Discrete-Time Cellular Neural Networks Implemented on Field-Programmable Gate-Arrays to Build a Virtual Sensor System

Image processing is one of the popular applications of Cellular Neural Networks. Macro enriched field-programmable gate-arrays can be used to realize such systems on silicon. At first glance a pipelined approach, based on circuit switching, seems promising. The digital implementation supports the handling of grey-level images at 180 to 240 Mpixels per second by exploiting the Xilinx Virtex-II macros to spatially unroll the local feedback. Later on, in order to overcome design limitations and thus enhance performance, the benefits of packet switching have been explored. The digital implementation is performed using Xilinx Virtex-II Pro P30. The advantages over the approach of circuit switching are discussed. Finally, the thesis illustrates the power of the different implementations experimentally. It is shown how these implementations can be used to measure from images or to create dynamic, autonomous processes that facilitate measurements within topographic maps. Applications range from image understanding to robot navigation