Photonic Monte Carlo Analysis and Deep Learning Predicting the Performance of Non-Invasive Glucose Detection Using Compact NIR Spectrometers-Reference-Cited by-同舟云学术

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Photonic Monte Carlo Analysis and Deep Learning Predicting the Performance of Non-Invasive Glucose Detection Using Compact NIR Spectrometers

Published:2021-12-05 Issue: Volume: Page:
ISSN:
Container-title:2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)
language:
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Author:

Khalil Karim D.¹,Zeyad Lama²,Maher Abdullah³,Othman Ahmed M.¹,Sabry Yasser M.¹

Affiliation:

1. Ain Shams University,Faculty of Engineering,Electronics and Communications Engineering Department,Cairo,Egypt

2. Ain Shams University,Faculty of Engineering,Computer and Systems Engineering Department,Cairo,Egypt

3. Benha University,Faculty of Engineering Shoubra,Communications and Electronics Engineering Department,Cairo,Egypt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9693993/9694107/09694263.pdf?arnumber=9694263

Reference22 articles.

1. All‐Silicon Double‐Cavity Fourier‐Transform Infrared Spectrometer On‐Chip

2. On-Chip Micro–Electro–Mechanical System Fourier Transform Infrared (MEMS FT-IR) Spectrometer-Based Gas Sensing

3. Correlation between blood glucose concentration in diabetics and noninvasively measured tissue optical scattering coefficient

4. Scattering-independent glucose absorption measurement using a spectrally resolved reflectance setup with specialized variable source-detector separations

5. Semi-Analytical Effective Layer Model for the Skin in the SWIR Spectral Range

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optical Modeling and Characterization of Lipid Emulsion Skin Phantoms for SWIR Diffuse Reflectance Spectroscopy;2022 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS);2022-06-06

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