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Banco do Nordeste do Brasil / A avaliaÃÃo educacional dispÃe de mÃtodos para a obtenÃÃo de dados que podem ser Ãteis para avaliar grupos de indivÃduos (alunos, professores, administradores, tÃcnicos e outros), projetos, produtos e materiais, instituiÃÃes e sistemas educacionais, nos seus diversos nÃveis e competÃncias. No campo da educaÃÃo em engenharia, os processos avaliativos podem ajudar os gestores a tomarem decisÃes e a realizarem mudanÃas em cursos de graduaÃÃo. Esta tese investiga de forma inÃdita uma nova abordagem para a anÃlise e interpretaÃÃo de dados no campo da educaÃÃo em engenharia com Ãnfase no processo de avaliaÃÃo, levando em consideraÃÃo dois aspectos de modo integrado: a) a percepÃÃo/opiniÃo dos estudantes sobre o contexto/ambiente educacional (Learning Context - LC) e b) os resultados/rendimentos obtidos pelos mesmos discentes (Learning Outcomes - LO). Para a realizaÃÃo desta pesquisa, foram coletados dados de estudantes do curso de graduaÃÃo em Engenharia de TeleinformÃtica (ETI) do Centro de Tecnologia (CT) da Universidade Federal do Cearà (UFC). Os dados de LC foram coletados a partir da aplicaÃÃo do instrumento SEEQ (Studentâs Evaluation of Educational Quality) da metodologia SETE (Student Evaluate Teaching Effetivecness). Os dados de LO foram coletados a partir das informaÃÃes dos resultados de desempenho da aprendizagem dos mesmos discentes. Na realizaÃÃo do processamento da informaÃÃo dos dados matriciais e tensoriais obtidos, foram utilizadas duas ferramentas matemÃticas: a decomposiÃÃo bilinear, por meio da AnÃlise de Componentes Principais (Principal Component Analysis - PCA) e a decomposiÃÃo multilinear tensorial por meio da AnÃlise de Fatores Paralelos (Parallel Factor Analysis - PARAFAC). Os resultados obtidos permitem identificar caracterÃsticas comuns e semelhanÃas em componentes curriculares, tanto em termos da percepÃÃo quanto do desempenho dos estudantes. Os modelos PCA e PARAFAC tambÃm demonstraram um potencial significativo para extrair informaÃÃes de dados relacionados com variÃveis latentes em contextos educativos. / Educational evaluation provides methods to obtain data that can be useful for evaluating groups of individuals (students, teachers, administrators, technicians and others), projects, products and materials, educational institutions and systems at different levels and skills. In engineering education, evaluation processes can help managers to make decisions and changes in undergraduate courses. This thesis investigates in unprecedented way a new approach to the analysis and interpretation of data in the field of engineering education with emphasis in the evaluation process, taking into account two aspects in an integrated manner: a) perception / opinion of students about the context / educational environment (Learning Context - LC) and b) the results / income earned by the same students (Learning outcomes - LO). For this research, we collected data related to undergraduate students in Teleinformatics Engineering (TEI), at Technology Center (CT) of the Federal University of Cearà (UFC). LC data were collected from the application of SEEQ (Studentâs Evaluation of Educational Quality) instrument of SETE (Student Teaching Evaluate Effetivecness) methodology. The LO data was collected from the information of the performance of the studentsâ learning outcomes. Carrying out the information processing of the obtained tensor and matrix data, we have used two mathematical tools: the bilinear decomposition, called Principal Component Analysis - PCA decomposition and the multilinear tensor decomposition by Parallel Factor Analysis - PARAFAC. The results allow us to identify common features and similarities in curriculum components, both in terms of perception as the performance of students. The PCA and PARAFAC models also showed significant potential to extract data information related to latent variables in educational settings.

Nesse trabalho introduzimos e desenvolvemos a teoria de hiper-ideais de aplicações multilineares contínuas e polinômios homogêneos contínuos entre espaços de Banach. A ideia central é refinar os conceitos de multi-ideais e de ideais de polinômios com o objetivo de explorar de forma mais aprofundada a natureza não-linear das aplicações envolvidas. Para isso tomamos a teoria de ideais de operadores lineares, aplicações multilineares e polinômios homogêneos, desenvolvida a partir dos trabalhos de Pietsch, tanto no caso linear como no caso multilinear, como referencial. Provamos resultados gerais para hiper-ideais, damos muitos exemplos ilustrativos, e desenvolvemos métodos para gerar hiper-ideais, tanto no caso multilinear como no caso polinomial. / In this work we introduce and develop the theory of hyper-ideals of multilinear mappings and homogeneous polynomials between Banach spaces. The main idea is to refine the concepts of multi-ideal and of ideal of polynomials with the purpose of exploring deeply the nonlinear nature of the underlying mappings. To do this we take the ideal theory of linear operators, multilinear mappings and homogeneous polynomials, developed from the works of Pietsch, both in the linear and nonlinear cases, as a reference. We prove general results for hyper-ideals, provide a number of illustrative examples, and develop methods to generate hyper-ideals of multilinear mappings, as well as of hyper-ideals of homogeneous polynomials.

Nesse trabalho introduzimos e desenvolvemos a teoria de hiper-ideais de aplicações multilineares contínuas e polinômios homogêneos contínuos entre espaços de Banach. A ideia central é refinar os conceitos de multi-ideais e de ideais de polinômios com o objetivo de explorar de forma mais aprofundada a natureza não-linear das aplicações envolvidas. Para isso tomamos a teoria de ideais de operadores lineares, aplicações multilineares e polinômios homogêneos, desenvolvida a partir dos trabalhos de Pietsch, tanto no caso linear como no caso multilinear, como referencial. Provamos resultados gerais para hiper-ideais, damos muitos exemplos ilustrativos, e desenvolvemos métodos para gerar hiper-ideais, tanto no caso multilinear como no caso polinomial. / In this work we introduce and develop the theory of hyper-ideals of multilinear mappings and homogeneous polynomials between Banach spaces. The main idea is to refine the concepts of multi-ideal and of ideal of polynomials with the purpose of exploring deeply the nonlinear nature of the underlying mappings. To do this we take the ideal theory of linear operators, multilinear mappings and homogeneous polynomials, developed from the works of Pietsch, both in the linear and nonlinear cases, as a reference. We prove general results for hyper-ideals, provide a number of illustrative examples, and develop methods to generate hyper-ideals of multilinear mappings, as well as of hyper-ideals of homogeneous polynomials.

The goal of this research was to examine the potential predictive ability of socioeconomic and demographic data for drivers on Kentucky crash occurrence. Identifying unique background characteristics of at-fault drivers that contribute to crash rates and crash severity may lead to improved and more specific interventions to reduce the negative impacts of motor vehicle crashes. The driver-residence zip code was used as a spatial unit to connect five years of Kentucky crash data with socioeconomic factors from the U.S. Census, such as income, employment, education, age, and others, along with terrain and vehicle age. At-fault driver crash counts, normalized over the driving population, were used as the dependent variable in a multivariate linear regression to model socioeconomic variables and their relationship with motor vehicle crashes. The final model consisted of nine socioeconomic and demographic variables and resulted in a R-square of 0.279, which indicates linear correlation but a lack of strong predicting power. The model resulted in both positive and negative correlations of socioeconomic variables with crash rates. Positive associations were found with the terrain index (a composite measure of road curviness), travel time, high school graduation and vehicle age. Negative associations were found with younger drivers, unemployment, college education, and terrain difference, which considers the terrain index at the driver residence and crash location. Further research seems to be warranted to fully understand the role that socioeconomic and demographic characteristics play in driving behavior and crash risk.

Denoising signals is an important task of digital signal processing. Many linear and non-linear methods for signal denoising have been developed. Wavelet based denoising is the most famous nonlinear denoising method lately. In the linear case, Kalman filter is famous for its easy implementation and real-time nature. Wavelet- Kalman filter developed lately is an important improvement over Kalman filter, in which the Kalman filter operates in the wavelet domain, filtering the wavelet coeffi- cients, and resulting in the filtered wavelet transform of the signal in real-time. The real-time filtering and multiresolution representation is a powerful feature for many real world applications. This study explains in detail the derivation and implementation of Real-Time Wavelet-Kalman Filter method to remove noise from signals in real-time. The filter is enhanced to use different wavelet types than the Haar wavelet, and also it is improved to operate on higer block sizes than two. Wavelet shrinkage is integrated to the filter and it is shown that by utilizing this integration more noise suppression is obtainable. A user friendly application is developed to import, filter and export signals in Java programming language. And finally, the applicability of the proposed method to suppress noise from seismic waves coming from eartquakes and to enhance spontaneous potentials measured from groundwater wells is also shown.

In this work, we study some generalized multipartite access structures and linear secret sharing schemes for their realizations. Given a multipartite set of participants with m compartments (or levels) and m conditions to be satisfied by an authorized set, we firstly examine the intermediary access structures arousing from the natural case concerning that any c out of m of these conditions suffice, instead of requiring anyone or all of the m conditions simultaneously, yielding to generalizations for both the compartmented and hierarchical cases. These are realized essentially by employing a series of Lagrange interpolations and a simple frequently-used connective tool called access structure product, as well as some known constructions for existing ideal schemes. The resulting schemes are non-ideal but perfect. We also consider nested multipartite access structures, where we let a compartment to be defined within another, so that the access structure is composed of some multipartite substructures. We extend formerly employed bivariate interpolation techniques to multivariate interpolation, in order to realize such access structures. The generic scheme we consider is perfect with a high probability such as 1-O(1/q) on a finite field F_q. In particular, we propose a non-nested generalization for the conventional compartmented access structures, which depicts a stronger way of controlling the additional participants.

CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / In this dissertation, the face recognition problem is investigated from the standpoint of multilinear algebra, more specifically the tensor decomposition, and by making use of Gabor wavelets. The feature extraction occurs in two stages: first the Gabor wavelets are applied holistically in feature selection; Secondly facial images are modeled as a higher-order tensor according to the multimodal factors present. Then, the HOSVD is applied to separate the multimodal factors of the images. The proposed facial recognition approach exhibits higher average success rate and stability when there is variation in the various multimodal factors such as facial position, lighting condition and facial expression. We also propose a systematic way to perform cross-validation on tensor models to estimate the error rate in face recognition systems that explore the nature of the multimodal ensemble. Through the random partitioning of data organized as a tensor, the mode-n cross-validation provides folds as subtensors extracted of the desired mode, featuring a stratified method and susceptible to repetition of cross-validation with different partitioning. / Nesta dissertaÃÃo o problema de reconhecimento facial à investigado do ponto de vista da Ãlgebra multilinear, mais especificamente por meio de decomposiÃÃes tensoriais fazendo uso das wavelets de Gabor. A extraÃÃo de caracterÃsticas ocorre em dois estÃgios: primeiramente as wavelets de Gabor sÃo aplicadas de maneira holÃstica na seleÃÃo de caracterÃsticas; em segundo as imagens faciais sÃo modeladas como um tensor de ordem superior de acordo com o fatores multimodais presentes. Com isso aplicamos a decomposiÃÃo tensorial Higher Order Singular Value Decomposition (HOSVD) para separar os fatores que influenciam na formaÃÃo das imagens. O mÃtodo de reconhecimento facial proposto possui uma alta taxa de acerto e estabilidade quando hà variaÃÃo nos diversos fatores multimodais, tais como, posiÃÃo facial, condiÃÃo de iluminaÃÃo e expressÃo facial. Propomos ainda uma maneira sistemÃtica para realizaÃÃo da validaÃÃo cruzada em modelos tensoriais para estimaÃÃo da taxa de erro em sistemas de reconhecimento facial que exploram a natureza multilinear do conjunto de imagens. AtravÃs do particionamento aleatÃrio dos dados organizado como um tensor, a validaÃÃo cruzada modo-n proporciona a criaÃÃo de folds extraindo subtensores no modo desejado, caracterizando um mÃtodo estratificado e susceptÃvel a repetiÃÃes da validaÃÃo cruzada com diferentes particionamentos.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-02T10:00:56Z No. of bitstreams: 1 fabriciovieiraoliveira.pdf: 1771957 bytes, checksum: 8c6b8d555c008276c082082c55021a5e (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-02T11:42:53Z (GMT) No. of bitstreams: 1 fabriciovieiraoliveira.pdf: 1771957 bytes, checksum: 8c6b8d555c008276c082082c55021a5e (MD5) / Made available in DSpace on 2016-02-02T11:42:53Z (GMT). No. of bitstreams: 1 fabriciovieiraoliveira.pdf: 1771957 bytes, checksum: 8c6b8d555c008276c082082c55021a5e (MD5) Previous issue date: 2014-04-24 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O presente trabalho tem por objetivo apresentar aplicações da teoria de ultraprodutos em Análise Funcional em espaços de Banach, especificamente nos problemas de extensão de funções holomorfas, constantes de polarização e ideais de operadores maximais. Também é realizada uma revisão dos conceitos relacionados a topologia, aplicações multilineares, ultrafiltros e ultraprodutos de espaços de Banach. / This is work aims to present a application of the ultraproducts theory in Functional Analysis in Banach spaces, specifically in the problems of extension of holomorphic functions, polarization constants and maximal operator ideals. Also is performed a review of concepts about topology, multilinear maps, ultrafilters and ultraproducts in Banach spaces.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively). Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.