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THz Pattern Recognition Experiments

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Terahertz Imaging for Biomedical Applications

Abstract

The work described in this chapter combines the different techniques discussed in previous chapters into case studies for the biomedical specimen identification. Section 9.1 outlines the THz experiment setup. In Sect. 9.2, an improvement in classification accuracy is demonstrated by applying wavelet-based techniques in the preprocessing of T-ray pulsed signals. In Sect. 9.3, three system identification schemes for discriminating between lactose, mandelic acid, and DL-mandelic acid THz transients is proposed, with application of a discrimination metric for the evaluation of classification performance. Section 9.4 represents the implication of AR and ARMA models on the WTs of measured T-ray pulse data for automatic classification of THz measurements, highlighting their potential in biomedical, pharmaceutical, and security applications. Section 9.5 illustrates that SVM learning algorithms are sufficiently powerful to detect patterns within noisy biomedical measurements. Case studies show effective discrimination of RNA samples and various powdered substances.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Dr. Xiaoxia Yin, Dr. Brian W.-H. Ng & Prof. Derek Abbott

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  1. Dr. Xiaoxia Yin
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  2. Dr. Brian W.-H. Ng
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  3. Prof. Derek Abbott
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Yin, X., Ng, B.WH., Abbott, D. (2012). THz Pattern Recognition Experiments. In: Terahertz Imaging for Biomedical Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1821-4_9

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  • DOI: https://doi.org/10.1007/978-1-4614-1821-4_9

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