Dear Colleagues,

Distributed monitoring has progressively gained in popularity given the growing need for continuous measurements of large structures or regions, e.g., cultivated fields, pipelines, tunnels, and viaducts. The output of this kind of monitoring is a distribution of the physical quantity of the itme of interest (like temperature, strain, moisture, etc.) along the entire structure, or the detection and location of anomalous values of the quantity at any point of the structure.

There are basically two ways to perform distributed monitoring. The first is distributed sensing, which uses cable-like elements (e.g. ,optical fibers) sensitive at every point along their length. The second is distributed sensor networks, which use a large number of sensor nodes with wired or wireless communication to obtain the desired measurements.

This Special Issue is addressed to both types of distributed monitoring. A quality contribution should illustrate a particularly effective solution using one of the two methods and highlights the validity of the proposed methodology for general problems or for a specific application. The reason for preferring either menthod in the discussed case should be outlined.

Prof. Nicola Giaquinto
Prof. Francesco Adamo
Guest Editors

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• Distributed sensing
• Reflectometric techniques
• Sensors networks
• Sensor swarms
• IoT measurements

Title: Detection and Characterization of Discontinuities in Cables with TDR and Neural Networks
Authors: Marco Scarpetta, Maurizio Spadavecchia, Francesco Adamo and Nicola Giaquinto
Affiliation: Department of Electrics and Electronics, Polytechnic of Bari
Abstract: In this paper, a convolutional neural network model for the detection and characterization of capacitive faults in coaxial cables is presented. The neural network analyzes time domain reflectometry signals and outputs predictions of discontinuity points, composed of a class (capacitive fault or line termination), an estimated position and an estimated capacity (only if a capacitive fault is detected). The neural network was trained using a great number of simulated signals, obtained using a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training procedure, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performance were assessed.