1,354,140 research outputs found
Validation of electromagnetic numerical simulations through measurements for an efficient deployment of complex systems
No full textA proper use of electromagnetic numerical simulations can lead to an efficient equipment integration and consequent successful deployment of complex systems and stations. In this paper, the level of accuracy attainable with modern numerical methods is discussed as a consequence of measurement campaign performed in complex systems and environments
Le Sezioni Unite si pronunciano sul danno da risoluzione per mancato pagamento dei canoni locatizi
No full textA seguito dei contrastanti orientamenti sorti in seno alla giurisprudenza di legittimità,
la sentenza in esame interviene a dirimere la questione concernente la corretta individuazione del danno risarcibile in caso di risoluzione anticipata del contratto di
locazione immobiliare per inadempimento del conduttore, nell’ipotesi di restituzione
dell’immobile da parte di quest’ultimo.
In light of the divergent interpretations that have arisen in the case law, the judgment in question addresses the issue concerning the proper determination of the recoverable damages in cases of early termination of a real estate lease agreement due to the tenant’s breach, particularly in circumstances where the property has been returned by the tenant
An intelligent system for electrical energy management in buildings
No full textRecent studies have highlighted that a significant part of the electrical energy consumption in residential and business buildings is due to an improper use of the electrical appliances. In this context, an automated power management system - capable of reducing energy wastes while preserving the perceived comfort level - would be extremely appealing. To this aim, we propose GreenBuilding, a sensor-based intelligent system that monitors the energy consumption and automatically controls the behavior of appliances used in a building. GreenBuilding has been implemented as a prototype and has been experimented in a real household scenario. The analysis of the experimental results highlights that GreenBuilding is able to provide significant energy savings
ARBITRARY VOXEL SELECTION FOR ACCELERATING A RAY TRACING-BASED FIELD PREDICTION MODEL IN URBAN ENVIRONMENTS
No full textLTE signal propagation in a maritime environment: Validation of a hybrid MoM-ray tracing prediction method
No full textThe present work shows how we were able to deploy a hybrid Method of Moments (MoM) - Ray Tracing electromagnetic approach to predict the measured power over a Long Term Evolution (LTE) radio link over the sea during two different measurement campaigns. Empirical models and pure ray-tracing code by themselves were not able to accurately reproduce the measured features, which were found to be caused by the receiving antenna interactions with the ship metallic structure. The correct result was obtained through a hybrid approach which combines the far field of the antenna and its surroundings. This approach could prove beneficial in critical applications where a precise estimation of received power and coverage range is desirable
Oil Spill Classification from Multi-Spectral Satellite Images: Exploring Different Machine Learning Techniques
No full textThis work describes the potential of oil spill classification from optical satellite images, as investigated by applying different machine learning techniques to a dataset of more than 300 oil spill candidates, which have been detected from multi-spectral satellite sensors during the years 2008 and 2009, over the entire area of the Mediterranean Sea. A set of geometrical and grey level features from Synthetic Aperture Radar (SAR) literature has been extracted from the regions of interest in order to characterize possible oil spills and feed the classification system. Results obtained by applying different machine learning classifiers to the dataset, and the achieved performance are discussed. In particular, as a first approach to oil spill classification, simple statistical classifiers and neural networks were used. Then, a more interpretable fuzzy rule-based classifier was employed, and performance evaluation was refined by exploiting Receiver Operating Characteristic (ROC) analysis. Finally, since oil spill dataset collection happens incrementally, a suitable technique for online classification was proposed, encompassing at the same time cost-oriented classification, in order to allow for a dynamic change of the misclassification costs. This latter goal has been achieved by building an ensemble of cost-oriented, incremental and decremental support vector machines, exploiting the concept of the ROC convex hull
Bathymetry Estimation from Multi-Spectral Satellite Images Using a Neuro-Fuzzy Technique
No full textThe aim of this work is the study and the development of a technique for bathymetry estimation, which is based on the exploitation of information contained in optical images, collected by satellite sensors. The use of satellite images allows to inspect a wide geographical area, and to produce the corresponding bathymetric map, in an economical way. Since remote areas can easily be observed by satellite sensors, remote sensing techniques could represent a useful support to Rapid Environmental Access (REA) activities, that consist in getting information on hardly accessible zones. Moreover, the exploitation of high resolution satellite sensors data, such as Quickbird data, allows to describe the coastal zone with high accuracy, although this implies a reduction of the spectral information. In this work we propose an accurate supervised method based on the use of a neuro-fuzzy system, whose input is made of only three spectral bands. The method consists in the application of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to the optical satellite image of the area of interest. We applied the technique to two Quickbird images of the same area, acquired in different years and in different meteorological conditions. In particular, the first one has been acquired in calm sea conditions, and is supplied with a large dataset of in-situ measured depths for the training and the validation of the method. The second image has been acquired in slight sea conditions and is supplied with a limited dataset of in-situ measured depths, collected along two transepts in the scene. These two cases allow to study and compare the performance of the presented technique, taking into account the effect of both meteorological conditions and training set size reduction on the overall performance. On the first image we achieved a mean STD of 36.7 cm for estimated water depths in the range [-18,-1] m. We then studied the performance of the method in realistic situations of limited in-situ data availability, that is using as a training set only data collected along closed paths in the same image. In this case we obtained a mean STD of 45 cm. In addition, we studied the effect of limited data availability together with unfavorable sea conditions by applying the method to the second image. In this latter case we achieved a mean STD of about 64 cm, which is still a good result
Building a Time Variant Cost-Oriented Classifier Using an Ensemble of SVMs on a Real Case Application
No full textThis paper shows an attempt to build a time variant cost-oriented classifier for two-class classification problems. Such classifier is based on a sliding window, and has been designed as an ensemble of Cost-Oriented Support Vector Machines (CO-SVMs). More precisely, we have integrated the Incremental lDecremental (ID) formulation of SVMs with the Cost-Oriented (CO) formulation, thus obtaining an ensemble of COID-SVMs. At each data arrival, the new pattern is classified by using a dynamic selection of the underlying COID-SVMs in the Receiver Operating Characteristic (ROC) space by means of the ROC convex hull method. Then, once the actual class label of the new pattern is known, the new data and the associated class label are used to perform an incremental learning by each COID-SVM. At the same time, each SVM is updated by performing the decremental learning of the data falling outside the sliding window. This allows to adapt the classification to time varying conditions. The methodology has been applied to the classification of oil spills at sea from remotely sensed optical images
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