Plataforma HW-SW para el disparo de fuegos artificiales

Desde la invención de la pólvora en China en torno al siglo IX, los fuegos artificiales han evolucionado de manera notable. En la actualidad, son habituales los espectáculos de fuegos artificiales de gran magnitud, en los que la sincronización y coordinación entre los distintos artificios pirotécnicos es prácticamente perfecta. Esto es posible gracias al desarrollo de sistemas de disparo eléctricos que permiten la automatización de estos espectáculos. Sin embargo, su precio es bastante elevado. En este trabajo se propone el desarrollo de una plataforma hardware /software capaz de realizar todas las funciones de un sistema de disparo profesional con un coste mucho más reducido. El prototipo presentado está compuesto de dos módulos, a los que se pueden conectar 24 artificios pirotécnicos a cada uno. Estos módulos son controlados por un ordenador central que ejecuta el software diseñado capaz de programar y ejecutar espectáculos pirotécnicos. La comunicación entre el ordenador central y los módulos se realiza de forma inalámbrica mediante la creación de una red WiFi.

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

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.

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.