699 research outputs found
Harnessing the power of complex light propagation in multimode fibers for spatially resolved sensing
The propagation of coherent light in multimode optical fibers results in a
speckled output that is both complex and sensitive to environmental effects.
These properties can be a powerful tool for sensing, as small perturbations
lead to significant changes in the output of the fiber. However, the mechanism
to encode spatially resolved sensing information into the speckle pattern and
the ability to extract this information is thus far unclear. In this paper, we
demonstrate that spatially dependent mode coupling is crucial to achieving
spatially resolved measurements. We leverage machine learning to quantitatively
extract this spatially resolved sensing information from three fiber types with
dramatically different characteristics and demonstrate that the fiber with the
highest degree of spatially dependent mode coupling provides the greatest
accuracy.Comment: 17 pages and 7 figure
Robust Super-Resolution Imaging Based on a Ring Core Fiber with Orbital Angular Momentum
Single fiber imaging technology offers unique insights for research and
inspection in difficult to reach and narrow spaces. In particular,
ultra-compact multimode fiber (MMF) imaging, has received increasing interest
over the past decade. However, MMF imaging will be seriously distorted when
subjected to dynamic perturbations due to time-varying mode coupling, and the
imaging of space objects via Gaussian beam will be relatively degraded at the
edge due to insufficient contrast. Here, a robust super-resolution imaging
method based on a ring core fiber (RCF) with orbital angular momentum (OAM) has
been proposed and experimentally demonstrated. The OAM modes propagating in the
RCF form a series of weakly-coupled mode groups, making our imaging system
robust to external perturbations. In addition, a spiral phase plate is used as
a vortex filter to produce OAM for edge enhancement, thus improving the image
resolution. Furthermore, a few-shot U-Transformer neural network is proposed to
enhance the resilience of the developed RCF-OAM imaging system against
environmental perturbations. Finally, the developed RCF-OAM imaging system
achieves biological image transmission, demonstrating the practicality of our
scheme. This pioneering RCF OAM imaging system may have broad applications,
potentially revolutionising fields such as biological imaging and industrial
non-destructive testing
Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning
Retrieving images transmitted through multi-mode fibers is of growing
interest, thanks to their ability to confine and transport light efficiently in
a compact system. Here, we demonstrate machine-learning-based decoding of
large-scale digital images (pages), maximizing page capacity for optical
storage applications. Using a millimeter-sized square cross-section waveguide,
we image an 8-bit spatial light modulator, presenting data as a matrix of
symbols. Normally, decoders will incur a prohibitive O(n^2) computational
scaling to decode n symbols in spatially scrambled data. However, by combining
a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using
efficient convolutional operations only. We compare trainable ray-tracing-based
with eigenmode-based twins and show the former to be superior thanks to its
ability to overcome the simulation-to-experiment gap by adjusting to optical
imperfections. We train the pipeline end-to-end using a differentiable
mutual-information estimator based on the von-Mises distribution, generally
applicable to phase-coding channels.Comment: 21 pages, 5 figure
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Machine learning approach for computing optical properties of a photonic crystal fiber
Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared
Intelligent self calibration tool for adaptive few-mode fiber multiplexers using multiplane light conversion
Space division multiplexing (SDM) is promising to enhance capacity limits of optical networks. Among implementation options, few-mode fibres (FMFs) offer high efficiency gains in terms of integratability and throughput per volume. However, to achieve low insertion loss and low crosstalk, the beam launching should match the fiber modes precisely. We propose an all-optical data-driven technique based on multiplane light conversion (MPLC) and neural networks (NNs). By using a phase-only spatial light modulator (SLM), spatially separated input beams are transformed independently to coaxial output modes. Compared to conventional offline calculation of SLM phase masks, we employ an intelligent two-stage approach that considers knowledge of the experimental environment significantly reducing misalignment. First, a single-layer NN called Model-NN learns the beam propagation through the setup and provides a digital twin of the apparatus. Second, another single-layer NN called Actor-NN controls the model. As a result, SLM phase masks are predicted and employed in the experiment to shape an input beam to a target output. We show results on a single-passage configuration with intensity-only shaping. We achieve a correlation between experiment and network prediction of 0.65. Using programmable optical elements, our method allows the implementation of aberration correction and distortion compensation techniques, which enables secure high-capacity long-reach FMF-based communication systems by adaptive mode multiplexing devices
Single-ended Recovery of Optical fiber Transmission Matrices using Neural Networks
Ultra-thin multimode optical fiber imaging technology promises
next-generation medical endoscopes that provide high image resolution deep in
the body (e.g. blood vessels, brain). However, this technology suffers from
severe optical distortion. The fiber's transmission matrix (TM) calibrates for
this distortion but is sensitive to bending and temperature so must be measured
immediately prior to imaging, i.e. \emph{in vivo} and thus with access to a
single end only. We present a neural network (NN)-based approach that quickly
reconstructs transmission matrices based on multi-wavelength reflection-mode
measurements. We introduce a custom loss function insensitive to global
phase-degeneracy that enables effective NN training. We then train two
different NN architectures, a fully connected NN and convolutional U-Net, to
reconstruct complex-valued fiber TMs through a simulated
single-ended optical fiber with error. This enables image
reconstruction with error. This TM recovery approach shows
advantages compared to conventional TM recovery methods: 4500 times faster;
robustness to 6\% fiber perturbation during characterization; operation with
non-square TMs and no requirement for prior characterization of reflectors.Comment: 13 pages, 9 figure
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