期刊
APPLIED SCIENCES-BASEL
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/app10113816
关键词
multimode fibers; deep learning; image classification; fiber-based optical computing; wavelength drift
类别
资金
- Swiss program: CEPF SFA
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.
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