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.

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.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.

O crime de condução perigosa de veículo rodoviário e a sua perceção pelos militares da GNR afetos a fiscalização rodoviária

The Republican National Guard has a core role concerning the security of Portugal’s roads since a significant part of them are under its responsibility. Dangerous driving is a crime that happens less times that other two crimes (driving without a license and driving under the influence), but it isn’t covered by the media like those two, much to the small number of reports written by the Security Forces. This work aims to approach the particularity of this crime given it poses a real threat and because its report requirements are far demanding. The thesis is divided in two parts. The first one develops the theory frame through the analysis of documents. The second part deals with the processing of fines from selected samples. The conclusion of this study states that there is a way of reducing situations that could result in this type of crime using fines. Other conclusion points to the fact that the time gap between the occurrence of the crime and a judge’s sentence is substantial, meaning that the military from the Republican National Guard often opt for the second option because the sentences and the security measures for this crime can often find parallel in other type of crimes. The about 200 sentences reached per day from courts all over the country when tied to the 700/800 criminal defenses mean that the National Authority for Road Security is overloaded with administrative work, which is a problem that could be surpassed with the implementation of a new document and a different access to the data of each driver through the National Authority for Road Security’s website.