Abstract
The use of concrete has been widespread in our society in housing and infrastructure, despite the environmental cost associated with its production. Its decay poses a social, economic, and environmental problem. Currently, the carbonation of cement paste is monitored through the measurement of its pH, with several optical fiber sensors (OFS) have been produced for this purpose. In the current work the focus is, also, on the carbonation monitoring of cement paste through an OFS, but not through pH measurements. Single fiber reflectance spectroscopy, previously employed to measure cement paste durability, is used to monitor the discoloration of cement paste caused by carbonation. As the carbonation front reaches the fiber tip embedded in the cement paste, the signal reflected onto the fiber increases. The accelerated carbonation of two limestone cement paste samples in an atmosphere of 100% CO2 was successfully monitored. The applicability of the sensor for operational use with ambient CO2 was confirmed through the measurement of carbonation at 3% CO2. The cross interference from water ingress and egress was also evaluated, and it didn't hinder the measurements of carbonation. Therefore, a novel OFS capable of measuring cement paste carbonation and durability, was achieved. © Published under licence by IOP Publishing Ltd.

Abstract
Bloch Surface Waves (BSW) consist of electromagnetic modes generated at the interface between a photonic crystal and an isotropic dielectric. This type of surface mode displays sharp resonances and high sensitivity to external refractive index variations, and thus appears to be an ideal candidate for usage in optical sensors. Nevertheless, design and optimization of photonic crystals is not a trivial task and constitutes an ongoing field of research. The sensitivity of BSW in both refractometric and adsorption sensing is calculated analytically using first-order perturbation theory for TE modes, allowing the understanding of how several physical parameters of the photonic crystal influence the sensitivity. Preliminary experimental results are presented, which aim to use the analytical calculations to allow for both refractometric and adsorption sensing in a single photonic crystal structure. © Published under licence by IOP Publishing Ltd.

Abstract
New paths to increase the sensing performance of plasmonic sensors have been reported in recent years. There are several methodologies to achieve such purpose, namely by optimizing the nanostructure, nanomaterial and even the sensing platform. Recently the use nanoparticles over plasmonic thin films have been reported and shown sensitivity enhancement, when compared to a bare thin film. Nevertheless, a nanomaterial combination between NP and thin film has not been studied. In this work it was studied such plasmonic materials in order to optimize not only refractometric sensitivity but also decrease the resultant plasmonic band width. It was found that for Au, Ag and Cu thin films, the deposition of plasmonic nanoparticles resulted in an overall refractometric sensitivity and figure of merit (FOM) increase. The larger FOM increase was obtained for the Ag thin film, from 42 to 162 when coupled to Si nanoparticles. The greater sensitivity increase was achieved for a Cu thin film coupled to a Si nanoparticle, with an increase from 1745 to 3230 nm/RIU. © Published under licence by IOP Publishing Ltd.

Abstract
The interrogation of optic fiber sensors usually relies in complex and costly equipment with low portability due to their size such as Optical Spectrum Analyzers (OSA) or high-resolution spectrometers. Because of this, micro spectrometer devices, such as Micro-Electromechanical Systems (MEMS) with Fabry-Pérot tunable filters, are emerging as simpler and compact alternatives capable of being used to acquire spectral information in a wide wavelength band. In this work it is described the development of an interrogation system capable of infrared spectroscopy using a MEMS Fabry-Pérot Interferometer (MEMS-FPI) with a spectral response in the 1350nm to 1650nm range. Its performance is tested with the interrogation of long period fiber gratings both as a refractive index sensor and as a temperature sensor. Deconvolution techniques such as Wiener filtering are used to reduce the impact of the tunable filter's impulse response in the measured signal. Results are comparable to those obtained using a typical OSA which shows the system's potential as a cheaper and more transportable alternative. © Published under licence by IOP Publishing Ltd.

Abstract
The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Tras-os-Montes region in Portugal are identified (Cobrancosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Abstract