Detail publikačního výsledku

Structured light enhanced machine learning for fiber bend sensing

ŠKVARENINA, Ľ.; ANGELUCCI, S.; CHEN, Z.; CLACK, A.; VALLES, A.; LAVERY, M.; HOLCMAN, V.

Originální název

Structured light enhanced machine learning for fiber bend sensing

Anglický název

Structured light enhanced machine learning for fiber bend sensing

Druh

Článek WoS

Originální abstrakt

The 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.

Anglický abstrakt

The 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.

Klíčová slova

optical;distortions

Klíčová slova v angličtině

optical;distortions

Autoři

ŠKVARENINA, Ľ.; ANGELUCCI, S.; CHEN, Z.; CLACK, A.; VALLES, A.; LAVERY, M.; HOLCMAN, V.

Rok RIV

2025

Vydáno

01.02.2024

Nakladatel

Optica Publishing Group

Místo

WASHINGTON

ISSN

1094-4087

Periodikum

OPTICS EXPRESS

Svazek

32

Číslo

5

Stát

Spojené státy americké

Strany od

7882

Strany do

7895

Strany počet

14

URL

https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7882&id=547088

Plný text v Digitální knihovně

http://hdl.handle.net/11012/245494

BibTex

@article{BUT188523,
  author="Sara {Angelucci} and Zhaozhohg {Chen} and Ľubomír {Škvarenina} and Alasdair {Clack} and Adam {Valles} and Martin {Lavery}",
  title="Structured light enhanced machine learning for fiber bend sensing",
  journal="OPTICS EXPRESS",
  year="2024",
  volume="32",
  number="5",
  pages="7882--7895",
  doi="10.1364/OE.513829",
  issn="1094-4087",
  url="https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7882&id=547088"
}

Dokumenty

oe-32-5-7882