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. Soc. Am. A39, 759 (2022)10.1364/JOSAA.446352], the proposed 1D speckle-based method utilizes only a 1D array (1×n pixels) of the structured light speckle pattern image (n × n pixels). This drastically reduces the computational cost, since the required data is reduced by a factor of 1/n. A custom-designed 1D convolutional neural network (1D-CNN) with only 2.4 k learnable parameters is trained and tested on 1D structured light speckle arrays for fast and accurate recognition. A comparative study is carried out between 2D speckle-based and 1D speckle-based array recognition techniques comparing the data size, training time, and accuracy. For a proof-of-concept for the 1D speckle-based structured light recognition, we have established a 3-bit free-space communication channel by employing structured light-shift keying. The trained 1D CNN has successfully decoded the encoded 3-bit gray image with an accuracy of 94%. Additionally, our technique demonstrates robust performance under noise variation showcasing its deployment in practical cost-effective real-world applications.

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 L G ml and H G pq beams, respectively, among all three channels. It demonstrates the directional independence and broadcasting capability of speckle-based decoding (SBD) in FSO communication using structured light. Further, we have extended the study from 2D to 1D SBD in one-to-three NLOS FSO communication channels to decrease the computational cost and to emphasize the importance of the 1D SBD approach.

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%. We have trained and tested our method against the influence of atmospheric turbulence by training the models with perturbed LG, HG, and superposition modes and found that models are still able to classify modes with an accuracy >98%. We have also trained and tested our model with experimental speckle images of LG modes generated by three different ground glasses. We have achieved a maximum accuracy of 96% for the most robust case, where the model is trained with all simulated and experimental data. The novelty of the technique is that one can do the mode classification just by using a small portion of the speckle fields because speckle grains contain the information about the original mode, thus eliminating the need for capturing the whole modal field, which is modal dependent.