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Practical method for determining inductance and capacitance of metamaterial resonators
(2012-02-16)
Li, J. N.
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Withayachumnankul, W.
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Chang, S. J.
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Abbott, D.
A practical approach to determining the distributed capacitance and inductance of individual metamaterial resonators is demonstrated. By loading a set of lumped capacitors onto the resonator, the resonance frequency can be measured as a function of the load capacitance. The capacitance and inductance values of the resonator are then determined by relating the equivalent circuit model to the measurements. Although the measurement is limited to the microwave region owing to the availability of suitable lumped capacitors, the concept is useful in simulations over a wider frequency range. The obtainable parameters are useful for further metamaterial analyses or syntheses. © 2012 The Institution of Engineering and Technology.
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Elastomeric silicone substrates for terahertz fishnet metamaterials
(2012-02-06)
Khodasevych, I. E.
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Shah, C. M.
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Sriram, S.
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Bhaskaran, M.
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Withayachumnankul, W.
In this work, we characterize the electromagnetic properties of polydimethylsiloxane (PDMS) and use this as a free-standing substrate for the realization of flexible fishnet metamaterials at terahertz frequencies. Across the 0.2-2.5 THz band, the refractive index and absorption coefficient of PDMS are estimated as 1.55 and 0-22 cm <sup>-1</sup>, respectively. Electromagnetic modeling, multi-layer flexible electronics microfabrication, and terahertz time-domain spectroscopy are used in the design, fabrication, and characterization of the metamaterials, respectively. The properties of PDMS add a degree of freedom to terahertz metamaterials, with the potential for tuning by elastic deformation or integrated microfluidics. © 2012 American Institute of Physics.
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T-ray relevant frequencies for osteosarcoma classification
(2006-04-17)
Withayachumnankul, W.
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Ferguson, B.
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Rainsford, T.
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Findlay, D.
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Mickan, S. P.
We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.
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T-rays in biomedicine and security
(2005-12-01)
Rainsford, T.
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Png, G. M.
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Withayachumnankul, W.
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Ferguson, B.
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Mickan, S. P.
With a number of commercial systems becoming increasingly available, and improved generation and detection techniques T-ray systems are increasingly finding new applications. One of the main technical challenges is to make systems low cost, compact and easy to use. In this paper we suggest some novel solutions for parameter extraction, image enhancement, improved signal-to-noise ratio and classification schemes. These tools are useful in a number of biomedical and security applications. © 2005 IEEE.

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