Computer-supported cooperative work:alguns temas de investigação

Os primeiros trabalhos sobre Computer-Supported Cooperative Work surgiram na segunda metade da década de 80, estabelecendo-se um campo de investigação interdisciplinar com enfoque no papel do computador e das tecnologias da comunicação no apoio do trabalho em grupo (Ishii et al., 1994). Ao abordar esta área de investigação torna-se claro que é necessário ter em conta a diversidade dos grupos e das tarefas que estes devem de utilizar, entre outros factores importantes. As implicações desta diversidade são discutidas ao nível concepção de interfaces de groupware, em que um maior envolvimento dos utilizadores nas fases iniciais parece ser necessário, e ao nível dos Sistemas de Apoio à Decisão em Grupo.

Bridging present and future of brain-computer interfaces: an assessment of impacts

Based on the report for the “Project III” unit of the PhD programme on Technology Assessment under the supervision of Prof. António B. Moniz. This report was discussed also at the 2nd Winter School on Technology Assessment held at Universidade Nova de Lisboa, Caparica Campus, Portugal on December 2011.

Estudo de Funcionalidades HTML5 para um Editor de Imagem na Internet

Hoje em dia existem múltiplas aplicações multimédia na Internet, sendo comum qualquer website apresentar mais de uma forma de visualização de informação além do texto como, por exemplo: imagens, áudio, vídeo e animação. Com aumento do consumo e utilização de Smartphone e Tablets, o volume de tráfego de internet móvel tem vindo a crescer rapidamente, bem como o acesso à internet através da televisão. As aplicações web-based ganham maior relevância devido à maior partilha ou consumo de conteúdos multimédia, com ou sem edição ou manipulação da mesma, através de redes sociais, como o Facebook. Neste documento é apresentado o estudo de alternativas HTML5 e a implementação duma aplicação web-based no âmbito do Mestrado de Engenharia Informática, ramo de Sistemas Gráficos e Multimédia, no Instituto Superior Engenharia do Porto (ISEP). A aplicação tem como objetivo a edição e manipulação de imagens, tanto em desktop como em dispositivos móveis, sendo este processo exclusivamente feito no lado do cliente, ou seja, no Browser do utilizador. O servidor é usado somente para o armazenamento da aplicação. Durante o desenvolvimento do projeto foi realizado um estudo de soluções de edição e manipulação de imagem existentes no mercado, com a respetiva análise de comparação e apresentadas tecnologias Web modernas como HTML5, CSS3 e JavaScript, que permitirão desenvolver o protótipo. Posteriormente, serão apresentadas, detalhadamente, as várias fases do desenvolvimento de um protótipo, desde a análise do sistema, à apresentação do protótipo e indicação das tecnologias utilizadas. Também serão apresentados os resultados dos inquéritos efetuados a um grupo de pessoas que testaram esse protótipo. Finalmente, descrever-se-á de forma mais exaustiva, a implementação e serão apontadas dificuldades encontradas ao longo do desenvolvimento, bem como indicadas futuras melhorias a introduzir.

Brain-computer interface (BCI): a methodological proposal to assess the impacts of medical applications in 2022

Based on the report for “Project IV” unit of the PhD programme on Technology Assessment (Doctoral Conference) at Universidade Nova de Lisboa (December 2011). This thesis research has the supervision of António Moniz (FCT-UNL and ITAS-KIT) and Armin Grunwald (Karlsruhe Institute of Technology-ITAS, Germany). Other members of the thesis committee are Mário Forjaz Secca (FCT-UNL) and Femke Nijboer (University of Twente, Netherlands).

Purification of complex biopharmaceuticals with new processes, advanced analytics and computer-aided process design tools

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Characterization and surface reconstruction of objects in tomographic images of composite materials

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Heterogeneous computing with an algorithmic skeleton framework

The Graphics Processing Unit (GPU) is present in almost every modern day personal computer. Despite its specific purpose design, they have been increasingly used for general computations with very good results. Hence, there is a growing effort from the community to seamlessly integrate this kind of devices in everyday computing. However, to fully exploit the potential of a system comprising GPUs and CPUs, these devices should be presented to the programmer as a single platform. The efficient combination of the power of CPU and GPU devices is highly dependent on each device’s characteristics, resulting in platform specific applications that cannot be ported to different systems. Also, the most efficient work balance among devices is highly dependable on the computations to be performed and respective data sizes. In this work, we propose a solution for heterogeneous environments based on the abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of the power of all CPU and GPU devices present in a system, without the need for different kernel implementations nor explicit work-distribution.To that end, we extended Marrow, an algorithmic skeleton framework for multi-GPUs, to support CPU computations and efficiently balance the work-load between devices. Our approach is based on an offline training execution that identifies the ideal work balance and platform configurations for a given application and input data size. The evaluation of this work shows that the combination of CPU and GPU devices can significantly boost the performance of our benchmarks in the tested environments, when compared to GPU-only executions.

STAR: Speech Therapy with Augmented Reality for children with autism spectrum disorders

Graphics based systems of Augmented and Alternative Communication are widely used to promote communication in people with Autism Spectrum Disorders. This study discusses an integration of Augmented Reality in communication interventions, by relating elements of Augmented and Alternative Communication and Applied Behaviour Analysis strategies. An architecture for an Augmented Reality based interactive system to assist interventions is proposed. STAR provides an Augmented Reality tool to assist interventions performed by therapists and support for parents to join in and participate in the child’s intervention. Finally we report on the usage of the Augmented Reality tool in interventions with children with Autism Spectrum Disorders.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.