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Fibre-optic based particle sensing via deep learning

Fibre-optic based particle sensing via deep learning
Fibre-optic based particle sensing via deep learning
We demonstrate the capability for the identification of single particles, via a neural
network, directly from the backscattered light collected by a 30-core optical fibre, when particles are illuminated using a single mode fibre-coupled laser light source. The neural network was shown to be able to determine the specific species of pollen with ~ 97% accuracy, along with the distance between the end of the 30-core sensing fibre and the particles, with an associated error of ± 6 µm. The ability to be able to classify particles directly from backscattered light using an optical fibre has potential in environments in which transmission imaging is neither possible nor suitable, such as sensing over opaque media, in the deep sea or outer space.
2515-7647
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Jain, Saurabh
bf4af598-26bf-47f4-a0a4-800095a23eb5
Xie, Yunhui
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MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael, David Tom
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Praeger, Matthew
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Loxham, Matthew
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Richardson, David
ebfe1ff9-d0c2-4e52-b7ae-c1b13bccdef3
Eason, Robert
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Mills, Benjamin
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Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Jain, Saurabh
bf4af598-26bf-47f4-a0a4-800095a23eb5
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Richardson, David
ebfe1ff9-d0c2-4e52-b7ae-c1b13bccdef3
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Jain, Saurabh, Xie, Yunhui, MacKay, Benita, Scout, McDonnell, Michael, David Tom, Praeger, Matthew, Loxham, Matthew, Richardson, David, Eason, Robert and Mills, Benjamin (2019) Fibre-optic based particle sensing via deep learning. Journal of Physics: Photonics.

Record type: Article

Abstract

We demonstrate the capability for the identification of single particles, via a neural
network, directly from the backscattered light collected by a 30-core optical fibre, when particles are illuminated using a single mode fibre-coupled laser light source. The neural network was shown to be able to determine the specific species of pollen with ~ 97% accuracy, along with the distance between the end of the 30-core sensing fibre and the particles, with an associated error of ± 6 µm. The ability to be able to classify particles directly from backscattered light using an optical fibre has potential in environments in which transmission imaging is neither possible nor suitable, such as sensing over opaque media, in the deep sea or outer space.

Text
Fibre_AI_Resubmitted_JAGJ - Accepted Manuscript
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Published date: 11 September 2019

Identifiers

Local EPrints ID: 434151
URI: https://eprints.soton.ac.uk/id/eprint/434151
ISSN: 2515-7647
PURE UUID: d1feda38-f9c5-4f28-8d12-ad70c3f24b86
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benita, Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Matthew Loxham: ORCID iD orcid.org/0000-0001-6459-538X
ORCID for David Richardson: ORCID iD orcid.org/0000-0002-7751-1058
ORCID for Robert Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 13 Sep 2019 16:30
Last modified: 14 Sep 2019 00:40

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