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.

Composition and structure variation for magnetron sputtered tantalum oxynitride thin films, as function of deposition parameters

Tantalum oxynitride thin films were produced by magnetron sputtering. The films were deposited usinga pure Ta target and a working atmosphere with a constant N2/O2ratio. The choice of this constant ratiolimits the study concerning the influence of each reactive gas, but allows a deeper understanding of theaspects related to the affinity of Ta to the non-metallic elements and it is economically advantageous.This work begins by analysing the data obtained directly from the film deposition stage, followed bythe analysis of the morphology, composition and structure. For a better understanding regarding theinfluence of the deposition parameters, the analyses are presented by using the following criterion: thefilms were divided into two sets, one of them produced with grounded substrate holder and the otherwith a polarization of −50 V. Each one of these sets was produced with different partial pressure of thereactive gases P(N2+ O2). All the films exhibited a O/N ratio higher than the N/O ratio in the depositionchamber atmosphere. In the case of the films produced with grounded substrate holder, a strong increaseof the O content is observed, associated to the strong decrease of the N content, when P(N2+ O2) is higherthan 0.13 Pa. The higher Ta affinity for O strongly influences the structural evolution of the films. Grazingincidence X-ray diffraction showed that the lower partial pressure films were crystalline, while X-rayreflectivity studies found out that the density of the films depended on the deposition conditions: thehigher the gas pressure, the lower the density. Firstly, a dominant -Ta structure is observed, for lowP(N2+ O2); secondly a fcc-Ta(N,O) structure, for intermediate P(N2+ O2); thirdly, the films are amorphousfor the highest partial pressures. The comparison of the characteristics of both sets of produced TaNxOyfilms are explained, with detail, in the text.

Thin films composed of Ag nanoclusters dispersed in TiO2: Influence of composition and thermal annealing on the microstructure and physical responses

Noble metal powders containing gold and silver have been used for many centuries, providing different colours in the windows of the medieval cathedrals and in ancient Roman glasses. Nowadays, the interest in nanocomposite materials containing noble nanoparticles embedded in dielectric matrices is related with their potential use for a wide range of advanced technological applications. They have been proposed for environmental and biological sensing, tailoring colour of functional coatings, or for surface enhanced Raman spectroscopy. Most of these applications rely on the so-called localised surface plasmon resonance absorption, which is governed by the type of the noble metal nanoparticles, their distribution, size and shape and as well as of the dielectric characteristics of the host matrix. The aim of this work is to study the influence of the composition and thermal annealing on the morphological and structural changes of thin films composed of Ag metal clusters embedded in a dielectric TiO2 matrix. Since changes in size, shape and distribution of the clusters are fundamental parameters for tailoring the properties of plasmonic materials, a set of films with different Ag concentrations was prepared. The optical properties and the thermal behaviour of the films were correlated with the structural and morphological changes promoted by annealing. The films were deposited by DC magnetron sputtering and in order to promote the clustering of the Ag nanoparticles the as-deposited samples were subjected to an in-air annealing protocol. It was demonstrated that the clustering of metallic Ag affects the optical response spectrum and the thermal behaviour of the films.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.