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Fiber-Based Optical Data Processing
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Fiber-Based Optical Data Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 9986

Special Issue Editor

Prof. Dr. Zeev Zalevsky

E-Mail Website
Guest Editor
Faculty of Engineering, Bar-Ilan University, 52900 Ramat-Gan, Israel
Interests: super resolution; bio-photonics; electro-optical nano devices; silicon photonics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The typical usage of fibers is in the transmission of information during optics communication or in performing imaging in fiber based endoscopes. However, recently, the usage of fiber in fields related to optical data processing was also demonstrated. Specifically single mode, multi mode, and multi core fibers were used in the realization of neural networks and in training/inference optical modules. The implementation of fiber-based optics in these directions is highly applicable, as it may use the already evolved infrastructure of optics communication in order to develop a good interface between the optical data processing unit and the external, usually electronic, environment.

In this Special Issue, we aim to provide a designated publishing platform for fiber-based optical data processing research, focusing on the integration of various types of fibers in optical data processing modalities, especially those based on artificial neural networks while including research work emphasizing the various possible fiber-based processing architectures, implementations, applications, and configurations. We invite researchers, investigators, and engineers working in this field to contribute original research or review articles. Papers on the latest technological developments and research breakthroughs using fibers for photonic data processing and computing are especially welcome.

Prof. Zeev Zalevsky
Guest Editor

Manuscript Submission Information

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Keywords

  • Fiber-based optical computing and data processing
  • Fiber-based fourier optics
  • Fiber-based artificial neural networks
  • Fiber-based deep learning architectures
  • Optical training/inference fiber-based architectures
  • Single/multi mode and single/multi core fibers processing architectures
  • Fiber-based optical storage
  • Fiber-based quantum computing
  • Processing of data from distributed fiber optics sensors

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Published Papers (3 papers)

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9 pages, 2836 KiB  
Article
Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift
by Eirini Kakkava, Navid Borhani, Babak Rahmani, Uğur Teğin, Christophe Moser and Demetri Psaltis
Appl. Sci. 2020, 10(11), 3816; https://doi.org/10.3390/app10113816 - 30 May 2020
Cited by 20 | Viewed by 4036
Abstract
Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along [...] Read more.
Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along its length is highly sensitive to wavelength changes. We use a tunable laser to implement a wavelength drift with a controlled bandwidth, aiming to estimate the DNN’s performance in different cases and identify the limitations. We find that when the DNNs are trained with a dataset which includes the noise induced by wavelength changes, successful classification of a speckle pattern can be performed even for a large wavelength bandwidth drift. A single training step is found to be sufficient for high classification accuracy, removing the need for time-consuming recalibration at each wavelength. Full article
(This article belongs to the Special Issue Fiber-Based Optical Data Processing)
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9 pages, 907 KiB  
Article
Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
by Sagie Asraf, Benjamin Lengenfelder, Michael Schmidt and Zeev Zalevsky
Appl. Sci. 2020, 10(2), 664; https://doi.org/10.3390/app10020664 - 17 Jan 2020
Viewed by 2007
Abstract
We propose a novel technique for measurements of Brillouin acoustic vibrations based on temporal tracking of back-reflected speckle patterns. The proposed method holds the potential to enhance some of the limiting factors in Brillouin frequency measurements while yielding increased spatial resolution and shorter [...] Read more.
We propose a novel technique for measurements of Brillouin acoustic vibrations based on temporal tracking of back-reflected speckle patterns. The proposed method holds the potential to enhance some of the limiting factors in Brillouin frequency measurements while yielding increased spatial resolution and shorter scanning times of the inspected fiber. Experimental results show the capabilities of the proposed method are presented, using a two pump-waves configuration. Full article
(This article belongs to the Special Issue Fiber-Based Optical Data Processing)
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9 pages, 2761 KiB  
Article
In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain
by Feng Wan, Baojian Wu, Feng Wen and Kun Qiu
Appl. Sci. 2019, 9(24), 5438; https://doi.org/10.3390/app9245438 - 11 Dec 2019
Cited by 1 | Viewed by 3291
Abstract
We propose an in-band measurement method of optical signal-to-noise ratio (OSNR) output from an all-optical regeneration system with a nonlinear power transfer function (PTF) according to the fact that there are different average gains of signal and noise. For the all-optical quadrature phase-shift [...] Read more.
We propose an in-band measurement method of optical signal-to-noise ratio (OSNR) output from an all-optical regeneration system with a nonlinear power transfer function (PTF) according to the fact that there are different average gains of signal and noise. For the all-optical quadrature phase-shift keying (QPSK) regenerator as an example, the output OSNR is derived from the input OSNR and the total gain of the degraded QPSK signal. Our simulation shows that the OSNR results obtained by this method are in agreement with those calculated from the error vector magnitude (EVM) formula. The method presented here has good applicability for different data rates but is also useful for analyzing the OSNR degradation of other nonlinear devices in optical communication links. Full article
(This article belongs to the Special Issue Fiber-Based Optical Data Processing)
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