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CyberUnits is a cross-platform class library for rapid development of high-performance computer simulations in life sciences. It supports modelling for biomedical cybernetics and systems biology with Object Pascal.
This project will create a user-friendly modeling system for studying animal social aggregations. The model codifies the a new hypothesis that the animal's state, kinematics, and the group's emergent properties are dynamically linked together, with each one affecting the others. The model will be constructed using Object-Oriented MatLab.
A MATLAB toolbox for simulating the movement of the iRobot Create. Contains multiple GUIs for creating maps and other input, showing the movement of the Create, and replaying a previously saved session.
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.
Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.
Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
Functions for WIImote 'N for Engineering (and for Educationnal purposes,too) (1) WinXP : Wii Remote library for Matlab/Simulink(MEX command and s-Function) (2) Linux : Wii Controller library for Scilab (MEX Command)
A MATLAB toolbox for time series analysis using state space models. Supports fully interactive model construction with MATLAB objects and efficient Kalman filter backend implemented in c.
Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples. It is also useful as way of exploring surprise theory. For more information see also: http://ilab.usc.edu/