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eraser

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  • ERASER - OpenLicht Knowledge Base

    Overview

    There are the following subprojects in this repository:

    • Base
    • Utility
      • commons.color: Utilities for converting color spaces (XYZ, RGB, HSB)
      • eraser.rest and eraser.spark: REST-API to communicate with the framework
      • eraser.starter: The main entry point to start the application
      • feedbackloop.*: Component specification and implementation of the MAPE-K feedback loop
      • project-template: Template to quickly create new subprojects used by make-new-project.py
    • Demos
      • integration and openhab-mock: Playground projects testing some features
      • skywriter-hue-integration: The first real demonstrator integrating the Skywriter gesture recogintion with a Hue using a simple rule
      • benchmark: Small benchmark to replay previously recoreded sensor events
    • Machine Learning Integration (deprecated)
      • org.openhab.action.machinelearn: openHAB connector of the Machine Learning Part
      • org.openlicht.action.reinforcementlearning: Reinforcement-Learning of preferences
      • stub.org.openhab.core.scriptengine.action: Stub to make the other two projects build

    Setup and Building

    This project uses Gradle as the build tool. For detailed information, see setup guidelines

    Running the demo

    There is a demo project, as explained above to showcase the framework. To start it, run ./gradlew :skywriter-hue-integration:run

    This demo is intended to run with a pre-configured openHAB instance and has some hard-coded settings. It will first try to connect to an MQTT broker (with a hard-coded IP) to listen on changes of item states. There is one rule implemented, namely to listen on changes of the item skywriter_xyz expecting MQTT messages of the form XVALUE,YVALUE,ZVALUE, and to change to state of another item iris1_item representing the HSB value of a Hue Iris.

    Trivia

    The name Eraser is a small pun on the term eRACR, which is an extension of RACR for event recognition. It was chosen to ease the search for a good repository image as it is based on a film.