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Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

Yin, X.X., Ng, B.W.H., Ferguson, B., Abbott, D. and Hadjiloucas, S. (2007) Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements. Journal of Biological Systems, 15 (4). pp. 551-571. ISSN 0218-3390

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To link to this article DOI: 10.1142/S0218339007002374

Abstract/Summary

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15490
Uncontrolled Keywords:Terahertz, Auto Regressive Moving Average (ARMA), Yule-Walker algorithm, Prony's method, soft threshold wavelet shrinkage de-noising, discrete wavelet transform (DWT), wavelet packet transform (WPT), Mahalanobis distance classifier , SPECTROSCOPY

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