Abstract
Composite structures integrity is sensible to service life. Their application in the aeronautical and space engineering implies the necessity to insure their integrity through non-destructive evaluations. On-line health monitoring procedure capable to detect, acquire, and identify damage in fibre reinforced plastic composite materials are necessary. Among the different non-destructive techniques, acoustic emission was chosen for its ability to detect evolutive defects during in-service life of structures. Traditionally, the AE waves are detected at the surface of the structure by piezoelectric transducers. Such transducers have some limitations (e.g. they can't be used at low/high temperature, and are sensible to electromagnetic interferences). Optic fibre sensors have revealed to be a good alternative. Due to their low dimensions they can be easily embedded in fibre reinforced composite at manufacturing. In this chapter is discussed the use of an optic fibre system developed for damage monitoring in composite materials from the rapid release of elastic strain energy they generate, detected in the form of elastic waves. Among the different optic fibre sensors, the Fabry-Pérot interferometer is chosen for its high sensitivity to transient phenomena. The propagating acoustic emission waves induce variations of the light in the interferometer. The difficulty when using such sensor remains the phase recovery. In this study an original set-up is proposed for phase recovery based on the generation of two quadrature-shifted phase interferometric signals from two fibre Bragg gratings. The optic fibre sensor is embedded in a cross-ply carbon fibre/epoxy laminate. The optic fibre sensor system successfully detects periodic ultrasonic waves propagating into the material as well as simulated acoustic emission waves. These tests demonstrate that the optic fibre system is suitable for damage detection from acoustic emission waves. Such in-service health monitoring methodology can be used to locate damage and to determine its severity.

Abstract
An indicator free optical fiber sensor for determination of carbon dioxide is presented. The sensing layer is based on the acid-basic equilibrium of phenol and of its derivative p-nitro-phenol that, in the presence of CO2, are prone to protonation introducing refractive index changes. The new sensitive layer is characterized and tested in different refractometric fiber optic sensor configurations. Using a fiber based interferometric setup, a CO 2 dependent refractive index change of ~0.05 RIU is observed, in the 10%-90% CO2 concentration range, demonstrating the membrane viability. Preliminary results are presented for an all-fiber LPG-based carbon dioxide sensor.

Abstract
A novel design for a miniature fiber Bragg grating-based temperature probe is presented. The sensor integrates a u-shaped lossless taper thus offering the advantages of a terminal temperature probe free from of strain cross-sensitivity and showing installation flexibility while enabling effective serial multiplexing. The experimental validation of the probe design is reported, being demonstrated lossless operation and effective elimination of strain cross-sensitivity up to 400 degrees C.

Abstract
In this Letter, we present a fiber loop mirror configuration based on a suspended twin-core fiber for sensing applications. Using the suspended twin-core fiber, the fringe pattern is due to the differential optical patch of the light in the two cores associated with a refractive index difference of similar to 10(-3), which indicates an advantage of this approach compared with those based on high-birefringent fibers, namely, the possibility of using a small length of fiber. The sensing configuration was characterized for torsion, temperature, and strain. Using the fast Fourier transform technique, it is possible to obtain measurand-induced amplitude variations of the fringe pattern. The results obtained indicate the viability of a temperature- and strain-independent torsion sensor. (C) 2010 Optical Society of America

Abstract
Pervasive environments offer an increasing number of services to a large number of people moving within these environments including timely information about where to go and when. People using these services interact with the system but they are also meeting other people and performing other activities as relevant opportunities arise. The design of such systems and the analysis of collective dynamic behaviour of people within them is a challenging problem. In previous work we have successfully explored a scalable analysis of stochastic process algebraic models of smart signage systems. In this paper we focus on the validation of a representative example of this class of models in the context of emergency egress. This context has the advantage that detailed data is available from studies with alternative analysis methods. A second aim is to show how realistic human behaviour, often observed in emergency egress, can be embedded in the model and how the effect of this behaviour on building evacuation can be analysed in an efficient and scalable way. © 2010 IEEE.

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.