Holographic sensing

Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become a...

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Bibliographic Details
Published in:Applied and computational harmonic analysis 2020-07, Vol.49 (1), p.296-315
Main Authors: Bruckstein, A.M., Ezerman, M.F., Fahreza, A.A., Ling, S.
Format: Article
Language:English
Subjects:
Cyclostationary data
Fusion frame
Holographic representation
Mean squared error estimation
Stochastic data
Wiener filter
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Summary:Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become available. Such representations are ideally suited for distributed storage and for the transmission of data packets over networks with unpredictable delays and or erasures. Several methods for holographic representations of signals and images have been proposed over the years and multiple description information theory also deals with such representations. Surprisingly, however, these methods had not been considered in the classical framework of optimal least-squares estimation theory, until very recently. We develop a least-squares approach to the design of holographic representation for stochastic data vectors, relying on the framework widely used in modeling signals and images.
ISSN:1063-5203
1096-603X
DOI:10.1016/j.acha.2019.03.001