Showing 5 open source projects for "network development platforms"

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    DaNNet

    DaNNet

    Deep Artificial Neural Network framework using Armadillo

    DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network.
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  • 2

    OpenRTDynamics

    A framework for implementing real-time control systems

    - - - T H I S P R O J E C T M O V E D T O G I T H U B - - - A framework for the implementation of advanced real-time control systems which uses signal/block-based schematics (like Simulink) extended by several features like state machines and online-exchangeable sub-simulations. Besides, this framework properly handles multiple threads, their communication, allows to synchronise control systems to external events (e.g. variable timers or incoming network packages) and provides...
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  • 3
    Bat2015

    Bat2015

    Bachelor of Science (Informatik)

    The toolkit glpk supports methods for mixed integer linear programming (MILP). These methods solve Capital Budgeting Problems (CBP). Unfortunately, glpk does not support any multithreading and there is no feature to distribute problems via network connections. Today, this is a pitiable sight, because modern computer systems are coupled by networks and support multi threading. We create a distributed system with Apache thrift and the C-API of glpk. Now, it is possible to use as many cores in...
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  • 4
    RGP: Red General de Posicionamiento (Project Number P022-06/E16 - Junta de Andalucía) -- General Network Positioning based on open software and hardware. This project is the development and test of the standard IEC-60870-5 application layer protocol
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  • 5
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary...
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