The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientific fields such as economics, finance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavelet-based Bayesian estimation procedure for the parameters of Gaussian ARFIMA(p, d, q) process is proposed. This entails calculating the exact variance-covariance matrix of given ARFIMA(p, d, q) process and transforming them into wavelet domains using two dimensional discrete wavelet transform (DWT2). Metropolis algorithm is used for sampling the model parameters from the posterior distributions. Simulations with different values of the parameters and of the sample size are performed. A real data application to the U.S. GNP data is also reported. Detection and estimation of multiple change points in the long memory parameter is also investigated. The reversible jump MCMC is used for posterior inference. Performances are evaluated on simulated data and on the Nile River dataset.

Safeguarding creative content in a digital form has become increasingly difficult. It is progressively easier to copy, modify and redistribute digital media, which causes great declines in business profits. For example, the International Federation of the Phonographic Industry estimates that in 2001 the worldwide sales of pirated music CDs were 475 million US dollars. While a large amount of time and money is committed to creating intellectual property, legal means have not proven to be sufficient for the protection of this property. Digital watermarking is a steganographic technique that has been proposed as a possible solution to this problem. A digital watermark hides embedded information about the origin, status, owner and/or destination of the data, often without the knowledge of the viewer or user. This dissertation examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT). Research has been done in this field, but which wavelet family is superior is not adequately addressed. This dissertation studies the influence of the wavelet family when using a blind, nonvisible watermark in digital media. The digital watermarking algorithm uses a database of multiple images with diverse properties. Various watermarks are embedded. Eight different families of wavelets with dissimilar properties are compared. How effective is each wavelet? To objectively measure the success of the algorithm, the influence of the mother wavelet, the imperceptibility of the embedded watermark and the readability of the extracted watermark, the Peak Signal-to-Noise Ratio and the Image Quality Index for each wavelet family and image are obtained. Two common categories of digital watermarking attacks are removing the watermark and rendering the watermark undetectable. To simulate and examine the effect of attacks on the images, noise is added to the image data. Also, to test the effect of reducing an image in size, each image containing the embedded watermark is compressed. The dissertation asks the questions: “Is the wavelet family chosen to implement the algorithm for a blind, nonvisible watermark in digital images of consequence? If so, which family is superior?” This dissertation conclusively shows that the Haar transform is the best for blind, non-visible digital watermarking.

A method for constructing a highly scalable bit stream for video coding is presented in detail and implemented in a demo application with a GUI in the Windows Vista operating system. The video codec uses the Discrete Wavelet Transform in both spatial and temporal directions together with a zerotree quantizer to achieve a highly scalable bit stream in the senses of quality, spatial resolution and frame rate. / I detta arbete presenteras en metod för att skapa en mycket skalbar videoström. Metoden implementeras sedan i sin helhet i programspråken C och C++ med ett grafiskt användargränssnitt på operativsystemet Windows Vista. I metoden används den diskreta wavelettransformen i såväl de spatiella dimensionerna som tidsdimensionen tillsammans med en nollträdskvantiserare för att åstakomma en skalbar videoström i avseendena bildkvalitet, skärmupplösning och antal bildrutor per sekund.

The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientific fields such as economics, finance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavelet-based Bayesian estimation procedure for the parameters of Gaussian ARFIMA(p, d, q) process is proposed. This entails calculating the exact variance-covariance matrix of given ARFIMA(p, d, q) process and transforming them into wavelet domains using two dimensional discrete wavelet transform (DWT2). Metropolis algorithm is used for sampling the model parameters from the posterior distributions. Simulations with different values of the parameters and of the sample size are performed. A real data application to the U.S. GNP data is also reported. Detection and estimation of multiple change points in the long memory parameter is also investigated. The reversible jump MCMC is used for posterior inference. Performances are evaluated on simulated data and on the Nile River dataset.

Manual sleep scoring, executed by visual inspection of the EEG, is a very time consuming activity, with an inherent subjective decisional component. Automatic sleep scoring could ease the job of the technicians, because faster and more accurate. Frequency information characterizing the main brain rhythms, and consequently the sleep stages, needs to be extracted from the EEG data. The approach used in this study involves a wavelet filter bank for the EEG frequency features extraction. The wavelet packet analysis tool in MATLAB has been employed and the frequency information subsequently used for the automatic sleep scoring by means of an artificial neural network. Finally, the automatic sleep scoring has been employed for epoching the fMRI data, thus allowing for studying brain resting state networks during sleep. Three resting state networks have been inspected; the Default Mode Network, The Attentional Network and the Salience Network. The networks functional connectivity variations have been inspected in both healthy and narcoleptic subjects. Narcolepsy is a neurobiological disorder characterized by an excessive daytime sleepiness, whose aetiology may be linked to a loss of neurons in the hypothalamic region.

In power transmission systems, locating faults is an essential technology. When a fault occurs on a transmission line, it will affect the whole power system. To find the fault location accurately and promptly is required to ensure the power supply. In this paper, the study of traveling wave theory, fault location method, Karrenbauer transform, and Wavelet transform is presented. This thesis focuses on single ended fault location method. The signal processing technique and evaluation study are presented. The MATLAB SimPowerSystem is used to test and simulate fault scenarios for evaluation studies.

Safeguarding creative content in a digital form has become increasingly difficult. It is progressively easier to copy, modify and redistribute digital media, which causes great declines in business profits. For example, the International Federation of the Phonographic Industry estimates that in 2001 the worldwide sales of pirated music CDs were 475 million US dollars. While a large amount of time and money is committed to creating intellectual property, legal means have not proven to be sufficient for the protection of this property. Digital watermarking is a steganographic technique that has been proposed as a possible solution to this problem. A digital watermark hides embedded information about the origin, status, owner and/or destination of the data, often without the knowledge of the viewer or user. This dissertation examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT). Research has been done in this field, but which wavelet family is superior is not adequately addressed. This dissertation studies the influence of the wavelet family when using a blind, nonvisible watermark in digital media. The digital watermarking algorithm uses a database of multiple images with diverse properties. Various watermarks are embedded. Eight different families of wavelets with dissimilar properties are compared. How effective is each wavelet? To objectively measure the success of the algorithm, the influence of the mother wavelet, the imperceptibility of the embedded watermark and the readability of the extracted watermark, the Peak Signal-to-Noise Ratio and the Image Quality Index for each wavelet family and image are obtained. Two common categories of digital watermarking attacks are removing the watermark and rendering the watermark undetectable. To simulate and examine the effect of attacks on the images, noise is added to the image data. Also, to test the effect of reducing an image in size, each image containing the embedded watermark is compressed. The dissertation asks the questions: “Is the wavelet family chosen to implement the algorithm for a blind, nonvisible watermark in digital images of consequence? If so, which family is superior?” This dissertation conclusively shows that the Haar transform is the best for blind, non-visible digital watermarking.

The estimation of optical flow has become a vital research field in image sequence analysis especially in past two decades, which found applications in many fields such as stereo optics, video compression, robotics and computer vision. In this thesis, the complex wavelet based algorithm for the estimation of optical flow developed by Magarey and Kingsbury is implemented and investigated. The algorithm is based on a complex version of the discrete wavelet transform (CDWT), which analyzes an image through blocks of filtering with a set of Gabor-like kernels with different scales and orientations. The output is a hierarchy of scaled and subsampled orientation-tuned subimages. The motion estimation algorithm is based on the relationship between translations in image domain and phase shifts in CDWT domain, which is satisfied by the shiftability and interpolability property of CDWT. Optical flow is estimated by using this relationship at each scale, in a coarse-to-fine (hierarchical) manner, where information from finer scales is used to refine the estimates from coarser scales. The performance of the motion estimation algorithm is investigated with various image sequences as input and the effects of the options in the algorithm like curvature-correction, interpolation kernel between levels and some parameter values like confidence threshold iv maximum number of CDWT levels and minimum finest level of detail are also experimented and discussed. The test results show that the method is superior to other well-known algorithms in estimation accuracy, especially under high illuminance variations and additive noise.

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

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)