Quantitative strain sensing in a multimode fiber using dual frequency speckle pattern tracking

2020 ◽  
Vol 45 (6) ◽  
pp. 1309
Author(s):  
Matthew J. Murray ◽  
Brandon Redding
2020 ◽  
Vol 10 (11) ◽  
pp. 3816
Author(s):  
Eirini Kakkava ◽  
Navid Borhani ◽  
Babak Rahmani ◽  
Uğur Teğin ◽  
Christophe Moser ◽  
...  

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.


2019 ◽  
Vol 27 (20) ◽  
pp. 28494 ◽  
Author(s):  
Matthew J. Murray ◽  
Allen Davis ◽  
Clay Kirkendall ◽  
Brandon Redding

1996 ◽  
Vol 21 (11) ◽  
pp. 785 ◽  
Author(s):  
D. Z. Anderson ◽  
M. A. Bolshtyansky ◽  
B. Ya. Zel’dovich

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