High-Resolution Spectrum Reconstruction of Cascaded FBG-FPI Sensor in Resource-Constrained Environments

Xu, H; Chen, S; Ren, S; Hou, X; Peng, H; Wang, G; Zhang, Y

High-Resolution Spectrum Reconstruction of Cascaded FBG-FPI Sensor in Resource-Constrained Environments

Xu, H Chen, S

Ren, S Hou, X Peng, H Wang, G Zhang, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2024, 24, (7), pp. 11878-11885
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Xu, H
dc.contributor.author	Chen, S https://orcid.org/0000-0001-9992-2264
dc.contributor.author	Ren, S
dc.contributor.author	Hou, X
dc.contributor.author	Peng, H
dc.contributor.author	Wang, G
dc.contributor.author	Zhang, Y
dc.date.accessioned	2024-08-07T04:59:13Z
dc.date.available	2024-08-07T04:59:13Z
dc.date.issued	2024-04-01
dc.identifier.citation	IEEE Sensors Journal, 2024, 24, (7), pp. 11878-11885
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/180256
dc.description.abstract	Monitoring external physical variables with fiber optic sensors requires highly efficient demodulation instruments, particularly for strategies based on spectrum analysis. However, balancing performance with cost is a significant challenge in resource-limited settings. To address these challenges, this article introduces a new framework that uses a simple yet effective multimodal fusion strategy based on deep learning. It achieves high-resolution and precise demodulation by reconstructing the original spectrum from a limited number of ultralow resolution samples. Specifically, these ultralow resolution spectra samples are transformed into images. A pretrained model, which contains extensive prior knowledge, encodes them as potential multilayer spectral features. Additionally, spectral drift is captured as projected intensity information, taking advantage of the wave decomposition multiplexing feature of arrayed waveguide gratings (AWGs). A simple neural network then fuses and decodes the multilayer spectral features and projected intensity information to produce high-resolution sensor spectra. We demonstrate the effectiveness of our approach with a cascade structure sensor that includes a fiber Bragg grating-Fabry-Perot interferometer (FBG-FPI) for strain monitoring. The results confirm that our framework can efficiently reconstruct spectra within a 75 nm wavelength range, achieving high resolution and precision.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2024.3365546
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	40 Engineering
dc.title	High-Resolution Spectrum Reconstruction of Cascaded FBG-FPI Sensor in Resource-Constrained Environments
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:59:02Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	7

Abstract:

Monitoring external physical variables with fiber optic sensors requires highly efficient demodulation instruments, particularly for strategies based on spectrum analysis. However, balancing performance with cost is a significant challenge in resource-limited settings. To address these challenges, this article introduces a new framework that uses a simple yet effective multimodal fusion strategy based on deep learning. It achieves high-resolution and precise demodulation by reconstructing the original spectrum from a limited number of ultralow resolution samples. Specifically, these ultralow resolution spectra samples are transformed into images. A pretrained model, which contains extensive prior knowledge, encodes them as potential multilayer spectral features. Additionally, spectral drift is captured as projected intensity information, taking advantage of the wave decomposition multiplexing feature of arrayed waveguide gratings (AWGs). A simple neural network then fuses and decodes the multilayer spectral features and projected intensity information to produce high-resolution sensor spectra. We demonstrate the effectiveness of our approach with a cascade structure sensor that includes a fiber Bragg grating-Fabry-Perot interferometer (FBG-FPI) for strain monitoring. The results confirm that our framework can efficiently reconstruct spectra within a 75 nm wavelength range, achieving high resolution and precision.

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http://hdl.handle.net/10453/180256