In this paper we present a study of the repeatability of an innovative electromagnetic tracking system (EMTS) for surgical navigation, developed to overcome the state of the art of current commercial systems, allowing for the placement of the magnetic field generator far from the operating table. Previous studies led to the development of a preliminary EMTS prototype. Several hardware improvements are described, which result in noise reduction in both signal generation and the measurement process, as shown by experimental tests. The analysis of experimental results has highlighted the presence of drift in voltage components, whose effect has been quantified and related to the variation of the sensor position. Repeatability in the sensor position measurement is evaluated by means of the propagation of the voltage repeatability error, and the results are compared with the performance of the Aurora system (which represents the state of the art for EMTS for surgical navigation), showing a repeatability error about ten times lower. Finally, the proposed improvements aim to overcome the limited operating distance between the field generator and electromagnetic (EM) sensors provided by commercial EM tracking systems for surgical applications and seem to provide a not negligible technological advantage.

Colonies of the endangered red sea pen Pennatula rubra (Cnidaria: Pennatulacea) sampled by trawling in the northwestern Mediterranean Sea were analyzed. Biometric parameters, such as total length, peduncle length, number of polyp leaves, fresh weight, and dry weight, were measured and related to each other by means of regression analysis. Ad hoc models for future inferencing of colonies size and biomass through visual techniques were individuated in order to allow a non-invasive study of the population structure and dynamics of P. rubra.

Monitoring 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.

The simulation of the propagation of electromagnetic (EM) waves is now widely used for both teaching and engineering/research purposes. In the context of teaching, there is no better way to present most EM topics, after presenting the basic mathematical concepts, than using simulators and showing realistic waveforms that illustrate what actually goes on in the system under analysis. In professional engineering or research work, there is an increasing need for EM simulations able to predict the behavior of modeled devices and systems, aiming to optimize design parameters (among other goals).

This work presents the application of a Time-Domain Reflectometry (TDR) inversion algorithm for localizing water along a bi-wire cable acting as a distributed sensing element (SE), and for evaluating the uncertainty of the water position measurement. The TDR inversion relies on a simplified yet effective gray-box circuital model of the measurement system that, without attempting a full-wave electromagnetic (EM) simulation, reproduces with good accuracy any actually observed reflectograms. The model parameters are estimated from a single acquired reflectogram so as to reproduce the measured signal, without a prior EM characterization of the system components. The model provides the water localization and enables extensive simulation campaigns under realistic variations in water position, stimulus pulse duration, and disturbance effects. A specific measurement setup, designed to perform repeated measurements in controlled laboratory conditions, is analyzed in detail as a case study. The water localization error of the measurement system is statistically evaluated in terms of confidence intervals, bias, and standard deviation, by means of simulated measurements of the model, with different water positions and TDR pulse durations. Then, the uncertainty evaluation is validated through 45 actual measurements, using multiple SEs, and the same water positions and pulse durations. The work proves the viability and the performance of the presented TDR inversion method for both localization measurements and for their uncertainty evaluation under different experimental conditions. More generally, it establishes a general framework for TDR measurements and uncertainty evaluation combining physical modeling, simulation-based uncertainty evaluation, and experimental verification.