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
1998
In: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998..
An emulated digital architecture implementing the CNN universal machine
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
In: 1998 international symposium on nonlinear theory and its applications. NOLTA'98. Proceedings. Crans-Montana, 1998. Vol. 2. 1998. (Presses Polytechniques et Universitaires Romandes.).
CNN computing infrastructure hosting nonlinear spatiotemporal algorithms - a review
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
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)