Greenow: um algoritmo de roteamento orientado a workspace para uma arquitetura de Internet do futuro

Current and future applications pose new requirements that Internet architecture is not able to satisfy, like Mobility, Multicast, Multihoming, Bandwidth Guarantee and so on. The Internet architecture has some limitations which do not allow all future requirements to be covered. New architectures were proposed considering these requirements when a communication is established. ETArch (Entity Title Architecture) is a new Internet architecture, clean slate, able to use application’s requirements on each communication, and flexible to work with several layers. The Routing has an important role on Internet, because it decides the best way to forward primitives through the network. In Future Internet, all requirements depend on the routing. Routing is responsible for deciding the best path and, in the future, a better route can consider Mobility aspects or Energy Consumption, for instance. In the dawn of ETArch, the Routing has not been defined. This work provides intra and inter-domain routing algorithms to be used in the ETArch. It is considered that the route should be defined completely before the data start to traffic, to ensure that the requirements are met. In the Internet, the Routing has two distinct functions: (i) run specific algorithms to define the best route; and (ii) to forward data primitives to the correct link. In traditional Internet architecture, the two Routing functions are performed in all routers everytime that a packet arrives. This work allows that the complete route is defined before the communication starts, like in the telecommunication systems. This work determined the Routing for ETArch and experiments were performed to demonstrate the control plane routing viability. The initial setup before a communication takes longer, then only forwarding of primitives is performed, saving processing time.

HIPERLOCAL, DADOS E APLICATIVOS: inovações no fazer jornalismo e comunicação

A evolução tecnológica na comunicação contemporânea estrutura sistemas digitais via redes de computadores conectados e exploração maciça de dispositivos tecnológicos. Os dados digitais captados e distribuídos via aplicativos instalados em smartphones criam ambiente dinâmico comunicacional. O Jornalismo e a Comunicação tentam se adaptar ao novo ecossistema informacional impetrado pelas constantes inovações tecnológicas que possibilitam a criação de novos ambientes e sistemas para acesso à informação de relevância social. Surgem novas ferramentas para produção e distribuição de conteúdos jornalísticos, produtos baseados em dados e interações inteligentes, algoritmos usados em diversos processos, plataformas hiperlocais e sistemas de narrativas e produção digitais. Nesse contexto, o objetivo da pesquisa foi elaborar uma análise e comparação entre produtos de mídia e tecnologia específicos. Se as novas tecnologias acrescentam atributos às produções e narrativas jornalísticas, seus impactos na prática da atividade e também se há modificação nos processos de produção de informação de relevância social em relação aos processos jornalísticos tradicionais e consolidados. Investiga se o uso de informações insertadas pelos usuários, em tempo real, melhora a qualidade das narrativas emergentes através de dispositivos móveis e se a gamificação ou ludificação altera a percepção de credibilidade do jornalismo. Para que assim seja repensado a forma de se produzir e gerar informação e conhecimento para os públicos que demandam conteúdo

Relatório de Estágio na Inficon AG e na PT Inovação & Sistemas

This document focuses the projects developed during two independent internships, which were carried out at Inficon AG and PT Inovação & Sistemas. Since the research areas of both internships are unrelated, individual abstracts are presented.

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.

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.

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.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.