Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy

被引:67
作者
Park, Hochong [1 ]
Son, Joo-Hiuk [2 ]
机构
[1] Kwangwoon Univ, Dept Elect Engn, Seoul 01897, South Korea
[2] Univ Seoul, Dept Phys, Seoul 02504, South Korea
基金
新加坡国家研究基金会;
关键词
terahertz imaging; terahertz time-domain spectroscopy; machine learning; classification; regression; supervised learning; feature extraction; CONVOLUTIONAL NEURAL-NETWORK; TRAUMATIC BRAIN-INJURY; TERAHERTZ SPECTROSCOPY; WATER-CONTENT; LIVER-INJURY; CLASSIFICATION; RECOGNITION; CANCER; INFORMATION; REGRESSION;
D O I
10.3390/s21041186
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields. Many of these tasks require the analysis of acquired terahertz signals to extract embedded information, which can be achieved using machine learning. Recently, machine learning techniques have developed rapidly, and many new learning models and learning algorithms have been investigated. Therefore, combined with state-of-the-art machine learning techniques, terahertz applications can be performed with high performance that cannot be achieved using modeling techniques that precede the machine learning era. In this review, we introduce the concept of machine learning and basic machine learning techniques and examine the methods for performance evaluation. We then summarize representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.
引用
收藏
页码:1 / 25
页数:25
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