Rational Convolution Roots of Isobaric Polynomials

In this paper, we exhibit two matrix representations of the rational roots of generalized Fibonacci polynomials (GFPs) under convolution product, in terms of determinants and permanents, respectively. The underlying root formulas for GFPs and for weighted isobaric polynomials (WIPs), which appeared in an earlier paper by MacHenry and Tudose, make use of two types of operators. These operators are derived from the generating functions for Stirling numbers of the first kind and second kind. Hence we call them Stirling operators. To construct matrix representations of the roots of GFPs, we use the Stirling operators of the first kind. We give explicit examples to show how the Stirling operators of the second kind appear in the low degree cases for the WIP-roots. As a consequence of the matrix construction, we have matrix representations of multiplicative arithmetic functions under the Dirichlet product into its divisible closure.Comment: 13 page

Using PostScript Programming Language in an Undergraduate Computer Graphics Course

Abstract. We report about the experiences of using the PostScript programming language in an undergraduate computer science and computer engineering course as a complementary tool besides OpenGL to teach basic concepts of computer graphics, especially affine transformations and hierarchical modeling using a transformation matrix stack mechanism. We can conclude that once the somewhat cryptic syntax of this stack-oriented language has been overcome, a natural computer graphics programming interface is available which permits a rapid understanding of essential concepts in graphics which can then easily be extrapolated to a 3-D interface like OpenGL. We would like to emphasize that the use of PostScript is not intended as an alternative to the standard graphics programming languages, but as an enrichment of the students programming skills in a completely distinct programming paradigm

A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment

AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis

The influence of anisotropic growth and geometry on the stress of solid tumors

Solid stresses can affect tumor patho-physiology in at least two ways: directly, by compressing cancer and stromal cells, and indirectly, by deforming blood and lymphatic vessels. In this work, we model the tumor mass as a growing hyperelastic material. We enforce a multiplicative decomposition of the deformation gradient to study the role of anisotropic tumor growth on the evolution and spatial distribution of stresses. Specifically, we exploit radial symmetry and analyze the response of circumferential and radial stresses to (a) degree of anisotropy, (b) geometry of the tumor mass (cylindrical versus spherical shape), and (c) different tumor types (in terms of mechanical properties). According to our results, both radial and circumferential stresses are compressive in the tumor inner regions, whereas circumferential stresses are tensile at the periphery. Furthermore, we show that the growth rate is inversely correlated with the stresses’ magnitudes. These qualitative trends are consistent with experimental results. Our findings therefore elucidate the role of anisotropic growth on the tumor stress state. The potential of stress-alleviation strategies working together with anticancer therapies can result in better treatments

The role of malignant tissue on the thermal distribution of cancerous breast

The present work focuses on the integration of analytical and numerical strategies to investigate the thermal distribution of cancerous breasts. Coupled stationary bioheat transfer equations are considered for the glandular and heterogeneous tumor regions, which are characterized by different thermophysical properties. The cross-section of the cancerous breast is identified by a homogeneous glandular tissue that surrounds the heterogeneous tumor tissue, which is assumed to be a two-phase periodic composite with non-overlapping circular inclusions and a square lattice distribution, wherein the constituents exhibit isotropic thermal conductivity behavior. Asymptotic periodic homogenization method is used to find the effective properties in the heterogeneous region. The tissue effective thermal conductivities are computed analytically and then used in the homogenized model, which is solved numerically. Results are compared with appropriate experimental data reported in the literature. In particular, the tissue scale temperature profile agrees with experimental observations. Moreover, as a novelty result we find that the tumor volume fraction in the heterogeneous zone influences the breast surface temperature

Mathematical modeling of the interplay between stress and anisotropic growth of avascular tumors

In this work, we propose a new mathematical framework for the study of the mutual interplay between anisotropic growth and stresses of an avascular tumor surrounded by an external medium. The mechanical response of the tumor is dictated by anisotropic growth, and reduces to that of an elastic, isotropic, and incompressible material when the latter is not taking place. Both proliferation and death of tumor cells are in turn assumed to depend on the stresses. We perform a parametric analysis in terms of key parameters representing growth anisotropy and the influence of stresses on tumor growth in order to determine how these effects affect tumor progression. We observe that tumor progression is enhanced when anisotropic growth is considered, and that mechanical stresses play a major role in limiting tumor growth

DIAGNÓSTICO PRECOCE DE DOENÇAS MAMÁRIAS USANDO IMAGENS TÉRMICAS E APRENDIZADO DE MÁQUINA

O câncer é uma doença que se origina de células mutantes, sem causas bem conhecidas ainda, que se reproduzem descontroladamente, aumentando a perfusão sanguínea e, consequentemente, ocasionando um aumento da temperatura da região tumoral. Essa temperatura é irradiada para a pele e pode ser medida por diversos dispositivos como termômetros e a câmeras térmicas. Na termografia médica (por câmeras infra-vermelho), após a aquisição da imagem térmica, é feita a análise e identificação de padrões térmicos. Tendo em vista que o corpo humano é um sistema praticamente simetrico em relação ao plano sagital (i.e. ao plano que divide o corpo em parte direita e esquerda) , a presença de uma grande alteração no padrão térmico entre as mamas esquerda e direita, é um importante indício de presença de patologias. Este trabalho tem por objetivo verificar a viabilidade do uso de técnicas de reconhecimento de padrões na classificação das imagens disponiveis no projeto ProENG  com pacientes saudáveis ou com portadoras de alguma patologia da mama. Para tanto, destas imagens são extraídas características que permitirão a sua classificação através de técnicas de Inteligência Artificial. Utilizou-se características de três grupos distindos: estatísticas simples, baseadas na geometria fractal e características de fundamentação geoestatística. Foram testados três classificadores, SVM, KNN e Naïve Bayes e duas técnicas de redução de características: PCA e Ganho de Informação. Os resultados se mostraram bastante promissores com uma acurácia próxima de 90% e área abaixo da curva ROC próxima de 0,9%

DETECÇÃO DE REGIÕES SUSPEITAS DE LESÃO NA MAMA EM IMAGENS TÉRMICAS UTILIZANDO SPATIOGRAM E REDES NEURAIS

Este trabalho propõe uma metodologia para identificar regiões suspeitas de lesão baseada nas assime-trias da mama esquerda e direita de imagens de termogramas. O estudo é pautado em imagens de pacientes do Hospital Universitário da Universidade Federal de Pernambuco (UFPE), capturadas por câmera infravermelha. Inicialmente as imagens são manualmente segmentadas. Em seguida, os seios são registrados usando a transformação B-spline. Além disso, como o corpo humano tem uma simetria radial das temperaturas, uma lesão, eventualmente, leva uma assimetria destas regiões, em seguida, o spatiogram é usado para identificar essas regiões assimétricas. Finalmente, apenas as regiões com temperaturas superiores à média são mantidas, com base no fato de que o câncer tem a temperatura mais elevada do que o restante mama. Após esse processo são extraídas características (Variação dos pixels, a média, o desvio padrão, o índice de Geary e Dimensão Fractal de Higuchi) para a classificação dessas regiões restantes em lesão ou não lesão utilizando-se uma rede neural artificial com perceptron em multicamadas. A metodologia apresentou 75% das regiões classificadas corretamente, indicando que o spatiogram e a média das temperaturas das regiões assimétricas são métodos bem promissores para identificação de regiões suspeitas de conter lesão.Palavras-chave: Termografia. Câncer. Spatiogram. Mama. Rede-neural.SUSPECT DETECTION OF REGIONS OF INJURY IN BREAST IN THERMAL IM-AGES USING SPATIOGRAM AND NEURAL NETWORKSAbstract: This paper proposes a methodology to identify suspicious regions of injury based on asymmetries of left and right breasts of thermograms images.. The study is based on images captured by infrared camera from patients at the University Hospital of the Federal University of Pernambuco. Initially the images are manually segmented. Then, the sinuses are recorded using the B-spline transformation. Furthermore, as the human body has a radial symmetry of temperatures, damage eventually leads asymmetry of these regions, then the spatiogram is used to identify those asymmetric.regions. Finally, only the regions with higher than average temperatures are maintained, based on the fact that the cancer has a higher temperature than the rest of the breast. After this process features are extracted (Variation of pixels, the mean, standard deviation, index Geary and Higuchi Fractal Dimension) for the classification of regions remaining in injury or no injury using an artificial neural network Multilayer perceptron. The methodology showed 75% of correctly classified regions, indicating that the spatiogram and the average temperatures of the asymmetric regions are well promises methods to identify regions suspected of containing lesion.Keywords: Thermography. Cancer. Spatiogram. Breast. Neural-network.DETECCIÓN DE ZONAS SOSPECHOSAS DE LESIÓN EN LA MAMA EN IMÁGENES TÉRMICAS UTILIZANDO SPATIOGRAM Y REDES NEURALESResumen: En este trabajo se propone una metodología para identificar las regiones sospechosas de lesión basado en las asimetrías de la mama izquierda y derecha de las imágenes termogramas. El estudio se basa en las imágenes capturadas por la cámara infrarroja de los pacientes en el Hospital Universitario de la Universidade Federal de Pernambuco. Inicialmente, las imágenes son segmentadas manualmente. Luego, los senos se registran utilizando la transformación B-spline. Además, como el cuerpo humano tiene  una simetría radial de temperaturas, daños eventualmente conducen a una asimetría de estas regiones, entonces el spatiogram se utiliza para identificar las regiones asimétricas. Finalmente, basado en el hecho de  que el cáncer tiene una temperatura más alta que el resto de la mama, sólo las regiones con temperaturas más alta que la temperatura media son mantenidas. Después de este proceso se extraen características (Variación de píxeles, la media, desviación estándar, Dimensión índice y Higuchi Geary fractal) para la clasificación de las regiones restantes en lesiones o ninguna lesión utilizando un perceptrón multicapa red neural artificial. La metodología mostró 75% regiones clasificados correctamente, lo que indica que las temperaturas spatiogram y media de las regiones son métodos asimétricos bien promete para identificar regiones sospechosas de contener lesión.Palabras clave: Termografía. Cáncer. Spatiogram. Mama. Redes neuronales

Anais do IX SIBGRAPI (1996) 167-172 Manipulating Facial Appearance Through Age Parameters

Nowadays a wide variety of warping applications is known, like educational ones and entertainment ones. The method presented in this work modifies conventional warping techniques in order to applicate it in ageing facial manipulation. We used ageing curves of facial region to show forward and backward ageing face