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

Time frequency analysis in terahertz pulsed imaging

Recent advances in laser and electro-optical technologies have made the previously under-utilized terahertz frequency band of the electromagnetic spectrum accessible for practical imaging. Applications are emerging, notably in the biomedical domain. In this chapter the technique of terahertz pulsed imaging is introduced in some detail. The need for special computer vision methods, which arises from the use of pulses of radiation and the acquisition of a time series at each pixel, is described. The nature of the data is a challenge since we are interested not only in the frequency composition of the pulses, but also how these differ for different parts of the pulse. Conventional and short-time Fourier transforms and wavelets were used in preliminary experiments on the analysis of terahertz pulsed imaging data. Measurements of refractive index and absorption coefficient were compared, wavelet compression assessed and image classification by multidimensional clustering techniques demonstrated. It is shown that the timefrequency methods perform as well as conventional analysis for determining material properties. Wavelet compression gave results that were robust through compressions that used only 20% of the wavelet coefficients. It is concluded that the time-frequency methods hold great promise for optimizing the extraction of the spectroscopic information contained in each terahertz pulse, for the analysis of more complex signals comprising multiple pulses or from recently introduced acquisition techniques

Temporal properties of gamma-ray bursts as signatures of jets from the central engine

A comprehensive temporal analysis has been performed on the 319 brightest GRBs with T90>2s from the BATSE current catalog. The rise times, fall times, full-widths at half maximum (FWHM), pulse amplitudes and pulse areas were measured and the frequency distributions are presented here. The distribution of time intervals between pulses is not random but compatible with a lognormal distribution when allowance was made for the 64 ms time resolution and a small excess (5%) of long duration intervals that is often referred to as a Pareto-Levy tail. A range of correlations are presented on pulse and burst properties. The rise and fall times, FWHM and area of the pulses are highly correlated with each other. The time intervals between pulses and pulse amplitudes of neighbouring pulses are correlated with each other. It was also found that the number of pulses, N, in GRBs is strongly correlated with the fluence and the duration and that can explain the well known correlation between duration and fluence. The GRBs were sorted into three catagories based on N i.e. 3=25. The properties of pulses before and after the stongest pulse were compared for the three catagories of bursts. This analysis revealed that the GRBs with large numbers of pulses have narrower and faster pulses and also larger fluences, longer durations and higher hardness ratios than the GRBs with smaller numbers of pulses.Comment: 19 pages, 22 figures. Submitted to A&A July 200

The complex behaviour of the microquasar GRS 1915+105 in the rho class observed with BeppoSAX. I: Timing analysis

GRS 1915+105 was observed by BeppoSAX for about 10 days in October 2000. For about 80% of the time, the source was in the variability class $\rho$, characterised by a series of recurrent bursts. We describe the results of the timing analysis performed on the MECS (1.6--10 keV) and PDS (15--100 keV) data. The X-ray count rate from \grss showed an increasing trend with different characteristics in the various energy bands. Fourier and wavelet analyses detect a variation in the recurrence time of the bursts, from 45--50 s to about 75 s, which appear well correlated with the count rate. From the power distribution of peaks in Fourier periodograms and wavelet spectra, we distinguished between the {\it regular} and {\it irregular} variability modes of the $\rho$ class, which are related to variations in the count rate in the 3--10 keV range. We identified two components in the burst structure: the slow leading trail, and the pulse, superimposed on a rather stable level. We found that the change in the recurrence time of the regular mode is caused by the slow leading trails, while the duration of the pulse phase remains far more stable. The evolution in the mean count rates shows that the time behaviour of both the leading trail and the baseline level are very similar to those observed in the 1.6--3 and 15--100 keV ranges, while that of the pulse follows the peak number. These differences in the time behaviour and count rates at different energies indicate that the process responsible for the pulses must produce the strongest emission between 3 and 10 keV, while that associated with both the leading trail and the baseline dominates at lower and higher energiesComment: Astronomy and Astrophysics, in pres

Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems

Electrocardiograms (ECG) record the electrical activity of the heart through 12 main signals called shunts. Medical experts examine certain segments of these signals in where they believe the cardiovascular disease is manifested. This fact is an important determining factor for designing expert systems for cardiac diagnosis, as it requires the direct expert opinion in order to locate these specific segments in the ECG. The main contributions of this paper are: (i) to propose a model that uses the full ECG signal to identify key characteristic points that define cardiac pathology without medical expert intervention and (ii) to present an expert system based on artificial neural networks capable of detecting bundle branch block disease using the previous approach. Cardiologists have validated the proposed model application and a comparative analysis is performed using the MIT-BIH arrhythmia database.Spanish project TSI-020302-2010-136 University of Málaga: 81434547001-

Quantum-Gravity Analysis of Gamma-Ray Bursts using Wavelets

In some models of quantum gravity, space-time is thought to have a foamy structure with non-trivial optical properties. We probe the possibility that photons propagating in vacuum may exhibit a non-trivial refractive index, by analyzing the times of flight of radiation from gamma-ray bursters (GRBs) with known redshifts. We use a wavelet shrinkage procedure for noise removal and a wavelet `zoom' technique to define with high accuracy the timings of sharp transitions in GRB light curves, thereby optimizing the sensitivity of experimental probes of any energy dependence of the velocity of light. We apply these wavelet techniques to 64 ms and TTE data from BATSE, and also to OSSE data. A search for time lags between sharp transients in GRB light curves in different energy bands yields the lower limit $M \ge 6.9 \cdot 10^{15}$ GeV on the quantum-gravity scale in any model with a linear dependence of the velocity of light $~ E/M$. We also present a limit on any quadratic dependence.Comment: This version is accepted for publication in Astronomy & Astrophysics. The discussion and introduction are extended making clear why the wavelet analysis should be superior to straight cross-correlation analysis. More details on compiled data are elaborated. 18 pages, 9 figures, A&A forma

Ultrasonic signal detection and recognition using dynamic wavelet fingerprints

A novel ultrasonic signal detection and characterization technique is presented in this dissertation. The basic tool is a simplified time-frequency (scale) projection which is called a dynamic wavelet fingerprint. Take advantage of the matched filter and adaptive time-frequency analysis properties of the wavelet transform, the dynamic wavelet fingerprint is a coupled approach of detection and recognition. Different from traditional value-based approaches, the dynamic wavelet fingerprint based technique is pattern or knowledge based. It is intuitive and self-explanatory, which enables the direct observation of the variation of non-stationary ultrasonic signals, even in complex environments. Due to this transparent property, efficient detection and characterization algorithms can be customized to address specific problems. Furthermore, artificial intelligence can be integrated and expert systems can be developed based on it.;Several practical ultrasonic applications were used to evaluate the feasibility and performance of this technique. The first application was ultrasonic materials sorting. Dynamic wavelet fingerprints of echoes from the surface of different plates were generated and then used to successfully identify corresponding plates.;The second application was ultrasonic periodontal probing. The dynamic wavelet fingerprint technique was used to expose the hidden trend of the complex waveforms. Taking the manual probing data as gold standard , a 40% agreement ratio was achieved with a tolerance limit of 1mm. However, statistically, lack of agreement was found in terms of the limits of agreement of Bland and Altman.;The third application was multi-mode Lamb wave tomography. The dynamic wavelet fingerprint technique was used to extract arrival times of transmitted Lamb wave modes. The overall quality of the estimated arrival times was acceptable in terms of their regular distributions and discernable variation patterns that correspond to specific defects. The tomographic images generated from estimated arrival times were also fine enough to indicate different defects in aluminum plates.;The last application was ultrasonic thin multi-layers inspection. High precision and robustness of a dynamic wavelet fingerprint based algorithm was demonstrated by processing simulated ultrasonic signals. When applied to practical data obtained from a plastic encapsulated IC package, multiple interfaces in the package were successfully detected

Experimental investigations of two-phase flow measurement using ultrasonic sensors

This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Wave Propagation

A wave is one of the basic physics phenomena observed by mankind since ancient time. The wave is also one of the most-studied physics phenomena that can be well described by mathematics. The study may be the best illustration of what is “science”, which approximates the laws of nature by using human defined symbols, operators, and languages. Having a good understanding of waves and wave propagation can help us to improve the quality of life and provide a pathway for future explorations of the nature and universe. This book introduces some exciting applications and theories to those who have general interests in waves and wave propagations, and provides insights and references to those who are specialized in the areas presented in the book