Influence of casting condition on the anisotropy of the fracture properties of Steel Fibre Reinforced Self-Compacting Concrete (SFRSCC)

Identification of the tensile constitutive behaviour of Fibre Reinforced Concrete (FRC) represents an important aspect of the design of structural elements using this material. Although an important step has been made with the introduction of guidance for the design with regular FRC in the recently published fib Model Code 2010, a better understanding of the behaviour of this material is still necessary, mainly for that with self-compacting properties. This work presents an experimental investigation employing Steel Fibre Self-Compacting Concrete (SFRSCC) to cast thin structural elements. A new test method is proposed for assessing the post-cracking behaviour and the results obtained with the proposed test method are compared with the ones resulted from the standard three-point bending tests (3PBT). Specimens extracted from a sandwich panel consisting of SFRSCC layers are also tested. The mechanical properties of SFRSCC are correlated to the fibre distribution by analysing the results obtained with the different tests. Finally, the stress-crack width constitutive law proposed by the fib Model Code 2010 is analysed in light of the experimental results.

Development and characterization of lipid-based nanosystems for controlled release of bioactive compounds

Tese de Doutoramento em Engenharia Química e Biológica.

Development of polymeric nanoparticles containing neuroprotective compounds of Hypericum perforatum

Tese de Doutoramento em Biologia das Plantas - MAP BIOPLANT

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.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

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.

Para um (novo) modelo de intervenção penal na União Europeia: uma reflexão a partir do princípio de legalidade como limite material de atuação da Procuradoria Europeia

Tese de Doutoramento em Ciências Jurídicas (área de especialização em Ciências Jurídicas - Públicas)

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Os novos desafios do direito tributário: em particular, a tributação do jogo online

Dissertação de mestrado em Direito Tributário e Fiscal

Determinação da medida da pena privativa de liberdade: um olhar crítico a partir do direito anglo-saxónico

Tese de Doutoramento em Ciências Jurídicas - Área de Ciências Jurídicas Públicas

A vinculação das sociedades por quotas nas situações de gerência plural e as limitações constantes do pacto social: análise dos institutos jurídicos e o sentido das decisões judiciais

Relatório de atividade profissional de mestrado em Direito dos Contratos e da Empresa

Trabalho infantil: as crianças no mundo do espetáculo, moda e publicidade

Dissertação de mestrado em Direitos Humanos

Dano reflexo nos acidentes de viação

Relatório de atividade profissional de mestrado em Direito dos Contratos e da Empresa

Dieta da torda-mergulheira, Alca torda, capturada acidentalmente em Portugal Continental

Dissertação de mestrado em Ecologia