Over the last ten years, terahertz (THz or T-ray) biomedical imaging has become a modality of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized, with increasing trials performed in a biomedical setting. Advanced digital image processing algorithms are greatly need to assist screening, diagnosis, and treatment. Pattern recognition algorithms play a critical role in the accurate and automatic process of detecting abnormalities when applied to biomedical imaging. This goal requires classification of meaningful physical contrast and identification of information in images, for example, distinguishing between different biological tissues or materials. T-ray tomographic imaging and detection technology contributes especially to our ability to discriminate opaque objects with clear boundaries and makes possible significant potential applications in both in vivo and ex vivo environments. The Thesis consists of a number of Chapters, which can be grouped in to three parts. The first part provides a review of the state-of-the-art regarding THz sources and detectors, THz imaging modes, and THz imaging analysis. Pattern recognition forms the second part of this Thesis, which is represented via combining several basic operations: wavelet transforms and wavelet based signal filtering, feature extraction and selection, along with classification schemes for THz applications. Signal filtering in this Thesis is achieved via wavelet based de-noising. The ultrafast pulses generated terahertz time-domain spectroscopy (THz-TDS), which is demonstrated to justify their decomposition in the wavelet domain as it can provide better de-noising performance. Feature extraction and selection of the terahertz measurements rely on observed changes in pulse amplitude and phase, as well as scattering characteristics of several different types of powder samples under study. Additionally, three signal processing algorithms are adopted for the evaluation of the complex insertion loss function of such samples as lactose, mandelic acid, and dl-mandelic acid: (i) standard evaluation by ratioing the sample with the background spectra, (ii) a subspace identification algorithm, and (iii) a novel wavelet packet identification procedure. These system identification algorithms enable THz measurements to be transformed to features for THz pattern recognition. Meanwhile, a novel feature extraction method involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA)models on the wavelet transforms of measured T-ray pulse responses of ex vivo osteosarcoma cells as well as other biomedical materials is presented. Classification schemes are carried out via simple and robust schemes, such as the linear Mahalanobis distance classifier, and the non-linear Support Vector Machine (SVM) classifier. In particular, SVMs are used as a learning scheme to achieve the identification of two classes of RNA samples and multiple classes of powered materials. Coherent terahertz detection hardware—THz time-domain spectroscopy (THz-TDS)—is used to obtain all the data for validation of these classification schemes. The past decade has witnessed the tremendous development of terahertz instruments for detecting, storing, analysing, and displaying images. Terahertz time-domain spectroscopy (THz-TDS) is a broadband technique that generates and detects THz radiation in a synchronous and coherent manner. By contrast, the newly developed THz quantum cascade laser is a narrow-band radiation source that provides potential for realising compact systems; they produce image data with higher average power levels. The third part of this Thesis discusses methods to improve the capability of both broad and narrow-band terahertz imaging, driven by computer-aided analytical techniques. A wavelet based reconstruction algorithm for terahertz computed tomography is represented to show how this algorithm can be used to rapidly reconstruct the region of interest (ROI) with a reduction in the measurements of terahertz responses, compared with a standard filtered back-projection technique. These reconstruction algorithms are applied to the analysis of acquired experimental data and to locally recover the two dimensional (2D) and three-dimensional (3D) structures of several optically opaque objects. Moreover, a segmentation technique based on two dimensional wavelet transforms is investigated for the identification of different materials from the reconstructed CT image. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1352839 / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2009

Σκοπός της παρούσας μεταπτυχιακής διπλωματικής εργασίας είναι η μελέτη και σχεδίαση ενός υδατογραφικού σχήματος για ηλεκτροεγκεφαλογραφικά (ΗΕΓ) σήματα επιληπτικών ασθενών. Το υδατογραφικό σχήμα που εφαρμόστηκε βασίζεται στο Μετασχηματισμό Κυματιδίων (wavelet transform) και είχε μέχρι πρότινος εφαρμοστεί μόνο σε ιατρικές εικόνες. Στόχος της ανάπτυξης του υδατογραφικού αυτού σχήματος, είναι η ενσωμάτωση πληροφοριών που έχουν μεγάλη αξία για διάγνωση και θεραπεία χωρίς όμως να αλλοιώνεται αισθητά το σήμα μετά την ενσωμάτωση των δεδομένων. Πριν την εφαρμογή όμως του υδατογραφικού μας σχήματος, απομονώνεται με έναν ειδικά σχεδιασμένο αλγόριθμο η περιοχή της επιληπτικής κρίσης γιατί είναι η περιοχή με τη μεγαλύτερη διαγνωστική αξία και στόχος είναι να παραμείνει εντελώς αναλλοίωτη. Η πρόοδος στον τομέα της τηλεϊατρικής έχει επιτρέψει τη μεταφορά ιατρικών σημάτων με στόχο τη διάγνωση και θεραπεία ασθενών που βρίσκονται σε απομακρυσμένες περιοχές. Κατά τη μεταφορά του υδατογραφημένου σήματος όμως μπορεί αυτό να υποστεί αλλοιώσεις που προέρχονται από συμπιέσεις του σήματος με σκοπό τη μείωση του μεγέθους τους, αλλά και αλλοιώσεις που προέρχονται από προσθήκη θορύβου. Για να εκτιμηθεί η αποτελεσματικότητα λοιπόν του υδατογραφικού μας σχήματος, εφαρμόζονται στο σήμα επιθέσεις συμπίεσης με διαφορετικά κατώφλια και επιθέσεις προσθήκης λευκού θορύβου με διαφορετικούς σηματοθορυβικούς λόγους SNR. Εφαρμόζοντας αυτές τις επιθέσεις, υπολογίζεται το ποσοστό ανάκτησης του υδατογραφήματος από το σήμα που έχει υποστεί επίθεση καθώς και το ποσοστό αλλοίωσης μεταξύ του αρχικού υδατογραφημένου σήματος και του υδατογραφημένου σήματος που έχει υποστεί επίθεση. Μεγαλύτερο ποσοστό ανάκτησης του υδατογραφήματος παρατηρείται όσο το κατώφλι συμπίεσης μικραίνει ενώ αντίθετα ο σηματοθορυβικός λόγος SNR μεγαλώνει. / The purpose of this thesis is the disquisition of a digital watermarking scheme for electroencephalogram (EEG) signals designed for epileptic patients. The watermarking scheme that has been applied in EEG signals is based on wavelet transform applied only in medical images. The objective implementing this digital watermarking scheme in EEG signals, is to embed important data with great significance in patient’s medical history. Furthermore, the scheme can be used for: diagnosis and cure, without distort the initial signal in such a way that leads in a misdiagnosis. Prior to implementation of our watermarking scheme, the area that presents the epileptic seizure is isolated with a specific designed algorithm so as the signal in this area remains undistorted. Nowadays, modern telecommunication infrastructure supports the possibility of delivering quality health care without the physical presence of medical experts. During the telecommunication signal transfers, the watermarking signals can be distorted due to compression methods or/and addition of white noise. In order to evaluate the efficiency of watermarking scheme, the signal is subjected to different kinds of attacks, such as compression with different compression thresholds, and attacks of adding white noise with different SNR ratio. After applying these attacks to the signal, it is computed the recovery ratio of the watermark and the distortion between the initial watermarked signal and the signal that has been subjected to the attacks. Given the results, the conclusion is that the smaller the compression threshold is, the better the recovery ratio of the watermark. On the other hand, in white noise attacks, the recovery ratio increases as the SNR ratio gets higher.

Pavement texture has been measured predominantly by using two-dimensional (2D) profile methods. The 2D profile based mean profile depth (MPD) is still the well accepted texture index which has been found inadequate to characterize pavement texture especially when tire/pavement friction and noise are involved. There is a lack of standard 3D texture indices which show strong correlation with pavement friction and noise. There is a need to use 3D texture measurement for more comprehensive understanding of texture. The objectives of this thesis are to characterize pavement surfaces using 3D texture parameters based on 3D texture measurement and to explore the relationship between 3D texture parameters, pavement friction, and pavement noise. Field tests are conducted at various pavement sections for the measurements of texture, friction, and noise. The tested pavements include Interstate highway, MnROAD test facilities, airport runway, and municipal streets. The findings and contributions of this thesis are: • The pavement surface texture is measured in a 3D manner by using a line-laser scanner with both horizontal sample interval and vertical accuracy better than 0.05 mm. • A 3D texture analysis procedure with discrete wavelet transform (DWT) is proposed to separate macrotexture from microtexture and to define texture indices independently. • 3D parameters for macrotextures and microtexture are proposed and verified by field tests. • The relationship between 3D and 2D macrotexture indices [i.e. SMTD and MPD; Sq and root mean square roughness (RMSR)] are established, which is useful for the purposes of data comparison between 3D and 2D methods. • The relationship is investigated between 3D macrotexture parameters (SMTD and Sq) and pavement friction and noise. • It is found that texture distribution indices (i.e. Ssk and Sku) are significant contributors to pavement friction and noise. The new 3D texture analysis procedure and texture indices proposed in this thesis can be used to characterize various pavement textures (concrete pavement, asphalt pavement, and pavement contains recycled materials) in 3D manner, to compare 3D with 2D texture measurement/indices for quality control purposes, and to evaluate and predict pavement friction and noise. / February 2016

O presente trabalho apresenta uma comparação de duas técnicas para a análise tempo - frequência em análise de qualidade de energia elétrica para sinais de tensão que contenham distúrbios individuais ou simultâneos. Dessa forma, o objetivo, desta dissertação, é encontrar uma ferramenta que forneça as características e parâmetros para a localização, identificação e classificação de tais distúrbios. O estudo consiste na análise do desempenho da Transformada Wavelet Discreta e da Transformada-S, principalmente, quando os sinais são analisados na presença de múltiplos distúrbios. Ambas as transformadas fornecem informação importante nos domínios do tempo e da frequência. No entanto, essas ferramentas não tem sido amplamente exploradas para análise de múltiplos distúrbios. Neste contexto, ambas as transformadas são testadas para conhecer seus desempenhos e suas capacidades de identificação e localização de eventos de qualidade de energia elétrica. Para finalizar, é projetado um sistema classificador baseado em arvore de decisão capaz de reconhecer quinze tipos de distúrbios diferentes. / This work presents a comparison of two methods for time-frequency analysis applied in Power Quality signals containing single or multiple disturbances. In this way, the aim of this work is to apply tools that supply the parameters and characteristics to identify, locate and classify Power Quality disturbances. For that, the proposed method analyzes the performance of the Wavelet and S transforms, mainly when the signals are with more than one disturbance type. Both mathematical tools supply important information on the time and frequency domain. However, these tools have not been thoroughly used to analyze multiple events locate Power Quality events. In this contest, both transforms are tested in order to assess their performance to identify and locate electrical power quality events. According to a decision tree classifier, fifteen types of single and combined power disturbances are well recognized.

Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-05-29Bitstream added on 2014-06-13T20:21:16Z : No. of bitstreams: 1 soares_wc_dr_ilha.pdf: 2948445 bytes, checksum: cf47c579c7e9a4f2d231373d9ed5f704 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho é feito um estudo dos métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método não-limiar para redução de ruído em sinais de voz no domínio wavelet. Em geral os sinais de voz podem estar contaminados com ruídos artificiais ou reais. O problema consiste que dado um sinal limpo adiciona-se o ruído branco ou colorido, obtendo assim o sinal ruidoso, ambos no domínio do tempo. O que se propõe neste trabalho, é aplicar a transformada wavelet, obtendo assim o sinal transformado no domínio wavelet, reduzindo ou atenuando o ruído sem o uso de limiar. Os métodos mais usados no domínio wavelet são os métodos de redução por limiar, pois permitem bons resultados para sinais contaminados por ruído branco, mas não são eficientes no processamento de sinais contaminados por ruído colorido, que é o tipo de ruído mais comum em situações reais. Nesses métodos, o limiar, geralmente, é calculado nos intervalos de silêncio e aplicado em todo o sinal. Os coeficientes no domínio wavelet são comparados com este limiar e aqueles que estão abaixo deste valor são eliminados ou reduzidos, fazendo assim uma aplicação linear deste limiar. Esta eliminação, na maioria das vezes, causa descontinuidades no tempo e na frequência no sinal processado. Além disso, a forma com que o limiar é calculado pode degradar os trechos de voz do sinal processado, principalmente nos casos em que o limiar depende fortemente da última janela do último trecho de silêncio. O método proposto nesta pesquisa consiste na execução de três processamentos, agindo de acordo com as suas características nas regiões de voz e silêncio, sem o uso de limiar. A execução dos três processamentos é sintetizada numa única função, denominada de função de transferência, que atua como um filtro no processamento do sinal... / In this work a study of the methods for speech noise reduction based on wavelets is done and, through this study, a new non-thresholding method for speech noise reduction in the wavelet domain is proposed. Generally, a speech signal may be corrupted by artificial or real noise. Let a clean signal be corrupted by white or colored noise, rising a noisy signal in time domain. This work proposes the wavelet application to which gives rise to in the wavelet domain. In this domain, noise is reduced or attenuated without a threshold use. After, the signal is recomposed using the inverse discrete wavelet transform. The most used methods in the wavelet domain wavelet are the thresholding reduction methods, because they allow good results for signals corrupted by white noise, but they do not have the same efficiency when processing signals corrupted by colored noise, this is the most common noise in real situations. In those methods, the threshold is usually calculated in the silence intervals and applied to the whole signal. The coefficients in the wavelet domain are compared with this threshold and those that have absolute value below this value are eliminated or reduced, making a linear application of this threshold. This elimination causes discontinuities in time and in the frequency of the processed signal. Besides, the form with that the threshold is applied can degrade the voice segments of the processed signal, principally in cases that the threshold depends strongly on the last window of the last silence segment. The method proposed in this research consists in the execution of three processing, acting according to their characteristics in the voice and silence segments, without the threshold use. The three processing execution is synthesized in an unique function, called transfer function, acting as a filter in the signal processing. This method has as main objective the overcoming... (Complete abstract click electronic access below)

[pt] Essa dissertação apresenta um codificador de imagens para baixas taxas de bits utilizando a decomposição da imagem em 10 sub-bandas através da aplicação da transformada wavelet. Uma técnica de redução de irrelevância visual é usada para descartar blocos de coeficientes das sub-bandas de alta freqüência (2 a 10). Os blocos remanescentes são classificados em bordas e não bordas e codificados através da técnica ECTCVQ ( Entropy-Constrained Trellis Coded Vector Quantization). Já a primeira sub-banda é codificada através da técnica PTCQ( Predictive Trellis Coded Quantization) com preservação de bordas. Na alocação de bits entre as sub-bandas é utilizado o algoritmo de Wersterink et al. Os resultados obtidos mostram um desempenho muito superior ao padrão JPEG, e bons resultados quando comparados a técnica de codificação de imagens recentes. / [en] This dissertation presentes a low bit rate image coder using a 10 sub-band image decomposition base don the wavelet transform. Some blocks of coefficients of the high frequency sub-bands are discarded by a technique of irrelevancy reduction. The remaning blocks are classified into edges and non-edges, and coded by ECTCVQ (Entropy- constrained Trellis Vector Quantization). The fist sub- band is coded by an edge preserving PTCQ (Predictive Trellis Coded Quantization), also proposed in this work. The Westerink et al. algorithm is used to allocate the bits between the sub-bands. The results obtained show that the performance of the proposed coder is Significantly superior so the one obtained with the standard JPEG. Moreover, good results are achivied as compared to recently proposed techniques of images coding.

4D flow MRI is an imaging technique able to provide relevant information on patients’ cardiac health condition both from a visual and a quantitative point of view. Its applicability is however limited by uncertainty in the data due to the presence of noise. A new class of filters, called divergence free filters, was recently proposed. They incorporate physical knowledge into the filtering of 4D flow data. One way to implement divergence filters is via wavelet transform. The filtering process using the Divergence Free Wavelet Transform can be carried out in a completely automated fashion and was shown to hold promising results. The focus of this thesis was thus put towards assessing the effect produced by these filters on a large cohort of patients. Time-resolved segmentations were incorporated into the filtering process as this was thought to enhance divergence reduction. They were also used to investigate the filtering in every region of the thoracic cardiovascular system. The assessment of the filters was carried out both from a visual and a quantitative perspective. In-house tools were used to compute clinically used parameters on the data before and after the filtering to investigate the introduced change. The results showed that the used method was able to reduce divergence like noise while preserving all the relevant information contained in the original data, in all the regions of the heart. Flow quantifications were essentially unchanged by the filtering suggesting that the method can be safely applied on 4D flow data.

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Electron beam welding is used for fabricating critical components for the aerospace and nuclear industries which demand high quality. The cost of materials and associated processes of fabrication is also very high. Therefore, manufacturing processes in these industries are highly controlled. However, it has been found that even minor changes in the electron beam gun itself can produce large variations in beam characteristics, leading to unpredictable welding performance. Hence, it is very important to ensure the beam quality prior to carrying out welds. This requires some kind of device and process to characterise the electron beam to indicate variations. A detailed review of different technologies used to develop devices to characterise electron beams has been carried out. At this time, it is uncommon for beam measurement to be carried out on production EBW equipment. Research carried out for this thesis is focused on development of a novel approach to characterise the electron beams using a slit-probe to maintain the quality of the welds. The challenge lies in deriving relevant features from the acquired probe signal which can effectively differentiate between the beams of different quality. Wavelet transformation, with its advantages over other methods for simultaneous time and frequency localization of signals, has found its application to feature extraction in many pattern based classifications. This technique has been used to analyse probe signals considering that different quality beams will possess unique signal profiles in the form of their distribution of energies with respect to frequency and time. To achieve the aim of the thesis, an experimental approach was used by carrying out melt runs on Ti-6Al-4V plates focusing on aerospace requirements, and varying beam properties and acquiring probe signals for all beam settings. Extracted features from the probe signals have been used in classification of the electron beams to ensure these will produce welds within the tolerance limits specified by aerospace standards for quality assurance. The features vector was compiled following statistical analysis to find the significant beam characteristics. By analysing the performance of classifier for different combination of parameters of the features vector, the optimum classification rate of 89.8% was achieved by using the parameters derived from wavelet coefficients for different decomposition levels. This work showed that the use of wavelet analysis and classification using features vectors enabled identification of beams that would produce welds out-of-tolerance. Keywords: Electron beam welding, probe devices, electron beam characterisation, quality assurance, wavelet transform, features vector, linear discriminant classifier, weld profiles, weld defects.

abstract: We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin’s procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N^2) to O(N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016