Large-scale remotely interrogated arrays of fiber-optic interferometric sensors for underwater acoustic applications

2003 ◽  
Vol 3 (1) ◽  
pp. 19-30 ◽  
Author(s):  
G.A. Cranch ◽  
P.J. Nash ◽  
C.K. Kirkendall
Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Wei Shi ◽  
Ye Tian ◽  
Antoine Gervais

AbstractThe tremendous growth of data traffic has spurred a rapid evolution of optical communications for a higher data transmission capacity. Next-generation fiber-optic communication systems will require dramatically increased complexity that cannot be obtained using discrete components. In this context, silicon photonics is quickly maturing. Capable of manipulating electrons and photons on the same platform, this disruptive technology promises to cram more complexity on a single chip, leading to orders-of-magnitude reduction of integrated photonic systems in size, energy, and cost. This paper provides a system perspective and reviews recent progress in silicon photonics probing all dimensions of light to scale the capacity of fiber-optic networks toward terabits-per-second per optical interface and petabits-per-second per transmission link. Firstly, we overview fundamentals and the evolving trends of silicon photonic fabrication process. Then, we focus on recent progress in silicon coherent optical transceivers. Further scaling the system capacity requires multiplexing techniques in all the dimensions of light: wavelength, polarization, and space, for which we have seen impressive demonstrations of on-chip functionalities such as polarization diversity circuits and wavelength- and space-division multiplexers. Despite these advances, large-scale silicon photonic integrated circuits incorporating a variety of active and passive functionalities still face considerable challenges, many of which will eventually be addressed as the technology continues evolving with the entire ecosystem at a fast pace.


Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2528 ◽  
Author(s):  
Hiroshi Yamazaki ◽  
Ichiro Kurose ◽  
Michiko Nishiyama ◽  
Kazuhiro Watanabe

In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes, in addition to immunity to electromagnetic interference and chemical corrosions. Thus, the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within (−6°, 4°), and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.


2021 ◽  
Author(s):  
Benjamin Schwarz ◽  
Korbinian Sager ◽  
Philippe Jousset ◽  
Gilda Currenti ◽  
Charlotte Krawczyk ◽  
...  

<p><span>Fiber-optic cables form an integral part of modern telecommunications infrastructure and are ubiquitous in particular in regions where dedicated seismic instrumentation is traditionally sparse or lacking entirely. Fiber-optic seismology promises to enable affordable and time-extended observations of earth and environmental processes at an unprecedented temporal and spatial resolution. The method’s unique potential for combined large-N and large-T observations implies intriguing opportunities but also significant challenges in terms of data storage, data handling and computation.</span></p><p><span>Our goal is to enable real-time data enhancement, rapid signal detection and wave field characterization without the need for time-demanding user interaction. We therefore combine coherent wave field analysis, an optics-inspired processing framework developed in controlled-source seismology, with state-of-the-art deep convolutional neural network (CNN) architectures commonly used in visual perception. While conventional deep learning strategies have to rely on manually labeled or purely synthetic training datasets, coherent wave field analysis labels field data based on physical principles and enables large-scale and purely data-driven training of the CNN models. The shear amount of data already recorded in various settings makes artificial data generation by numerical modeling superfluous – a task that is often constrained by incomplete knowledge of the embedding medium and an insufficient description of processes at or close to the surface, which are challenging to capture in integrated simulations.</span></p><p><span>Applications to extensive field datasets acquired with dark-fiber infrastructure at a geothermal field in SW Iceland and in a town at the flank of Mt Etna, Italy, reveal that the suggested framework generalizes well across different observational scales and environments, and sheds new light on the origin of a broad range of physically distinct wave fields that can be sensed with fiber-optic technology. Owing to the real-time applicability with affordable computing infrastructure, our analysis lends itself well to rapid on-the-fly data enhancement, wave field separation and compression strategies, thereby promising to have a positive impact on the full processing chain currently in use in fiber-optic seismology.</span></p>


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