Abstract
Digital photogrammetry and GIS-based mapping are increasingly recognised as powerful tools in littoral issues. This paper considers the interoperability framework for high-resolution imagery acquisition and the development of coastal geoscience maps. The layered system architecture of the cartographic methodology is also explained. Moreover, it highlights a new approach to assessing heterogeneous geologic, geomorphological and maritime environments. The main goal of the present study was to test a new concept for photogrammetric images in order to assist modelling techniques, spatial analysis and coastal conceptual models. This approach proposes a methodological approach to coastal zone monitoring and to maritime forcing conditions evaluating. This approach will allow: (1) the acquisition of a large archive of high-resolution imagery; (2) the development of a coastal database including the entire data field and in situ assessments; (3) the study of coastal dynamics and shoreline evolution; (4) the assessment of the rock platforms and hydraulic structures; (5) the production of coastal geosciences maps. An integrated coastal geoscience and engineering methodology was outlined in NW of Iberian Peninsula (South Galicia and North/Central Portugal regions). This paper reports on the increased knowledge of the studied regions, providing essential data concerning coastal geo-morphodynamics. The overall assessment revealed additional evidence of erosion issues, which contributes to a better understanding of the hydraulic conditions. The main results are presented in regional coastal geoscience maps and local approach-outputs that could help government, local authorities and stakeholders to develop coastal management plans and to recommend strategies.

Abstract
This work describes a preliminary methodological framework for the assessment of coastal volcanic rocky platforms in Pico Island (Azores). This study also deals with the importance of GIS-based mapping and unmanned aerial vehicles (UAVs) whichwere tested in two studied areas (Lajes and Madalena sites). This research gives an overview about the UAVs work flow and its application for photogrammetric assessment of coastal volcanic rocky platforms. The main purpose of this study is to explore the influence of basaltic lava coastal platforms and associated boulder strewn, which are well differentiated in Lajes and Madalena sites. In general, this research describes the image acquisition process and the UAV LiDAR technology and the aerial surveys to acquire the geodatabase which will enable the design of geoscience and geotechnical maps on coastal volcanic rock platforms. The images analysis will allow: (i) boulder geotechnical description and evaluation; (ii) shore and coastal platform assessment; (iii) propose monitoring coastal plans (short to long-term); (iv) rock boulders (basaltic blocks) mobility, movement trend, imbrication, indicating flow and source direction; (v) obtain DTM and contour lines to generate 3D models of the coastal area. All these data is crucial to understand the coastal dynamics of the sites and develop an applied mapping which couples GIS technologies and UAV based spatial platform. It was also developed a GNSS (Global Navigation Satellite System) datasheet which incorporates the entire database of ground control points, using high resolution GPS system for the georeferencing and differential correction process. This research presents the preliminary results of the coastal geotechnics mapping for volcanic environments. Furthermore, high resolution image acquisition, georeferencing and differential correction of the ground control points using high accuracy GPS, were also thorough analysed to improve the general methodology presented herein. Finally, it was proposed a preliminary integrated coastal engineering study for the rocky platform zoning and short to long-term monitoring in selected sites on Pico Island. The study was partially financed by FEDEREU COMPETE Funds and FCT (GeoBioTec|UA: UID/GEO/04035/2013) and LABCARGA|ISEP reequipment program: IPP-ISEP|PAD’2007/08. © 2016 Taylor & Francis Group, London.

Abstract
This work focus on the simulation of gas pipeline dynamic models in view to develop a leakage detection tool. The gas dynamics in the pipes is represented by a system of nonlinear partial differential equations. The linear partial differential equations is reduced to a transfer function model. Taking advantage of an electrical analogy, a pipeline can be represented by a two port network where gas mass flows behave like electrical currents and pressures like voltages. Thence, four transfer functions quadripole models are found to describe the gas pipeline dynamics, depending on the variable of interest at the boundaries. These models are simple enough to be used in the control and management of the network. These models have been validated using operational data and used to simulate a leakage. © Copyright 2016, PSIG, Inc.

Abstract
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the "curse of dimensionality", a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes.

Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

Abstract
In this paper an identification method for statespace LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.