Search

Wireless Body Area Networks (WBANs) are formed by autonomous devices that collect biometric data from the human body. Such devices (sensors) commonly use low transmitting power. Thus, even small postural variations can cause disconnections. Based on this problem, we propose an algorithm for cluster topology generation, based on anomalous topologies. The proposed algorithm determines metrics for the quality and stability of the clusters, based on a recent history of previous clusters. In order to perform a more realistic evaluation of the proposed algorithm, we consider a real-life typical movement pattern. The Omnet++ was considered, and simulation results showed that the proposed algorithm outperformed the LEACH protocol concerning both: i) energy conservation, and ii) packet delivery ratio in a WBAN with mobility.

The detection of events in time series is an important task in several areas of knowledge where operations monitoring is essential. Experts often have to deal with choosing the most appropriate event detection method for a time series, which can be a complex task. There is a demand for benchmarking different methods in order to guide this choice. For this, standard classification accuracy metrics are usually adopted. However, they are insufficient for a qualitative analysis of the tendency of a method to precede or delay event detections. Such analysis is interesting for applications in which tolerance for "close" detections is important rather than focusing only on accurate ones. In this context, this paper proposes a more comprehensive event detection benchmark process, including an analysis of temporal bias of detection methods. For that, metrics based on the time distance between event detections and identified events (detection delay) are adopted. Computational experiments were conducted using real-world and synthetic datasets from Yahoo Labs and resources from the Harbinger framework for event detection. Adopting the proposed detection delay-based metrics helped obtain a complete overview of the performance and general behavior of detection methods.

The Wireless Body Area Networks (WBANs) are a special case ofthe Wireless Sensor Networks (WSNs) and, in general, are responsiblefor gathering and transmitting biometric data from a givenpatient. One of the features of the WSNs is the self–configuration;i.e., the capability that a given parameter can be adjusted regardingsome occurrence in execution time. As stated, the objective of theWBANs is the gathering, through several sensors, of biometric dataof a patient. In general, because of possible daily activities and/orpathologies, some sensors can be more demanded (or required) incertain periods of the day. In these situations, information of thesementioned sensors are more relevant and require larger data deliverypriority. Thus, we propose QoSBody-AODV, a variation of theAODV routing protocol, which performs the dynamic adjustmentof the message’s priority, turning the network functioning to besensitive to the physical activities performed by the patient, as wellas pathological information.

Sensitive information being shared on the internet is growing. Becauseof this, it is increasingly necessary to take security measureswhilst this information travels in the network. Digital steganographyallows one to send sensitive information in a hidden manner.Although there is a plethora of techniques for such a goal, findingan appropriate one is not always simple. This paper implementsand compares spatial-domain digital steganography techniques inboth RGB and grayscale images. A frequency-domain heuristic forreducing the visual impact of digital steganography in grayscaleimages is presented. As another result of this work, a dataset is alsoavailable in the Kaggle platform with 18 GB of images, containingsecret messages using the techniques under study. In addition, aPython language library was also made available in the PyPI repository,allowing for both concealment and revelation of messagesusing the presented digital steganography methods.

Ao analisar séries temporais é possível observar mudanças significativas no comportamento das observações que frequentemente caracterizam a ocorrência de eventos. Eventos se apresentam como anomalias, pontos de mudança, ou padrões frequentes. Na literatura existem diversos métodos para detecção de eventos. Entretanto, a busca por um método adequado para uma série temporal não é uma tarefa simples, principalmente considerando-se que a natureza dos eventos muitas vezes não é conhecida. Neste contexto, este trabalho apresenta Harbinger, um framework para integração e análise de métodos de detecção de eventos. O Harbinger foi avaliado em dados sintéticos e reais, onde foi possível constatar que suas funcionalidades promovem a seleção de métodos e a compreensão dos eventos detectados.

Every year several landslides are observed in Brazil, causing a major impact on the population which lives in risk areas. These events are permanent concerns for the local population, and also for the authorities responsible for monitoring the risk areas. This work presents UnderApp, a system for remote monitoring of landslides based on a Wireless Underground Sensor Network (WUSN). The sensor network is responsible to collect data in real time about rainfall and soil moisture, which are the main metrics for predicting the eminence of landslides. Then, the collected data is stored in a Web Server and sent to an App designed for smartphones to provide the data visualization by the local population and public authorities. A prototype was implemented and initial tests were performed to ratify the feasibility of the data collection and storage. Moreover, the App interface and its main features are presented as well.

An Internet of Things (IoT) environment usually is composed by sensor nodes connected to the Internet, which constitutes a Wireless Sensor Network (WSN). In this work, the Sensor Allocation Problem (SAP) for a WSN is addressed, which defines the position of sensor nodes according to their different operation modes while pursuing the optimization of network efficiency with respect to performance parameters. An optimization methodology based on a Genetic Algorithm is proposed, in order to solve the SAP. Case studies are performed in order to evaluate the efficiency of the proposed solution method.

In recent years, the search to make cities smart often has been a strategy designed to mitigate problems generated by urban population growth. To improve the population's quality of life and optimize the use of resources and infrastructure, applications in various fields have been developed. Public bus services are widely deployed in cities around the world because they provide cost-effective public transportation. Most of the time the citizens are not provided information about the buses in real-time (location, route, etc). This paper address this lack of information on public bus services and presents ShareBus, an information system to improve urban mobility. The system architecture and its main functionalities are described. A preliminary implementation of the system is also presented which is evaluated through real tests. The system uses the idea of collaboration to obtain and maintain data, either from users or companies, keeping it as much as possible updated.

The Volterra series consists of a powerful method for the identification of non-linear relationships. However, the identification of the series active basis sets requires intense research in order to reduce the computational burden of such a procedure. This is a result of a large number of measurements being required in order to produce an adequate estimate, due to overparameterization issues. In this work, we present a robust hierarchical evolutionary technique which employs a heuristic initialization and provides robustness against noise. The advanced solution is based on a genetic algorithm which improves on the computational complexity of existing methods without harming the identification accuracy. The impact of the parameters calibration is evaluated for different signal-to-noise levels and several nonlinear systems considered in the literature.

Computer models have been an important tool to determine soil bulk density. This soil property is fundamental to estimate soil carbon reserves and consequently to understand the global carbon cycle. The estimation of soil bulk density is not a trivial task since it demands an intensive and often impractical work. The purpose of this paper is to evaluate the performance of a pedotransfer function against an Artificial Neural Networks to estimate soil bulk density for soils at Brazilian biomes. The first one consists of a linear model composed of a Least Square method. The latter employs a robust committee of multilayer perceptron networks and a model selection procedure based on k-fold cross-validation. The data are composed of 3404 soil layers distributed in different Brazilian regions and with different uses. The proposed non-linear regressor presents higher precision when compared to the linear model, and requires less information to do so. Additionally, the developed solution brings to light the assumed relationship between soil bulk density and some soil chemical properties.

Due to the constant innovations in communications tools, several educational institutions are continually evaluating the adoption of new communication tools (NCT) for their adopted learning platforms (LP). Notably, many educational institutions are interested in checking if NCT is bringing benefits in their teaching and learning process. We can state an important problem that tackles this interest as for how to identify when NCT is providing a significantly different complementary communication flow concerning the current communication tools (CCT) provided at LP. This paper presents the Mixed Graph Framework (MGF) to address the problem of measuring the complementarity of an NCT in the scenario where some CCT is already established. Since we are interested in the methodological process, we evaluated MGF using synthetic data. Our experiments observed that the MGF was able to identify whether an NCT produces significant changes in the overall communications of an LP according to some centrality measures.

Reliability analysis of power systems has been attracting increasing attention. Regulatory agencies establish reliability standards that, if infringed, result in costly fines for the utility suppliers. A special concern pertains to the distribution networks on which most failures occur. The allocation of switches is a possible strategy to improve reliability, by allowing network reconfiguration to isolate contingencies and restore power to dark areas. This paper proposes an optimization methodology to allocate switches on radially operated distribution networks. The solution framework considers sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The approach minimizes the costs of allocation and energy not supplied, under reliability and flow capacity constraints. The solution framework is based on memetic algorithm concepts with a structured population. Case studies with a large network and real-world scenarios were used to evaluate the methodology. The results indicate that significant cost reductions can be achieved using the proposed solutions.

Due to the constant innovations in communications tools, several organizations are constantly evaluating the adoption of new communication tools (NCT) with respect to current ones. Especially, many organizations are interested in checking if NCT is really bringing benefits in their production process. We can state an important problem that tackles this interest as for how to identify when NCT is providing a significantly different complementary communication flow with respect to the current communication tools (CCT). This paper presents the Mixed Graph Framework (MGF) to address the problem of measuring the complementarity of a NCT in the scenario where some CCT is already established. We evaluated MGF using synthetic data that represents an enterprise social network (ESN) in the context of well-established e-mail communication tool. Our experiments observed that the MGF was able to identify whether a NCT produces significant changes in the overall communications according to some centrality measures.

Time series prediction has been attracting interest of researchers due to its increasing importance in decision-making activities in many fields of knowledge. The demand for better accuracy in time series prediction furthered the arising of many machine learning time series prediction methods (MLM). Choosing a suitable method for a particular dataset is a challenge and demands established benchmark methods (BM) for performance assessment. Suppose a particular BM is selected, and an experimental comparison is made with a particular MLM. If the latter does not provide better prediction results for the same dataset, this indicates that some improvements are needed for the MLM. Regarding this matter, adopting a well-established, easy to interpret, and tuned BM is desirable. This paper presents a framework for systematic benchmarking some MLM against well-known Linear Methods (LM), namely Polynomial Regression and models in the ARIMA family, used as BM for univariate time series prediction. We implemented such a framework within the R-Package named TSPred. This implementation was evaluated using a wide number of datasets from past prediction competitions. The results show that fittest LM provided by TSPred are adequate BM for univariate time series predictions.

A plataforma de gerencia de conteudo didatico foi criada para atender as demandas no consumo de informacoes digitais da atu- alidade. A criacao desta plataforma foi focada em atender ao pu- blico alvo da instituicao CEFET/RJ Campus Petropolis. A demanda criada por este publico constituido de alunos e visitantes se tor- nou um desafio no aprimoramento da divulgacao das informa- coes. Foram realizados estudos e testes a fim de encontrar manei- ras mais simples e objetivas de entregar o conteudo aos usuarios. Alem da disponibilizacao do conteudo, foram criadas estrategias de seguranca. Estas estrategias estavam relacionadas ao trafego e armazenamento das informacoes durante requisicoes dos usu- arios. A tolerância a falhas tambem foi abordada de forma a pro- porcionar uma recuperacao no menor tempo possivel em caso de falhas ou ataques ao servidor de conteudos.

Nos dias atuais, em grande parte dos ambientes educacionais, o processo de comunicação com os alunos é realizada de forma ad hoc, com baixa padronização e pouca interação entre as partes. Entretanto, uma comunicação mais dinâmica é demandada pelo perfil jovem da maior parte dos alunos. Desta forma, os dispositivos móveis apresentam-se como um canal eficiente e flexível de atingir esse público. Nesse cenário, apresentamos uma plataforma ubíqua e pervasiva para prover a distribuição de conteúdo educacional utilizando um modelo sensível a contexto. Assim, a plataforma pode atender às demandas específicas informadas pelo usuário, e também reagir ao contexto atual do mesmo, enviando conteúdos personalizados conforme aspectos pré-definidos como localização, perfil, dentre outros.

Orientadores: Christiano Lyra Filho, Celso Cavellucci Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação Resumo: Grande parte das falhas em sistemas elétricos de potência ocorrem por consequência de falhas permanentes nas redes de distribuição. Agências reguladoras definem índices de confiabilidade para quantificar e avaliar a qualidade da distribuição de energia. A violação dos limites estabelecidos podem resultar em multas significativas para a distribuidora de energia. Um dos objetivos ao se realizar a instalação de chaves em redes de distribuição é criar a possibilidade de re-energizar a maior quantidade de clientes no menor tempo possível através da transferência de carga para sistemas que não tiveram seu fornecimento de energia interrompido. Esta tese estuda o problema de alocação de chaves (PAC) em sistemas radiais de distribuição de energia elétrica e propõe a instalação otimizada desses dispositivos em locais apropriados das redes, a fim de melhorar a confiabilidade do sistema pela redução do período que os consumidores ficam sem energia. Uma metodologia baseada nos conceitos de algoritmo memético juntamente com uma população estruturada é proposta neste trabalho para alocar chaves seccionadoras e de manobra, manuais e automáticas, com diferentes capacidades. A função objetivo utilizada busca minimizar o custo de alocação das chaves e o custo da energia não distribuída sob restrições de confiabilidade e fluxo de carga em todos os componentes da rede. É apresentado também um estudo multiobjetivo para o PAC, que procura alocar chaves minimizando simultaneamente os custos de instalação das chaves e da energia não distribuída e maximizando a confiabilidade da rede, sob restrições de fluxos. A abordagem proposta para resolver o PAC mono-objetivo também foi utilizada no PAC multiobjetivo, juntamente com o método do ?-restrito. A metodologia proposta tem o seu bom desempenho confirmado por diferentes estudos de casos com redes reais de grande porte localizadas no estado de São Paulo Abstract: Most failures in electric power systems occur as a result of permanent faults in distribution networks. Regulatory agencies establish reliability standards indices for quantify and evaluate the quality of power distribution. The infringe of established limits can result in costly fines for the utility suppliers. One of the aim when perform the switches allocation in distribution networks is the possibility of re-energize the largest amount of customers in the shortest possible time by transferring load to other power systems which don¿t had their energy supply interrupted. This thesis studies the switch allocation problem (SAP) in radial systems of electrical power distribution and proposes an optimized installation of these devices in appropriate locations of network, in order to improve the reliability system by the reducing of the period that consumers remains without power. A methodology based on the concepts of memetic algorithm with a structured population is proposed in this thesis to allocate sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The objective function used seeks to minimize the switches allocation and the energy not supplied costs under constraints of reliability and load flow. A Multi-objective study for SAP is presented, to perform the switches allocation seeks minimize simultaneously the switches installation and energy not supplied costs and maximize the network reliability, under constraints of load flow. The proposed approach to solve the SAP monocriteria was also used in SAP multi-criteria along with the ?-constraint method. The proposed methodology has its good performance confirmed by several case studies with real large networks located in the state of São Paulo Doutorado Automação Doutora em Engenharia Elétrica

Orientador: Paulo Morelato França Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação Resumo: O objetivo desta dissertação é desenvolver uma metodologia eficiente para solucionar o problema de agrupamento capacitado multicritério (PACM), no qual objetos com pesos associados são dados, os quais devem ser particionados em agrupamentos com capacidade limitada. Neste trabalho, o PACM está ambientado em um problema de reagrupamento de lotes urbanos, nos quais devem ser realizadas as leituras dos medidores de energia elétrica por concessionárias de distribuição de energia. A operação de leitura dos medidores é realizada sobre lotes geograficamente definidos e é desempenhada sobre rotas percorridas uma vez por mês pelos leituristas. A motivação deste trabalho é atribuída ao fato de que, com o passar do tempo, o tamanho e o formato dos lotes vão ficando obsoletos, devido a modificações introduzidas na conformação atual, desarranjando o equilíbrio entre os lotes e desatualizando as rotas. Por esse motivo é importante realizar um reagrupamento dos lotes buscando a diminuição dos custos operacionais de leitura, assim como a minimização dos custos e transtornos causados pelas modificações. O método proposto para resolver o problema abordado nesta dissertação é um algoritmo baseado na metaheurística GRASP (Greedy randomized adaptive search procedure). A eficiência do método proposto é testada sobre uma série de instâncias geradas e sobre uma rede real. Os experimentos computacionais demonstram a eficiência do método. Abstract: The aim of this dissertation is to develop an eficient methodology to solve the multicriteria redistricting capacitated problem (PACM), in which objects with associated weights are given, which must be partitioned into groups with limited capacity. In this work, the PACM is inserted in to a reassignment problem of urban clusters of clients, in which the readings of the eletric energy measurement must be performed by the company of energy distribution. The reading operation is performed over lots geographically defined is performed once a month by the readers. The motivation of this work is due to the fact that the size and shape of the lots become obsolete after some time, due to modifications introduced in the current conformation, desarranging the balance between the lots and outdating the routes. For this reason it is important to achieve a reassignment of the lots trying to decrease the operational costs of reading, as well as minimizing the costs and inconvenience caused by the changes. The proposed method to solve the problem addressed in this dissertation is a algorithm based on GRASP (Greedy randomized adaptive search procedure) metaheuristic. The efectiveness of the proposed method is tested on a large number of generated instances and on a real network. Computational experiments demonstrate the efectiveness of the proposed approach. Mestrado Automação Mestre em Engenharia Elétrica

The capacitated redistricting problem (CRP) has the objective to redefine, under a given criterion, an initial set of districts of an urban area represented by a geographic network. Each node in the network has different types of demands and each district has a limited capacity. Real-world applications consider more than one criteria in the design of the districts, leading to a multicriteria CRP (MCRP). Examples are found in political districting, sales design, street sweeping, garbage collection and mail delivery. This work addresses the MCRP applied to power meter reading and two criteria are considered: compactness and homogeneity of districts. The proposed solution framework is based on a greedy randomized adaptive search procedure and multicriteria scalarization techniques to approximate the Pareto frontier. The computational experiments show the effectiveness of the method for a set of randomly generated networks and for a real-world network extracted from the city of São Paulo.