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Genetic algorithm based optimization for terahertz time-domain adaptive sampling
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Li, Kaidi, Chen, Xuequan, Shen, Shuaiqi, Zhang, Rui and Pickwell-MacPherson, Emma (2019) Genetic algorithm based optimization for terahertz time-domain adaptive sampling. IEEE Transactions on Terahertz Science and Technology, 9 (6). pp. 675-683. doi:10.1109/TTHZ.2019.2935635 ISSN 2156-342X.
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WRAP-Genetic-algorithm-optimization-terahertz time-sampling- MacPherson-2019.pdf - Accepted Version - Requires a PDF viewer. Download (956Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TTHZ.2019.2935635
Abstract
We propose a genetic algorithm (GA) based method to improve the sampling efficiency in THz time domain spectroscopy (THz-TDS). For a typical time domain THz signal, most information are contained in a short region of the pulse which needs to be densely sampled, while the other regions fluctuating around zero can be represented by fewer points. Based on this clustering feature of the THz signal, we can use much fewer sampling points and optimize the distribution by using a GA to achieve an accurate scanning in less time. Both reflection and transmission measurements were conducted to experimentally verify the performance. The measurement results show that the sampling time can be greatly reduced while maintaining very high accuracy both in the time-domain and frequency-domain compared with a high-resolution step scan. This method significantly improves the measurement efficiency. It can be easily adapted to most THz-TDS systems equipped with a mechanical delay stage for fast detection and THz imaging.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Terahertz spectroscopy , Genetic algorithms | ||||||||||||
| Journal or Publication Title: | IEEE Transactions on Terahertz Science and Technology | ||||||||||||
| Publisher: | IEEE | ||||||||||||
| ISSN: | 2156-342X | ||||||||||||
| Official Date: | November 2019 | ||||||||||||
| Dates: |
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| Volume: | 9 | ||||||||||||
| Number: | 6 | ||||||||||||
| Page Range: | pp. 675-683 | ||||||||||||
| DOI: | 10.1109/TTHZ.2019.2935635 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Date of first compliant deposit: | 19 September 2019 | ||||||||||||
| Date of first compliant Open Access: | 19 September 2019 | ||||||||||||
| RIOXX Funder/Project Grant: |
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