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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 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 originalInglés
Título de la publicación alojadaLaser Communication and Propagation through the Atmosphere and Oceans VIII
EditoresJeremy P. Bos, Alexander M. J. van Eijk, Stephen Hammel
EditorialSPIE
ISBN (versión digital)9781510629592
DOI
EstadoPublicada - 2019
EventoLaser Communication and Propagation through the Atmosphere and Oceans VIII 2019 - San Diego, Estados Unidos
Duración: 12 ago 201914 ago 2019

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen11133
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

ConferenciaLaser Communication and Propagation through the Atmosphere and Oceans VIII 2019
País/TerritorioEstados Unidos
CiudadSan Diego
Período12/08/1914/08/19

Nota bibliográfica

Publisher Copyright:
© 2019 SPIE.

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