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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/138504 |
| _version_ | 1826122841419939840 |
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| author | Bruckstein, Alfred Marcel Ezerman, Martianus Frederic Fahreza, Adamas Aqsa Ling, San |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Bruckstein, Alfred Marcel Ezerman, Martianus Frederic Fahreza, Adamas Aqsa Ling, San |
| author_sort | Bruckstein, Alfred Marcel |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:54:47Z |
| format | Journal Article |
| id | ntu-10356/138504 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:54:47Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1385042023-02-28T19:52:22Z Holographic sensing Bruckstein, Alfred Marcel Ezerman, Martianus Frederic Fahreza, Adamas Aqsa Ling, San School of Physical and Mathematical Sciences Engineering::Computer science and engineering Science::Mathematics Cyclostationary Data Fusion Frame 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. Accepted version 2020-05-07T10:06:28Z 2020-05-07T10:06:28Z 2020 Journal Article 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 1063-5203 https://hdl.handle.net/10356/138504 10.1016/j.acha.2019.03.001 2-s2.0-85063096622 1 49 296 315 en Applied and Computational Harmonic Analysis https://doi.org/10.21979/N9/G2Z0KZ © 2020 Elsevier. All rights reserved. This paper was published in Applied and Computational Harmonic Analysis and is made available with permission of Elsevier. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Science::Mathematics Cyclostationary Data Fusion Frame Bruckstein, Alfred Marcel Ezerman, Martianus Frederic Fahreza, Adamas Aqsa Ling, San Holographic sensing |
| title | Holographic sensing |
| title_full | Holographic sensing |
| title_fullStr | Holographic sensing |
| title_full_unstemmed | Holographic sensing |
| title_short | Holographic sensing |
| title_sort | holographic sensing |
| topic | Engineering::Computer science and engineering Science::Mathematics Cyclostationary Data Fusion Frame |
| url | https://hdl.handle.net/10356/138504 |
| work_keys_str_mv | AT brucksteinalfredmarcel holographicsensing AT ezermanmartianusfrederic holographicsensing AT fahrezaadamasaqsa holographicsensing AT lingsan holographicsensing |