Terahertz Spectrum Analysis Based on Empirical Mode Decomposition
- 217 Downloads
Precise identification of terahertz absorption peaks for materials with low concentration and high attenuation still remains a challenge. Empirical mode decomposition was applied to terahertz spectrum analysis in order to improve the performance on spectral fingerprints identification. We conducted experiments on water vapor and carbon monoxide respectively with terahertz time domain spectroscopy. By comparing their absorption spectra before and after empirical mode decomposition, we demonstrated that the first-order intrinsic mode function shows absorption peaks clearly in high-frequency range. By comparing the frequency spectra of the sample signals and their intrinsic mode functions, we proved that the first-order function contains most of the original signal’s energy and frequency information so that it cannot be left out or replaced by high-order functions in spectral fingerprints detection. Empirical mode decomposition not only acts as an effective supplementary means to terahertz time-domain spectroscopy but also shows great potential in discrimination of materials and prediction of their concentrations.
KeywordsTerahertz spectroscopy Empirical mode decomposition Absorption peaks Spectrum analysis Water vapor
The authors acknowledge helpful discussions with Dr. Yuqiang Deng from the National Institute of Metrology, China. This work is supported by the National Key R&D Program of China (Grant Nos. 2016YFC0801300 and 2016YFC0801200) and the National Natural Science Foundation of China (Grant Nos. 61575103 and 61675111).
- 8.J. B. Sleiman, J. El Haddad, J. Perraud, L. Bassel, B. Bousquet, N. Palka, and P. Mounaix, in Infrared, Millimeter, and Terahertz waves (IRMMW-THz), 2014 39th International Conference on (IEEE, 2014), pp. 1.Google Scholar
- 9.Z. Jin, A. Wada, J. Shin, N. Yugami, and R. Kodama, in Journal of Physics: Conference Series (IOP Publishing, 2016), p. 012040.Google Scholar
- 10.W. Tu, S. Zhong, Y. Shen, and A. Incecik, OcEng 111, 582 (2016).Google Scholar
- 11.N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences (The Royal Society, 1998), pp. 903.Google Scholar
- 13.B. Weng, G. Xuan, J. Kolodzey, and K. E. Barner, in Genomic Signal Processing and Statistics, 2006. GENSIPS’06. IEEE International Workshop on (IEEE, 2006), pp. 63.Google Scholar