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The degree of flexibility brought to the next generation of aircraft by Integrated Modular Avionics (IMA) concept potentially enables dynamic reconfiguration of the avionics systems, e.g. as a means of fault containment. So far, most of the research in this field has focused on real-time task scheduling and dynamic resource (re)allocation.In this paper, we show that these are just some aspects of the more general problem of IMA reconfiguration. Focusing on one of the subproblems, human involvement, we provide evidence of the need and criticality of pilot-system real-time interaction during reconfiguration.In this regard, we present a novel approach to solve the complex combinatorial problem of IMA reconfiguration in real-time whilst providing support for pilot's involvement by means of automatic generation of explanations of reconfiguration actions, in order to augment her situation awareness. Our approach is based on Explanation-based Constraint Programming.

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In this paper, we present a generic platform for autonomous medical monitoring and diagnostics. We validated the platform in the context of arrhythmia detection with publicly available databases. The big advantage of this platform is its capacity to deal with various types of physiological signals. Many pre-processing steps are performed to bring the input information into a uniform state that will be explored by a machine learning algorithm. Since this block plays a crucial role in the entire processing pipeline, three different methods were evaluated for detection and classification of anomalies. The results presented in this work are validated on cardiac beats, where the highest accuracy was obtained on the classification of normal beats (94%). On the other hand, atrial fibrillation and premature ventricular contraction beats were classified with an accuracy of 78%.

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