Classifying beams carrying orbital angular momentum with machine learning: tutorial

Aero-optical effects, part II. Sources of aberrations: tutorial

This paper serves as part II of a two-part tutorial on "aero-optical effects." In part I, we provide introductory material with an emphasis on system-level considerations, particularly for those who are new to the field of aero-optics. In part II, we move on to survey several sources of aberrations. For example, we cover foundational sources like boundary layers and shear layers, as well as miscellaneous sources like mechanical contamination, shock waves, and aero acoustics. Throughout part II, we emphasize drivers for system-level performance, which appropriately builds on the system-level considerations covered in part I. This emphasis will inform future efforts looking to develop airborne-laser systems flying at subsonic, supersonic, and hypersonic speeds.

Aero-optical effects, part I. System-level considerations: tutorial

This paper serves as part I of a two-part tutorial on "aero-optical effects." We first present background information to assist with our introduction of the topic. Next, we use the aerodynamic environment associated with a hemisphere-on-cylinder beam director to decompose the resulting aberrations (that arise due to aero-optical effects) in terms of piston, tilt, and higher-order phase errors. We also discuss the performance implications that these phase errors have on airborne-laser systems. Recognizing the complexity of these environments, we then discuss how one measures these phase errors using standard wavefront-sensing approaches and the impact these phase errors have on imaging performance. These system-level considerations provide the material needed to survey several sources of aberrations such as boundary layers and shear layers, as well as mechanical contamination, shock waves, and aero-acoustics-all of which we cover in part II of this two-part tutorial.

Machine-learning-assisted orbital angular momentum recognition using nanostructures

The recognition of orbital angular momentum (OAM) in light beams holds significant importance in optical communication. The majority of current OAM recognition techniques are highly sensitive to stringent alignment issues. The speckle-based OAM recognition method reported in J. Opt. Soc. Am. A39, 759 (2022)JOAOD61084-752910.1364/JOSAA.446352 is alignment-free in the transverse direction of light propagation and has been shown to operate successfully in the far-field region using macrostructures. This study introduces a proof-of-concept for speckle-learned OAM recognition with nanostructures, relaxing the strict alignment requirements in both the transverse and along the direction of light propagation. When the OAM beam interacts with random inhomogeneities at micron and/or nanoscale, it generates an OAM speckle field. Initially, a comprehensive examination of the dynamic evolution of OAM speckle fields, ranging from near field to far field, has been conducted using a ground glass diffuser, featuring random phase inhomogeneities at the micron scale. Subsequently, the investigation proceeds to randomly grown ZnO nanosheets on an aluminum substrate. To achieve rapid and precise OAM recognition, a tailored three-layer CNN is trained and tested on OAM speckle fields ranging from near field to far field to attain an accuracy surpassing 92%. This research expands the technique's applicability, enabling recognition of OAM across near-field to far-field regimes, while leveraging micro- to nanostructures.

Neural network classification of beams carrying orbital angular momentum after propagating through controlled experimentally generated optical turbulence

We generate an alphabet of spatially multiplexed Laguerre-Gaussian beams carrying orbital angular momentum, which are demultiplexed at reception by a convolutional neural network (CNN). In this investigation, a methodology for optimizing alphabet design for best classification rates is proposed, and three 256-symbol alphabets are designed for performance evaluation in optical turbulence. The beams were propagated in three environments: through underwater optical turbulence generated by Rayleigh-Bénard (RB) convection (C n2≅10-11 m -2/3), through a simulated propagation path derived from the Nikishov spectrum (C n2≅10-13 m -2/3), and through optical turbulence from a thermal point source located in a water tank (C n2≅10-10 m -2/3). We report a classification accuracy of 93.1% for the RB environment, 99.99% in simulation, and 48.5% in the point source environment. The project demonstrates that the CNN can classify the complex alphabet symbols in a practical turbulent flow that exhibits strong optical turbulence, provided sufficient training data is available and testing data is representative of the specific environment. We find the most important factor in a high classification accuracy is a diversification in the intensity profiles of the alphabet symbols.

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.

Welcoming 2024: editorial

Editor-in-Chief Olga Korotkova summarizes the Journal's progress in 2023, outlines near-future plans, and introduces the editors who recently joined the board.