No description available.

No description available.

No description available.

No description available.

Dissertação apresentada para obtenção do grau de Mestre em Manutenção Industrial, na Faculdade de Engenharia da Universidade do Porto

Dissertação apresentada para obtenção do grau de Mestre em Construção de Edifícios, na Faculdade de Engenharia da Universidade do Porto, sob a orientação do Engenheiro Costa Manso

No description available.

No description available.

The title of the thesis focuses the attention on hyperspectral image segmentation, that is, we want to detect salient regions in a hyperspectral image and isolate them as accurate as possible. This purpose presents two main problems: Firstly, the fact of using hyperspectral imaging not only give us a huge amount of information, but we also have to face the problem of selecting somehow the information avoiding redundancies.Secondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.

En este trabajo se presenta una metodología basada en tecnologías de representación, anotación y procesamiento semántico para la evaluación de preguntas abiertas o desarrollo en e-learning. Dicha metodología se fundamenta en la anotación semántica de los cursos, de las preguntas esperadas, y de las respuestas dadas a estas preguntas. Para ello, se emplean técnicas estándar de anotación semántica, y una metodología que evalúa el conocimiento que se almacena, para dar las calificaciones de las respuestas. La metodología fue evaluada en cursos reales y en modalidades de enseñanza diferentes, obteniendo resultados evaluados de forma satisfactoria con el empleo de métodos estadísticos. Además, se ha implementado la plataforma OeLE, que automatiza el empleo de la metodología. / In this work, a methodology based on knowledge representation, semantic annotation and processing for the assessment of open questions in eLearning is presented. This methodology is based on the semantic annotation of courses, expected answers to the questions and the students' answers to such questions. For this purpose, standard semantic annotation techniques are used, as well as a methodology that evaluates the knowledge stored for marking the answers. This methodology has been validated in real settings in both traditional and eLearning courses by using the OeLE platform, which implements it. The results were statistically analyzed, which satisfactory conclusions were obtained.