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Neural Network for Optical Pathlength Demodulation in Wavelength Shifting Interferometry

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Abstract

Current demodulation algorithms in wavelength shifting interferometry fail to reach the theoretical sensitivity limit set by the Cramér-Rao bound. Simulations show that neural networks improve upon existing techniques and approach this limit.

© 2019 The Author(s)

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