Cargando…

Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy

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 machin...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Hochong, Son, Joo-Hiuk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914669/
https://www.ncbi.nlm.nih.gov/pubmed/33567605
http://dx.doi.org/10.3390/s21041186
_version_ 1783657057476411392
author Park, Hochong
Son, Joo-Hiuk
author_facet Park, Hochong
Son, Joo-Hiuk
author_sort Park, Hochong
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7914669
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79146692021-03-01 Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy Park, Hochong Son, Joo-Hiuk Sensors (Basel) Review 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. MDPI 2021-02-08 /pmc/articles/PMC7914669/ /pubmed/33567605 http://dx.doi.org/10.3390/s21041186 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Park, Hochong
Son, Joo-Hiuk
Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title_full Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title_fullStr Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title_full_unstemmed Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title_short Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
title_sort machine learning techniques for thz imaging and time-domain spectroscopy
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914669/
https://www.ncbi.nlm.nih.gov/pubmed/33567605
http://dx.doi.org/10.3390/s21041186
work_keys_str_mv AT parkhochong machinelearningtechniquesforthzimagingandtimedomainspectroscopy
AT sonjoohiuk machinelearningtechniquesforthzimagingandtimedomainspectroscopy