Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106164
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Photonics Research Institute | en_US |
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.creator | Peng, Y | en_US |
| dc.creator | Xiao, Y | en_US |
| dc.creator | Chen, W | en_US |
| dc.date.accessioned | 2024-05-03T00:45:34Z | - |
| dc.date.available | 2024-05-03T00:45:34Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/106164 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Optical Society of America | en_US |
| dc.rights | © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement (https://opg.optica.org/library/license_v2.cfm#VOR-OA). Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. | en_US |
| dc.rights | Journal © 2023 | en_US |
| dc.rights | The following publication Yang Peng, Yin Xiao, and Wen Chen, "High-fidelity and high-robustness free-space ghost transmission in complex media with coherent light source using physics-driven untrained neural network," Opt. Express 31, 30735-30749 (2023) is available at https://dx.doi.org/10.1364/OE.498073. | en_US |
| dc.title | High-fidelity and high-robustness free-space ghost transmission in complex media with coherent light source using physics-driven untrained neural network | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.spage | 30735 | en_US |
| dc.identifier.epage | 30749 | en_US |
| dc.identifier.volume | 31 | en_US |
| dc.identifier.issue | 19 | en_US |
| dc.identifier.doi | 10.1364/OE.498073 | en_US |
| dcterms.abstract | It is well recognized that it is challenging to realize high-fidelity and high-robustness ghost transmission through complex media in free space using coherent light source. In this paper, we report a new method to realize high-fidelity and high-robustness ghost transmission through complex media by generating random amplitude-only patterns as 2D information carriers using physics-driven untrained neural network (UNN). The random patterns are generated to encode analog signals (i.e., ghost) without any training datasets and labeled data, and are used as information carriers in a free-space optical channel. Coherent light source modulated by the random patterns propagates through complex media, and a single-pixel detector is utilized to collect light intensities at the receiving end. A series of optical experiments have been conducted to verify the proposed approach. Experimental results demonstrate that the proposed method can realize high-fidelity and high-robustness analog-signal (ghost) transmission in complex environments, e.g., around a corner, or dynamic and turbid water. The proposed approach using the designed physics-driven UNN could open an avenue for high-fidelity free-space ghost transmission through complex media. | en_US |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Optics express, 11 Sept 2023, v. 31, no. 19, 498073, p. 30735-30749 | en_US |
| dcterms.isPartOf | Optics express | en_US |
| dcterms.issued | 2023-09-11 | - |
| dc.identifier.isi | WOS:001080799200001 | - |
| dc.identifier.eissn | 1094-4087 | en_US |
| dc.identifier.artn | 498073 | en_US |
| dc.description.validate | 202405 bcrc | en_US |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | OA_Scopus/WOS | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | Guangdong Basic and Applied Basic Research Foundation | en_US |
| dc.description.fundingText | Hong Kong Polytechnic University | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.description.oaCategory | VoR allowed | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| oe-31-19-30735.pdf | 3.85 MB | Adobe PDF | View/Open |
Page views
76
Last Week
0
0
Last month
Citations as of Nov 9, 2025
Downloads
58
Citations as of Nov 9, 2025
SCOPUSTM
Citations
4
Citations as of Jun 21, 2024
WEB OF SCIENCETM
Citations
13
Citations as of Dec 18, 2025
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.



