Electrical system/environment interactions on the planet Mars

The Martian environment is a diverse environment with which systems will interact in numerous ways. Preliminary thoughts on electrical system/environment interactions which might be of interest to system designers at all stages of system design are presented. These interactions are primarily related to electrical charging, contamination, and Martian surface sand and dust

Wavelet transform and terahertz local tomography

Copyright © 2007 SPIE - The International Society for Optical Engineering. Copyright 2007 Society of Photo-Optical Instrumentation Engineers. This paper was published in Novel Optical Instrumentation for Biomedical Applications III, edited by Christian D. Depeursinge Proc. of SPIE-OSA Biomedical Optics, SPIE Vol. 6631, 663113 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.We use the theory of two dimensional discrete wavelet transforms to derive inversion formulas for the Radon transform of terahertz datasets. These inversion formulas with good localised properties are implemented for the reconstruction of terahertz imaging in the area of interest, with a significant reduction in the required measurements. As a form of optical coherent tomography, terahertz CT complements the current imaging techniques and offers a promising approach for achieving non-invasive inspection of solid materials, with potentially numerous applications in industrial manufacturing and biomedical engineering. © 2007 SPIE-OSA.Xiaoxia Yin and Brian W.-H. Ng and Bradley Fergusona and Derek Abbot

Support vector machine applications in terahertz pulsed signals feature sets

Copyright © 2007 IEEE. All Rights Reserved.In the past decade, terahertz radiation (T-rays) have been extensively applied within the fields of industrial and biomedical imaging, owing to their noninvasive property. Support vector machine (SVM) learning algorithms are sufficiently powerful to detect patterns hidden inside noisy biomedical measurements. This paper introduces a frequency orientation component method to extract T-ray feature sets for the application of two- and multiclass classification using SVMs. Effective discriminations of ribonucleic acid (RNA) samples and various powdered substances are demonstrated. The development of this method has become important in T-ray chemical sensing and image processing, which results in enhanced detectability useful for many applications, such as quality control, security detection and clinic diagnosis.Xiaoxia Yin, Brian W.-H. Ng, Bernd M. Fischer, Bradley Ferguson, and Derek Abbot

Wavelet based segment detection and feature extraction for 3D T-ray CT pattern classification

Copyright © 2006 IEEEThis paper explores three dimensional (3D) Terahertz (T-rays) computed tomographic (CT) classification based on T-ray functional imaging techniques. The target objects are separated by their refractive indices, which are indicated by the intensity in the images. Segmentation techniques are employed to identify the position of each pixel belonging to the different classes. Wavelet methods are applied to the detected T-ray pulsed responses for feature extraction. A Mahalanobis distance classifier is selected for the final classification task. This paper presents T-ray CT classification techniques that allow analysis of measured T-ray transmission image statistics and that automatically identify materials within a heterogeneous structure.X.X. Yin, B.W.-H. Ng, B. Ferguson, S.P. Mickan, D. Abbot

Statistical model for the classification of the wavelet transforms of T-ray pulses

©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This study applies Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) modeling to wavelet decomposed terahertz pulsed signals to assist biomedical diagnosis and mail/packaging inspection. T-ray classification systems supply a wealth of information about test samples to make possible the discrimination of heterogeneous layers within an object. In this paper, the classification of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of seven different powder samples are demonstrated. A correlation method and an improved Prony’s method are investigated in the calculation of the AR and ARMA model parameters. These parameters are obtained for models from second to eighth orders and are subsequently used as feature vectors for classification. For pre-processing, wavelet de-noising methods including the SURE (Stein’s Unbiased Estimate of Risk) and heuristic SURE soft threshold shrinkage algorithms are employed to de-noise the normalised T-ray pulsed signals. A Mahalanobis distance classifier is used to perform the final classification. The error prediction covariance of AR/ARMA modeling and the classification accuracy are calculated and used as metrics for comparison.X.X. Yin, B.W.-H. Ng, B. Ferguson, S.P. Mickan, D. Abbot

T-ray computed tomography

We demonstrate a tomographic imaging modality that uses pulsed terahertz (THz) radiation to probe the optical properties of three-dimensional (3D) structures in the far-infrared. This THz-wave computed tomography (T-ray CT) system provides sectional images of objects in a manner analogous to conventional CT techniques such as x-ray CT.The transmitted amplitude and phase of broadband pulses of THz radiation are measured at multiple projection angles. The filtered backprojection algorithm is then used to reconstruct the target object, including both its 3D structure and its frequency-dependent far-infrared optical properties.Bradley Ferguson, Shaohong Wang, Doug Gray, Derek Abbot, and X.-C. Zhan

The Dust Content of Galaxy Clusters

We report on the detection of reddening toward z ~ 0.2 galaxy clusters. This is measured by correlating the Sloan Digital Sky Survey cluster and quasar catalogs and by comparing the photometric and spectroscopic properties of quasars behind the clusters to those in the field. We find mean E(B-V) values of a few times 10^-3 mag for sight lines passing ~Mpc from the clusters' center. The reddening curve is typical of dust but cannot be used to distinguish between different dust types. The radial dependence of the extinction is shallow near the cluster center suggesting that most of the detected dust lies at the outskirts of the clusters. Gravitational magnification of background z ~ 1.7 sources seen on Mpc (projected) scales around the clusters is found to be of order a few per cent, in qualitative agreement with theoretical predictions. Contamination by different spectral properties of the lensed quasar population is unlikely but cannot be excluded.Comment: 4 pages, 3 figure