1,720,985 research outputs found
Riflettometria con forme d’onda stepped frequency
No full textLe tecniche basate sulla riflettometria sono molto utilizzate ¬in diverse applicazioni di misura, come, per esempio, le misure di umidità e salinità dei materiali e del suolo; le misure del livello dei liquidi; il rilevamento di perdite in tubature d’acqua sotterranee; per monitorare il movimento del terreno e lo stato delle frane. Nel tempo sono stati sviluppati diversi metodi di riflettometria, che si differenziano in base al segnale di riferimento utilizzato e alle tecniche di analisi dei segnali misurati. Essi possono essere divisi, essenzialmente, in metodi nel dominio del tempo (TDR), nei quali si inviano impulsi stretti o gradini, e metodi nel dominio della frequenza (FDR), che usano come segnali di riferimento sinusoidi a diverse frequenze. Esistono, ovviamente, molte altre varianti tra cui si possono citare la riflettometria nel dominio tempo-frequenza, che usa segnali chirp Gaussiani, e la Sequence TDR e Spread Spectrum TDR, che usano segnali pseudo-casuali.
In questa memoria, che riassume un lavoro in fase di pubblicazione, viene analizzata una nuova tecnica di riflettometria proposta recentemente nell’ambito della diagnostica dei cavi. Questa tecnica, chiamata stepped-frequency waveform reflectometry (SFWR), prevede l’utilizzo di segnali sinusoidali limitati nel tempo come segnali di riferimento e di tecniche di analisi nel dominio tempo-frequenza (distribuzione di Rihaczek). La tecnica permette, in questo modo, di sfruttare sia i vantaggi della TDR che quelli della FDR, pur richiedendo dispositivi per la generazione e la misura del segnale più semplici ed economici rispetto alle tecniche FDR. In la tecnica SFWR è stata sviluppata ipotizzando un modello quadratico per la funzione di propagazione delle onde nei cavi e coefficienti di riflessione indipendenti dalla frequenza. In questo documento viene proposto, prima di tutto, uno studio teorico del problema, grazie al quale si riescono a sfruttare tutte le informazioni disponibili sul segnale riflesso misurato. Viene definito, quindi, un algoritmo di stima progettato appositamente per lavorare con i segnali di riflettometria che si hanno in risposta alle sinusoidi limitate nel tempo e che generalizza quello proposto in anche a situazioni in cui sono presenti una funzione di propagazione e un coefficiente di riflessione con comportamenti in frequenza sconosciuti. La tecnica è stata inoltre provata su cavi simulati piuttosto che in prove reali, in modo da conoscere tutti i parametri dei cavi e poter determinare gli errori direttamente riconducibili all’algoritmo di stima
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Full text linkMonitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation
Split Ring Resonator Network and Diffused Sensing Element Embedded in a Concrete Beam for Structural Health Monitoring
Full text linkThe aim of this work is to propose two different and integrated sensors for the structural
health monitoring of concrete beams. In particular, a diffused sensing element and a split ring
resonator network are presented. The first sensor is able to detect the variations in the dielectric
properties of the concrete along the whole beam length, for a diffuse monitoring both during the
important concrete curing phase and also for the entire life cycle of the concrete beams. The resonators
instead work punctually, in their surroundings, allowing an accurate evaluation of the permittivity
both during the drying phase and after. This allows the continuous monitoring of any presence of
water both inside the concrete beam and at points that can be critical, in the case of beams in dams,
bridges or in any case subject to a strong presence of water which could lead to deterioration, or
worse, cause serious accidents. Moreover, the
A new dataset of satellite images for deep learning-based coastline measurement
No full textCoastline monitoring over time is crucial to promptly detect and address environmental problems such as coastal erosion. Satellite imaging offers a great opportunity for this kind of tasks, but proper analysis tools are required to identify sea and land regions. Several techniques have been proposed over time for satellite images analysis, typically based on the direct computation of a water probability index for each pixel. In more recent years, however, research was focused on the usage of deep learning techniques for sea-land segmentation and coastline detection. For these methods, a large dataset of labelled samples is required but often not available. In this paper, we propose a method for the automatic generation of a dataset of labelled satellite images, containing both sea and land regions. The automatic labelling method is based on the combination of information retrieved from publicly available coastline data and from satellite images themselves and can be used to generate a large number of sea-land segmented samples
Metrology for AI: Quality Evaluation of the SNOWED Dataset for Satellite Images Segmentation
No full textThe paper tackles the issue of evaluating the quality of datasets for AI-based systems, by examining a specific case related to land-water segmentation of satellite images. The quality of the samples of a public dataset is automatically assessed using a new method, based on the best match with labels computed with the normalized difference water index (NDWI). Then, the assessment is used for identifying samples with labels of doubtful value, and for extracting a higher quality subset. The quality of the subset is validated by training a neural model for satellite image analysis, and by testing it also on a completely independent set of satellite images. The study, besides providing a concrete new method to check and improve datasets for water-land segmentation of satellite images, demonstrates the general importance of evaluating dataset quality
The SNOWED Dataset and Its Application to Po River Monitoring Through Satellite Images
No full textThis paper presents an approach for the creation of a water/land segmentation dataset using a combination of satellite imagery and certified shoreline measurements. The dataset is created by selecting Sentinel-2 Level 1C satellite images that align with certified shoreline measurements obtained from the NOAA Continually Updated Shoreline Product (CUSP) program. The effectiveness of the proposed dataset is demonstrated through its application in a water monitoring scenario, specifically in assessing water quantity fluctuations in a region of Po river in Italy. Given the very good results obtained in this application, the dataset proves to be effective in training neural networks for water/land segmentation tasks. This preliminary research contributes to address the increasing environmental challenges, particularly in hydrogeologically vulnerable areas, by providing a reliable dataset for accurate shoreline segmentation and long-term monitoring applications
A New Processing Method to Segment Olive Trees and Detect Xylella Fastidiosa in UAVs Multispectral Images
No full textIn this paper, a new approach for fast detection of Xylella fastidiosa bacterium symptoms on olive trees is presented. Images are taken using a multirotor unmanned aerial vehicle (UAV) equipped with a multispectral camera. A new segmentation algorithm to recognize trees is applied and images are then classified using linear discriminant analysis. It has been applied to selected sites in the Southern Italy where multispectral images of olive orchards have been acquired. The developed algorithm seems to be very promising thanks to its high mean Sørensen–Dice similarity coefficient, which demonstrates the feasibility of a correct tree individuation, and its sensitivity in detecting infected trees
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