Pedro Brandão Silva

Spectroscopic characterization of a sample of metal-poor solar-type stars from the HARPS planet search program - Precise spectroscopic parameters and mass estimation

Stellar metallicity strongly correlates with the presence of planets and their properties. To check for new correlations between stars and the existence of an orbiting planet, we determine precise stellar parameters for a sample of metal-poor solar-type stars. This sample was observed with the HARPS spectrograph and is part of a program to search for new extrasolar planets. The stellar parameters were determined using an LTE analysis based on equivalent widths (EW) of iron lines and by imposing excitation and ionization equilibrium. The ARES code was used to allow automatic and systematic derivation of the stellar parameters. Precise stellar parameters and metallicities were obtained for 97 low metal-content stars. We also present the derived masses, luminosities, and new parallaxes estimations based on the derived parameters, and compare our spectroscopic parameters with an infra-red flux method calibration to check the consistency of our method in metal poor stars. Both methods seems to give the same effective temperature scale. Finally we present a new calibration for the temperature as a function of B − V and [Fe/H]. This was obtained by adding these new metal poor stars in order to increase the range in metallicity for the calibration. The standard deviation of this new calibration is ∼50 K.

Evaluation of Features for Leaf Discrimination

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. A database with 15 classes and 171 leaf samples was considered for the evaluation of these measures using linear discriminant analysis and hierarchical clustering. The results obtained match the human visual shape perception with an overall accuracy of 87%.

Procedural content graphs for urban modeling

Massive procedural content creation, for example, for virtual urban environments, is a difficult, yet important challenge. While shape grammars are a popular example of effectiveness in architectural modeling, they have clear limitations regarding readability, manageability, and expressive power when addressing a variety of complex structural designs. Moreover, shape grammars aim at geometry specification and do not facilitate integration with other types of content, such as textures or light sources, which could rather accompany the generation process. We present procedural content graphs, a graph-based solution for procedural generation that addresses all these issues in a visual, flexible, and more expressive manner. Besides integrating handling of diverse types of content, this approach introduces collective entity manipulation as lists, seamlessly providing features such as advanced filtering, grouping, merging, ordering, and aggregation, essentially unavailable in shape grammars. Hereby, separated entities can be easily merged or just analyzed together in order to perform a variety of context-based decisions and operations. The advantages of this approach are illustrated via examples of tasks that are either very cumbersome or simply impossible to express with previous grammar approaches.

Multi-modal natural interaction in game design: a comparative analysis of player experience in a large scale role-playing game

Previous work on player experience research has focused on identifying the major factors involving content creation and interaction. This has encouraged a large investment in new types of physical interaction artefacts (e.g. Wiimote™, Rock Band™, Kinect™). However, these artefacts still require custom interaction schemes to be developed for them, which critically limits the number of commercial videogames and multimedia applications that can benefit from those. Moreover, there is currently no agreement as to which factors better describe the impact that natural and complex multi-modal user interaction schemes have on users’ experiences—a gap in part created by the limitations in adapting this type of interaction to existing software. Thus, this paper presents a generic middleware framework for multi-modal natural interfaces which enables game-independent data acquisition that encourages further advancement on this domain. Furthermore, our framework can then redefine the interaction scheme of any software tool by mapping body poses and voice commands to traditional input means (keyboard and mouse). We have focused on digital games, where the use of physical interaction artefacts has become mainstream. The validation methods for this tool consisted of a series of increasing difficulty stress tests, with a total of 25 participants. Also, a pilot study was conducted on a further 16 subjects which demonstrated mainly positive impact of natural interfaces on player’s experience. The results supporting this were acquired when subjects played a complex commercial role-playing game whose mechanics were adapted using our framework; statistical tests on the obtained Fun ratings, along with subjective participant opinions indicate that this kind of natural interaction indeed has a significant impact on player’s experience and enjoyment. However, different impact patterns emerge from this analysis, which seem to fit with standing theories of player experience and immersion.

GEMINI: A Generic Multi-Modal Natural Interface Framework for Videogames

In recent years videogame companies have recognized the role of player engagement as a major factor in user experience and enjoyment. This encouraged a greater investment in new types of game controllers such as the WiiMoteTM, Rock BandTM instruments and the KinectTM. However, the native software of these controllers was not originally designed to be used in other game applications. This work addresses this issue by building a middleware framework, which maps body poses or voice commands to actions in any game. This not only warrants a more natural and customized user-experience but it also defines an interoperable virtual controller. In this version of the framework, body poses and voice commands are respectively recognized through the Kinect’s built-in cameras and microphones. The acquired data is then translated into the native interaction scheme in real time using a lightweight method based on spatial restrictions. The system is also prepared to use Nintendo’s WiimoteTM as an auxiliary and unobtrusive gamepad for physically or verbally impractical commands. System validation was performed by analyzing the performance of certain tasks and examining user reports. Both confirmed this approach as a practical and alluring alternative to the game’s native interaction scheme. In sum, this framework provides a game-controlling tool that is totally customizable and very flexible, thus expanding the market of game consumers.

Procedural modeling of urban environments for digital games development

This paper describes a procedural modeling methodology for urban environments, capable of reproducing real-world structures based on geographical information, operating on a spatial database engine, with applications in the field of digital gaming.

A Collaborative Environment for Urban Landscape Simulation

Digital maps are very commonly used in the analysis, administration and representation of urban spaces. Despite the degree of information they can provide, they still pose some difficulties in decision making due to the three-dimensional nature of such ever-changing settings. This paper presents a collaborative solution for large virtual environment recreation aimed at urban landscape simulation. By providing an extensive interactive three-dimensional virtualization of a real-world city, multiple users may contribute with additional data, to either improve the model fidelity or to preview the impact of certain urban changes. This is achieved by employing procedural modeling methods to generate a basic three-dimensional city model from real-world data and a set of parameters, which can later be refined through human intervention. A multi-user simulation platform has been conceived, allowing the collaborative management of the model data, as well as the simulation of possible urban landscape changes, introducing therefore more advanced analysis and representation features to study and discuss the impact of certain decisions on the urban landscape.

Differential scorecards for binary and ordinal data

Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Score- cards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplic- ity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.