Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138504
Title: Holographic sensing
Authors: Bruckstein, Alfred Marcel
Ezerman, Martianus Frederic
Fahreza, Adamas Aqsa
Ling, San
Keywords: Engineering::Computer science and engineering
Science::Mathematics
Issue Date: 2020
Source: Bruckstein, A. M., Ezerman, M. F., Fahreza, A. A., & Ling, S. (2020). Holographic sensing. Applied and Computational Harmonic Analysis, 49(1), 296-315. doi: 10.1016/j.acha.2019.03.001
Journal: Applied and Computational Harmonic Analysis
Abstract: 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.
URI: https://hdl.handle.net/10356/138504
ISSN: 1063-5203
DOI: 10.1016/j.acha.2019.03.001
DOI (Related Dataset): https://doi.org/10.21979/N9/G2Z0KZ
Rights: © 2020 Elsevier. All rights reserved. This paper was published in Applied and Computational Harmonic Analysis and is made available with permission of Elsevier.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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