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- W64756854 abstract "The Hopfield network is a classical and interesting type of artificial neural networks that can be employed for storing multiple patterns. As such, the Hopfield Network can serve as a content-addressable, auto-associative memory (AAM) whereby a stored pattern can be retrieved from an incomplete or damaged copy of itself. Recently, it has been demonstrated that a Hopfield Network can be applied as an AAM for storing complex holograms. Despite the success, the structure of the AAM is rather cumbersome as a pair of networks is required for storing the real and the imaginary components of the complex hologram. In this paper, we proposed an enhanced method to alleviate this shortcoming with a single line-partitioned hetero-associative memory (LP-HAM). Briefly, the LP-HAM is trained to memorize the imaginary components of a collection of binary holograms, so that each of them can be recalled from its corresponding real component. Subsequently, the input (real component) and the output (recalled imaginary component) images will be combined to form the complex hologram." @default.
- W64756854 created "2016-06-24" @default.
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- W64756854 date "2014-01-01" @default.
- W64756854 modified "2023-09-27" @default.
- W64756854 title "A Line-Partitioned Heteroassociative Memory for Storing Binary Fresnel Hologram" @default.
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- W64756854 doi "https://doi.org/10.1007/978-3-319-12637-1_39" @default.
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