Abstract
Breast Cancer Conservative Treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as Breast Cancer Conservative Treatment. cosmetic results (BCCT. core) software tool. Although the satisfying results, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system as a low-cost tool, the incorporation of the Microsoft Kinect sensor in BCCT evaluations was studied. The aim with this work is to enable the simultaneous detection of breast contour and breast peak points using depth-map data. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. Additionally, comparatively to previous research, the procedure for detecting prominent points was automated.

Abstract
Media content adaptation is the action of transforming media files to adapt to device capabilities, usually related to mobile devices that require special handling because of their limited computational power, small screen size and constrained keyboard functionality. Image retargeting is one of such adaptations, transforming an image into another with different size. Tools allowing the author to imagery once and automatically retarget that imagery for a variety of different display devices are therefore of great interest. The performance of these algorithms is directly related with the preservation of the most important regions and features of the image. In this work, we introduce an algorithm for automatically retargeting images. We explore and extend a recently proposed algorithm on the literature. The central contribution is the introduction of the stable paths for image resizing, improving both the computational performance and the overall quality of the resulting image. The experimental results confirm the potential of the proposed algorithm.

Abstract
Breast Cancer Conservative Treatment (BCCT) is considered the gold standard of breast cancer treatment. However, the aesthetic outcome is diverse and very difficult to evaluate in a consistent way partly due to the weak reproducibility of the subjective methods in use. T his motivated the research on the objective methods. BCCT.core is a very recent software that objectively and automatically evaluates the aesthetic outcome of BCCT. However, as in other approaches, the system only uses frontal patient information, disregarding volumetric perception on lateral measurements. In the current work we investigate the improvement of the BCCT.core model by introducing lateral information extracted from patients images. We compare the performance of the model currently used on BCCT.core with the model developed in this study. Experimental results suggest that with lateral measurements the model presents better performance, however improvements are not significant. We can conclude that is essential to use robust models on the BCCT, and the input of 3D models will probably help to obtain better results. © 2010 IEEE.

Abstract
This paper presents two initiatives run by groups of engineering students at the University of Porto: the Microelectronics Students' Group and BioStar. These groups are student-led initiatives that promote different scientific fields through self-guided projects. Both experiences have proven to be very successful in increasing the undergraduate student's interest in science and technology. This work reports the activities, organization and main methodologies employed by these groups, which can be seen as successful approaches to enhance the technical curriculum of students. © 2012 IEEE.

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