Structured light enhanced machine learning for fiber bend sensing

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dc.contributor.authorAngelucci, Saracs
dc.contributor.authorChen, Zhaozhohgcs
dc.contributor.authorŠkvarenina, Ľubomírcs
dc.contributor.authorClack, Alasdaircs
dc.contributor.authorValles, Adamcs
dc.contributor.authorLavery, Martincs
dc.coverage.issue5cs
dc.coverage.volume32cs
dc.date.issued2024-02-01cs
dc.description.abstractThe intricate optical distortions that occur when light interacts with complex media, such as few- or multi -mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase -noise and allow for the use of inter modalcoupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all -optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional -neural -network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies.en
dc.formattextcs
dc.format.extent7882-7895cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationOPTICS EXPRESS. 2024, vol. 32, issue 5, p. 7882-7895.en
dc.identifier.doi10.1364/OE.513829cs
dc.identifier.issn1094-4087cs
dc.identifier.orcid0000-0002-9203-0359cs
dc.identifier.other188523cs
dc.identifier.researcheridAAC-8505-2019cs
dc.identifier.scopus57192984953cs
dc.identifier.urihttp://hdl.handle.net/11012/245494
dc.language.isoencs
dc.publisherOptica Publishing Groupcs
dc.relation.ispartofOPTICS EXPRESScs
dc.relation.urihttps://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7882&id=547088cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1094-4087/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectopticalen
dc.subjectdistortionsen
dc.titleStructured light enhanced machine learning for fiber bend sensingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-188523en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:40:35en
sync.item.modts2025.01.17 18:40:23en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav fyzikycs
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