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Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi

Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi
Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi
The identification of mixtures of particles in a solution via analysis of scattered light can be a complex task, due to the multiple scattering effects between different sizes and types of particles. Deep learning offers the capability for solving complex problems without the need for a physical understanding of the underlying system, and hence offers an elegant solution. Here, we demonstrate the application of convolutional neural networks for the identification of the concentration of microparticles (silicon dioxide and melamine resin) and the solution salinity, directly from the scattered light. The measurements were carried out in real-time using a Raspberry Pi, light source, camera, and neural network computation, hence demonstrating a portable and low-cost environmental marine sensor.
2515-7620
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
McDonnell, Michael, David Tom
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Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Loxham, Matthew
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Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Heath, Daniel J
d53c269d-90d2-41e6-aa63-a03f8f014d21
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Xie, Yunhui, MacKay, Benita, Scout, Praeger, Matthew, McDonnell, Michael, David Tom, Heath, Daniel J, Loxham, Matthew, Eason, Robert and Mills, Benjamin (2019) Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi. Environmental Research Communications, 1 (1). (doi:10.1088/2515-7620/ab14c9).

Record type: Article

Abstract

The identification of mixtures of particles in a solution via analysis of scattered light can be a complex task, due to the multiple scattering effects between different sizes and types of particles. Deep learning offers the capability for solving complex problems without the need for a physical understanding of the underlying system, and hence offers an elegant solution. Here, we demonstrate the application of convolutional neural networks for the identification of the concentration of microparticles (silicon dioxide and melamine resin) and the solution salinity, directly from the scattered light. The measurements were carried out in real-time using a Raspberry Pi, light source, camera, and neural network computation, hence demonstrating a portable and low-cost environmental marine sensor.

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More information

Accepted/In Press date: 29 March 2019
e-pub ahead of print date: 29 March 2019
Published date: 30 April 2019

Identifiers

Local EPrints ID: 430807
URI: https://eprints.soton.ac.uk/id/eprint/430807
ISSN: 2515-7620
PURE UUID: da130fb2-80cb-43cf-aef1-72f96f02d82d
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 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: 14 May 2019 16:30
Last modified: 15 May 2019 00:37

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