Optical Computing for Adaptive Signal Processing and Associative Memories

Citation

Hong, John Hyunchul (1987) Optical Computing for Adaptive Signal Processing and Associative Memories. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/3vpt-fn50. https://resolver.caltech.edu/CaltechETD:etd-06142006-094757

Abstract

Optical techniques for performing two computing tasks are investigated. First, acousto-optical systems that implement adaptive filtering structures are presented for operation in environments that are not well characterized a priori or are time-varying. Theoretical analyses along with experimental confirmations are given to identify the important system parameters that affect the performance. Extensions of the systems to the multidimensional domain of phased array signal processing are discussed as well as novel implementations that use photorefractive crystals as time-integrating elements.

Also investigated are various associative memory models. An acousto-optic implementation of the so-called Hopfield model is presented. The system's storage capacity and attraction radius are characterized experimentally and are shown to agree with computer simulations. Secondly, an upper bound is derived for the storage capacity of holographic associative memories that use planar holograms. It is shown that if the space bandwidth product of the hologram is N₂, then the holographic memory can store at most N₂/N₃ associations, where N₃ is the number of pixels in each output item. Finally, associative memories whose performance is invariant with respect to shifts in the input pattern position are considered. It is shown that nonlinear interconnections are required to achieve shift invariant operation, and optical implementations are discussed.

Thesis Files