Smart Machine Vision for Universal Spatial Mode Reconstruction
Jos\'e D. Huerta-Morales, Chenglong You, Omar S. Maga\~na-Loaiza, and Shi-Hai Dong, Roberto de J. Le\'on-Montiel, Mario A., Quiroz-Ju\'arez

TL;DR
This paper introduces a low-power image sensor that acts as an artificial neural network to detect and reconstruct distorted orbital angular momentum beams, enhancing communication systems with efficient, light-based solutions.
Contribution
A novel low-cost, low-power image sensor functions as an AI neural network for real-time reconstruction of distorted OAM beams, reducing processing time and energy consumption.
Findings
Achieved 95% reconstruction efficiency for various OAM modes.
Demonstrated successful reconstruction of hybrid non-orthogonal modes.
Showed potential for low-power, light-based communication devices.
Abstract
Structured light beams, in particular those carrying orbital angular momentum (OAM), have gained a lot of attention due to their potential for enlarging the transmission capabilities of communication systems. However, the use of OAM-carrying light in communications faces two major problems, namely distortions introduced during propagation in disordered media, such as the atmosphere or optical fibers, and the large divergence that high-order OAM modes experience. While the use of non-orthogonal modes may offer a way to circumvent the divergence of high-order OAM fields, artificial intelligence (AI) algorithms have shown promise for solving the mode-distortion issue. Unfortunately, current AI-based algorithms make use of large-amount data-handling protocols that generally lead to large processing time and high power consumption. Here we show that a low-power, low-cost image sensor can…
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Taxonomy
TopicsOrbital Angular Momentum in Optics · Optical Wireless Communication Technologies · Optical Polarization and Ellipsometry
