Abstract
Portugal stands out as a recognized country in the production of superior quality wine, the two main reasons being the edaphoclimatic conditions and the grapevine heritage. To maximize quality it is important that the various steps of the winemaking process be submitted to effective control and monitoring. Since the alcoholic fermentation is a crucial stage of the winemaking process, this paper describes a low cost prototype to perform the supervision and control of the alcoholic fermentation process in a winery. To demonstrate the viability of the practical application of this solution in real conditions, a prototype was installed in the winery of Escola Superior Agraria de Coimbra (ESAC), to control the fermentation temperature of white must in a medium size vat.

Abstract
Depression and loneliness among the elderly are one of the biggest problems affecting the world population. This leads to elderly isolation which is a major risk factor for suicide. Moreover, if isolation is coupled with physical illness and incapacitation, such a risk increases exponentially. To fight back this problem, roboticists have been proposing solutions to autonomously monitor, support and even promote physical activities among the elderly. Nevertheless, those appear as very high-cost and complex solutions that require an advanced technical expertise. Recent off-the-shelf solutions, such as the well-known NAO robot, emerged as possible alternatives. An extension to the NAO robot, denoted as RIA, is being developed at the Engineering Institute of Coimbra (ISEC). The RIA is not only built for a social interaction with the elderly but also as an autonomous tool to promote professional care through the analysis of health and environmental parameters. Therefore, the RIA robot is an adapted NAO low-cost platform equipped with several sensors that can measure different parameters like body temperature, blood pressure and heart rate. By validating this valuable platform, the foundations were laid for a whole new paradigm in elderly care.

Abstract
The performance of football players within game context can be analyzed based on their ability to break or (re)balance the attacker-defender dyad. In this context, the analysis of each sub-phase (e.g., 1v1, 2v2) presents a feature that needs to be taken into account in sports analysis. This study aims to investigate the interpersonal dynamics dyad formed by the attacker and the defender in 1v1 situations with a goalkeeper. A sample of 11 football male players (age: 17.91 +/- 1.04 years) with 8.6 +/- 1.52 years of practice experience participated in the study. Analyzing the 1v1 sub-phase, results show that the distance, speed and angular amplitude between the attacker and the defender increases, especially when the attacker attempts to overtake the defender (i.e., score a goal). We conclude that decision-making emerges from the perception that players draw from the action, actively and consistently interacting to find solutions to emerging problems within the game context.

Abstract
PhenoSat is an experimental software tool that produces phenological information from satellite vegetation index time series. The main characteristics and functionalities of the PhenoSat tool are presented, and its performance is compared against observed measures and other available software applications. A multiyear experiment was carried out for different vegetation types: vineyard, low shrublands, and seminatural meadows. Temporal satellite normalized difference vegetation index (NDVI) data provided by MODerate resolution Imaging Spectroradiometer and Satellite Pour l'Observation de la Terre VEGETATION were used to test the ability of the software in extracting vegetation dynamics information. Three important PhenoSat features were analyzed: extraction of the main growing season information, estimation of double growth season parameters, and the advantage of selecting a temporal region of interest. Seven noise reduction filters were applied: cubic smoothing splines, polynomial curve fitting, Fourier series, Gaussian models, piecewise logistic, Savitzky-Golay (SG), and a combination of the last two. The results showed that PhenoSat is a useful tool to extract NDVI metrics related to vegetation dynamics, obtaining high significant correlations between observed and estimated parameters for most of the phenological stages and vegetation types studied. Using the combination of SG and piecewise logistic to fit the NDVI time series, PhenoSat obtained correlations higher than 0.71, except for the seminatural meadow start of season. The selection of a temporal region of interest improved the fitting process, consequently providing more reliable phenological information.

Abstract
Earth Observation Satellite (EOS) data have a great potential for land cover mapping, which is mostly based on high resolution images. However, in tropical areas the use of these images is seriously limited due to the presence of clouds. This paper evaluates the ability of temporal-based image classification methods to produce land cover maps in tropical regions. A new approach is proposed for land cover classification and updating based exclusively on temporal series data, illustrated with a practical test using SPOT VEGETATION satellite images from 1999 to 2011 for Rondonia (Amazon), Brazil. Using the GLC2000 as reference, a Normalized Difference Vegetation Index (NDVI) time series of 15 distinct land cover classes (LCC) were created. Two classifiers were used (Euclidean Distance and Dynamic Time Warping) to produce maps of land cover changes for 1999-2011. Due to the difficulties in discriminating 15 LCC in the Amazon region, a hierarchical aggregation was performed by joining the initial classes gradually up to four broad classes. The land cover changes in the 1999-2011 period were evaluated using criteria based on the classification results for the individual years. The comparison with reference data showed consistent results, proving that this approach is able to produce accurate land cover maps using exclusively temporal series EOS data.

Abstract
The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes). For each class, the normalized difference vegetation index (NDVI) time-series are extracted from SPOT VGT images and a hierarchical aggregation is done using two different methods: one that preserves the initial signatures throughout the hierarchical process, and another that recalculates the signatures for each aggregation level. The average classification agreement was above 89% using 26 classes. Reducing the number of classes improves classification agreement. In order to study the influence of temporal variability in the classification results, the methodology was applied on data from 1999, 2001, 2008, and 2010. With 26 classes, the best average classification agreement obtained was 94.5% with annual data, against 74.1% with interannual data.