Solution Classification With Portable Smartphone-Based Spectrometer System Under Variant Shooting Conditions by Using Convolutional Neural Network

被引：5

作者：

Kong, Weichao ^{[1
]}

Kuang, Dengfeng ^{[1
]}

Wen, Yuxiang ^{[1
]}

Zhao, Mengxian ^{[1
]}

Huang, Jinhui ^{[2
]}

Yang, Chen ^{[2
]}

机构：

[1] Nankai Univ, Inst Modern Opt, Tianjin Key Lab Microscale Opt Informat Sci & Tec, Tianjin 300350, Peoples R China

[2] Nankai Univ, Coll Environm Sci & Engn, Sino Canada Joint R&D Ctr Water & Environm Safety, Tianjin 300350, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2020年 / 20卷 / 15期

基金：

中国国家自然科学基金;

关键词：

Ions; Cameras; Convolution; Optical sensors; Reliability; Mobile handsets; CNNs; portable smartphone spectrometer; spectral classification; PHOSPHATE; WATER; PLATFORM; CNN;

D O I：

10.1109/JSEN.2020.2983733

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The rapid development of smartphone-based devices has facilitated on-site measurements, which approach to laboratory-level instrument performance. However, the reliability of these instruments is inevitably limited by the external environment factors. Here, we report on a portable smartphone spectrometer without any external power supply. Using this spectrometer to analyze the diffractive patterns of multiple samples under variant shooting conditions, we verify the influence of external parameters on spectrum. Convolutional neural network is proposed here to perform solution classification under variant shooting conditions and the classification prediction accuracy is more than 99.5%.

引用

页码：8789 / 8796

页数：8

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