Abstract
As with most disciplines, the best way to learn and understand the relevant theoretical aspects related to industrial automation is by applying them to solve concrete problems. In order to increase the motivation of students a physical model of a manufacturing cell was acquired, which is, however, easily susceptible to being damaged through incorrect control sequences. To allow students safe access to this physical model, an interlocking application was designed and implemented. Additionally, a configurable software based simulator was also devised, which allows the students to work away from the laboratory in their own time. These tools have resulted in a higher flexibility in organizing courses and class work, due to their configurability and the way the physical model was integrated into the laboratory.

Abstract
This paper describes a business and technological model proposal, known as Inov@Douro, intended to support and to promote competitive and sustained precision agriculture practices in the Portuguese Douro Region. Our approach is based on a distributed cooperative network, tailored 10 meet the specific needs of viticulture enterprises which also explore tourism as a valuable national and international business source. Instead of focusing on operational issues, such as remote sensing and data transmission, among others, we present the Inov@Douro model from the knowledge generation point-of-view, intended to support the multidisciplinary concept of a cooperation approach among regional partners. As a result, this collaborative approach might be the most suited technological tool to promote precision agriculture sustainability practices based on a symbiotic cooperation with the tourism sector. The Inov@Douro model aims to represent a new working style for this unique region, where the concept of public and private information is a key feature to achieve the desired success as a knowledge network. As a guideline to attain the implementation of such a model. information technology and infrastructures tools are discussed in order to promote precision agriculture practices while giving valuable and dynamic tourist information to the general public.

Abstract
In this manuscript we explore the feasibility of using LWUV-VIS-SWNIR (340 - 1100 nm) spectroscopy to classify Saccharomyces cerevisiae colony structures in YP agar and YPD agar, under different growth conditions, such as: i) no alcohol; ii) 1% (nu/nu) Ethanol; iii) 1% (nu/nu) 1-Propanol; iv) 1% (nu/nu) 1-butanol; v) 1 % (nu/nu) Isopropanol; vi) 1% (nu/nu) (+/-)-1-Phenylethanol; vii) 1% (nu/nu) Isoamyl alcohol; viii) 1% (nu/nu) tert-Amyl alcohol (2-Methyl-2-butanol); and ix) 1% (nu/nu) Amyl alcohol. Results show that LWUV-VISSWNIR spectroscopy has the potential for yeasts metabolic state identification once the spectral signatures of colonies differs from each others, being possible to acheive 100% of classification in UV-VIS and VIS-SWNIR. The UV-VIS region present high discriminant information (350-450 nm), and different responses to UV excitation were obtained. Therefore, high precision is obtained because UV-VIS and VIS-NIR exhibit different kinds of information. In the future, high precision analytical chemistry techniques such as mass spectroscopy and molecular biology transcriptomic studies should be performed in order to understand the detailed cell metabolism and genomic phenomena that characterize the yeast colony state.

Abstract
One of the most studied bioprocesses is fermentation by yeasts. Although this is true, there is still the lack of real-time instrumentation that is capable of providing detailed information on metabolic state of fermentations. In this research we explore the possibility of using UV-VIS-SWNIR spectroscopy as a high-output, non-destructive and multivariate methodology of monitoring beer fermentation. We herein report the implementation of the a fibber optics sensor and the capacity for detecting key parameters by partial least squares regression for biomass, extract, pH and total sugars. Results show that UV-VIS-SWNIR is a robust technique for monitoring beer fermentations, being able to provide detailed information spectroscopic fingerprinting of the process. Calibrations were possible to obtain for all the studied parameters with R2 of 0.85 to 0.94 in the UV-VIS region and 0.95 to 0.97 in the VIS-SWNIR region. This preliminary study allowed to conclude that further improvements in experimental methodology and signal processing may turn this technique into a valuable instrument for detailed metabolic studies in biotechnology.

Abstract
The aim of the present study was to develop a hierarchical bioclimatic model for forecasting olive crop yields in the Alentejo region of south-eastern Portugal. The model was estimated for three different developmental stages: (1) at flowering, using only the regional pollen index (RPI); (2) at fruit growth using RPI and a plant water requirements index (PWRI) and (3) at fruit maturing using RPI plus a water requirements index plus a phytopathological index (PPI). Olive airborne pollen was sampled from 1999 to 2007, using a Cour trap installed in Reguengos de Monsaraz. The meteorological parameters used in the calculation of the post-flowering indices corresponded to data from a meteorological station located near the airborne sampling point. At the flowering stage, 0.66 of the regional olive yield can be explained by the RPI with an average deviation between observed and predicted production of 0.15 for the forecast model internal validation and of 0.19 for the cross-validation. The addition of the variable PWRI to the forecasting model explained an additional 026 of the variation, while the PPI explained an additional 0.05. The final bioclimatic model, with all the three variables tested, explained 0.97 of the regional olive fruit yield being the average deviation between observed and predicted production of 0.04 for the internal validation of the model and of 0.07 for the external validation. The hierarchical nature of this bioclimatic model, along three different development stages, enabled the prediction to be updated as the growing season progressed.

Abstract
There are several data fusion methods widely used to produce a high resolution multi-spectral image from a pair of images - a panchromatic high resolution and a multi-spectral lower resolution image. Although the fused images can be visually satisfactory, it is not clear whether they provide additional information for quantitative measurements made from satellite images. A methodology to evaluate data fusion algorithms is proposed, based on the production of synthetic images that reproduce real satellite images. An experiment was conducted testing the performance of six data fusion methods in the production of NDVI values for land parcels from SPOT HRG and Landsat TM data. The fusion methods evaluated were: Brovey, IHS Hexcone, IHS Cylinder, PCA, Wavelet IHS and Wavelet Single Band. The best data fusion method overall was found to be Wavelet IHS, although better results were obtained by using directly the lower resolution multi-spectral data instead. The software tools developed and a number of test images datasets are freely available at the SITEF website (www.fc.up.pt/sitef).