This paper discusses the use of diagnostics based on machine learning (ML) within a flight test context. The paper begins by discussing some of the problems associated with instrumenting a test aircraft and how they could be ameliorated using ML-based diagnostics. We then describe a number of types of supervised ML algorithms which can be used in this context. In addition, key practical aspects of applying these algorithms, such as feature engineering and parameter selection, are also discussed. The paper then outlines a real-world application developed by Curtiss-Wright, called Machine Learning for Advanced System Diagnostics (MLASD). This description includes key challenges that were encountered during the development process and how suitable input features were identified. Real-world results are also presented. Finally, we suggest some further applications of ML techniques, in addition to describing other areas of development.

<p>Cyber physical systems are deployed in a wide range of applications from sensor nodes in a factory setting to drones in defense applications. This distributed setting of nodes or processes often needs to reach agreement on a set of values. Byzantine Agreement protocols address this issue of reaching an agreement in an environment where a malicious entity can take control over a set of nodes and deviates the system from its normal operation. However these protocols do not consider the energy consumption of the nodes. We explore Byzantine Agreement protocols from an energy efficient perspective providing both <i>energy resilience</i> where the actions of the Byzantine nodes can not adversely effect the energy consumption of non-malicious nodes as well as <i>fairness</i> in energy consumption of nodes over multiple rounds of agreement.</p>

of Doctoral Thesis Title: Components and Services in Resource-Constrained Envi- ronments Author: Tomáš Pop tomas.pop@d3s.mff.cuni.cz Department: Department of Distributed and Dependable Systems Faculty of Mathematics and Physics Charles University in Prague Advisor: Prof. František Plášil plasil@d3s.mff.cuni.cz Abstract Appliances of every day use such as consumer electronics, automotive and telecommunication devices as well as various kinds of control systems have be- come a common and important part of our everyday lives. In comparison to general-purpose desktop systems, they are limited in terms of various resources, for example memory, CPU power, and battery capacity. Component-based soft- ware engineering is a well-established development technique, which has found its way to industry and has been successfully used for a long time in multiple do- mains. However, the industrially adopted component frameworks were designed for general-purpose systems and they can be hardly used in resource-constrained environments. In the thesis, we explore the potential of component-based system engineer- ing in resource-constrained environments. Stemming from a survey of existing component frameworks targeted to this domain, we identify the most important approaches to overcome resource constraints in...