Department of Electronics Engineering, Kwangwoon University, Seoul 01897, Korea.
Department of Physics, University of Seoul, Seoul 02504, Korea.
Sensors (Basel). 2021 Feb 8;21(4):1186. doi: 10.3390/s21041186.
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.
太赫兹成像和时域光谱学已广泛应用于各种生物医学和工程领域的测试样品特性的描述。这些任务中的许多都需要分析所获得的太赫兹信号以提取嵌入式信息，这可以通过机器学习来实现。最近，机器学习技术发展迅速，许多新的学习模型和学习算法已经被研究。因此，结合最先进的机器学习技术，太赫兹应用可以实现使用机器学习时代之前的建模技术无法实现的高性能。在这篇综述中，我们介绍了机器学习的概念和基本机器学习技术，并研究了性能评估的方法。然后，我们总结了使用机器学习进行的太赫兹成像和时域光谱学的代表性实例。
