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
1996
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 24 (3)., pp. 315-339.
Methods for constructing physiologically motivated neuromorphic models in CNNs
Information theoretical approach to LEQG-optimal stochastic control and H-infinity optimal control. In: Technical Report of the Systems and Controls Laboratory, SCL - 001 - 1996
College of Engineering University of California, Berkeley, pp. 21.
New results and measurements related to dynamic image coding using CNN universal chips. (Memorandum of the Electronics Research Laboratory, UCB/ERL M96/58)
In: Extended finite state models of language. Proceedings of the ECAI'96 workshop. 12th European conference on artificial intelligence. Budapest, 1996..
Colonies: a multi-agent approach to language generation