Design methodology for application-specific electromagnetic energy harvesters

For energy harvesters to be used efficiently, they have to be adapted to the respective application. For kinetic excitations, electromagnetic harvesters are very promising as they allow a high degree of freedom in the design which in turn permits optimally adapted designs. A corresponding design methodology has been developed in a current research project. It is implemented as a design tool in MATLAB®, which performs an automated comparison between different basic structures. Prior to presenting first results of these structural comparisons, the general structure of the design process is explained. It is shown that the application-specific requirements are most important for the evaluation of the basic structures

In-Network Detection of Anomaly Regions in Sensor Networks with Obstacles

Abstract: In the past couple of years, sensor networks have evolved to a powerful infrastructure component for monitoring and tracking events and phenomena in many application domains. An important task in processing streams of sensor data is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in a sensor network. In this paper, we present an approach that facilitates the efficient computation of such anomaly regions from sensor readings. We propose an algorithm to derive spatial regions from individual anomalous sensor readings, with a particular focus on obstacles present in the sensor network. We improve this approach by proposing a distributed in-network processing technique where the region detection is performed at the sensor nodes. We demonstrate the advantages of this strategy over a centralized processing strategy by utilizing a cost model for real sensors and sensor networks.

In-network detection of anomaly regions in sensor networks with obstacles

Abstract: In the past couple of years, sensor networks have evolved to a powerful infrastructure component for monitoring and tracking events and phenomena in many application domains. An important task in processing streams of sensor data is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in a sensor network. In this paper, we present an approach that facilitates the efficient computation of such anomaly regions from sensor readings. We propose an algorithm to derive spatial regions from individual anomalous sensor readings, with a particular focus on obstacles present in the sensor network. We improve this approach by proposing a distributed in-network processing technique where the region detection is performed at the sensor nodes. We demonstrate the advantages of this strategy over a centralized processing strategy by utilizing a cost model for real sensors and sensor networks.

Handbuch Künstliche Intelligenz : ein Praxisleitfaden für Unternehmen

Die Arbeitsgruppe (AG) Künstliche Intelligenz (KI) von Mittelstand-Digital tauscht sich zu Ergebnissen und Herausforderungen im Themenkomplex Künstliche Intelligenz in regelmäßigen Expertenrunden aus. Teilnehmen können alle Zentren des Mittelstand-Digital-Netzwerks. Unternehmen können so direkt von unserem Erfahrungsaustausch profitieren. Der gezielte Einsatz KI-basierter Lösungen hat eine signifikante Bedeutung für und Auswirkung auf die Wirtschaftlichkeit von Unternehmen. Durch die Arbeit in der AG Künstliche Intelligenz werden Unternehmen dazu befähigt, ein besseres Verständnis der Methoden Künstlicher Intelligenz zu entwickeln sowie daraus entstehende neue Potentiale und Chancen für das eigene Unternehmen zu erkennen. Den Unternehmen wird anhand von Best-Practice Beispielen aus dem Mittelstand-Digital Netzwerk Orientierung gegeben sowie diese bei der Einführung KI-basierter Lösungen unterstützt