Your browser doesn't support javascript.
loading
Phase Function Effects on Identification of Terahertz Spectral Signatures Using the Discrete Wavelet Transform.
Khani, Mahmoud E; Winebrenner, Dale P; Arbab, M Hassan.
Afiliação
  • Khani ME; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA.
  • Winebrenner DP; Department of Electrical Engineering, and the Applied Physics Laboratory, University of Washington, Seattle, WA, 98195 USA.
  • Arbab MH; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA.
IEEE Trans Terahertz Sci Technol ; 10(6): 656-666, 2020 Nov.
Article em En | MEDLINE | ID: mdl-33738125
We describe the application of the Discrete Wavelet Transform (DWT) in extracting the characteristic absorption signatures of materials from terahertz reflection spectra. We compare the performance of different mother wavelets, including Daubechies, Least Asymmetric (LA), and Coiflet, based on their phase and gain functions and filter lengths. We show that the phase functions of the wavelet and scaling filters result in spectral shifts to the absorption lines in the wavelet domain. We provide a solution by calculating advancement coefficients necessary to achieve effective zero-phase-function DWT. We demonstrate the utility of this signal processing technique using α-lactose monohydrate/polyethylene samples with different levels of rough surface scattering. In all cases, the DWT-based algorithm successfully extracts resonant signatures at 0.53 and 1.38 THz, even when they are obscured by the rough surface scattering effects. The DWT analysis with accompanying phase corrections can be utilized as a robust technique for material identification in non-destructive evaluation (NDE) using terahertz spectroscopy.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article