Open Source MATLAB Software Development Software
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MATLAB Software Development Software
View 6258 business solutionsBrowse free open source MATLAB Software Development Software and projects below. Use the toggles on the left to filter open source MATLAB Software Development Software by OS, license, language, programming language, and project status.
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Octave Forge
A collection of packages providing extra functionality for GNU Octave
Octave Forge is a central location for collaborative development of packages for GNU Octave. The Octave Forge packages expand Octave's core functionality by providing field specific features via Octave's package system. See https://octave.sourceforge.io/packages.php for a list of all available packages. GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. Refer to https://www.octave.org for more information. -
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GNSS-SDR
An open source software-defined GNSS receiver
An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework. -
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MATLAB and Simulink for Students
An awesome list of helpful resources for students learning MATLAB
Are you a new MATLAB user seeking helpful tips and tricks? Are you a member of a student society in search of engaging workshops? Or perhaps you're looking for opportunities to test your MATLAB skills through student competitions or challenges? Look no further! Our awesome list repository below is a resource that caters to all these needs. Whether you're starting from scratch or aiming to enhance your existing knowledge, you'll find a wealth of information to help you learn MATLAB and make progress on your journey as a student. Explore the repository now and unlock the potential of MATLAB. -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: https://arma.sourceforge.net/download.html * Documentation: https://arma.sourceforge.net/docs.html * Bug reports: https://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/armadillo-lib/armadillo-code -
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JOELib/JOELib2 is a cheminformatics library which supports SMARTS substructure search, descriptor calculation, processing/filtering pipes, conversion of file formats, 100% pure Java, and interfaces to external programs (e.g. Ghemical) are available.
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Exclusively Dark Image Dataset
ExDARK dataset is the largest collection of low-light images
The Exclusively Dark (ExDARK) dataset is one of the largest curated collections of real-world low-light images designed to support research in computer vision tasks under challenging lighting conditions. It contains 7,363 images captured across ten different low-light scenarios, ranging from extremely dark environments to twilight. Each image is annotated with both image-level labels and object-level bounding boxes for 12 object categories, making it suitable for detection and classification tasks. The dataset was created to address the lack of large-scale low-light datasets available for research in object detection, recognition, and enhancement. It has been widely used in studies of low-light image enhancement, deep learning approaches, and domain adaptation for vision models. Researchers can also explore its associated source code for low-light image enhancement tasks, making it an essential resource for advancing work in night-time and low-light visual recognition. -
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EIDORS (electrical impedance and diffuse optical tomography reconstruction software) provides software, algorithms and reference data for image reconstruction and modelling.
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PlatEMO
Evolutionary multi-objective optimization platform
Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms published in top journals after 2010. Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values. PlatEMO provides a powerful and friendly GUI, where users can configure all the settings and perform experiments in parallel via the GUI without writing any code. -
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SuiteSparse
The official SuiteSparse library: a suite of sparse matrix algorithms
The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University. -
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Kalibr Allan
IMU Allan standard deviation charts
kalibr_allan is a utility repository that provides scripts and tools for calculating IMU noise parameters for use in Kalibr and other IMU filtering systems. While manufacturers typically provide “white noise” values in IMU datasheets, the bias instability and random walk parameters must be determined experimentally. This project enables users to compute those values using Allan variance analysis from recorded IMU data. The workflow involves recording IMU measurements with the device stationary, converting ROS bag files into MATLAB-compatible formats, and then running MATLAB scripts to generate Allan deviation plots. These plots are analyzed to determine noise density and random walk parameters for both gyroscopes and accelerometers. The repository also includes example data and plots from real sensors such as the XSENS MTI-G-700, Tango Yellowstone Tablet, and ASL-ETH VI-Sensor, providing reference points for interpretation. -
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
The Grey Wolf Optimizer for Path Planning is a MATLAB-based implementation of the Grey Wolf Optimizer (GWO) algorithm designed for UAV path and trajectory planning. It allows simulation of both two-dimensional and three-dimensional UAV trajectory planning depending on parameter setups. The tool provides built-in functions to configure different UAV environments and supports multiple optimization objectives. It includes progress visualization to help monitor the optimization process during simulations. Users can adjust objective function weights and experiment with multiple heuristic search strategies to explore optimal solutions. This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community. -
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Continuation Core and Toolboxes (COCO)
Toolboxes for parameter continuation and bifurcation analysis.
Development platform and toolboxes for parameter continuation, e.g., bifurcation analysis of dynamical systems and constrained design optimization. This material is based upon work partially supported by the National Science Foundation under Grant No. 1016467 and the Danish research council (FTP) under the project number 0602-00753B. Any opinions, findings, and conclusions or recommendations expressed on this site are those of the authors and do not necessarily reflect the views of the National Science Foundation or other funding sources. Documentation and tutorials are available for the following toolboxes: * ep : continuation and bifurcations of equilibrium points * coll : continuation of constrained collections of trajectory segments, including multi-segment boundary-value problems * po : continuation and bifurcations of periodic orbits in smooth and hybrid systems * recipes : collection of examples from the book Recipes for Continuation -
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BRAHMS is a Modular Execution Framework for dynamical systems. It knits together independently-authored software modules implementing dynamical processes into an integrated system, and supervises the deployment and execution of that system.
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GTK+ IOStream
GTK+iostream, Data plots, ORB, Neural Networks, WSOLA
Create GTK+ interfaces using IOStream style code. Its simple and quick! Also providing simple data plotting (similar to Matlab/Octave), ORB (Object Request Broker), and Neural Network computations. You can create GTK+ GUIs in a few lines of code. Labels<<"Thanks for reading"; (HBox<<Labels).show(); or even one line : (HBox() << (Labels()<<"Thanks for reading")).show(); Inline code destructs the classes, but leaves the widgets/callbacks operating. This is great for embedded memory saving code. You can also plot like so : Plot figure; figure.plot(x, y, x.size()); Plotting utilises GtkDataBox.sf.net ORB utilises www.zeroc.com Feed forward neural networks with different activation functions. Audio playback/recording and digital signal processing (DSP) - utilising Jack www.jackaudio.org -
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Gait Tracking With x-IMU
Foot tracking algorithm demonstrated in Seb Madgwick's 3D Tracking
This repository provides the source code for a foot-tracking algorithm using an inertial measurement unit (IMU) attached to the foot, as demonstrated in Seb Madgwick’s “3D Tracking with IMU” video (March 2011). The method performs dead reckoning to integrate foot motion and corrects for drift at each foot strike (when the foot touches the ground) to reset error accumulation. The algorithm was part of gait analysis research, estimating foot trajectories in 3D over time. The repo is marked “no longer maintained,” and has been superseded by newer versions of Gait-Tracking. Capability to compare walking trajectories in experiments. Integration of IMU data (accelerometer + gyroscope). -
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MARRMoT
Modular Assessment of Rainfall-Runoff Models Toolbox
MARRMoT (Modular Assessment of Rainfall-Runoff Models Toolbox) is a MATLAB toolbox designed for hydrological modeling. It allows users to simulate and assess various rainfall-runoff models, providing tools for calibration, sensitivity analysis, and comparison of model performance. -
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MBeautifier
MBeautifier is a MATLAB source code formatter, beautifier
MBeautifier is a lightweight M-Script-based MATLAB source code formatter usable directly in the MATLAB Editor. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts. -
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Plotly Graphing Library for MATLAB
Plotly Graphing Library for MATLAB
Create interactive charts in your web browser with MATLAB® and Plotly. -
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segyio
Fast Python library for SEGY files
Segyio is a small LGPL-licensed C library for easy interaction with SEG-Y and Seismic Unix formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome. -
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GeoPDEs
A package for Isogeometric Analysis in Octave and Matlab
GeoPDEs is a suite of software tools for research on Isogeometric Analysis of PDEs. GeoPDEs is free software implemented in Octave and fully compatible with Matlab. GeoPDEs is no longer developed at SF, and has moved to GitHub. Please visit http://rafavzqz.github.io/geopdes/ The mailing list will remain active. Releases up to 2.0.4 can be found here. From version 2.1.0 onward, see https://github.com/rafavzqz/geopdes -
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Optimizer_sovkov
Constructing and optimizing general mathematical and physical models
We present the package Optimizer, aimed at constructing and optimizing general mathematical models of phenomena of versatile nature. It is written in the Matlab algorithmic language and is executed in the Matlab environment with partial functionality in Octave. The convenient visual interface and the detailed manuals are provided. The main benefit of the package is its capability to construct models of any level of complexity in a block-by-block manner. Elementary model blocks can be collected in libraries, and we have already written many such library programs. Many of these programs are already available along with the main package. Currently, the main focus of these is computational quantum mechanics, analysis and simulation of molecular spectra, and general-purpose approximants. The package provides the most reliable modern strategies for linear and non-linear model optimization, regularization, and hypothesis tests. Parallel computing is supported. -
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Waterloo
Java-based scientific graphics
Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab. -
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detectMITE
Detection of Miniature Inverted Repeat Transposable Elements
detectMITE - a MATLAB-based tool for detecting miniature inverted repeat transposable elements (MITEs) in genomes. [1] Who are we? Please visit website: http://bioinfolab.miamioh.edu [2] How to cite detectMITE? Ye C, Ji G, Liang C (2016) detectMITE: A novel approach to detect miniature inverted repeat transposable elements in genomes. Sci. Rep. 6, 19688. http://www.nature.com/articles/srep19688 Ye C, Ji G, Li L, Liang C (2014) detectIR: A Novel Program for Detecting Perfect and Imperfect Inverted Repeats Using Complex Numbers and Vector Calculation. PLoS ONE 9(11): e113349. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113349 [3] detectMITE user manual Please visit the Wiki page of this website. [4] detectMITE Q&A For Q&A, please visit the Blog page of this website. [5] Update history To see the update history, go to https://sourceforge.net/p/detectmite/wiki/UpdateHistory/
