Observa-se nos tempos atuais um crescente interesse e demanda por novas tecnologias de sensoriamento e interação. A monitoração, com o objetivo de reconhecimento de movimentos humanos, permite oferecer serviços personalizados em diferentes áreas, dentre elas a área de cuidados médicos. Essa monitoração pode ser realizada por meio de diferentes técnicas como o uso de câmeras de vídeo, instrumentação do ambiente onde o indivíduo habita, ou pelo uso de dispositivos pessoais acoplados ao corpo. Os dispositivos acoplados ao corpo apresentam vantagens como baixo custo, uso confortável, além de muitas vezes serem despercebidos pelo usuário, diminuindo a sensação de invasão de privacidade durante a monitoração. Além disso, o dispositivo sensor pode ser facilmente acoplado ao corpo pelo próprio usuário, tornando o seu uso efetivo. Deste modo, este trabalho apresenta o desenvolvimento de um sistema que emprega técnicas de inteligência computacional e um acelerômetro facilmente acoplado ao punho do usuário para efetuar, de maneira confortável e não invasiva, o reconhecimento de movimentos básicos da rotina de uma pessoa. Aplicando máquinas de vetores de suporte para classificar os sinais e a razão discriminante de Fisher para efetuar a seleção das características mais significativas, o sistema apresentou uma taxa de sucesso em torno de 93% no reconhecimento de movimentos básicos efetuados por indivíduos monitorados. O sistema apresenta potencialidade para ser integrado a um hardware embarcado de baixo custo, responsável pelo gerenciamento da aquisição dos dados e pelo encaminhamento das informações a um sistema de monitoramento ou armazenamento. As informações providas por este sistema podem ser destinadas à promoção da saúde e bem estar do indivíduo, bem como utilizadas em diagnósticos ou monitoramento remoto de pacientes em um ambiente de vida assistida. / Nowadays it is observed a growing interest and demand for new sensing technologies and interaction. Monitoring with the objective of recognizing human movements, allows us to offer personalized services in different areas, among them healthcare. This monitoring can be performed through the use of different techniques such as the use of video cameras, living environment instrumentation, or the use of personal devices attached to the body, also known as wearable devices. These wearable devices have some advantages such as low cost, comfortable to use, and are often unnoticed by the user, reducing the feeling of privacy invasion during the monitoring. In addition, the sensing device can be easily attached to the body by the user itself, making its use effective. Thus, this work presents the development of a system that uses computational intelligence techniques and an accelerometer which is easily attached to the users wrist to perform, in a comfortable and non-invasive manner, the recognition of basic movements of a persons routine. By applying support vector machines to classify the signals and Fishers discriminant ratio to select the most significant features, the system has shown a success rate of 93% in the recognition of basic movements performed by monitored individuals. The system has the potential to be integrated into a low-cost embedded hardware, which is responsible for managing the data acquisition and routing the movement data to a remote monitoring system or storage. The information provided by the system can be designed to promote the health and wellness of the individual, as well used in diagnostics or remote patient monitoring in an ambient assisted living (AAL).

Modeling a biological systems, is a cyclic process which involves constructing a model from current theory and beliefs and then validating that model against the data. If the data does not match, qualitatively or quantitatively then there may be a problem with either our beliefs or the current theory. At the same time directly finding a model from the existing data would make generalizing results difficult. A considerable difficultly in this process is how to specify the model in the first place. There is a need to be practice which accounts for the growing use of mathematical and statistical methods. However, as a systems becomes more complex, standard mathematical approaches may not be sufficient. In the field of ecology, the standard techniques involve discrete maps, and continuous models such as ODE's. The intent of this work is to present the mathematics necessary to study hybrids of these two models, then consider two case studies. In first case we con sider a coral reef with continuous change, except in the presence of hurricanes. The results of the data are compared quantitatively and qualitatively with simulation results. For the second case we consider a model for rabies with a periodic birth pulse. Here the analysis is qualitative as we demonstrate the existence of a strange attractor by looking at the intersections of the stable and unstable manifold for the saddle point generating the attractor. For both cases studies the introduction of a discrete event into a continuous system is done via a Dirac Distribution or Measure. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

From locating a secure home, foraging for food, running away from predators, spatial navigation is an integral part of everyday life. Multiple brain regions work together to form a three-dimensional representation of our environment; specifically, place cells, grid cells, border cells & head direction cells are thought to interact and influence one another to form this cognitive map. Head direction (HD) cells fire as the animal moves through space, according to directional orientation of the animal’s head with respect to the laboratory reference frame, and are therefore considered to represent the directional sense. Interestingly, inactivation of head direction cell-containing brain regions has mixed consequences on spatial behavior. Current methods of identifying HD cells are limited to in vivo electrophysiological recordings in a dry-land environment. We first developed a dry-land version of the MWM in order to carry out behavioral-recording paired studies. Additionally, to learn about HD cells function we quantified expression of neuronal activation marker (c-Fos), and L-amino acid transporter 4 (Lat4) in neurons found within the HD cell dense anterodorsal thalamic nucleus (ADN) in mice after exploratory behavior in an open field, or forward unidirectional movement on a treadmill. We hypothesize that the degree to which ADN neurons are activated during exploratory behavior is influenced by the range of heading directions sampled. Additionally, we hypothesize that c-Fos and Lat4 are colocalized within ADN neurons following varying amounts of head direction exposure. Results indicate that following free locomotion of mice in an open field arena, which permitted access to 360° of heading, a greater number of ADN neurons express c-Fos protein compared to those exposed to a limited range of head directions during locomotion in a treadmill. These findings suggest that the degree of ADN neuronal activation was dependent upon the range of head directions sampled. We observed a high degree of colocalization of c-Fos and Lat4 within ADN suggesting that Lat4 may be a useful tool to manipulate neuronal activity of HD cells. Identifying genetic markers specific to ADN helps provide an essential understanding of the spatial navigation system, and supports development of therapies for cognitive disorders affecting navigation. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Cidades Inteligentes são sistemas urbanos que usam as Tecnologias da Informação e Comunicação (TICs) para tornar a infraestrutura e os serviços públicos de uma cidade mais interativos, acessíveis e eficientes aos seus habitantes. Com isso, surge a necessidade de proporcionar novos tipos de serviços que busquem auxiliar na organização da cidade, no bem-estar das pessoas e auxiliem a melhorar a governança da cidade. Nesse contexto, o Sensoriamento Urbano é um paradigma emergente, situado no escopo de Cidades Inteligentes, que combina a ubiquidade de smartphones e de diferentes tipos de sensores para coletar dados que retratam diferentes aspectos da cidade. Um aspecto importante nesse processo de sensoriamento é a transmissão dos dados coletados para serem processados por um sistema centralizado remoto. Isso demanda uma ampla e constante cobertura de infraestrutura de rede de comunicação, fato que nem sempre é possível. As Redes Oportunistas surgem como uma alternativa complementar e inovadora para situações como essa, onde as aplicações precisam transmitir dados porém a infraestrutura de rede é intermitente ou mesmo inexistente. Entretanto, com o uso de Redes Oportunistas, as aplicações além de herdarem os seus benefícios também herdam os desafios existentes na área relacionados à tomada de decisão de encaminhamento das mensagens. Dessa forma, a presente tese busca responder ao questionamento de como intensificar a disseminação de conteúdo e o encaminhamento de mensagens em aplicações de Sensoriamento Urbano que fazem uso de Redes Oportunistas como paradigma complementar de comunicação. Para isso é proposto o Situs, um componente de software baseado em Redes Oportunistas que utiliza Sensibilidade à Situação com vistas à proatividade nas tomadas de decisões de roteamento para com isso intensificar a entrega de mensagens. Ele emprega Lógica Fuzzy para a compreensão da situação e uma rede neural chamada Echo State Network (ESN) para efetuar a projeção de situações. Os resultados experimentais demonstraram que a sua performance supera algumas das principais iniciativas existentes na literatura. Por fim, pode-se concluir que ele é capaz de preencher as lacunas do estado da arte apresentadas durante o desenvolvimento da tese sendo capaz de proporcionar um comportamento proativo com o uso de Sensibilidade à Situação. / Smart cities are urban systems that uses Information and Communication Technologies (ICTs) to make infrastructure and public services in a more interactive, accessible and efficient city to its inhabitants. With this comes the need to provide new types of services that seeks to assist in the organization of the city, the well-being of people and assist to improve the governance of the city. In this context, urban sensing is an emerging paradigm, sited in the Smart Cities scope, combining the ubiquity of smartphones with the capability of measuring o sensors to collect data that depict different aspects of the city. This ecosystem consists of different types of mobile and fixed devices orchestrated by a computational architecture that encompass the full sensing process. An important aspect of this process is the transmission of data collected for processing by a remote central system. This requires a broad and constant coverage of communication network infrastructure, a fact that is not always possible. The Opportunistic Networks emerge as an innovative and complementary alternative for situations like this where the applications needs to transmit data but the network infrastructure is intermittent or unavailable. However, using Opportunistic Networks, applications as well as inherit its benefits also inherit the existing challenges in the area related to decision-making of messages forwarding. Thus, this thesis seeks to answer the question of how to improve the dissemination of content and message routing of urban sensing applications that makes use of Opportunistic networks as complementary communication paradigm. Therefore, it proposes Situs, a software component based on Opportunistic Networks that uses Situation Awareness towards a proactivity in making routing decisions. For such task, it applies fuzzy logic for situation comprehension and a king of neural network called Echo State Network (ESN) for situation projection. The results of the experiments showed that their performance outperforms some existent initiatives in literature. Finally, we argue it fullfills the gaps of state of art presented in this thesis and could provide a proactive behaviour with usage of situation awareness.

A relação entre o preço futuro e o preço a vista é um fator que requer muita atenção e planejamento das atividades de comercialização agropecuária. As previsões de preços permitem fornecer a redução das incertezas dentro do mercado de carne bovina auxiliando na determinação da quantidade a ser produzida bem como no estabelecimento de políticas governamentais apropriadas e sustentáveis. Este trabalho tem como objetivo definir um modelo matemático capaz de predizer os preços de carne bovina usando inteligência computacional a partir da análise do ARIMA, Análise de Risco e Redes Neurais Artificiais, identificando aspectos quantitativos relacionados à lógica da decisão na formação do preço de venda, utilizando-se de séries temporais, a fim de explorar as correlações que impactam com maior frequência no preço de venda de carne bovina, por entender que este conhecimento pode aperfeiçoar os instrumentos de avaliação no processo de tomada decisão. A pesquisa caracteriza-se como descritiva, explicativa e quantitativa pois busca-se identificar fatores determinantes para a ocorrência dos fenômenos observados nas séries temporais de preço do boi gordo, traduzindo-se em números as informações classificadas e analisadas com o uso de técnicas estatísticas. Neste estudo foi utilizado a metodologia de séries temporais, aplicada à série histórica de preços do Boi Gordo no período de 23 de julho de 1997 a 18 de fevereiro de 2013, obtida junto ao Centro de Estudos Avançados em Economia Aplicada (CEPEA) da ESALQ/USP/Piracicaba. Estes dados representam o indicador de preço do Boi Gordo ESALQ/BM&F/BOVESPA utilizado como referencial para as negociações de compra e venda de contratos futuros. Os resultados demonstram a eficiência dos modelos propostos para a simulação e instrumentalização, ou seja, permitem avaliar o comportamento linear e não linear do modelo como ferramenta para a geração de informações e redução dos riscos que contribuirá para reduzir a subjetividade no processo de tomada de decisão. / The relationship between the future price and the cash down price of beef is a factor that requires much attention and planning in agricultural market activities. Price previews help to reduce uncertainties in the beef market and to assist in the determination of the quantity of beef to be produced as well as in the establishment of proper and sustainable governmental policies. This work aims to establish a mathematical model capable of predicting the price of beef using computational intelligence from the ARIMA analysis, Risk Analysis and Artificial Neural Network,by identifying the quantitative aspects related to the logic of decision in the formation of selling prices. This is done by using temporal series in order to explore the correlations that impact with greater frequency on the selling price of beef meat. Knowledge thus produced could improve the assessment instruments in the process of decision making. This research is characterized as descriptive, explanatory and quantitative because it intends to identify the determining factors for the occurrence of the observed phenomena in the time series of livestock prices where the classified and analyzed data result in numbers by the use of statistical techniques. The time series methodology was used. The historical series of livestock prices in the period from July 23rd 1997 to February 18th 2013 was obtained in the Center for Advanced Studies in Applied Economics (CEPEA) from ESALQ/USP/Piracicaba. This represents the livestock price indicator ESALQ/BM&F/BOVESPA that is used as a reference for the negotiations of purchase and sale in future contracts. The results show the effectiveness of the proposed models for simulation and orchestration, that is, they allow assessment of linear and non-linear behavior of the model as a tool for the generation of data and risk reductions that will contribute to less subjectivity in the decision-making process.

Abstract not available

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2009. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 5, 2010) Includes bibliographical references (p. 152-159).