Learning and Intelligence in Embedded Systems
(Lernen und Intelligenz in Eingebetteten Systemen)
Embedded systems have become very widespread and used nearly everywhere. They are usually designed to perform specific predefined tasks based on a known input domain. However, many embedded systems are required to interact with new environments, that have new or unknown inputs, and provide intelligent decisions. Hence, the need arises to adopt machine learning techniques in embedded systems to be able to learn from examples and adapt to changing conditions. However, some resource-constrained embedded systems may not be able to run specific computation-intensive techniques.
In this proseminar, we will explore machine learning techniques and applications for embedded systems. Furthermore, we will discuss the techniques that are suitable for resource-constrained embedded systems.
Date | Speaker | Topic |
19.10.2016 | Anas Toma | Introduction |
26.10.2016 | Anas Toma | How to present |
23.11.2016 | Leon Schubert | Emotional cognitive mechanisms |
30.11.2016 | Anas Toma | Artificial neural networks in embedded systems |
07.12.2016 | Philipp Verstege | Localization and Tracking |
14.12.2016 | Timo Gojowczyk | Energy harvesting level adaptation |
21.12.2016 | Hendrik Hildebrandt | Passive and active learning |
11.01.2017 | Philipp Koppenstein | Power supply voltage and processor frequency levels adaptation |
18.01.2017 | Julian Hankel | Adaptive sensing and its policies |
25.01.2017 | Mandy Nicolaus | Introduction to fault diagnosis systems |
01.02.2017 | Anas Toma | Last meeting and discussion |
15.02.2017 | All participants | Final presentations |
Note: the presentations were scheduled randomly in the second lecture.
Time | Speakers |
09:30 - 11:00 | Leon Schubert, Philipp Verstege and Timo Gojowczyk |
11:00 - 11:10 | Break |
11:10 - 12:10 | Hendrik Hildebrandt and Philipp Koppenstein |
12:10 - 13:10 | Break |
13:10 - 14:10 | Julian Hankel and Mandy Nicolaus |