Computational Optical Sensing and Processing Laboratory
The main research challenge of our laboratory is to derive precise abstract information or decisions from large complex noisy topological data sets captured by one or multiple optical sensors. We apply special optical arrangements such as different holographic setups and fluorescent illuminations for microscopic imaging, or multi-spectral camera-based patient monitoring systems or wide-angle multi-camera systems in monitoring. The heavy computational load is handled by many-core processor arrays, such as GPUs in desktop applications or embedded low-power systems.
Publication date
1999
In: 38th IEEE conference on decision and control. Conference proceedings. Phoenix, 1999..
Stochastic approximation for function minimization under quantization error
Solving the shortest path problem in the CNN framework - a comparison of different approaches. (Research report of the Analogical and Neural Computing laboratory DNS-7-1999.)
Combination of adaptive resonance theory (ART) and cellular neural network (CNN) chips into a high-speed pattern recognition system. ( Research report of the Analogical and Neural Computing Laboratory, DNS-1-1999.)
In: IEEE International Symposium on Circuits and Systems, ISCAS 2015 Proceedings - IEEE International Symposium on Circuits and Systems (2015-J) IEEE, New York, pp. 1981-1984. ISSN 0271-4310
Emulating massively parallel non-Boolean operators on FPGA