Sistema de notificação e monitorização de acidentes para motociclos

A percentagem de utilização de motociclos tem vindo a crescer relativamente à utilização de veículos automóveis. Provavelmente, este número continuará a aumentar anualmente devido a diversos fatores: a mobilidade, a flexibilidade de circulação e o menor gasto de combustível. No entanto, um dos principais problemas da condução de motociclos é o elevado risco de acidente, comparativamente com os veículos automóveis. A segurança do condutor e passageiros, quando sujeitos a um acidente, merece total atenção. Convém, pois, encontrar soluções com capacidade de prevenir ou ajudar a minimizar o número de mortalidade que possam ocorrer diariamente. Uma maior atenção às medidas de segurança rodoviária veio diminuir o número de acidentes, no entanto, quando estes acontecem, a ajuda das unidades médicas demora algum tempo a chegar. Sabe-se que nestas situações, qualquer segundo pode fazer a diferença. Este é o problema que o autor deseja resolver. Na presente dissertação, o autor pretende demonstrar como desenvolveu um sistema para motociclos com a capacidade de reconhecer um acidente, enviando um alerta (SMS) com a informação do local da ocorrência (GPS). Este sistema foi preparado para operar em locais isolados com pouco ou nenhum tráfego rodoviário. Implementou-se no referido sistema uma tecnologia sem fios e fiabilizou-se um método capaz de ser utilizado em diversos modelos de motociclos. Procedeu-se à realização de interfaces que permitem monitorizar e possibilitar o reconhecimento da informação sobre o condutor e sobre o acidente, em tempo real.

Context-aware framework for collaborative applications

Future pervasive environments will take into consideration not only individual user’s interest, but also social relationships. In this way, pervasive communities can lead the user to participate beyond traditional pervasive spaces, enabling the cooperation among groups and taking into account not only individual interests, but also the collective and social context. Social applications in CSCW (Computer Supported Cooperative Work) field represent new challenges and possibilities in terms of use of social context information for adaptability in pervasive environments. In particular, the research describes the approach in the design and development of a context.aware framework for collaborative applications (CAFCA), utilizing user’s context social information for proactive adaptations in pervasive environments. In order to validate the proposed framework an evaluation was conducted with a group of users based on enterprise scenario. The analysis enabled to verify the impact of the framework in terms of functionality and efficiency in real-world conditions. The main contribution of this thesis was to provide a context-aware framework to support collaborative applications in pervasive environments. The research focused on providing an innovative socio-technical approach to exploit collaboration in pervasive communities. Finally, the main results reside in social matching capabilities for session formation, communication and coordinations of groupware for collaborative activities.

Context-awareness privacy in data communications

Internet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

We propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify

Handling Heterogeneity in Collaborative Networked Surgical Simulators

Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee -- However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support -- For these reasons, a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training of users adopting different medical roles during the training sesión -- Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in the training session -- To provide successful training involving good collaborative performance, CNVSS should handle heterogeneity factors such as users’ machine capabilities and network conditions, among others -- Several systems for collaborative training of surgical procedures have been developed as research projects -- To the best of our knowledge none has focused on handling heterogeneity in CNVSS -- Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors -- Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative sesión -- In this document, the development of a context-aware architecture for collaborative networked virtual surgical simulators, in order to handle the heterogeneity involved in the collaboration session, is proposed -- To achieve this, the following main contributions are accomplished in this thesis: (i) Which and how infrastructure heterogeneity factors affect the collaboration of two users performing a virtual surgical procedure were determined and analyzed through a set of experiments involving users collaborating, (ii) a context-aware software architecture for a CNVSS was proposed and implemented -- The architecture handles heterogeneity factors affecting collaboration, applying various adaptation mechanisms and finally, (iii) A mechanism for handling heterogeneity factors involved in a CNVSS is described, implemented and validated in a set of testing scenarios

Reserva de recursos MSRP para o switch de tempo-real HaRTES

Os mecanismos e técnicas do domínio de Tempo-Real são utilizados quando existe a necessidade de um sistema, seja este um sistema embutido ou de grandes dimensões, possuir determinadas características que assegurem a qualidade de serviço do sistema. Os Sistemas de Tempo-Real definem-se assim como sistemas que possuem restrições temporais rigorosas, que necessitam de apresentar altos níveis de fiabilidade de forma a garantir em todas as instâncias o funcionamento atempado do sistema. Devido à crescente complexidade dos sistemas embutidos, empregam-se frequentemente arquiteturas distribuídas, onde cada módulo é normalmente responsável por uma única função. Nestes casos existe a necessidade de haver um meio de comunicação entre estes, de forma a poderem comunicar entre si e cumprir a funcionalidade desejadas. Devido à sua elevada capacidade e baixo custo a tecnologia Ethernet tem vindo a ser alvo de estudo, com o objetivo de a tornar num meio de comunicação com a qualidade de serviço característica dos sistemas de tempo-real. Como resposta a esta necessidade surgiu na Universidade de Aveiro, o Switch HaRTES, o qual possui a capacidade de gerir os seus recursos dinamicamente, de modo a fornecer à rede onde é aplicado garantias de Tempo-Real. No entanto, para uma arquitetura de rede ser capaz de fornecer aos seus nós garantias de qualidade serviço, é necessário que exista uma especificação do fluxo, um correto encaminhamento de tráfego, reserva de recursos, controlo de admissão e um escalonamento de pacotes. Infelizmente, o Switch HaRTES apesar de possuir todas estas características, não suporta protocolos standards. Neste documento é apresentado então o trabalho que foi desenvolvido para a integração do protocolo SRP no Switch HaRTES.

Demonstrador para rede tempo-real HaRTES

A utilização de sistemas embutidos distribuídos em diversas áreas como a robótica, automação industrial e aviónica tem vindo a generalizar-se no decorrer dos últimos anos. Este tipo de sistemas são compostos por vários nós, geralmente designados por sistemas embutidos. Estes nós encontram-se interligados através de uma infra-estrutura de comunicação de forma a possibilitar a troca de informação entre eles de maneira a concretizar um objetivo comum. Por norma os sistemas embutidos distribuídos apresentam requisitos temporais bastante exigentes. A tecnologia Ethernet e os protocolos de comunicação, com propriedades de tempo real, desenvolvidos para esta não conseguem associar de uma forma eficaz os requisitos temporais das aplicações de tempo real aos requisitos Quality of Service (QoS) dos diferentes tipos de tráfego. O switch Hard Real-Time Ethernet Switching (HaRTES) foi desenvolvido e implementado com o objetivo de solucionar estes problemas devido às suas capacidades como a sincronização de fluxos diferentes e gestão de diferentes tipos de tráfego. Esta dissertação apresenta a adaptação de um sistemas físico de modo a possibilitar a demonstração do correto funcionamento do sistema de comunicação, que será desenvolvido e implementado, utilizando um switch HaRTES como o elemento responsável pela troca de informação na rede entre os nós. O desempenho da arquitetura de rede desenvolvida será também testada e avaliada.

MANICURE/PEDICURE TECHNICIANS: BIOSECURITY KNOWLEDGE AND PRACTICES FOR VIRAL HEPATITIS

Objetivo: Investigar o conhecimento e as práticas de biossegurança para hepatites virais de manicures/pedicures. Métodos: Estudo descritivo, transversal, quantitativo, através de questionário, utilizando instrumento de coleta de dados autoaplicado elaborado pelos pesquisadores, contendo dados da população (sexo, idade, tempo de atuação profissional) e conhecimentos básicos sobre transmissão de hepatite e práticas de biossegurança e higiene. Resultados: Entrevistaram-se 96 manicures/pedicures que atuam no Noroeste do Paraná. A maioria das profissionais já ouviu falar da patologia, mas somente 41,7% (n=40) fizeram o exame para detecção do vírus da hepatite; 38,39% (n=77) relataram como via de transmissão o sangue e 31,8% (n=63), a relação sexual. A reutilização de materiais descartáveis foi relatada por 60,4% (n=58); 55,2% (n=53) realizam esterilização de materiais e 27,1% (n=26) não a realizam. Não ficou evidenciada associação significativa entre tempo de profissão e as variáveis utilizadas: ouviu sobre hepatite (p=0,77025), realização de exames (p=0,035476), reutilização de materiais descartáveis (p=0,42691), lavagem de mãos (p=0,32876), uso de luvas descartáveis (p=0,33752) e esterilização de materiais (p=0,84443). Conclusão: As manicures entrevistadas não conhecem as exigências da Vigilância Sanitária no que concerne à prevenção da transmissão de hepatites.

Utilização do mapa espacial de radiação e dose acumulada como ferramenta para otimização de doses em pacientes e trabalhadores nas clínicas de medicina nuclear

This study has as general aim to propose a spatial map of doses as an auxiliary tool in assessing the need for optimization of the workplace in nuclear medicine services. As specific aims, we assessed the workers individual dosimetry; we analyzed the facilities of the nuclear medicine services; and we evaluated environment exposure rates. The research is characterized as a case study, with an exploratory and explanatory nature. It was conducted in three Nuclear Medicine Services, all established in the Northwest of the Paraná State. Results indicated that the evaluated dose rates and workers dosimetry, in all the dependencies of the surveyed services, are within the limits of annual doses. However some exceeded the limits recommended in the standard CNEN-NN 3:01 (2014). It was concluded that the spatial map dose is an important tool for nuclear medicine services because it facilitates the visualization of areas with highest concentration of radiation, and also helps in the constant review of these measures and resources, aiding in the identification of any failures and shortcomings, providing resources to correct any issues and prevent their repetition. The spatial map dose is also important for the regular inspection, evaluating if the radiation protection objectives are being met.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.