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

Research output: Contribution to conferencePaper

2 Scopus citations

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.
Original languageAmerican English
DOIs
StatePublished - 1 Jan 2019
EventProceedings of SPIE - The International Society for Optical Engineering -
Duration: 1 Jan 2019 → …

Conference

ConferenceProceedings of SPIE - The International Society for Optical Engineering
Period1/01/19 → …

Keywords

  • digital image processing
  • Laser beam transmission
  • neural networks
  • optical vortices
  • pattern recognition

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