1D speckle-learned structured light recognition.

In this Letter, we introduce a novel, to the best of our knowledge, structured light recognition technique based on the 1D speckle information to reduce the computational cost. Compared to the 2D speckle-based recognition [J. Opt.

Speckle-based structured light shift-keying for non-line-of-sight optical communication.

We report an experimental proof of concept for speckle-based one-to-three non-line-of-sight (NLOS) free space optical (FSO) communication channels employing structured light shift-keying. A 3-bit gray image of resolution 100×100 pixels is encoded in Laguerre-Gaussian or Hermite-Gaussian beams and decoded using their respective intensity speckle patterns via trained 1D convolutional neural network. We have achieved an average classification accuracy of 96% and 93% using and beams, respectively, among all three channels.

Speckle-based deep learning approach for classification of orbital angular momentum modes.

We present a speckle-based deep learning approach for orbital angular momentum (OAM) mode classification. In this method, we have simulated the speckle fields of the Laguerre-Gauss (LG), Hermite-Gauss (HG), and superposition modes by multiplying these modes with a random phase function and then taking the Fourier transform. The intensity images of these speckle fields are fed to a convolutional neural network (CNN) for training a classification model that classifies modes with an accuracy >99.