Abstract
An analytical model based on geometrical optics and multilayer transfer matrix method is applied to the surface plasmonic resonance supported by fibre taper structures in the context of optical sensing applications. Phase interrogation is considered in particular as a methodology to attain enhanced sensitivities, and the performance of the sensing heads as function of the metal clad and taper parameters is analyzed. General topics concerning the actual relevance of plasmonics are also presented, first in a global perspective and then when applied to sensing.

Abstract
Many optical systems based on Surface Plasmon Resonance (SPR) have been developed for work as refractometers, chemical sensors or even for measure the thickness of metal and dielectric thin films. These kinds of systems are usually large, expensive and cannot be used for remote sensing. Optical fiber sensors based on SPR has been widely studied for the last 20 years with several configurations mostly using multimode optical fibers with large cores and plastic claddings. Sensors based on SPR present very high sensitivity to refractive index variations when compared to the traditional refractive index sensors. Here we propose a SPR sensor based in a single mode fiber. The fiber end is chemically etched by emersion in a 48% hydrofluoric acid solution, resulting a single mode fiber with the cladding removed in a small section. A resonance dip around 1580 nm was attained in good agreement with the simulation scenario that takes into account the real characteristics of the fiber.

Abstract
This study was concerned with shoreline change and cliff recession. Galicia (north-west Spain) comprises a very energetic and diversified coast. The study focused on the analysis of the coastal dynamics and the spatio-temporal changes of coastal morphology for the years 1956, 2003, 2006 and 2008, using the digital shoreline analysis system (DSAS) extension. Estimation of the rates of erosion and accretion along the pilot site (Fisterra/Finisterre area) was performed. In addition, a continuous coastline along Galicia was integrated into a geographical information system project which comprises an interactive database with key information. A coastal susceptibility map (erosion/accretion) was created based on the DSAS results for the short-term approach and cross-checked with knowledge of the area in terms of geology, geomorphology and landslide occurrences. Aspects related to the engineering solutions, land-use planning or environmental management were considered in the recommended strategy, as well as the impact and disturbance severity analysis for each action used. This research was developed to provide useful information about the Galicia territory and to give reliable data for the coastal management plan supported by the council. Such plan addresses some changes to the coastal policy and encourages future issues.

Abstract
In this article, an algorithm to identify LPV State Space models is proposed. The LPV State Space system is in the companion reachable canonical form. Both the state matrix and the output vector coefficients are linear combinations of a set of nonlinear basis functions dependent on the scheduling signal. This model structure, although simple, can describe accurately the behaviour of many nonlinear systems by an adequate choice of the scheduling signal. The identification algorithm minimises a quadratic criterion of the output error. Since this error is a linear function of the output vector parameters, a separable nonlinear least squares approach is used to minimise the criterion function by a gradient method. The derivatives required by the algorithm are the states of LPV systems that need to be simulated at every iteration. The effectiveness of the algorithm is assessed by two simulated examples.

Abstract
There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

Abstract
An indirect downsampling approach for continuous-time input/output system identification is proposed. This modus operandi was introduced to system identification through a sub-space algorithm, where the input/output data set is partitioned into lower rate m subsets. Then, a state-space discrete-time model is identified by fusing the data subsets into a single one. In the present work the identification of the input/output downsampled model is performed by a least squares and a simplified refined instrumental variables (IV) procedures. In this approach, the inter-sample behaviour is preserved by the addition of fictitious inputs, leading to an increase of excitation requirements of the input signal. This over requirement is removed by directly estimating from the data the parameters of the transfer function numerator. The performance of the method is illustrated using the Rao-Garnier test system.