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Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 21, Iss 1186, p 1186 (2021)
- Publication Year :
- 2020
-
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 representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.
- Subjects :
- Computer science
Terahertz radiation
Feature extraction
02 engineering and technology
Review
lcsh:Chemical technology
Machine learning
computer.software_genre
01 natural sciences
Biochemistry
supervised learning
Analytical Chemistry
010309 optics
terahertz imaging
0103 physical sciences
lcsh:TP1-1185
Time domain
Electrical and Electronic Engineering
Spectroscopy
Instrumentation
business.industry
feature extraction
Supervised learning
021001 nanoscience & nanotechnology
Atomic and Molecular Physics, and Optics
machine learning
classification
regression
Artificial intelligence
0210 nano-technology
business
computer
terahertz time-domain spectroscopy
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....70791f89cb4bee078666ac8b12a9e9dc