Publication detail

Structured light enhanced machine learning for fiber bend sensing

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

Original Title

Structured light enhanced machine learning for fiber bend sensing

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

optical;distortions

Authors

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

Released

1. 2. 2024

Publisher

Optica Publishing Group

Location

WASHINGTON

ISBN

1094-4087

Periodical

OPTICS EXPRESS

Year of study

32

Number

5

State

United States of America

Pages from

7882

Pages to

7895

Pages count

14

URL

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

Full text in the Digital Library

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="14",
  doi="10.1364/OE.513829",
  issn="1094-4087",
  url="https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7882&id=547088"
}