Open Source MATLAB Algorithms
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MATLAB Algorithms
View 5730 business solutionsBrowse free open source MATLAB Algorithms and projects below. Use the toggles on the left to filter open source MATLAB Algorithms by OS, license, language, programming language, and project status.
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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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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: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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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 The repo is marked “no longer maintained,” and has been superseded by newer versions of Gait-Tracking.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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Image Harmonization Dataset iHarmony4
The first large-scale public benchmark dataset for image harmonization
This repository provides the iHarmony4 dataset, which is a large-scale dataset designed for image harmonization tasks. Image harmonization involves adjusting the appearance of a foreground in a composite image so that it is consistent with the background (in color, tone, illumination, etc.). The iHarmony4 dataset comprises four sub-datasets (HCOCO, HAdobe5k, HFlickr, Hday2night), each making composite images by combining a foreground from one image with a background from another, along with associated ground truth harmonized images and foreground masks. The dataset is intended as a benchmark resource to enable and standardize research in image harmonization. Each composite sample has: composite image, foreground mask, and corresponding real harmonized image. -
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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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NTU RGB-D
Info and sample codes for "NTU RGB+D Action Recognition Dataset"
The “NTU RGB+D” repository provides access to a large-scale dataset for human action recognition (and its extension, NTU RGB+D 120). The dataset includes multiple modalities (RGB video, depth sequences, infrared video, 3D skeletal joint data) captured with multiple Kinect v2 cameras simultaneously. The repository also contains MATLAB / Python demo scripts for loading, visualizing, and processing skeleton data, mapping between modalities, and handling dataset structure. Multi-modal action recognition dataset, RGB, depth, infrared, skeletal data. Split into background / evaluation sets for one-shot evaluation (in the extended dataset). -
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Omniglot
Omniglot data set for one-shot learning
This repository hosts the Omniglot dataset for one-shot learning, containing handwritten characters across multiple alphabets along with stroke data. It includes both MATLAB and Python starter scripts (e.g. demo.m, demo.py) to illustrate how to load the images and stroke sequences and run baseline experiments (such as classification by modified Hausdorff distance). The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each instance. Includes stroke data (time-sequenced coordinates) per sample. The repository is intended as a benchmark dataset in few-shot / meta-learning research, not as a plug-and-play detection or classification engine. Pre-split “background” and “evaluation” alphabets for standard benchmarking. -
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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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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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Smart Algorithm
Repository implementing a variety of intelligent algorithms
Smart-Algorithm is a repository implementing a variety of intelligent / metaheuristic optimization algorithms (e.g. Genetic Algorithm, Ant Colony, Particle Swarm, Immune Algorithm). The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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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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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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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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The NUMIPAD library implements several methods/algorithms to solve inverse problems and adaptive decomposition (i.e. Tikhonov regularization, Total Variation, Basis Pursuit, etc.)
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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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This project aims to develop a colour-based vision processing system for use in RoboCup. We are using a CCD camera for input to an FPGA. The system locates coloured objects and outputs detected corners.
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PopED (Population Experimental Design) is a software tool for computing optimal experimental designs. The software has been developed with an emphasis on drug trials based on population models (non-linear mixed effects models).
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A cross-platform library that computes fast and accurate SIFT image features. libsiftfast provides Octave/Matlab scripts, a command line interface, and a python interface (siftfastpy). Optimized with SIMD instructions and OpenMP .
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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/ -
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WM Hyperintensities Segmentation Toolbox
Open Source White Matter Hyperintensities Segmentation Toolbox
Wisconsin White Matter Hyperintensity Segmentation [W2MHS] and Quantification Toolbox is an open source MatLab toolbox designed for detecting and quantifying White Matter Hyperintensities (WMH) in Alzheimer’s and aging related neurological disorders. WMHs arise as bright regions on T2- weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer’s disease (AD) with regard to its prognosis. Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies. -
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ACADO Toolkit
Toolkit for Automatic Control and Dynamic Optimization
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization. ACADO Toolkit is implemented as self-contained C++ code and comes along with user-friendly MATLAB interface. The object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines. -
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CoRoPa stands for Computational Rough Paths. The aim of CoRoPa is to provide a software framework for various ideas related to Rough Path Theory, including rough differential equations and the digital description of serial data streams.
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Esta é a documentação de um projeto de conclusão de curso feito na conclusão do curso de Engenharia de Computação-UNIVALI.É um conjunto de arquivos C e Matlab contendo a implementação da Lógica Fuzzy e simulações em sistema de enegia.
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C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
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High-order HMM in Matlab
Implementation of duration high-order hidden Markov model in Matlab.
Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition.
