Machine learning techniques for thz imaging and time-domain spectroscopy

Hochong Park, Joo Hiuk Son

Research output: Contribution to journalReview articlepeer-review

51 Scopus citations

Abstract

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 repre-sentative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.

Original languageEnglish
Article number1186
Pages (from-to)1-25
Number of pages25
JournalSensors
Volume21
Issue number4
DOIs
StatePublished - 2 Feb 2021

Keywords

  • Classifica-tion
  • Feature extraction
  • Machine learning
  • Regression
  • Supervised learning
  • Terahertz imaging
  • Terahertz time-domain spectroscopy

Fingerprint

Dive into the research topics of 'Machine learning techniques for thz imaging and time-domain spectroscopy'. Together they form a unique fingerprint.

Cite this