Hundred and thousands of wireless sensor node comprise wireless sensor network (WSN), WSN can be applied in many situations, because a wireless sensor node have small size and wireless transmission advantages, the battery provide sensor node power, but the battery¡¦s power is limit, therefore, energy efficiency is a critical issue, routing protocol can make better energy consumption and loading balance, Clustering is useful routing protocol in WSN and provides the direction of energy efficiency. The ARC (Adaptive Re-Clustering) is based on clustering; ARC can reduce the whole WSN power consumption and protect less residue power cluster-head. The experiments prove that ARC can improve network reliability and extend network lifetime.

Wireless Sensor Network (WSN) is composed of micro sensor nodes and it represents that they are small in size and cheap in cost but own limited capacity of computation and operation time. WSN is used to detect and sense events like temperature, earthquake, creature activities, atmospheric pressure and so on. By the property of wireless data transmission, WSN can be rapidly deployed and easily built up. In other hand, lifetime of WSN has been constrained by the batteries built in each sensor node. To transmit sensed data back to the base station spends the most energy for the WSN, and thus how to operate efficiently will be the key to extend the operating time of the WSN. There are a lot of related researches that proposed many routing protocols to maximize WSN lifetime and clustering is a proven routing protocol for WSN energy efficiency. The clustering method group nearby nodes together and choose one of them as a cluster-head that will transmit data back. The most important issue of clustering method is to choose which as a cluster-head. Usually, cluster-head will be chosen by probability and normal nodes will choose their own cluster-head by distance. Global-fit and Energy-Efficient (GFEE) algorithm, which is based on global-fit concept, is proposed to enhance lifetime of WSN. GFEE not only chooses cluster-head by probability and taking turns, but also bases on residual energy. All other nodes choose their cluster-head by distance and total energy consumption. Nodes with low power should be protected by some mechanisms. Experiments approved GFEE, especially in the situations of nodes widely spread or long distance transmission.

Abstract Cardiovascular disease is one of the leading causes of death in Canada. Atherosclerosis is considered the primary cause for cardiovascular disease. Optical coherence tomography (OCT) provides a means to minimally invasive imaging and assessment of textural features of atherosclerotic plaque. However, detecting atherosclerotic plaque by visual inspection from Optical Coherence Tomography (OCT) images is usually difficult. Therefore we developed unsupervised segmentation algorithms to automatically detect atherosclerosis plaque from OCT images. We used three different clustering methods to identify atherosclerotic plaque automatically from OCT images. Our method involves data preprocessing of raw OCT images, feature selection and texture feature extraction using the Spatial Gray Level Dependence Matrix method (SGLDM), and the application of three different clustering techniques: K-means, Fuzzy C-means and Gustafson-Kessel algorithms to segment the plaque regions from OCT images and to map the cluster regions (background, vascular tissue, OCT degraded signal region and Atherosclerosis plaque) from the feature-space back to the original preprocessed OCT image. We validated our results by comparing our segmented OCT images with actual photographic images of vascular tissue with plaque. / October 2015

Systems created within the aspect-oriented paradigm (AOP) are difficult for programmers to understand fully. AOP suggests moving crosscutting concerns scattered throughout class code into an individual module, known as an aspect. The process of aspect weaving injects the crosscutting concern back into class code at specific locations, known as joinpoints. A side effect of the weaving process is aspect interference-when aspect code creates unexpected results at a joinpoint. Therefore, developing an understanding of locations that could either cause or exhibit aspect interference problems is essential to developing an interference-free AOP system. This study used the interference potential (IP) and interference causality potential (ICP) metrics, and derived a new metric called total interference potential (TIP), to classify areas of potential interference problems. In addition, the project performs a hierarchical agglomerative clustering using the three metrics. Experiments conducted on two AOP systems identified clusters within each program that could cause or exhibit aspect interference problems. Results showed the merit of using clustering analysis as a technique to locate portions of a system to review or alter to prevent interference problems.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents a method to accurately determine the location and amount of pedestrians detected by different vehicles equipped with a Pedestrian Autonomous Emergency Braking (PAEB) system, taking into consideration the inherent inaccuracy of the pedestrian sensing from these vehicles. In the thesis, a mathematical model of the pedestrian information generated by the PAEB system in the V2V network is developed. The Greedy-Medoids clustering algorithm and constrained hierarchical clustering are applied to recognize and reconstruct actual pedestrians, which enables a subject vehicle to approximate the number of the pedestrians and their estimated locations from a larger number of pedestrian alert messages received from many nearby vehicles through the V2V network and the subject vehicle itself. The proposed methods determines the possible number of actual pedestrians by grouping the nearby pedestrians information broadcasted by different vehicles and considers them as one pedestrian. Computer simulations illustrate the effectiveness and applicability of the proposed methods. The results are more integrated and accurate information for vehicle Autonomous Emergency Braking (AEB) systems to make better decisions earlier to avoid crashing into pedestrians.

Multivariate data is prevalent in modern applications, yet it often presents significant analytical challenges. Factor models can offer an effective tool to address issues associated with large-scale datasets. In this dissertation, we propose two novel Bayesian factors models. These models are designed to effectively reduce the dimensionality of the data, as the number of latent factors is typically much smaller than that of the observation vectors. Therefore, our proposed models can achieve substantial dimension reduction. Our first model is for spatiotemporal areal data. In this case, the region of interest is divided into subregions, and at each time point, there is one univariate observation per subregion. Our model writes the vector of observations at each time point in a factor model form as the product of a vector of factor loadings and a vector of common factors plus a vector of error. Our model assumes that the common factor evolves through time according to a dynamic linear model. To represent the spatial relationships among subregions, each column of the factor loadings matrix is assigned intrinsic conditional autoregressive (ICAR) priors. Therefore, we call our approach the Dynamic ICAR Spatiotemporal Factor Models (DIFM). Our second model, Bayesian Clustering Factor Model (BCFM) assumes latent factors and clusters are present in the data. We apply Gaussian mixture models on common factors to discover clusters. For both models, we develop MCMC to explore the posterior distribution of the parameters. To select the number of factors and, in the case of clustering methods, the number of clusters, we develop model selection criteria that utilize the Laplace-Metropolis estimator of the predictive density and BIC with integrated likelihood. / Doctor of Philosophy / Understanding large-scale datasets has emerged as one of the most significant challenges for researchers recently. This is particularly true for datasets that are inherently complex and nontrivial to analyze. In this dissertation, we present two novel classes of Bayesian factor models for two classes of complex datasets. Frequently, the number of factors is much smaller than the number of variables, and therefore factor models can be an effective approach to handle multivariate datasets. First, we develop Dynamic ICAR Spatiotemporal Factor Model (DIFM) for datasets collected on a partition of a spatial domain of interest over time. The DIFM accounts for the spatiotemporal correlation and provides predictions of future trends. Second, we develop Bayesian Clustering Factor Model (BCFM) for multivariate data that cluster in a space of dimension lower than the dimension of the vector of observations. BCFM enables researchers to identify different characteristics of the subgroups, offering valuable insights into their underlying structure.

Necessity to sustain resources has risen in recent years with significant number of people affected by lack of access to essential resources. Framing policies that support environmental sustainability is necessary for addressing the issue. Effective policies necessitate access to a framework which assesses and keeps track of sustainability. Conventional frameworks that support such policy-making involve ranking of countries based on a weighted sum of several environmental performance metrics. However, the selection and weighing of metrics is often biased. This study proposes a new framework to assess environmental sustainability of countries via leveraging unsupervised learning. Specifically, this framework harnesses a clustering technique and tracks progressions in terms of shifts within clusters over time. It is observed that using the proposed framework, countries can identify specific ways to improve their progress towards environmental sustainability.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T15:53:56Z No. of bitstreams: 1 Esdras Adriano Barbosa dos Santos.pdf: 719323 bytes, checksum: 7defd3639c47f0575744d0faadba2053 (MD5) / Made available in DSpace on 2016-07-05T15:53:56Z (GMT). No. of bitstreams: 1 Esdras Adriano Barbosa dos Santos.pdf: 719323 bytes, checksum: 7defd3639c47f0575744d0faadba2053 (MD5) Previous issue date: 2008-02-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Image analysis is frequently used by ophthalmologists as part of the diagnostic procedure. Inspection of the vascular structure of the retina may reveal early stages of pathologies such as diabetic retinopathy, and there have been various efforts to develop more efficient methods for diagnosing such diseases. Currently, identification of abnormalities requires a laborious inspection of a large number of images from the part of specialists, and there is a necessity of automating this process to provide auxiliary diagnostic tools of high speed and precision. One of the lines of research conducted in the direction of differentiating between healthy and pathological retinal images uses the concept of fractal dimension. Recently it was shown that the vascular structure of the human retina is not a simple fractal, but rather a multifractal, characterized by a non trivial multifractal spectrum. In this work, multivariate clustering methods are applied to the results of the multifractal analysis, in order to establish the sensitivity of this analysis, and its ability to differentiate between the normal and pathological cases of the human retina. The variables used for this purpose are the elements of the multifractal spectrum f (a) and the generalized dimension D(q), from which three distinct sets of variables were chosen. The clustering methods used for this study are the Ward method, K-means, PAM and Fuzzy C-means. As a measure ofvalidation of the obtained groups the cophenetic correlation was used for the Ward method,and the silhouette graphs for K-means, PAM and Fuzzy C-means. The results show that for the skeletonized images 70-80% of the pathological images were correctly classified (depending on the method and the variables used), while for the original segmented images clustering produces worse results. This fact indicates that the width of the vessels exerts less influence on the conclusions of the current analysis in comparison with the length distribution and the ramification structure. Thus, we may conclude that the multifractal analysis, with adequate pre-processing of the images and choice of variables, can be used for detection of pathological cases, as part of the pre-diagnostic procedure. / A análise de imagens é freqüentemente praticada por oftalmologistas para diagnóstico de doenças. A inspeção da vascularização da retina pode revelar inícios de doenças como a retinopatia diabética. Desta forma, existem vários esforços para o desenvolvimento de métodos mais eficazes no diagnóstico destas doenças. A identificação de anormalidades requer uma trabalhosa inspeção de uma grande quantidade das imagens por especialistas. Assim sendo, há necessidade de desenvolvimento de softwares para o auxílio dos oftalmologistas na busca de uma diagnose mais rápida e mais precisa. O uso da dimensão fractal na busca de diferenciação entre retinas com e sem patologias é mais um dos ramos de pesquisa realizados nesta área. Recentemente, foi mostrado que a retina humana não é um fractal simples, mas um multifractal, caracterizado pelo espectro multifractal não trivial. Neste trabalho, foram aplicados métodos de agrupamento nos resultados da análise multifractal para verificar a sensibilidade desta análise na diferenciação entre casos patológicos e casos normais da retina humana. As variáveis usadas são os elementos de espectro multifractal f (a) e dimensões generalizadas D(q), das quais foram escolhidos três conjuntos distintos. Os métodos de agrupamento usados para análise foram o método de Ward, K-médias, PAM e Fuzzy c-means. Como medida para a validação dos grupos obtidos, foiusada a correlação cofenética para o método de Ward e gráficos de silhueta e silhueta média para os métodos K-médias, PAM e Fuzzy c-means. Os resultados mostraram que, para imagens esqueletonizadas, 70-80% das retinas patológicas (dependendo do método e do conjunto de variáveis usadas) foram agrupadas corretamente, enquanto que para as imagens segmentadas originais, o agrupamento não apresentou resultados tão satisfatórios. Este fato indica que a largura dos vasos apresenta menor influência para as conclusões da análise atual, em comparação com o comprimento dos vasos e suas ramificações. Diante disso, é possível concluir que a análise multifractal, aliada ao pré-processamento adequado das imagens e a escolha adequada das variáveis, pode ser utilizada para detecção de casos patológicos ou para o pré-diagnóstico.

O envolvimento do consumidor é fundamental nas fases iniciais de projetos de produtos inovadores, para a coleta de informações sobre interesses e preferências orientadores do processo de geração de ideias e conceitos de novos produtos. Uma das formas de viabilizar este envolvimento é utilizando ferramentas do tipo Sistema de Engenharia Kansei. Esse tipo de ferramenta permite a tradução de inputs de grande volume de consumidores em configurações de produtos otimizados para auxiliar a equipe de projeto, no Processo de Desenvolvimento de Produtos. Há duas principais limitações nos Sistemas de Engenharia Kansei propostos na literatura. A primeira é a operacionalização do envolvimento do consumidor na etapa de definição do campo das propriedades, i.e., captação dos dados de entrada dos consumidores. A segunda é a continuidade do envolvimento, com a intenção de fornecer informações atualizadas à equipe de projetos de produtos. Este trabalho propõe e testa procedimento automático para apoiar a definição do campo das propriedades utilizando inputs indiretos de consumidores obtidos em lojas virtuais, empregando e adaptando métodos utilizados em aplicações de Sistemas de Engenharia Kansei e Sistemas de Recomendação Híbridos. O procedimento automático fornece como resultado principal uma lista de produtos e propriedades, obtidos da realidade, representativos do domínio Kansei para utilização nas etapas posteriores de um Sistema de Engenharia Kansei. O teste do procedimento automático demonstrou que a dissimilaridade presente no conjunto inicial de produtos determina o número máximo de produtos representativos do domínio; e, que o grupo de produtos e propriedades representativos do domínio, obtido da aplicação do procedimento automático, pode apresentar disparidade com relação a um grupo referencial obtido utilizando método de planejamento de experimentos, embora atenda aos critérios informados na literatura seminal de Engenharia Kansei. / The customer involvement is critical in the early stages of innovative projects, to collect information about guiding interests and preferences of the process of generating ideas and concepts of new products. One way to facilitate this involvement is using the type system of Kansei Engineering tools. This type of tool allows the translation of large volume of inputs of consumers in products optimized settings to assist the project team, the Product Development Process. There are two main limitations in Kansei Engineering Systems proposed in the literature. The first is the operationalization of consumer involvement in the step of defining the field of properties, i.e., capture the input data consumer. The second is the continued involvement with the intention to provide updated information to the team of product designs. This thesis proposes and tests automatic procedure to support the definition of the properties field using indirect inputs of consumers obtained in virtual stores, using and adapting methods used in applications of Kansei Engineering Systems and Hybrid Recommender Systems. The automatic procedure provides as main result a list of products and properties obtained from reality, representative of Kansei domain for use in the later stages of a Kansei Engineering System. The automatic test procedure showed that the dissimilarity present in the initial product set determines the maximum number of products representative of the field; and that the product group and representative properties of the domain obtained from the application of the automatic procedure can present disparity with respect to a reference group obtained using planning method of experiments, although meets the criteria given in the seminal literature Kansei Engineering.

O envolvimento do consumidor é fundamental nas fases iniciais de projetos de produtos inovadores, para a coleta de informações sobre interesses e preferências orientadores do processo de geração de ideias e conceitos de novos produtos. Uma das formas de viabilizar este envolvimento é utilizando ferramentas do tipo Sistema de Engenharia Kansei. Esse tipo de ferramenta permite a tradução de inputs de grande volume de consumidores em configurações de produtos otimizados para auxiliar a equipe de projeto, no Processo de Desenvolvimento de Produtos. Há duas principais limitações nos Sistemas de Engenharia Kansei propostos na literatura. A primeira é a operacionalização do envolvimento do consumidor na etapa de definição do campo das propriedades, i.e., captação dos dados de entrada dos consumidores. A segunda é a continuidade do envolvimento, com a intenção de fornecer informações atualizadas à equipe de projetos de produtos. Este trabalho propõe e testa procedimento automático para apoiar a definição do campo das propriedades utilizando inputs indiretos de consumidores obtidos em lojas virtuais, empregando e adaptando métodos utilizados em aplicações de Sistemas de Engenharia Kansei e Sistemas de Recomendação Híbridos. O procedimento automático fornece como resultado principal uma lista de produtos e propriedades, obtidos da realidade, representativos do domínio Kansei para utilização nas etapas posteriores de um Sistema de Engenharia Kansei. O teste do procedimento automático demonstrou que a dissimilaridade presente no conjunto inicial de produtos determina o número máximo de produtos representativos do domínio; e, que o grupo de produtos e propriedades representativos do domínio, obtido da aplicação do procedimento automático, pode apresentar disparidade com relação a um grupo referencial obtido utilizando método de planejamento de experimentos, embora atenda aos critérios informados na literatura seminal de Engenharia Kansei. / The customer involvement is critical in the early stages of innovative projects, to collect information about guiding interests and preferences of the process of generating ideas and concepts of new products. One way to facilitate this involvement is using the type system of Kansei Engineering tools. This type of tool allows the translation of large volume of inputs of consumers in products optimized settings to assist the project team, the Product Development Process. There are two main limitations in Kansei Engineering Systems proposed in the literature. The first is the operationalization of consumer involvement in the step of defining the field of properties, i.e., capture the input data consumer. The second is the continued involvement with the intention to provide updated information to the team of product designs. This thesis proposes and tests automatic procedure to support the definition of the properties field using indirect inputs of consumers obtained in virtual stores, using and adapting methods used in applications of Kansei Engineering Systems and Hybrid Recommender Systems. The automatic procedure provides as main result a list of products and properties obtained from reality, representative of Kansei domain for use in the later stages of a Kansei Engineering System. The automatic test procedure showed that the dissimilarity present in the initial product set determines the maximum number of products representative of the field; and that the product group and representative properties of the domain obtained from the application of the automatic procedure can present disparity with respect to a reference group obtained using planning method of experiments, although meets the criteria given in the seminal literature Kansei Engineering.