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

Malki, Suleyman LU (2006)
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Cellular Neural Networks, Image processing, Discrete-Time Cellular Neural Networks, Packet switching, FPGA, Velocity measurement, Autowaves, Robot navigation
pages
95 pages
ISBN
91-7167-040-8
language
English
LU publication?
yes
id
b7919862-3475-4a15-9b51-dbe74f17e245 (old id 617950)
date added to LUP
2007-12-10 14:11:19
date last changed
2016-09-19 08:45:16
@misc{b7919862-3475-4a15-9b51-dbe74f17e245,
  abstract     = {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.},
  author       = {Malki, Suleyman},
  isbn         = {91-7167-040-8},
  keyword      = {Cellular Neural Networks,Image processing,Discrete-Time Cellular Neural Networks,Packet switching,FPGA,Velocity measurement,Autowaves,Robot navigation},
  language     = {eng},
  note         = {Licentiate Thesis},
  pages        = {95},
  title        = {Discrete-Time Cellular Neural Networks Implemented on Field-Programmable Gate-Arrays to Build a Virtual Sensor System},
  year         = {2006},
}