Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

Jose Delpiano; Gustavo L. Funes; Jaime E. Cisternas; Sebastian Galaz; Jaime A. Anguita

doi:10.1117/12.2529303

Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

Jose Delpiano^*, Gustavo L. Funes, Jaime E. Cisternas, Sebastian Galaz, Jaime A. Anguita

^*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

16 Citas (Scopus)

Resumen

Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.

Idioma original	Inglés
Título de la publicación alojada	Laser Communication and Propagation through the Atmosphere and Oceans VIII
Editores	Jeremy P. Bos, Alexander M. J. van Eijk, Stephen Hammel
Editorial	SPIE
ISBN (versión digital)	9781510629592
DOI	https://doi.org/10.1117/12.2529303
Estado	Publicada - 2019
Evento	Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019 - San Diego, Estados Unidos Duración: 12 ago. 2019 → 14 ago. 2019

Serie de la publicación

Nombre	Proceedings of SPIE - The International Society for Optical Engineering
Volumen	11133
ISSN (versión impresa)	0277-786X
ISSN (versión digital)	1996-756X

Conferencia

Conferencia	Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	12/08/19 → 14/08/19

Nota bibliográfica

Publisher Copyright:
© 2019 SPIE.

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10.1117/12.2529303

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Delpiano, J., Funes, G. L., Cisternas, J. E., Galaz, S., & Anguita, J. A. (2019). Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence. En J. P. Bos, A. M. J. van Eijk, & S. Hammel (Eds.), Laser Communication and Propagation through the Atmosphere and Oceans VIII Artículo 1113305 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11133). SPIE. https://doi.org/10.1117/12.2529303

Delpiano, Jose ; Funes, Gustavo L. ; Cisternas, Jaime E. et al. / Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence. Laser Communication and Propagation through the Atmosphere and Oceans VIII. editor / Jeremy P. Bos ; Alexander M. J. van Eijk ; Stephen Hammel. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{5c817651bb494410a7cc4b62c683c85c,

title = "Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence",

abstract = "Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.",

keywords = "Laser beam transmission, digital image processing, neural networks, optical vortices, pattern recognition",

author = "Jose Delpiano and Funes, {Gustavo L.} and Cisternas, {Jaime E.} and Sebastian Galaz and Anguita, {Jaime A.}",

note = "Funding Information: The authors thankfully acknowledge conversations with F. Bernuy on data augmentation techniques for deep learning. This work was partially funded by the Millennium Institute for Research in Optics (MIRO) and the Advanced Center of Electrical and Electronic Engineering (AC3E) (CONICYT/FB0008). Publisher Copyright: {\textcopyright} 2019 SPIE.; Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019 ; Conference date: 12-08-2019 Through 14-08-2019",

year = "2019",

doi = "10.1117/12.2529303",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Bos, {Jeremy P.} and {van Eijk}, {Alexander M. J.} and Stephen Hammel",

booktitle = "Laser Communication and Propagation through the Atmosphere and Oceans VIII",

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Delpiano, J , Funes, GL , Cisternas, JE, Galaz, S & Anguita, JA 2019, Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence. En JP Bos, AMJ van Eijk & S Hammel (eds.), Laser Communication and Propagation through the Atmosphere and Oceans VIII., 1113305, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11133, SPIE, Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019, San Diego, Estados Unidos, 12/08/19. https://doi.org/10.1117/12.2529303

Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence. / Delpiano, Jose ; Funes, Gustavo L.; Cisternas, Jaime E. et al.
Laser Communication and Propagation through the Atmosphere and Oceans VIII. ed. / Jeremy P. Bos; Alexander M. J. van Eijk; Stephen Hammel. SPIE, 2019. 1113305 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11133).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

AU - Delpiano, Jose

AU - Funes, Gustavo L.

AU - Cisternas, Jaime E.

AU - Galaz, Sebastian

AU - Anguita, Jaime A.

N1 - Funding Information: The authors thankfully acknowledge conversations with F. Bernuy on data augmentation techniques for deep learning. This work was partially funded by the Millennium Institute for Research in Optics (MIRO) and the Advanced Center of Electrical and Electronic Engineering (AC3E) (CONICYT/FB0008). Publisher Copyright: © 2019 SPIE.

PY - 2019

Y1 - 2019

N2 - Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.

AB - Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.

KW - Laser beam transmission

KW - digital image processing

KW - neural networks

KW - optical vortices

KW - pattern recognition

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U2 - 10.1117/12.2529303

DO - 10.1117/12.2529303

M3 - Conference contribution

AN - SCOPUS:85076922579

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Laser Communication and Propagation through the Atmosphere and Oceans VIII

A2 - Bos, Jeremy P.

A2 - van Eijk, Alexander M. J.

A2 - Hammel, Stephen

PB - SPIE

T2 - Laser Communication and Propagation through the Atmosphere and Oceans VIII 2019

Y2 - 12 August 2019 through 14 August 2019

ER -

Delpiano J , Funes GL , Cisternas JE, Galaz S, Anguita JA. Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence. En Bos JP, van Eijk AMJ, Hammel S, editores, Laser Communication and Propagation through the Atmosphere and Oceans VIII. SPIE. 2019. 1113305. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2529303

Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

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