Abstract
The aim of this work is to implement an adaptive PID SISO feedback control to obtain a fine adjustment of an electric vehicle (EV) driving system. Our research work is done under different operating conditions, namely, variable battery voltage and variable load. A comparison between conventional and adaptive PID algorithms is established when they are applied to the above mentioned conditions. Experimental results indicate that the adaptive PID controller leads to a faster response and a better stability. Furthermore, the adaptive PID controller follows a given reference velocity faster and more smoothly than the conventional PID controller.

Abstract
Grapevine phenology observations are essential for ecological adaptability of grape varieties, crop management and crop modelling. Phenological events have traditionally been ground based, with observations mainly providing information concerning grape varieties over a limited spatial area and few in-season observations. Time-series of satellite imagery can rapidly provide a synoptic and objective view of grape vegetation dynamics that may be used for vineyard management. Ten-day VEGETATION image composites from 1999 to 2007 were used to examine temporal profile in the Normalized Difference Vegetation Index (NDVI) and their relationship with ground based observation of grapevine phenology. In Portugal is Douro wine region, 2 suitable tests sites with over 70% or more of their area occupied by grapevines were selected. A number of NDVI metrics were obtained for each year through logistic model adjusted to NDVI time series after noise reduction using a Savitzky-Golay filter. The comparison of ground-based vineyard phenology and satellite-derived flowering, show an average spread deviation of 3 days. The satellite derived full canopy date was significantly correlated to the veraison date (r=0.87; n=7; p<0.02). The data set provided by the VEGETATION sensor proved to be a valuable tool for vineyard monitoring, mainly for inter-annual comparisons on regional scale.

Abstract
The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (VI) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite VI from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of VIs and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each VI time series, as well as to extract a number of NDVI and EVI metrics by computing derivatives. There was weak to reasonable agreement between VIs-metrics from MODIS and ground based derived phenology. The NDVI had a great sensitivity to crop growth changes during start of growth season, whereas the EVI exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both VI from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODIS(EVI). These results suggest a great sensibility of MODIS(EVI) to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

Abstract
The segmentation stage is a key aspect of an object-based image analysis system. However, the segmentation quality is usually difficult to evaluate for satellite images. The Synthetic Image TEsting Framework (SITEF) is a tool to evaluate and compare image segmentation results. This paper presents an example of the use of SITEF for the evaluation of a segmentation algorithm, using a SPOT HRG satellite image with 6 vegetation land cover classes identified in an agricultural area. The segmentation results were evaluated under various perspectives, including the parcel size and shape, the land cover types, and the parameters used in the segmentation algorithm.

Abstract
A forecast model for estimating the annual variation in regional wine yield based on remote sensing was developed for the main wine regions of Portugal. Normalized Difference Vegetation Index (NDVI) time-series obtained by the VEGETATION sensor, on board the most recent Satellite Pour l'Observation de la Terre (SPOT) satellite, over the period 1998-2008 were used for four test sites located in the main wine regions of Portugal: Douro (two sites), Vinhos Verdes and Alentejo. The CORINE (Coordination of Information on the Environment) Land Cover maps from 2000 were initially used to select the suitable regional test sites. The NDVI values of the second decade of April of the previous season to harvest were significantly correlated to the wine yield for all studied regions. The relation between the NDVI and grapevine induction and differentiation of the inflorescence primordial or bud fruitfulness during the previous season is discussed. This NDVI measurement can be made about 17 months before harvest and allows us to obtain very early forecasts of potential regional wine yield. Appropriate statistical tests indicated that the wine yield forecast model explains 77-88% of the inter-annual variability in wine yield. The comparison of official wine yield and the adjusted prediction models, based on 36 annual data records for all regions, shows an average spread deviation between 2.9% and 7.1% for the different regions. The dataset provided by the VEGETATION sensor proved to be a valuable tool for vineyard monitoring, mainly for inter-annual comparisons on a regional scale due to their high data acquisition rates and wide availability. The accuracy, very early indication and low-cost of the developed forecast model justify its use by the winery and viticulture industry.

Abstract
The goal of the "Soft Core Robot" project is to set the way for a robot with minimalistic component and pin count, based on custom computing. Additionally, the "Joint Wheel Motion Controller" specifically takes advantage of custom computing in order to privilege angular precision over linear velocity which is interesting for some applications including mobile robotics. The proposed "Soft Core Robot" is expected to produce a small but very powerful and versatile robot. The chosen processing platform is based on a FPGA plus Microprocessor in the same chip. The proposed example application is the Fire Fighting contest at Instituto Politecnico da Guarda, in Portugal. The Joint Wheel Controller is not yet fully operational but results for the control of a single motor are presented. The overall status of the project is addressed, as well as the pros and cons of the approaches taken.