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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.


  • Preliminary meeting: 07.02.2019, Seminarraum E18, OH16
  • Start of course: 04.04.2019
  • Classroom sessions: Thursdays 12:15-13:45, Seminarraum E18, OH16
  • Maximum number of participants: 16
  • Semester: Sommersemester 2019
  • Language: English
  • Lecturer: Dr. Anas Toma
  • Reference book: Intelligence for Embedded Systems - A Methodological Approach, Cesare Alippi, Springer International Publishing Switzerland 2014


  1. Introductory presentation
  2. The students choose their topics
  3. Each student presents his topic as a rehearsal presentation for discussion, evaluation and feedback
  4. The students write a short summary report
  5. Final presentations


  • Emotional cognitive mechanisms
  • Artificial neural networks in embedded systems
  • Localization and Tracking
  • Energy harvesting level adaptation
  • Passive and active learning
  • Power supply voltage and processor frequency levels adaptation
  • Adaptive sensing and its policies
  • Introduction to fault diagnosis systems

Schedule of the lectures and the rehearsal talks:

This table will be updated during the semester.

04.04.2019 Anas Toma Introduction

Notes: Students' rehearsal presentations will be scheduled randomly in the second lecture.

This schedule is subject to change during the semester.