcesinteractive composite laminate calculator (link)finding stiffness matrices a, b and d (efunda link)
a matlab package implementing the set membership global optimization (smgo) algorithm.
aboutthe getting started demo by speedgoat, takes you through the typical real-time simulation and testing workflows using simple exercises that include simulink models and speedgoat real-time target machines with i/o connected to a device under test (dut) with an electric dc motor, switches, and leds. the reference application leverages the speedgoat getting started demo kit, which features an electric servo dc motor via an h-bridge and potentiometer feedback.this kit seamlessly operates with the cost-effective io397 configurable i/o module and simulink-programmable fpga i/o module and is delivered with a custom fpga implementation file integrating:pwm generationi2c communicationand general purpose digital i/o code moduleslearn how to integrate analog and digital i/o driver blocks to your model create a real-time application from simulink, and download it to a speedgoat real-time target machine connect the real-time target machine to a simple device under test (dut) including an electric dc motor, leds, and switches implment pwm and i2c communication on configurable fpga monitor, log, and tune signal parameters on the flygetting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationrelated contentrapid prototyping for electric motor control designshil testing of electric motor control systemsmotor controls with matlab & simulinkdonwloadmatlab r2023a: click on the "download from github" button above.matlab r2022bmatlab r2022amatlab r2021bmatlab r2021amatlab r2020b
learn how touse vehicle dynamics blockset™ to model complex vehicle dynamics and driving maneuverscreate real-time simulations of virtual vehicles with simulink real-time™ and speedgoat target hardware employ simulink’s interface to unreal engine® and visualize driving scenarios set up a driving simulator with pedals and a steering wheel to perform driver-in-the loop testingkey benefitsstart with library blocks and pre-built reference applications out-of-the-box save time and money by replacing in-vehicle testing with virtual vehicle simulators customize your vehicle model and easily interface with your simulink controls efficiently iterate and test edge scenarios, in a repeatable, reproducible and safe environment rapidly deploy your models on real-time hardware without leaving matlab® and simulink getting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationrelated contentsimscape vehicle templates | speedgoatbuilding real-time driver-in-the-loop simulatorsautonomous vehicles in driving scenariosdownloadmatlab r2021b: click on "download" button at the top of the page.matlab r2021amatlab r2020b matlab r2020a matlab r2019b
how to create accurate motor models and fine-tune controller gains by collecting data from hardware monitor and control your real-time application directly from simulink® models or with instrument panel apps configure your controller model to generate compact and fast c code for any target microcontroller automatically run test-cases and prove that your embedded motor controller meets requirements using hardware-in-the-loop testingkey benefitsdesign and test motor controls with matlab® and simulink®-integrated solutions that enable you to fully adopt model-based design test your motor controls early and often. cut back development costs by exposing design flaws as early as possible shorten time-to-market of your motor controls by using automated code generation and automated testingprotect your investment by reusing the same real-time equipment to test both, early control prototypes and final embedded controllers getting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationrelated contentrapid prototyping for electric motor control designshil testing of electric motor control systemsmotor controls with matlab & simulinkrapid control prototyping of pmsmdownloadmatlab r2023a: click on "download" button at the top of the page.matlab r2022b matlab r2022a matlab r2021b matlab r2021a matlab r2020b
learn how touse vision hdl toolbox™ to model a lane detection algorithm in simulink®auto-generate vhdl code from your simulink® model and deploy to speedgoat simulink programmable fpga i/oprocess high-resolution video streams with high sample frequency in real-timedirectly access video i/o such as hdmi with low latencykey benefitsbuild vision-based advanced driver assistance systems (adas) and automated driving systems with hardware-proven subsystemseliminate time-consuming and error-prone steps with automated hdl code generationquickly perform design iterations and test your algorithms continuouslybridge the gap between algorithm development and hardware deployment with matlab®, simulink® and speedgoat hardware solutionsgetting startedopen matlab and open simulink project fileclick in 'getting started' project shortcutfollow steps in the documentationrelated contentlane detection on fpgas simplifying the development of computer vision systems videovision processing for fpga
these files describe an experiment performed on phasor measurement unites dataset that is made publicly available . the goal of the experiment is to train a deep network to be resilient against any adversarial attacks. a specific robust feature engineering and a deep learning are involved in model reconstructions. fast gradient sign method and basic iterative method are involved in this case.notes: (i) to be able to produce experiments provided in of these codes, you have to run the "*.m" files in the directory in alphabetical order. (ii) then you can plot results starting by any "plot...*.m" files.link to the original paper: cite our work as:berghout, t.; benbouzid, m.; amirat, y. towards resilient and secure smart grids against pmu adversarial attacks: a deep learning-based robust data engineering approach. electronics 2023, 12, 2554.
introductionthe ramanujan sums were first proposed by srinivasan ramanujan in 1918, and have become exceedingly popular in the fields of signal processing,time-frequency analysis and shape recognition. the sums are by nature, orthogonal. this results in them offering excellent conservation of energy, which is a property shared by fourier transform as well.
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if the physical units toolbox is on your matlab path, there is nothing to initialize, import, add to your workspace, or pass to functions - simply multiply/divide by u.(unitname) to attach physical units to a variable. for example, to define a speed using a supported unit: carspeed = 100 * u.kph. or, define a speed with an unsupported unit as a combination of supported units: snailspeed = 20 * u.m/u.week.variables with physical units attached are of the class dimvar ("dimenensioned variable"). math operations performed on dimensioned variables will automatically perform dimensional analysis and can create new units or cancel units and return a normal variable.on a global or per-project basis, you can customize to use the base unit system of your choice (e.g. ft-lb-s instead of the si m-kg-s), as well as customize preferred display units. display units for any given variable can also be customized. variables will display in the command window or plot, etc. in terms of those units. most common matlab functions will work with physical units, including many types of plots (with added axis labels).
includes classes for reading cggtts and rinex observation files.
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bladepoint😎 📚 my skill setstools ml&dl devops 🏆 my trophy📱 connect with me 📊 github stats 💻 stackoverflow activity"feed action request limit reached" - facebook sdkcan not upload images to face bookoauthexception when posting to facebook wall using php?rewrite rule in htaccess is not working properlyhtaccess is not properly working 🎧 i'm listening no activity trackedfollowers 35 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide secretariatv gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin woottpp big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 khinthandarkyaw98 kincsescsaba esin followers 35 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide secretariatv gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 khinthandarkyaw98 aymanehrouch kincsescsaba esin followers 36 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 khinthandarkyaw98 aymanehrouch kincsescsaba esin followers 36 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 khinthandarkyaw98 aymanehrouch kincsescsaba esin followers 37 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 khinthandarkyaw98 aymanehrouch kincsescsaba esin followers 36 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 khinthandarkyaw98 aymanehrouch kincsescsaba esin followers 35 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid mathewtbenjamin big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 35 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 nikolaivladislav klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 33 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 33 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 chiemelie700 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 33 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 33 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 53jk1 aymanehrouch kincsescsaba esin followers 33 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 34 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa bastndev weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam gsy2020 bepb 53jk1 aymanehrouch kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa bastndev weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 32 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa bastndev weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 31 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 31 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 30 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 30 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm alineai21 lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 codepromoter dinosoid big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 27 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 28 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 28 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 27 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide gravatapreta alcalalandscapingpavers kingparis1 carinalisboa weldhappy phpfriend95 moeindeveloper92 giangpro89 athosss23 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 23 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 24 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 24 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 23 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 23 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 24 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 24 edobor1234 raphaellouisandress naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 23 edobor1234 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 22 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide alcalalandscapingpavers kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 22 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 22 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam 53jk1 kincsescsaba esin followers 22 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 22 naveedghazi dosmthai mistergymer larygereg56 barbiemmm lilyoung01worldwide christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin followers 21 naveedghazi dosmthai mistergymer larygereg56 barbiemmm christmannh kingparis1 weldhappy moeindeveloper92 giangpro89 akyongpogi28 renanpariiz djf0301 big-fish-00 gety09 klcreech howard13kam bepb 53jk1 kincsescsaba esin
tpms-designeran open-source matlab toolbox for generation, analysis and visualisation of tpms-like structures and other 3d objects.install instructionsto install the complete toolbox in matlab, downloadthe latest release of tpms designer release and include files in your path within matlab.to install just the gui application, download and runtpms-designer/app/tpms designer gui.mlappinstallfor users without matlab, you may download the stand-alone version.download and run tpms-designer/app/tpmsdesigner_installer_web.exethis may take a while and will prompt you to install the relevant version of the free matlab runtime.
brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings: meg, eeg, fnirs, ecog, depth electrodes and animal electrophysiology. our objective is to share a comprehensive set of user-friendly tools with the scientific community using meg/eeg as an experimental technique. for physicians and researchers, the main advantage of brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. we are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in meg/eeg research.
fieldtrip is the matlab software toolbox for meg and eeg analysis that is being developed by a team of researchers at the donders institute for brain, cognition and behaviour in nijmegen, the netherlands in close collaboration with collaborating institutes.fieldtrip offers advanced analysis methods of meg, eeg, and invasive electrophysiological data, such as time-frequency analysis, source reconstruction using dipoles, distributed sources and beamformers and non-parametric statistical testing. it supports the data formats of all major meg systems (ctf, elekta/neuromag, 4d, yokogawa) and of most popular eeg systems, and new formats can be added easily. fieldtrip contains high-level functions that you can use to construct your own analysis protocols in matlab. furthermore, it easily allows developers to incorporate low-level algorithms for new eeg/meg analysis methods.furthermore, it offers a frequently updated website [1] with tutorials, example data and other documentation. there is an active community centered around the email discussion list [2].fieldtrip is the mostly used software for meg data analysis and developed in close collaboration with related open source projects, such as eeglab, spm, brainstorm and mne.please cite the fieldtrip reference paper [3] when you have used fieldtrip for data analysis in your study.[1]
input is a thermal rgb image from a thermal camera, such as the flir one camera, that has a pseudocolored image and a colorbar all in the same image. user is asked to draw a rectangle around the thermal image part of the image, and a rectangle around the colorbar part of the image. user is asked for the min and max temperatures at the end of the colorbar. it then uses the embedded color bar to create a mapping of rgb color into temperatures in degrees f or c. it then determines temperatures, in those calibrated units, from the pixel color for every pixel in the image and makes a temperature image. the output temperature image is a 2-d floating point matrix where the pixel values are in units of degrees, rather than rgb color levels.
converts crsp daily prices to adjusted prices and total returns and puts them in a format for use with the backtesting framework.: quantitative asset management: factor investing and machine learning for institutional investing: 9781264258444: robbins, michael: books
downloads and converts adjusted prices to a format for use with the backtesting framework.: quantitative asset management: factor investing and machine learning for institutional investing: 9781264258444: robbins, michael: books
load adjusted prices from bloomberg in a format for use with the backtesting.: quantitative asset management: factor investing and machine learning for institutional investing: 9781264258444: robbins, michael: books
scrape fiducient capital markets assumptions and other tables from the portfolio engineer. : quantitative asset management: factor investing and machine learning for institutional investing: 9781264258444: robbins, michael: books
the geometry and image-based bioengineering add-on for matlabhttp://gibboncode.org/gibbon (the geometry and image-based bioengineering add-on) is an open-source matlab toolbox by kevin m. moerman and includes an array of image and geometry visualization and processing tools and is interfaced with free open source software such as tetgen, for robust tetrahedral meshing, and febio for finite element analysis. the combination provides a highly flexible image-based modelling environment and enables advanced inverse finite element analysis.important cite as: k. m. moerman, “gibbon: the geometry and image-based bioengineering add-on,” j. open source softw., vol. 3, no. 22, p. 506, feb. 2018, doi: 10.21105/joss.00506example sentence to cite this work: "... the mesh was created using the open source toolbox gibbon (v3.5.0, moerman et al. 2018, )"
an open source multi-modality radiation treatment planning sytem
pch_em_demo.mlx presents a full demo of the starting point algorithm (startingpoint.m) and histogram accelerated pch-em algorithm (pch_em.m).
purposedynaprog is a matlab toolbox to solve a finite horizon multi-stage deterministic decision problem, which is a problem where a decision must be made at each stage for a system that evolves through a finite number of stages, minimizing the total cost incurred.installing dynaprogthe most straightforward way to install dynaprog is to directly install it from matlab's add-on explorer or from the file exchange. doing this also installs the documentation in matlab's help browser.contactfor all questions or suggestions, feel free to contact me at federico.miretti@polito.it.
this code simulates an iot sensor network with adjustable parameters, including the number of sensors, communication radius, and events. it constructs a sensor network with random positions and simulates a predefined number of random events at various sensors. the code then calculates the communication delay for each event, considering the distance between sensors and their communication radius. it presents a bar chart displaying the communication count per sensor, a histogram illustrating the distribution of communication delays, and computes the average communication delay.
the constraint-based reconstruction and analysis toolbox is a matlab software suite for quantitative prediction of cellular and multicellular biochemical networks with constraint-based modelling. it implements a comprehensive collection of basic and advanced modelling methods, including reconstruction and model generation as well as biased and unbiased model-driven analysis methods.it is widely used for modelling, analysing and predicting a variety of metabolic phenotypes using genome-scale biochemical networks.-- manuscript available under doi.org/10.1038/s41596-018-0098-2
arm robotics with 6dof
a novel and powerful metaheuristic optimizer, named the growth optimizer (go), is proposed. its main design inspiration originates from the learning and reflection mechanisms of individuals in their growth processes in society. learning is the process of individuals growing up by acquiring knowledge from the outside world. reflection is the process of checking the individual’s own deficiencies and adjusting the individual’s learning strategies to help the individual’s growth. this work simulates this growth behavior mathematically and benchmarks the proposed algorithm on a total of 30 international test functions of the 2017 ieee congress on evolutionary computation real-parameter boundary constraint benchmark (cec 2017 test suite). a total of 50 state-of-the-art metaheuristic algorithms participated in the comparison process. the results of the convergence accuracy comparison and the two nonparametric statistics based on the friedman test and the wilcoxon signed-rank test showed that go provides competitive results compared to the 50 state-of-the-art metaheuristic algorithms tested. in addition, to verify that go has the ability to solve different real-world optimization problems, go was applied to two different types of real-world optimization problems: the multiple sequence alignment (msa) problem based on the hidden markov model (hmm) and the multithresholding image segmentation problem based on kapur’s entropy method. go provided more promising results than other metaheuristic techniques, especially in terms of solution quality and avoidance of local optima.
digital pulse
plot function for graphical representation of heat exchanger networks as a grid.cc by-sa 4.0 david huber / tu wiendavid.huber@tuwien.ac.at
calculating the discrete fourier transform (dft) of non-periodic signals will produce large artifacts caused by the underlying periodicity assumptions that attempt to reconcile the offset from the pre- to post-step data. as a step response is transient by nature, detrending techniques can't be used to improve the spectral calculation. additionally, attempts to convolve the signal with windows such as the hanning window solve the end-point discontinuity but negatively impact the spectral outcome. gans-nahman published a method to turn step-inputs into duration-limited signals by appending a reflected version of the signal to the end of the original step response. this code outputs the spectra from this improved procedure and can also output the constructed waveform if desired. requirements:this function can handle multiple step-like signals provided that they share all correspond to the same time vector where time = 0 indicates the initial response to a step input (ie. any dead-time has been removed). data must exist prior to t = 0, and each of the signals must be normalized such that the signal is 0 for t < 0 and it approaches 1 as it reaches equilibrium. included example:the mat file gnexamp.mat is included to show the operation of the code. the mat file should include an nx1 time vector t spanning from -7 to 35 ms, nx2 data vector d, and sampling frequency fs. [f,f0] = gansnahmanfunc(t,d,2.75,fs,"a");after loading in the example mat data, inputting the above line will reproduce the time-domain waveforms seen in this code's thumbnail. "a" represents one of the preset low-pass filters included (chebyshev type ii at 50 khz). changing 2.75 to other values will change the time-domain signal included but will minimally affect the spectra. for the included data, this value should not exceed ~7.5 ms where the "step-input" portion of the signal ends. omitting "a" will skip the low-pass filtering entirely. the function can be modified to output the time series data by adding dgn and dorig to the outputs of the function. these are structures that include the constructred time signal (ex: dgn.t) and the constructed data signals (ex: dgn.d). comments:the function was originally written for data sampled at 5 mhz for ~50 ms. as such the code is written to expect the time vector in milliseconds and the sampling frequency in hz. efforts were made to make this version as robust and flexible as possible so warnings or errors should occur if the t and fs are not in these relative units. an additional leftover from the original version of this code is the optional inclusion of low-pass filters. these filters are applied before the reflected wave reconstruction if the region of interest is far below the nyquist frequency. to use existing filters or to input new ones, the dsp system toolbox is required as it uses fdesign.lowpass to generate the filter. lastly, there are plots that can be generated in this function, by toggling skipallplots to true, all of the plots will be supressed.citation for gans-nahman paper: gans, w. l., and nahman, n. s. “continuous and discrete fourier transforms of steplike waveforms.” ieee transactions on instrumentation and measurement, vols. im-31, no. 2, 1982, pp. 97–101. .
matlab interface to the liquid instruments moku devices
pushover-3dframesfunctions and subroutines for the static non-linear anlaysis of 3d frames
this code is explained in the following youtube video: more of my youtube videos, check out my matlab tutorial playlist: subscribe to my channel (engineering with dr. kelsey joy):
a trajectory optimization library for matlab
these functions provide a fast, simple interface for generating maximum length sequences with favorable autocorrelation properties for spread-spectrum communication waveforms. sequences are generated through a simulated linear feedback shift register (lfsr) implementation in the 'fibonacci' configuration (). the returned sequences are provided as a sequence of "1" and "-1" values. this code is intended for researchers and engineers who need to generate a large number of distinct spreading sequences for testing various dsss or other spread spectrum detection and analysis algorithms (e.g., monte carlo simulations over multiple spreading codes/lengths). there are two functions associated with the generator:[seq,poly,seed] = generaterandomspreadingsequence(length); this function allows the user to input a specified length (which must be an integer of the form 2^m - 1), and receive a randomly selected mls ('seq') meeting this length. the sequence is chosen uniformly randomly from all possible primitive polynomials and feedback shift register initialization seed values. in addition to the sequence, the chosen polynomial and seed values are provided to the user, in a bit-vector format for reference as well.seq = generateknownspreadingsequence(poly,seed); this function allows the user to specify the primitive polynomial and initial seed values for an equivalent linear feedback shift register, then receive the associated mls as the output 'seq'. this function is called by 'generaterandomspreadingsequence()' to obtain the sequence after the random polynomial/seed values are established.there are a few noteworthy limitations to this implementation:the largest mls that can be randomly generated with this code is 65535 (i.e., 2^16 - 1). this limitation is due to the use of the matlab function 'primpoly' that only produces primitive polynomials up to 16th order. there is no strict limit on generating longer sequences with the 'generateknownspreadingsequence()' function, assuming you can independently provide a longer primitive polynomial as input. longer codes require more processing time. generating codes of length 65535 take ~50 msec on my machine, whereas generating codes of length 1023 take ~5 msec.the function 'generateknownspreadingsequence(poly,seed)' does not validate that the provided polynomial is primitive, nor does it validate that the input seed is nonzero. non-primitive polynomials are still accepted and generate sequences, but the sequences will not be maximum length (i.e., they will repeat). seed values of zero will cause the output to be all "-1" values.the function 'generaterandomspreadingsequence(length)' makes use of the matlab function 'int2bit()' which was only introduced in r2021b. i have included a workaround in the code that uses the now-deprecated 'de2bi()' function instead, which predates r2006a. if you are using a version of matlab older than r2021b, you should comment out the line containing 'int2bit()' and uncomment the line containing 'de2bi()' instead.the matlab functions 'primpoly()', 'int2bit()', and 'de2bi()' require the communications toolbox to be installed. these functions are used within the 'generaterandomspreadingsequence()' function. the 'generateknownspreadingsequence()' function should not require any toolboxes beyond the basic matlab installation to work.
this pushover analysis function is based entirely on flexure hinges formations at the ends of the structural elements composing a structural plane frame. a uniform seismic load incrementation is carried out on each step until a collapse or stiffness degradation criteria is reached. such function has proved to have a great potential for its implementation in teaching the pushover method in structural mechanics and applied sciences through the simulation of collapse mechanisms. not only p-delta collapse graphics are obtained, but also the evolution of the collapse mechanism deformation of the structure and the seismic collapse safety factors (csf), including as well the evaluation of some basic damage indices (di) such as the inter-story drift di, the inter-story plastic drift di and the deformation based di.note: it is necessary to download the calfem toolbox to run this function. visit: byggmek.lth.se/english/calfem/
simple gene correlation analysis (sgca) is the heart of lcs. sgca clusters gene expression profiles according to their correlation distance to “ideal (logical) phenotypes” (ips). it thus differs from common clustering methods, such as k-means, or self-organizing maps, which cluster the gene expression profiles according to their mutual similarity. in sgca, all cluster centers are already given through the experimental design and further assump-tions are necessary. since sgca clusters are defined by the experimental design, they are often more readily interpretable than “unbiased” clusters, that may or may not correlate, with the experimental groups, because their “meaning” is retained. sgca allows to logically connect and compare different transcriptomics experiments through downstream sgea enrichment analysis (see below).sgca, the base application of lcs, is published under a cc by-nc-nd 4.0 license (). these terms also apply to redistribution of this application that was developed based on sgca. briefly, this application can be copied and redistributed under the condition that appropriate credit is given (see citation). lcs must not be used for commercial purposes. if you remix, transform, or build upon this application, you may not distribute the modified material.disclaimer: this is an experimental application that did not undergo thorough testing. therefore, interpret sgca & sgea results with due diligence, e.g., perform sanity checks with orthogonal methods, etc. before publishing.feedback: to improve the lcs application, your feedback is very welcome. please provide a sufficient descrip-tion of issues encountered by e-mail to sgcafeedback@gmail.com.sgca citation: ma y, hui kl, gelashvili z, niethammer p. oxoeicosanoid signaling mediates early antimicrobial de-fense in zebrafish. cell rep. 2023 jan 31;42(1):111974. doi: 10.1016/j.celrep.2022.111974. epub 2023 jan 10. pmid: 36640321; pmcid: pmc9973399.
a neurorobot is a robot controlled by a computer simulation of a biological brain. at backyard brains we use neurorobots to teach neuroscience in schools. to learn more, see this demo video and our 2020 research publication.
a graphical user interface for generating solutions of quasi-linear partial differential equations (pdes) using the method of characteristics. the interface provides a visual representation of the solution surface, including the characteristic lines. a slider allows for dynamic visualization of the parameterization variable gamma along the solution surface. users can easily adjust pde coefficients, initial conditions, and the spatial and temporal domains using input windows. the application is developed as an educational tool to illustrate moc concept for the course "control of distributed parameter systems" during my phd time at the institute of automatic control, rwth aachen university.acknowledgments: quasilinear pde solution function inspired from: prof. tobias von petersdorff's course "math 462, spring 2012: partial differential equations for scientists and engineers",
neural networks for multivariable function approximation & surface fittingintroductionneural networks possess the property of universal approximation, which means that given enough parameters, a neural net can approximate any multivariable continuous function to any desired level of accuracy.featuresrapidly train neural networks with just a few lines of code.% network set uplayerstruct=[inputdimension,10,10,10,outputdimension];nn=initialization(layerstruct);% train networkoption.maxiteration=600;nn=optimizationsolver(data,label,nn,option);outperforms the curve fitting app and regression learner in matlab for fitting multivariable functions, complex regression problems without any additional toolboxes.general & high-precesion multivariable function approximation. standard templatethere are currently two standard templates available for users to quickly call the main functions: "simplifiedworkflow.m" and "customizableworkflow.m" the purpose of "simplifiedworkflow.m" is to assist beginners in quickly getting started, while the other template provides more flexibility.instruction and examplefor detailed instructions on how to use the package, please refer to "generalguide.mlx", which provides a step-by-step demonstration on how to approximate the logo of matlab using neural networks. "curvefittingfromnoisydata.mlx" demonstrates how to use neural nets to handle noisy data and estimate derivatives."customizableworkflow.m" demonstrates the standard workflow of using neural networks for following multivariable function approximation. tips and considerations for training neural networksplease refer to the "tips for training neural networks.mlx" which provides detailed yet straightforward instructions to easily address the mentioned issues.ensure that the data (i.e., input x) is distributed in similar magnitude. otherwise, it can make neural network training challenging. therefore, it is recommended that users always preprocess their data (i.e., perform normalization) before starting the optimization process. if you are unfamiliar with preprocessing methods, the package also provides basic algorithms that should be sufficient for most situations.make sure the standard deviation of the labels is not too small, as it can also make it difficult to train the neural network. the package includes built-in functions to handle this situation.types of neural netsmultilayer perceptron (ann)residual neural network (resnet) (make optimization easier for deep nns.)optimization solversstochastic gradient descents (sgd)stochastic gradient descents with momentum (sgdm)adaptive momentum estimation (adam)broyden-fletcher-goldfarb-shanno method (bfgs)
etopo website has removed the xyz output format. in order to convert the geotiff files into the conventional xyz files for the numerical model, such as comcot tsunami model, i wrote a simple m code for converting geotiff to xyz.simply change the file name to "exportimage.tiff" and run this code.enjoy!tso-ren
this is the development branch (unstable) of the jsonlab toolbox. the download link is directly pointed to the latest commit in the "master" branch of the jsonlab github repository at please use this branch with caution: it may contain the latest bug fixes, but in the meantime, it may also contain partially implemented features, and may be unstable. if you need a stable release, please download from the below link insteadhttps:// previous stable release of jsonlab:** jsonlab 2.0 beta (magnus - beta) is released on 10/24/2019.**release url: implementation for jdata specification draft 2 - on: is a free and open-source implementation of a json/ubjson/messagepack encoderand a decoder in the native matlab language. it can be used to convert a matlabdata structure (array, struct, cell, struct array, cell array, and objects) intojson/ubjson/messagepack formatted strings, or to decode ajson/ubjson/messagepack file into matlab data structure. jsonlab supports bothmatlab and [ gnu octave] (a free matlab clone).json ([ javascript object notation]) is a highly portable,human-readable and [ "fat-free"] text formatto represent complex and hierarchical data. it is as powerful as [ xml]but less verbose. json format is widely used for data-exchange in applications.ubjson ([ universal binary json]) is a binary json format, specificallyspecifically optimized for compact file size and better performance while keepingthe semantics as simple as the text-based json format. using the ubjsonformat allows to wrap complex binary data in a flexible and extensiblestructure, making it possible to process complex and large datasetwithout accuracy loss due to text conversions. messagepack is another binaryjson-like data format widely used in data exchange in web/native applications.it is slightly more compact than ubjson, but is not directly readable comparedto ubjson.we envision that both json and its binary counterparts will play importantroles as mainstream data-exchange formats for scientific research.it has both the flexibility and generality as offered by other populargeneral-purpose file specifications, such as [ hdf5]but with significantly reduced complexity and excellent readability.towards this goal, we have developed the jdata specification ( standardize serializations of complex scientific data structures, such asn-d arrays, sparse/complex-valued arrays, trees, maps, tables and graphs usingjson/binary json constructs. the text and binary formatted jdata files aresyntactically compatible with json/ubjson formats, and can be readily parsedusing existing json and ubjson parsers.please note that data files produced by ``saveubjson`` may utilize a special"optimized header" to store n-d (n>1) arrays, as defined in the jdata specification draft 2.this feature is not supported by ubjson specification draft 12. to produceubjson files that can be parsed by ubjson-draft-12 compliant parsers, you mustadd the option ``'nestarray',1`` in the call to ``saveubjson``.please find detailed online help at links: [1]
his is a toolbox to send matlab jobs from your local machine (the client) to an hpc cluster running the slurm scheduler (the server). as long as you have unlimited licenses to run matlab on the cluster, this allows you to run many jobs in parallel, without the distributed computing server license. this only works for "dumb" parallelism though; messaging between jobs is not used. a simple gui shows information from the slumr acccounting log on the server, can retrieve data and log files, and allows you to restart failed jobs.