Research project

Powerful microcontrollers have become more available and affordable, enabling the implementation of pattern recognition algorithms in embedded systems. These systems consist of electronic components such as sensors, actors, data transmission devices and microcontrollers, which demand restrictions on power consumption, device dimensions and processing time. Parts of the pattern recognition pipeline which are implemented on microcontrollers include the preprocessing of sensor data, the feature extraction and the use of preselected classification algorithms.

This research project addresses the development of pattern recognition methods and algorithms on embedded systems that take into account the mentioned requirements on the target platforms. The development process of these kinds of systems will be supported in applied research projects. An analysis of the processing power and storage capacity needed for pattern recognition algorithms to run on a target hardware platform is part of our research goals. Closely related is the case where the hardware resources are known (e.g. memory, processing power, arithmetic operations) and an adequate classification algorithm has to be selected which delivers the best possible classification results for the given hardware restrictions. Since embedded systems are often battery powered devices, the power consumption of the entire system is crucial point in the design process. Therefore, the efficient energy usage in our methods is essential for many applications. Example applications of pattern recognition in embedded systems can be found in the automotive, sports or medical engineering sectors.