The development of a serious game requires perfect knowledge of the learning domain to obtain the desired results. But it is also true that this may not be enough to develop a successful serious game. First of all, the player has to feel that he is playing a game where the learning is only a consequence of the playing actions. Otherwise, the game is viewed as boring and not as a fun activity and engaging. For example, the player can catch some items in the scenario and then separate them according to its type (i.e., recycle them). Thus, the main action for player is catching the items in the scenario where the recycle action is a second action, which is viewed as a consequence of the first action. Sometimes, the game design relies on a detailed approach based on the ideas of the developers because some educational content are difficult to integrate in the games, while maintaining the fun factor in the first place. In this paper we propose a new methodology of design and development of serious games that facilitates the integration of educational contents in the games. Furthermore, we present a serious game, called “Clean World”, created using this new methodology.
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This paper introduces a tool to automatically generate meta-data from game sprite sheets. MuSSE is a tool developed to extract XML data from sprite sheet images with non-uniform -multi-sized -sprites. MuSSE (Multi-sized Sprite Sheet meta-data Exporter) is based on a Blob detection algorithm that incorporates a connected-component labeling system. Hence, blobs of arbitrary size can be extracted by adjusting component connectivity parameters. This image detection algorithm defines boundary blobs for each individual sprite in a sprite sheet. Every specific blob defines a sprite characteristic within the sheet: position, name and size, which allows for subsequent data specification for each blob/image. Several examples on real images illustrate the performance of the proposed algorithm and working tool.
-Brain-Machine Interfaces (BMIs) provide direct communication between the brain and external devices.They can be used to restore or augment human motor function and have been the focus of great interest in the last two decades in parallel with steady advances in microelectronics and our general understanding of the brain. In general, BMIs can be classified in invasive and non-invasive, depending on the need for surgical access to position the electrodes that will record the activity of neural ensembles. In the current work, we propose a new method for controlling a non-invasive, EEG-based, three class BMI, using motor imagery. For the classifier, we used an artificial neural network (ANN) implementation that learned to distinguish among three task-related EEG patterns. Key-words -Neural Networks, Brain-Machine Interface, EEGResumo -Interfaces Cérebro-Máquina (ICMs) provêm comunicação direta entre o cérebro e dispositivos externos. Elas podem ser usadas para restaurar ou aumentar funcionamento motor humano e tem sido foco de grande interesse nas últimas duas décadas em paralelo com avanços consistentes em microeletrônica e do nosso entendimento geral do cérebro. Em geral, ICMs podem ser classificadas em invasivas e não-invasivas, dependendo da necessidade de acesso através de cirurgia para o posicionamento dos eletrodos que irão registrar a atividade de agrupamento neuronais. Neste trabalho nós propomos um novo método para controlar uma interface não-invasiva, baseada em EEG, de três classes, utilizando imagética motora. Para a classificação, utilizamos uma implementação de rede neural artificial (RNA) que aprendeu a distinguir entre os três padrões de EEG relacionados com as tarefas.
The design of an umbilical cable begins with the definition of the operational functions it must implement and the environmental conditions to that it will be subjected to. Those functions suggest the components that it must have, usually chosen from a pre-defined set. Also, structural elements must be added, based on project and manufacture requirements, so the cable can withstand the environmental conditions of use. All the components must be geometrically arranged and the cross section of the cable must be defined, usually with the help of CAD software. But the structural behavior of the designed cable must be analyzed under several environmental conditions, using numerical and analytical tools. If this behavior does not fulfill the desired structural requirements, the cable must be redesigned: structural and functional components must be changed and the cross section of the umbilical must be rearranged in an iterative process. This article presents an environment that integrates a CAD tool dedicated to the design of the cross section of an umbilical cable with structural analysis tools, both analytical (Utilflex) and numerical (UFLEX2D). The CAD tool architecture is based on a component-instance model that enables both drawing and reusing of components. It also can export the designed cross-section to AutoCAD using AutoLISP language. Finally, it automates the generation of the data-sheet of the designed umbilical both in AutoLISP and Microsoft Word, including the basic structural properties calculated by means of analytical formulae.
This paper introduces a tool to semi-automatically generate meta-data from game sprite sheets. MuSSE is a tool developed to extract XML data from sprite sheet images with non-uniform – multi-sized – sprites. MuSSE (Multi-sized Sprite Sheet meta-data Exporter) is based on a Blob detection algorithm that incorporates a connected-component labeling system. Hence, blobs of arbitrary size can be extracted by adjusting component connectivity parameters. This image detection algorithm defines boundary blobs for each individual sprite in a sprite sheet. Every specific blob defines a sprite characteristic within the sheet: position, name and size, which allows for subsequent data specification for each blob/image. Those blobs are carefully optimized through an imbued threshold selection. This work also presents a parser to organize these meta-data into a readable solution for game engines. The parser is built to read XML metadata generated through MuSSE and allow developers to set up game objects that can be used by an engine. Several examples on real images illustrate the performance of the proposed algorithm and working tool.
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