Real-time particle pollution sensing using machine learning
Real-time particle pollution sensing using machine learning
Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.
27237-27246
Grant-Jacob, James
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MacKay, Benita S.
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Baker, James
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Heath, Daniel J.
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Xie, Yunhui
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Loxham, Matthew
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Eason, Robert W.
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Mills, Ben
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15 October 2018
Grant-Jacob, James
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MacKay, Benita S.
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
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Heath, Daniel J.
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Xie, Yunhui
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Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James, MacKay, Benita S., Baker, James, Heath, Daniel J., Xie, Yunhui, Loxham, Matthew, Eason, Robert W. and Mills, Ben
(2018)
Real-time particle pollution sensing using machine learning.
Optics Express, 26 (21), .
(doi:10.1364/OE.26.027237).
Abstract
Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.
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OX_Pollution_JAGJ_corrected_Final
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Available under License Other.
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oe-26-21-27237
- Version of Record
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Accepted/In Press date: 11 September 2018
e-pub ahead of print date: 3 October 2018
Published date: 15 October 2018
Identifiers
Local EPrints ID: 425056
URI: http://eprints.soton.ac.uk/id/eprint/425056
ISSN: 1094-4087
PURE UUID: f6e24ab5-5f39-4bd8-a273-e6c4553c99e7
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Date deposited: 09 Oct 2018 16:30
Last modified: 16 Mar 2024 04:18
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Contributors
Author:
James Grant-Jacob
Author:
Benita S. MacKay
Author:
James Baker
Author:
Daniel J. Heath
Author:
Yunhui Xie
Author:
Robert W. Eason
Author:
Ben Mills
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