Métodos de suavização através de processos de difusão é frequentemente utilizado como etapa prévia em diferentes procedimentos em imagens. Apesar da difusão anômala ser um processo físico conhecido, ainda não é aplicada à suavização de imagens como a difusão clássica. Esta dissertação propõe e relata a implementação e avaliação de filtros de difusão anômala, tanto isotrópico quanto anisotrópico, como um método de melhoramento em imagens ponderadas em difusão (DWI) e imagens de tensor de difusão (DTI) dentro do imageamento por ressonãncia magnética (MRI). Aqui propõe-se generalizar a difusão anisotrópica e isotrópica com o conceito de difusão anômala em processamento de imagens. Como metodologia implementou-se computacionalmente as equações de difusão bidimensional e aplicou às imagens MRI para avaliar o seu potencial como filtro de melhoramento. Foram utilizadas imagens de ressonância magnética de aquisição DTI em voluntários saudáveis. Os resultados obtidos neste estudo foram a verificação que métodos baseados em difusão anômala melhoram a qualidade em processamento das imagens DTI e DWI quando observadas medidas de qualidade como a relação sinal ruído (SNR) e índice de similaridade estrutural (SSIM), e assim determinou-se parâmetros ótimos para as diferentes imagens e situações que foram avaliadas em função dos seus parâmetros de controle, em especial o parâmetro anômalo, chamado de q. Os resultados apresentados aqui permitem prever não apenas uma melhora na qualidade das imagens DTI e DWI resultantes do processamento proposto, como também possível redução de repetições na sequência de aquisição de MRI para um SNR predeterminado. / Smoothing methods through diffusion processes is often used as a preliminary step in different procedures in images. Although the anomalous diffusion is a known physical process, it is not applied to image smoothing as the classical diffusion. This paper proposes and describes implementation and evaluation of anomalous diffusion filters, both isotropic and anisotropic, as a method of improving on diffusion-weighted images (DWI) and diffusion tensor images (DTI) within the magnetic resonance imaging (MRI). Hereby is proposed to generalize the isotropic and anisotropic diffusion with the concept of anomalous diffusion in image processing. The methodology is implemented computationally as bidimensional diffusion equations and applied to MRI images to evaluate its potential as a filter for quality improvement. We used DTI and DWI imaging to acquire from healthy volunteers as image set. The results of this study verified that methods based on anomalous diffusion improved DWI and DTI image processing when observed quality measures such as signal to noise ratio (SNR) and structural similarity index (SSIM), and determined filter optimal parameters for different images and situations evaluated in terms of their control parameters, particularly the anomalous parameter, called q. The results presented here can predict not only an improvement in the quality of DWI and DTI images resulting from the proposed method, and additionally the possible reduction of repetitions following acquisition of MRI for a predetermined SNR.

Structural health monitoring (SHM) is an active research area devoted to the assessment of the structural integrity of critical components of aerospace, civil and mechanical systems. Guided wave methods have been proposed for SHM of plate-like structures using permanently attached piezoelectric transducers, which generate and sense waves to evaluate the presence of damage. Effective interrogation of structural health is often facilitated by sensors and actuators with the ability to perform directional scanning. In this research, the novel class of Frequency Steerable Acoustic Transducers (FSATs) is proposed for directional generation/sensing of guided waves. The FSATs are characterized by a spatial arrangement of the piezoelectric material which leads to frequency-dependent directionality. The resulting FSATs can be employed both for directional sensing and generation of guided waves, without relying on phasing and control of a large number of channels. Because there is no need for individual control of transducer elements, hardware and power requirements are drastically reduced so that cost and hardware limitations of traditional phased arrays can be partially overcome. The FSATs can be also good candidates for remote sensing and actuation applications, due to their hardware simplicity and robustness. Validation of the proposed concepts first employs numerical methods. Next, the prototyping of the FSATs allows an experimental investigation confirming the analytical and numerical predictions. Imaging algorithm based on frequency warping is also proposed to enhance results representation.

In this paper we present an econometric framework aimed at obtaining projections of income growth in Europe at the regional level. We account for model uncertainty in terms of the choice of explanatory variables, as well as the nature of the spatial spillovers of output growth and human capital investment. Building on recent advances in Bayesian model averaging, we construct projected trajectories of income and human capital simultaneously, while integrating out the effects of other covariates. This approach allows us to assess the potential contribution of future educational attainment to economic growth and income convergence among European regions over the next decades. Our findings suggest that income convergence dynamics and human capital act as important drivers of income growth for the decades to come. In addition we find that the relative return of improving educational attainment levels in terms of economic growth appears to be higher in peripheral European regions. (authors' abstract) / Series: Department of Economics Working Paper Series

In this paper I will give a brief and general overview of the characteristics of spatial data, why it is useful to use such data and how to use the information included in spatial data. The first question to be answered is: how to detect spatial dependency and spatial autocorrelation in data? Such effects can for instance be found by calculating Moran's I, which is a measure for spatial autocorrelation. The Moran's I is also the basis for a test for spatial autocorrelation (Moran's test). Once we found some spatial structure we can use special models and estimation techniques. There are two famous spatial processes, the SAR- (spatial autoregressive) and the SMA- (spatial moving average process) process, which are used to model spatial effects. For estimation of spatial regression models there are mainly two different possibilities, the first one is called spatial filtering, where the spatial effect is filtered out and standard techniques are used, the second one is spatial two stage least square estimation. Finally there are some results of a spatial analysis of R&D spillovers data (for a panel dataset with 22 countries and 20 years) shown. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The finite-difference time-domain (FDTD) method is one of most widely used computational electromagnetics (CEM) methods to solve the Maxwell's equations for modern engineering problems. In biomedical applications, like the microwave imaging for early disease detection and treatment, the human tissues are considered as lossy and dispersive materials. The most popular model to describe the material properties of human body is the Debye model. In order to simulate the computational domain as an open region for biomedical applications, the complex-frequency-shifted perfectly matched layers (CFS-PML) are applied to absorb the outgoing waves. The CFS-PML is highly efficient at absorbing the evanescent or very low frequency waves. This thesis investigates the stability of the CFS-PML and presents some conditions to determine the parameters for the one dimensional and two dimensional CFS-PML.The advantages of the FDTD method are the simplicity of implementation and the capability for various applications. However the Courant-Friedrichs-Lewy (CFL) condition limits the temporal size for stable FDTD computations. Due to the CFL condition, the computational efficiency of the FDTD method is constrained by the fine spatial-temporal sampling, especially in the simulations with the electrically small objects or dispersive materials. Instead of modifying the explicit time updating equations and the leapfrog integration of the conventional FDTD method, the spatial filtered FDTD method extends the CFL limit by filtering out the unstable components in the spatial frequency domain. This thesis implements filtered FDTD method with CFS-PML and one-pole Debye medium, then introduces a guidance to optimize the spatial filter for improving the computational speed with desired accuracy.

Métodos de suavização através de processos de difusão é frequentemente utilizado como etapa prévia em diferentes procedimentos em imagens. Apesar da difusão anômala ser um processo físico conhecido, ainda não é aplicada à suavização de imagens como a difusão clássica. Esta dissertação propõe e relata a implementação e avaliação de filtros de difusão anômala, tanto isotrópico quanto anisotrópico, como um método de melhoramento em imagens ponderadas em difusão (DWI) e imagens de tensor de difusão (DTI) dentro do imageamento por ressonãncia magnética (MRI). Aqui propõe-se generalizar a difusão anisotrópica e isotrópica com o conceito de difusão anômala em processamento de imagens. Como metodologia implementou-se computacionalmente as equações de difusão bidimensional e aplicou às imagens MRI para avaliar o seu potencial como filtro de melhoramento. Foram utilizadas imagens de ressonância magnética de aquisição DTI em voluntários saudáveis. Os resultados obtidos neste estudo foram a verificação que métodos baseados em difusão anômala melhoram a qualidade em processamento das imagens DTI e DWI quando observadas medidas de qualidade como a relação sinal ruído (SNR) e índice de similaridade estrutural (SSIM), e assim determinou-se parâmetros ótimos para as diferentes imagens e situações que foram avaliadas em função dos seus parâmetros de controle, em especial o parâmetro anômalo, chamado de q. Os resultados apresentados aqui permitem prever não apenas uma melhora na qualidade das imagens DTI e DWI resultantes do processamento proposto, como também possível redução de repetições na sequência de aquisição de MRI para um SNR predeterminado. / Smoothing methods through diffusion processes is often used as a preliminary step in different procedures in images. Although the anomalous diffusion is a known physical process, it is not applied to image smoothing as the classical diffusion. This paper proposes and describes implementation and evaluation of anomalous diffusion filters, both isotropic and anisotropic, as a method of improving on diffusion-weighted images (DWI) and diffusion tensor images (DTI) within the magnetic resonance imaging (MRI). Hereby is proposed to generalize the isotropic and anisotropic diffusion with the concept of anomalous diffusion in image processing. The methodology is implemented computationally as bidimensional diffusion equations and applied to MRI images to evaluate its potential as a filter for quality improvement. We used DTI and DWI imaging to acquire from healthy volunteers as image set. The results of this study verified that methods based on anomalous diffusion improved DWI and DTI image processing when observed quality measures such as signal to noise ratio (SNR) and structural similarity index (SSIM), and determined filter optimal parameters for different images and situations evaluated in terms of their control parameters, particularly the anomalous parameter, called q. The results presented here can predict not only an improvement in the quality of DWI and DTI images resulting from the proposed method, and additionally the possible reduction of repetitions following acquisition of MRI for a predetermined SNR.

In this paper we put forward a Bayesian Model Averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS-2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates. After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe. Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers.

We estimate the speed of income convergence for a sample of 196 European NUTS 2 regions over the period 1985-1999. So far there is no direct estimator available for dynamic panels with strong spatial dependencies. We propose a two-step procedure, which involves first spatial filtering of the variables to remove the spatial correlation, and application of standard GMM estimators for dynamic panels in a second step. Our results show that ignorance of the spatial correlation leads to potentially misleading results. Applying a system GMM estimator on the filtered variables, we obtain a speed of convergence of 6.9 per cent and a capital elasticity of 0.43. / Series: EI Working Papers / Europainstitut

Radio frequency interference (RFI) mitigation enables radio astronomical observation in frequency bands that are shared with many modern satellite and ground based devices by filtering out the interference in corrupted bands. The present work documents the development of a beamformer (spatial filter) equipped with RFI mitigation capabilities. The beamformer is intended for systems with antenna arrays designed for large bandwidths. Because array data post processing on large bandwidths would require massive memory space beyond feasible limits, there is a need for a RFI mitigation system capable of doing processing on the data as it arrives in real-time; storing only a data reduced result into long term memory. The real-time system is designed to be implemented on both the FLAG phased array feed (PAF) on the Green Bank telescope in West Virginia, as well as future radio astronomy projects. It will also serve as the anti-jamming component in communications applications developed for the United States office of naval research (ONR). Implemented on a graphical processing unit (GPU), this beamformer demonstrates a working single step filter using nVidia's CUDA technology, technology with high-speed parallelism that makes real-time RFI mitigation possible.

Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region. The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques. The real-world deployment and usability are described for this system.