VeranstaltungsortK.0.01Stiftungssaal der Kärntner SparkasseVeranstalter Büro des RektoratsBeschreibungKontaktBirgit Moser (birgit-maria.moser@aau.at) Anmeldepflichtig!per Mail an birgit-maria.moser@aau.at

VeranstaltungsortL4.1.04Veranstalter Fakultät für Technische WissenschaftenBeschreibungInternet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.Vortragende(r)Dr. Hari Prabhat GuptaKontaktKerstin Smounig (kerstin.smounig@aau.at)

VeranstaltungsortB4.1.114Veranstalter Fakultät für Technische WissenschaftenBeschreibungThe use of drones, a.k.a. unmanned aerial vehicles (UAVs) as a flying radio access network (RAN) is currently gaining significant attention. It holds promises as a complement to classical fixed infrastructure by allowing ultra flexible deployments, with use cases ranging from disaster recovery scenarios to improving the performance and coverage of the network. Beyond obvious challenges within regulatory, control, navigation, and operational domains, the deployment of autonomous flying-RANs also come with a number of exciting new research problems such as the issue of autonomous real-time placement of the drones in non-trivial propagation scenarios (i.e. scenarios where the optimal placement is not just dictated by a trivial geometry or statistical argument due to shadowing effects, e.g. in cities). We present several different approaches, lying at the cross-roads between machine learning, signal processing, and optimization. Some approaches involve the reconstruction of a city map from sampled radio measurements which can have application beyond the realm of communications.Vortragende(r)Omid Esrafilian, MScKontaktKerstin Smounig (kerstin.smounig@aau.at)