Abstract

Abstract
The purpose of the research herein presented is the automatic detection of lung boundaries in posterior-anterior digital chest radiographs. The precise location of the two lungs is important in a computer-aided diagnosis system as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by an experienced radiologist. For this particular purpose, two programs with friendly interfaces were developed. The achieved comparison results demonstrate the good performance of the automatic method.

Abstract
This paper reports on the implementation of a required course on electronic instrumentation for informatics engineering students. These students had proved in previous years to require a special approach, as their motivation for this subject was low. The course focuses on the basic concepts of Electronic Instrumentation, including analogue-to-digital and digital-to-analogue conversion and introducing the concept of instrument control. The course was organised in a set of lectures coupled with laboratory experiments, and a small project. To foster collaborative learning all the students work on the same laboratory experiments, simultaneously, organised in groups of two. The students' feedback on the course was more positive than in previous years, although not substantially so in terms of results. We feel that a change of attitude took place effectively, and that this could be attributed to the laboratory experiments using LabVIEW.

Abstract
It is known that neurons in the primary visual cortex are characterized by receptive fields that are selective for speciftc spatial orientations. It is believed that the sense of object motion and velocity are also related to these fields, as objects in natural scenes are represented by a sparse set of statistically independent components, such as edges. Currently, computational models of receptive fields consider only spatial components, and thus cannot account for time-varying sensory stimuli. In this paper, a model based on independent components analysis and cellular neural networks is proposed. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes.

Abstract

Abstract