• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • No language data
  • Tagged with
  • 3
  • 3
  • 3
  • 2
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Optical processing of neural networks

Neil, Mark Andrew Aquilla January 1989 (has links)
No description available.
2

Optics in Hopfield content addressable memories

Barron, Kenneth Falconer January 1993 (has links)
No description available.
3

Cascaded linear shift invariant processing in pattern recognition

Reed, Stuart January 2000 (has links)
Image recognition is the process of classifying a pattern in an image into one of a number of stored classes. It is used in such diverse applications as medical screening, quality control in manufacture and military target recognition. An image recognition system is called shift invariant if a shift of the pattern in the input image produces a proportional shift in the output, meaning that both the class and location of the object in the image are identified. The work presented in this thesis considers a cascade of linear shift invariant optical processors, or correlators, separated by fields of point non-lineari ties, called the cascaded correlator. This is introduced as a method of providing parallel, shiftinvariant, non-linear pattern recognition in a system that can learn in the manner of neural networks. It is shown that if a neural network is constrained to give overall shift invariance, the resulting structure is a cascade of correlators, meaning that the cascaded correlator is the only architecture which will provide fully shift invariant pattern recognition. The issues of training of such a non-linear system are discussed in neural network terms, and the non-linear decisions of the system are investigated. By considering digital simulations of a two-stage system, it is shown that the cascaded correlator is superior to linear filtering for both discrimination and tolerance to image distortion. This is shown for theoretical images and in real-world applications based on fault identification in can manufacture. The cascaded correlator has also been proven as an optical system by implementation in a joint transform correlator architecture. By comparing simulated and optical results, the resulting practical errors are analysed and compensated. It is shown that the optical implementation produces results similar to those of the simulated system, meaning that it is possible to provide a highly non-linear decision using robust parallel optical processing techniques.

Page generated in 0.0725 seconds