Welcome to the website of the Intelligent Embedded Systems (IES) Lab at the University of Kassel, Germany.

We develop and apply innovative techniques that increase the “machine intelligence” of embedded computer systems.

A computer system can be characterized as being “intelligent” if it is aware of its own skills and needs and if it is able to use this knowledge to maintain or even to improve its abilities in a dynamic environment. Moreover, if such a system interacts with other, similar systems or humans, it must be aware of their skills and needs in order to communicate efficiently, to collaborate with them, or to behave pro-actively. Thus, machine learning is a key issue in our work. Some other topics are soft computing, pattern recognition, data mining, specification techniques for embedded systems, and real-time systems.

If you are interested in our work, do not hesitate to drop us a line. We will be happy to answer your questions.

Bernhard Sick

09/18/2009: New Research Associate

We welcome Ferdinand Kastl who joins the CIS group as new research assistant on October, 1.


The journal article “Periodical Switching between Related Goals for Improving Evolvability to a Fixed Goal in Multi-Objective Problems" (S. J. Ovaska, B. Sick, A. H. Wright) has been accepted for publication in the Information Sciences journal of Elsevier.

07/29/2009: New journal article available

The journal article “Processing Short-Term and Long-Term Information With a Combination of Polynomial Approximation Techniques and Time-Delay Neural Networks” (E. Fuchs, C. Gruber, T. Reitmaier, B. Sick) is now available online at the website of the IEEE Transactions on Neural Networks (DOI: 10.1109/TNN.2009.2024679)

07/12/2009: Funding approved

The German Research Foundation (DFG) approved the funding for our research project “On-Line Fusion of Functional Knowledge Within Distributed Sensor Networks” in the third phase of the priority program “Organic Computing”.

07/05/2009: Journal article accepted

The journal article “On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling” (A. Hofmann, B. Sick) has been accepted for publication in the IEEE Transactions on Dependable and Secure Computing.