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Due to the limitations of electromagnetic signals, underwater scenarios increase the complexity of developing accurate navigation systems. In the last decades, Ultra-Short BaseLine (USBL) positioning systems have been widely and efficiently used for Autonomous Underwater Vehicles (AUVs) localization, endorsing to be a suitable solution to limit the navigation drift without requiring periodic surfacing for Global Positioning System (GPS) resets. Typically, in the localization context, USBL measurements are exploited as observations within the on-board navigation filter where, most of the time, Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) solutions are employed. In a break-away from the above-mentioned approaches, in this study, the localization task is solved as a Maximum A Posteriori (MAP) estimation problem. The presented solution is validated through the use of data gathered in October 2020 during EUMarineRobots (EUMR) tests in La Spezia (Italy) within the activities of the SEALab, the joint research laboratory between the Naval Support and Experimentation Center (Centro di Supporto e Sperimentazione Navale, CSSN) of the Italian Navy and the Interuniversity Center of Integrated Systems for the Marine Environment (ISME).

This article illustrates a data acquisition methodological process based on Structure from Motion (SfM) processing confronted with terrestrial laser scanner (TLS) and integrated into a Historic Building Information Model (HBIM) for architectural Heritage’s management. This process was developed for the documentation of Cáceres’ Almohad wall bordering areas, a UNESCO World Heritage Site. The case study’s aim was the analysis, management and control of a large urban area where the urban growth had absorbed the wall, making it physically inaccessible. The methodology applied was the combination of: clouds and meshes obtained by SfM, with images acquired from Unmanned Aerial Vehicle (UAV) and Single Lens Reflex (SLR) and terrestrial photogrammetry, and finally, with clouds obtained by TLS. The outcome was a smart-high-quality three-dimensional study model of the inaccessible urban area. The final result was two-fold. On one side, there was a methodological result, a low cost and accurate smart work procedure to obtain a three-dimensional parametric HBIM model that integrates models obtained by remote sensing. On the other side, a patrimonial result involved the discovery of a XII century wall’s section, that had supposedly been lost, that was hidden among the residential buildings. The article covers the survey campaign carried out by the research team and the techniques applied.

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction works. The impact of these factors can affect traffic flow parameters by influencing travel time, density, occupancy, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.

The aim of the present paper is to propose innovative approaches able to improve the capability of classical damage indicators in detecting the damage position in linear systems. In particular, starting from classical indicators based on the change of the flexibility matrix and on the change of the modal strain energy, the proposed approaches consider two data fusion procedures both based on the Dempster-Shafer theory. Numerical applications are reported in the paper in order to assess the reliability of the proposed approaches considering different damage scenarios, different sets of modes of vibration and the presence of errors affecting the accounted modes of vibrations.

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains., Ines Mendes was recipient of a fellowship from the Portuguese Science Foundation, FCT (SFRH/BD/74798/2010). This work was supported by FCT I.P. through the strategic funding UID/BIA/04050/2013, and the project PTDC/AGR-ALI/121062/2010.

International audience; This paper exposes the Navigation and Control technology embedded in a recently commercialized micro Unmanned Air Vehicle (UAV), the AR.Drone, which cost and performance are unprecedented among any commercial product for mass markets. The system relies on state-of-the-art indoor navigation systems combining low-cost inertial sensors, computer vision techniques, sonar, and accounting for aerodynamics models.

The use of damage indicators based on changes of the systems’ dynamic properties before and after the occurrence of damage, generally represents a good tool in detecting the presence, the position and also the severity of the damage itself. However, signal noises, difficulties to excite systems for identifying the significant modes of vibrations, particularly the ones more sensible to the damage scenarios and also the presence of multiple damage locations can particularly affect the efficiency of these indicators. In recent years, new algorithms and innovative techniques devoted to improve the efficiency of the classical damage indicators have been carried out in many studies. In this context, the paper presents an approach for damage identification of linear systems developed by combining the use of classical damage indicators based on the modal strain energy (MSE) with a multi-stage data-fusion procedure. In particular, modal strain energy change ratios (MSECR) are evaluated by accounting different sets of modes of vibration. Then, the obtained MSECRs are converted in local decisions and involved in a multi-stage data-fusion process which provides indices able to detect the damage location and its extent. The approach provides a significant improvement of the efficiency of the damage indicators particularly in the presence of noises and multiple damages as shown by the numerical applications reported in the paper.

Nowadays the necessity to reveal the hidden information from complex data sets is increasing due to the development of high-throughput instrumentation. The possibility to jointly analyze data sets arising from different sources (e.g. different analytical determinations/platforms) allows capturing the latent information that would not be extracted by the individual analysis of each block of data. Several approaches are proposed in the literature and are generally referred to as data fusion approaches. In this work a mid level data fusion is proposed for the characterization of three varieties ( Salamino di Santa Croce , Grasparossa di Castelvetro , Sorbara ) of Lambrusco wine, a typical PDO wine of the district of Modena (Italy). Wine samples of the three different varieties were analyzed by means of 1 H-NMR spectroscopy, Emission-Excitation Fluorescence Spectroscopy and HPLC-DAD of the phenolic compounds. Since the analytical outputs are characterized by different dimensionalities (matrix and tensor), several multivariate analyses were applied (PCA, PARAFAC, MCR-ALS) in order to extract and merge, in a hierarchical way, the information present in each data set. The results showed that this approach was able to well characterize Lambrusco samples giving also the possibility to understand the correlation between the sources of information arising from the three analytical techniques.

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.