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.

Randomness of optical turbulence generated by Rayleigh-Bénard convection using intensity statistics.

The experimental study of optical turbulence proves difficult due to challenges in generating controllable conditions in a laboratory environment. Confined water tanks that produce Rayleigh-Bénard (RB) convection are one method to generate optical turbulence using a controllable temperature gradient. It is of utmost concern to quantify the properties of the optical turbulence generated for characterization of other optical applications such as imaging, sensing, or communications. In this experimental study a Gaussian beam is propagated through a RB water tank where two intensity measurements are made at the receiver's pupil and focal plane. The pupil and focal plane results include quantification of the intensity fluctuation distribution, scintillation distribution, and refractive index structure constant at various values of the temperature gradient. The angle of arrival fluctuations is also calculated at the focal plane to obtain a second estimate of C n2. The pupil plane estimate for C n2 using scintillation index and focal plane angle of arrival fluctuations is compared to preliminary predictions of C n2 as a function of RB temperature gradient showing C n2âˆ¼Δ T 4/3. The outcomes of the study confirm that the RB process produces intensity fluctuations that follow gamma-gamma and log-normal probability density functions. Estimates of the refractive index structure constant C n2 produce the same trends with different magnitudes when measured from the pupil and focal plane.

Structured illumination and image enhancement of three-dimensional and moving objects at a distance via incoherent Fourier ptychography.

We experimentally apply incoherent Fourier ptychography to enhance the resolution of recorded images by projecting known, uncorrelated, random patterns at high speed onto 3D moving and distant objects. We find that the resolution enhancement factor can be greater than 2, depending on the projection and camera optics.

Synchronous optical intensity and phase measurements to characterize Rayleigh-Bénard convection.

Propagation of a laser beam through the Rayleigh-Bénard (RB) convection is experimentally investigated using synchronous optical wavefront and intensity measurements. Experimental results characterize the turbulence strength and length scales, which are used to inform numerical wave optic simulations employing phase screens. Experimentally found parameters are the refractive index structure constant, mean flow rate, kinetic and thermal dissipation rates, Kolmogorov microscale, outer scale, and shape of the refractive index power spectrum using known models. Synchronization of the wavefront and intensity measurements provide statistics of each metric at the same instance in time, allowing for two methods of comparison with numerical simulations. Numerical simulations prove to be within agreement of experimental and published results. Synchronized measurements provided more insight to develop reliable propagation models. It is determined that the RB test bed is applicable for simulating realistic undersea environments.

Transport-based pattern recognition versus deep neural networks in underwater OAM communications.

Comparisons between machine learning and optimal transport-based approaches in classifying images are made in underwater orbital angular momentum (OAM) communications. A model is derived that justifies optimal transport for use in attenuated water environments. OAM pattern demultiplexing is performed using optimal transport and deep neural networks and compared to each other. Additionally, some of the complications introduced by signal attenuation are highlighted. The Radon cumulative distribution transform (R-CDT) is applied to OAM patterns to transform them to a linear subspace. The original OAM images and the R-CDT transformed patterns are used in several classification algorithms, and results are compared. The selected classification algorithms are the nearest subspace algorithm, a shallow convolutional neural network (CNN), and a deep neural network. It is shown that the R-CDT transformed images are more accurate than the original OAM images in pattern classification. Also, the nearest subspace algorithm performs better than the selected CNNs in OAM pattern classification in underwater environments.

Machine learning-aided classification of beams carrying orbital angular momentum propagated in highly turbid water: publisher's note.

This publisher's note corrects the name of an author of J. Opt. Soc. Am. A37, 1662 (2020)JOAOD60740-323210.1364/JOSAA.401153.

Machine learning-aided classification of beams carrying orbital angular momentum propagated in highly turbid water.

A set of laser beams carrying orbital angular momentum is designed with the objective of establishing an effective underwater communication link. Messages are constructed using unique Laguerre-Gauss beams, which can be combined to represent four bits of information. We report on the experimental results where the beams are transmitted through highly turbid water, reaching approximately 12 attenuation lengths. We measured the signal-to-noise ratio in each test scenario to provide characterization of the underwater environment. A convolutional neural network was developed to decode the received images with the objective of successfully classifying messages quickly. We demonstrate near-perfect classification in all scenarios, provided the training set includes some images taken under the same underwater conditions.

Designing laser beams carrying OAM for a high-performance underwater communication system.

We present a design methodology for creating a distinct laser beam set suitable for detection by using only the recorded intensity pattern. We consider four coherent Laguerre-Gaussian beams carrying orbital angular momentum (OAM) to form the basis for optical communication. The complex electric fields of the beams are superimposed to create 16 dissimilar intensity patterns. The presented beam set design method considers the beam generation hardware limitations and aims to minimize the correlation among the messages and maximize their intensity differences. After propagating the 16 messages through a water channel, we measured high correlation, intensity similarity, and R-squared values for the identical messages and low values for the different ones. Distinct clustering between the measurements for the matching messages and the rest allows us to set a threshold in the gap among the groupings and successfully classify the received images.