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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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 |
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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 |