Showing 6 open source projects for "malware-samples"

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  • 1
    Behaviour Control Framework

    Behaviour Control Framework

    A C++ framework that uses inhibition trees for behaviour control.

    The Behaviour Control Framework is a generic platform-independent C++ framework that implements a control architecture consisting of a hierarchical network of behaviour layers. These layers each contain behaviours that can inhibit one another and interact to achieve control. A full discussion of the structure and ideas behind this framework can be found in the Doxygen documentation that comes with the release. This documentation is well worth a read before you decide if the library is for...
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  • 2
    State Controller Library

    State Controller Library

    A C++ framework that implements generalised finite state machines.

    The State Controller Library is a generic platform-independent C++ framework that allows finite state machines and multi-action planning generalisations thereof to be realised. The structure and implementation of this library focuses on the application of finite state machines to real-time control loops, but can be reasonably adapted for virtually any other application, even completely unrelated to control systems. An emphasis has been placed on having very low overhead so as not to hurt...
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  • 3

    NN Image Recognition (with source-code)

    This is ANN trained application to predict digits from 0 - 9.

    It can predict digits from 0-9 with Artificial Neural Network. I trained ANN with 100 samples of each digit. It takes input of 20x20 pixel image and predicts it with Neural Network. It may predict wrong digit due to very low sample data but it work 90% correctly. Note: JRE 1.6 is required to run this application.
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  • 4
    Code samples and programs for RepRap, OBD-II, ARM, AVR and windows programs.
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  • 5
    C# Framework that implements the iRobot Create OI specification, which is an expansion of the Roomba SCI spec. Also included is a diagnostic app, & an app with samples that use the framework.
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  • 6
    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/
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