Deep-learning-enhanced ice thickness measurement using Raman scattering

Abstract

In ice thickness measurement (ICM) procedures based on Raman scattering, a key issue is the detection of ice–water interface using the slight difference between the Raman spectra of ice and water. To tackle this issue, we developed a new deep residual network (DRN) to cast this detection as an identification problem. Thus, the interface detection is converted to the prediction of the Raman spectra of ice and water. We enabled this process by designing a powerful DRN that was trained by a set of Raman spectral data, obtained in advance. In contrast to the state-of-the-art Gaussian fitting method (GFM), the proposed DRN enables ICM with a simple operation and low costs, as well as high accuracy and speed. Experimental results were collected to demonstrate the feasibility and effectiveness of the proposed DRN.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

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GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration

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