Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Power Line Communication and Sensing Using Time Series Forecasting.

Smart electrical grids rely on data communication to support their operation and on sensing for diagnostics and maintenance. Usually, the roles of communication and sensing equipment are different, i.e., communication equipment does not participate in sensing tasks and vice versa. Power line communication (PLC) offers a cost-effective solution for joint communication and sensing for smart grids. This is because the high-frequency PLC signals used for data communication also reveal detailed information regarding the health of the power lines that they travel through. Traditional PLC-based power line or cable diagnostic solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In this paper, we develop a power line sensing technique that can detect various types of cable anomalies without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the approximation that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to build an anomaly detector which identifies the occurrence of a cable fault. We demonstrate the effectiveness and universality of our sensing solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.

Physical layer security in light-fidelity systems.

Light-fidelity (LiFi) is a light-based wireless communication technology which can complement radio-frequency (RF) communication technologies for indoor applications. Although LiFi signals are spatially more contained than RF signals, the broadcasting nature of LiFi also makes it susceptible to eavesdropping. Therefore, it is important to secure the transmitted data against potential eavesdroppers. In this paper, an overview of the recent developments pertaining to LiFi physical layer security (PLS) is provided, and the main differences between LiFi PLS and RF PLS are explained. LiFi achievable secrecy rates and upper bounds are then investigated under practical channel models and transmission schemes. Beamforming and jamming, which received significant research attention recently as a means to achieve PLS in LiFi, are also investigated under indoor illumination constraints. Finally, future research directions of interest in LiFi PLS are identified and discussed. This article is part of the theme issue 'Optical wireless communication'.

SWAVE Imaging of Placental Elasticity and Viscosity: Proof of Concept.

The placenta is the interface between the fetus and the mother and is vital for fetal development. Ultrasound elastography provides a non-invasive way to examine in vivo the stiffness of the placenta; increased stiffness has previously been linked to fetal growth restriction. This study used a previously developed dynamic elastography method, called shear wave absolute vibro-elastography, to study 61 post-delivery clinically normal placentas. The shear wave speeds in the placenta were recorded under five different low-frequency mechanical excitations. The elasticity and viscosity were estimated through rheological modeling. The shear wave speeds at excitation frequencies of 60, 80, 90, 100 and 120 Hz were measured to be 1.23 ± 0.44, 1.67 ± 0.76, 1.74 ± 0.72, 1.80 ± 0.78 and 2.25 ± 0.80 m/s. The shear wave speed values we obtained are consistent with previous studies. In addition, our multi-frequency acquisition approach enables us to provide viscosity estimates that have not been previously reported.