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

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

The Large Scale Curvature of Networks

Understanding key structural properties of large scale networks are crucial for analyzing and optimizing their performance, and improving their reliability and security. Here we show that these networks possess a previously unnoticed feature, global curvature, which we argue has a major impact on core congestion: the load at the core of a network with N nodes scales as N^2 as compared to N^1.5 for a flat network. We substantiate this claim through analysis of a collection of real data networks across the globe as measured and documented by previous researchers.Comment: 4 pages, 5 figure

Spin Squeezing under Non-Markovian Channels by Hierarchy Equation Method

We study spin squeezing under non-Markovian channels, and consider an ensemble of $N$ independent spin-1/2 particles with exchange symmetry. Each spin interacts with its own bath, and the baths are independent and identical. For this kind of open system, the spin squeezing under decoherence can be investigated from the dynamics of the local expectations, and the multi-qubit dynamics can be reduced into the two-qubit one. The reduced dynamics is obtained by the hierarchy equation method, which is a exact without rotating-wave and Born-Markov approximation. The numerical results show that the spin squeezing displays multiple sudden vanishing and revival with lower bath temperature, and it can also vanish asymptotically.Comment: 7 pages, 4 figure

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

Spiking Neurons Learning Phase Delays

Time differences between the two ears are an important cue for animals to azimuthally locate a sound source. The first binaural brainstem nucleus, in mammals the medial superior olive, is generally believed to perform the necessary computations. Its cells are sensitive to variations of interaural time differences of about 10 μs. The classical explanation of such a neuronal time-difference tuning is based on the physical concept of delay lines. Recent data, however, are inconsistent with a temporal delay and rather favor a phase delay. By means of a biophysical model we show how spike-timing-dependent synaptic learning explains precise interplay of excitation and inhibition and, hence, accounts for a physical realization of a phase delay

The Interpretations For the Low and High Frequency QPO Correlations of X-ray Sources Among White Dwarfs, Neutron Stars and Black Holes

It is found that there exists an empirical linear relation between the high frequency \nhigh and low frequency \nlow of quasi-periodic oscillations (QPOs) for black hole candidate (BHC), neutron star (NS) and white dwarf (WD) in the binary systems, which spans five orders of magnitude in frequency. For the NS Z (Atoll) sources, $\nu_{high}$ and $\nu_{low}$ are identified as the lower kHz QPO frequency and horizontal branch oscillations (HBOs) \nh (broad noise components); for the black hole candidates and low-luminosity neutron stars, they are the QPOs and broad noise components at frequencies between 1 and 10 Hz; for WDs, they are the ``dwarf nova oscillations'' (DNOs) and QPOs of cataclysmic variables (CVs). To interpret this relation, our model ascribes $\nu_{high}$ to the Alfv\'en wave oscillation frequency at a preferred radius and $\nu_{low}$ to the same mechanism at another radius. Then, we can obtain \nlow = 0.08 \nhigh and the relation between the upper kHz QPO frequency \nt and HBO to be \nh \simeq 56 ({\rm Hz}) (\nt/{\rm kHz})^{2}, which are in accordance with the observed empirical relations. Furthermore, some implications of model are discussed, including why QPO frequencies of white dwarfs and neutron stars span five orders of magnitude in frequency. \\Comment: 11 pages, 1 figure, accepted by PAS

User Intent Prediction in Information-seeking Conversations

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201