LineLab-A Transmission Line Simulator for Distributed Sensing Systems: Open-Source MATLAB Code for Simulating Real-World Transmission Lines

D’Aucelli, Giuseppe Maria; Giaquinto, Nicola; Andria, G.

doi:10.1109/map.2018.2839908

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…Cables have been simulated using LineLab software [23], a MATLAB-based simulator of quasi-TEM transmission lines. It can simulate transmission lines with arbitrary dispersion models, and arbitrary profiles of the frequency-dependent primary parameters 𝑅, 𝐶, 𝐺, 𝐿.…”

Section: Test Results On Simulated Cablesmentioning

confidence: 99%

“…-a concise but complete theoretical framework of the SFWR technique; -signal estimation algorithms designed specifically to work with sinusoidal bursts reflected by a transmission line; -measurement techniques free from assumptions about the propagation function and the reflection coefficient; -the optimization of the above techniques for the special case of reflection coefficient independent on the frequency, examined in [22]; -simulation results, using a MATLAB simulator already developed and validated by the authors [23], in order to evaluate accurately the errors due to the algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry

Giaquinto

Scarpetta

Spadavecchia

2020

IEEE Trans. Instrum. Meas.

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The paper presents algorithms to realize effectively and accurately the stepped-frequency waveform reflectometry (SFWR), i.e. the reflectometric technique based on the use of sinusoidal bursts. This technique is useful for monitoring the health status of connection cables, but has many other applications, like other reflectometric techniques. The paper outlines the theory of SFWR, highlighting the problems associated to the transient components in the reflected signals; presents a method to minimize the effect of the transients, estimating the frequency response function (FRF) of interest with very low systematic error; shows how to use the FRF to locate and characterize faults in cables; evaluates accurately, using simulated cables with exactly known characteristics, the errors associated to the proposed methods. Overall, the paper demonstrates how the SFWR technique can be effectively used for testing cables, and in general determine, via reflectometry, parameters of interest of transmission lines.

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Section: Test Results On Simulated Cablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry

Giaquinto

Scarpetta

Spadavecchia

2020

IEEE Trans. Instrum. Meas.

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“…The neural network proposed in this paper required a TDR signal such as those described in Section 2.1 as input data and a list of the impedance discontinuity points in the cable as target data. A TL simulator [ 20 ] was adopted to produce an adequate number of samples, each of them accompanied by a label containing information about the faults in the cable and the cable length.…”

Section: Methodsmentioning

confidence: 99%

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks

Scarpetta

Spadavecchia

Adamo

et al. 2021

Sensors

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In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with 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 process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

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“…Educators within the EM community have been seeking innovative ways to improve students' understanding on the rather challenging subject. Many previous efforts have been focused on providing visualizations through computer-based softwares and simulation tools [1]- [11]. While these computer softwares and simulation tools are effective to help students better understand the concepts of various EM topics, they generally lack touch-based interactivity and inefficiency for ubiquitous, seamless teaching and learning.…”

Section: Introductionmentioning

confidence: 99%

“…However, the method thus far is only applicable for uncoupled transmission lines, and it is not suitable for coupled ones found in common coupled line structures. Most computerbased softwares on the topic of transmission lines theory [7], [8], [11] are also targeted at uncoupled transmission lines only. In EM engineering curricula, coupled line structures such as coupled line filters and directional couplers still form an important part of syllabi.…”

Section: Introductionmentioning

confidence: 99%

Mobile Teaching and Learning of Coupled-Line Structures: The multiple-1D coupled-line finite-difference time-domain method

Tan

Heh

2020

IEEE Antennas Propag. Mag.

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In this article, mobile teaching and learning of coupled line structures using multiple one-dimensional coupled line finite-difference time-domain (M1-D CL-FDTD) method are presented. The formulation of M1-D CL-FDTD method applicable for coupled line structures is provided. The method bypasses the more computationally intensive full-wave 3-D method and it is well-suited for implementation on mobile devices. Several mobile apps are developed to provide interactive visualization of EM wave propagation in coupled line structures. Such visualization allows the underlying resonating and coupling mechanisms to be elucidated clearly and improves students' understanding of coupled line theories. Mobile teaching and learning of various coupled line structures including coupled transmission lines, coupled line bandpass filter, directional coupler and branch line coupler are demonstrated and discussed.

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LineLab-A Transmission Line Simulator for Distributed Sensing Systems: Open-Source MATLAB Code for Simulating Real-World Transmission Lines

Cited by 9 publications

References 15 publications

Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry

Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks

Mobile Teaching and Learning of Coupled-Line Structures: The multiple-1D coupled-line finite-difference time-domain method

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