ADIMA: Automatic Configuration by Peripheral Detection and Adaptive Distributed Task Execution for Integrated Modular Avionics Platforms

This paper shows that even a very simple peripheral, like an LED, can be automatically detected and utilized by commercial-off-the-shelf IMA devices and thus enable a bit of a plug-and-fly system. The detection is performed based on electrical properties using a static feed-forward neural network. This paper extends our previous research by more precise training data, new features, detailed performance assessments and multi-device system function auto-configuration. We trained multiple different network architectures for two distinct data pools, created a C implementation for the networks and executed these one IMA devices. Furthermore, the configuration data and library are strictly separated to allow for a reusable detection code base for different IMA devices. We implemented four different approaches to solve the unknown classification problem. The final demonstration incorporates two IMA-devices, a host and a proxy, that detect their own peripherals, exchange the peripheral information, and the host commands the proxy with a suitable application.