• Title/Summary/Keyword: Multicore/multimode optical fiber

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Concentric Core Fiber Design for Optical Fiber Communication

  • Nadeem, Iram;Choi, Dong-You
    • Journal of information and communication convergence engineering
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    • v.14 no.3
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    • pp.163-170
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    • 2016
  • Because of rapid technological advancements, increased data rate support has become the key criterion for future communication medium selection. Multimode optical fibers and multicore optical fibers are well matched to high data rate throughput requirements because of their tendency to support multiple modes through one core at a time, which results in higher data rates. Using the numerical mode solver OptiFiber, we have designed a concentric core fiber by investigating certain design parameters, namely core diameter (µm), wavelength (nm), and refractive index profile, and as a result, the number of channels, material losses, bending losses, polarization mode dispersion, and the effective nonlinear refractive index have been determined. Space division multiplexing is a promising future technology that uses few-mode fibers in parallel to form a multicore fiber. The experimental tests are conducted using the standard second window wavelength of 1,550 nm and simulated results are presented.

Deep Learning: High-quality Imaging through Multicore Fiber

  • Wu, Liqing;Zhao, Jun;Zhang, Minghai;Zhang, Yanzhu;Wang, Xiaoyan;Chen, Ziyang;Pu, Jixiong
    • Current Optics and Photonics
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    • v.4 no.4
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    • pp.286-292
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    • 2020
  • Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.