Abstract

Abstract
Technology is present in almost all aspects of our modern lives, and technological advances have been more and more prevalent. One would expect this to facilitate everybody's life, rendering common tasks more practical and easier to perform, but that is often not the case - at least not for the elderly. Older people have significant difficulties in using technological equipment due to the visual, physical, cognitive and hearing limitations associated with the natural aging process; these aspects must be taken into account if the intention is that of integrating technology in the lives of the elderly. In order to improve the quality of life of the elderly, as well as to prevent their social isolation, a mobile device application was developed to act as an intelligent remote control. This mobile application deals with data from two different platforms that were previously developed for improving the quality of life of the senior population: HbbTV and +Social. The features built into this virtual remote control include the integration with the +Social platform, as well as a speech recognition mechanism, and the possibility of making and receiving video calls with an integrated contacts list. A comprehensive study was carried out, focusing on recommendations and rules for graphical interfaces for the elderly, so as to allow equipping the application with adequate usability standards. The most relevant result was the definition of a list of guidelines used to steer the development of the interface. An investigation of mechanisms and technologies for enabling the implementation of the required functionalities was also carried out in order to define methodologies for freeing the user from the need of possessing any level of computer literacy. A series of usability tests was also performed to validate the viability of the remote control application. These tests allowed assessing the quality of the application, especially concerning the Graphical User Interface, as well as to receive feedback from potential end users. Based on the data collected during the test phase we conclude that this product is useful and that it responds to a current need.

Abstract
Eyesight is an important sense that helps humans to avoid dangers and navigate in our world. Blind people usually have enhanced accuracy and sensibility of their other natural senses in order to sense their surroundings. But sometimes this is not enough because the human senses can be affected by external sources of noise or disease. That is why technology has been used to develop many assistive tools. Artifacts like white canes or braille compasses help blind people moving around in the environment. In this article, the use of a system that detects and recognize nearby objects or obstacles is proposed, giving an audible feedback to the user. It is designed as a wireless system in order to be comfortable to be carried by the user. The system helps visual impaired people to move around in indoor or outdoor scenarios. The goals of this blind guide system is to detect obstacles that white canes or dogs cannot, extending its detection range. © 2016 ACM.

Abstract
In clinical practice, high-resolution computed tomography (HRCT) is a reference in the diagnosis, quantification, and follow-up of lung emphysema. In this study, differential lacunarity analysis was applied to HRCT images of the chest in order to quantify the texture of healthy and emphysematous regions of the lung parenchyma. The used approach to compute lacunarity is a multiscale technique that takes advantage of the extensive scale used in the acquisition of CT images. The results show that the extracted features are discriminatory of the considered lung patterns, being suitable to integrate clinical applications for the characterization of disease patterns in HRCT images.

Abstract

Abstract
The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test, p value < 0.01) during CV. An accuracy of 95.8 +/- 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 +/- 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis.