Search Results (99)

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

With the widespread diffusion of personal Internet of Things (IoT) devices, Electromagnetic Side-Channel Attacks (EM-SCAs), which exploit electromagnetic emissions to uncover critical data such as cryptographic keys, are becoming extremely common. Existing shielding approaches typically rely on bulky or opaque materials, which limit integration in modern IoT environments; this motivates the need for a transparent, lightweight, and easily integrable solution. Thus, to address this threat, we propose the use of electromagnetic metasurfaces with shielding capabilities, fabricated with an optically transparent conductive film. This film can be easily integrated into glass substrates, offering a novel and discrete shielding solution to traditional methods, which are typically based on opaque dielectric media. The paper presents two proof-of-concept case studies for shielding against EM-SCAs. The first one investigates the design and fabrication of a passive metasurface aimed at shielding emissions from chip processors in IoT devices. The metasurface is conceived to attenuate a specific frequency range, characteristic of the considered IoT processor, with a target attenuation of 30 dB. At the same time, the metasurface ensures that signals from 4G and 5G services are not affected, thus preserving normal wireless communication functioning. Conversely, the second case study introduces an active metasurface for dynamic shielding/transmission behavior, which can be modulated through diodes according to user requirements. This active metasurface is designed to block undesired electromagnetic emissions within the 150–465 MHz frequency range, which is a common band for screen gleaning security threats. The experimental results demonstrate an attenuation of approximately 10 dB across the frequency band when the shielding mode is activated, indicating a substantial reduction in signal transmission. Both the case studies highlight the potential of transparent metasurfaces for secure and dynamic electromagnetic shielding, suggesting their discrete integration in building windows or other environmental structural elements. Full article

Modern power systems have become increasingly complex, making the detailed modeling and analysis of large-scale networks computationally demanding and often impractical. Therefore, network reduction techniques are essential for representing a smaller area of interest while preserving the electrical behavior of the complete system. For electromagnetic transient (EMT) studies, such as load rejection analysis, reduced networks are commonly derived using classical methods like Kron reduction under maximum power transfer conditions. However, this approach can lead to discrepancies in load flow and short-circuit levels between the reduced and complete systems. In addition, Kron reduction may introduce negative resistances in the reduced-order model, compromising system stability by producing non-passive equivalents and potentially causing unrealistic or numerically unstable EMT simulations. To address these limitations, this paper proposes an optimization-based approach, termed PSO-OPF-Kron, which integrates Optimal Power Flow (OPF) with the Particle Swarm Optimization (PSO) algorithm to refine the equivalent network parameters. The method optimally determines power injections, bus voltages, transformer tap settings, and impedances to align the reduced model with the full system’s operating point and short-circuit levels. Validation on the IEEE 39-bus system demonstrates that the proposed method significantly improves accuracy and numerical stability, ensuring reliable EMT simulations for load rejection studies. Full article

With the evolution of semiconductor devices, power electronics systems are trending towards higher frequencies and greater integration, leading to increasingly severe electromagnetic interference (EMI) issues. As an effective means of suppressing EMI, EMI filters have been extensively researched consequently. Over the past few decades, research on EMI filters has yielded a wealth of valuable achievements. However, the existing literature lacks a comprehensive and systematic collation of different EMI filters. In order to fill this gap, this work presents a thorough survey of EMI filters. According to their principles and implementation methods, these EMI filters can be broadly categorized into three types: Passive EMI Filters (PEFs), Active EMI Filters (AEFs) and integrated electromagnetic EMI filters (IEFs). Based on the review of the principles for each category, this paper analyzes their respective advantages, drawbacks, and development status. Through organization and categorization, this work aims to provide a reference for researchers and designers. Full article

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the $30\%R$ initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over $70\%$, and improves the final adjustment accuracy by more than $95\%$ relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions. Full article

With the recent increase in the threat posed by unmanned aerial vehicles (UAVs) operating in environments where conventional detection systems such as radar, optical, or acoustic detection are impractical, attention is paid to methods for detecting low-flying UAVs with small radar cross-section (RCS). The most commonly used detection methods are radar detection, which is susceptible to electromagnetic (EM) interference, and optical detection, which is susceptible to weather conditions and line-of-sight. This research aims to demonstrate the possibility of using passive optical fiber Bragg grating (FBG) as a sensitive element array for low-flying UAV detection and localization. The principle is as follows: an optical signal that propagates through an optical fiber can be modulated due to the FBG reaction on the air pressure caused by a low-flying (even hovering) UAV. As a result, a small target—the DJI Avata drone can be detected and tracked via intensity surge determination. In this paper, the experimental setup of the proposed FBG-based UAV detection system, measurement results, as well as methods for analyzing UAV-caused downwash are presented. High-speed data reading and processing were achieved for low-flying drones with the possible presence of EM clutter. The proposed system has shown the ability to, on average, detect an overpassing UAV’s flight height around 85 percent and the location around 87 percent of the time. The key advantage of the proposed approach is the comparatively straightforward implementation and the ability to detect low-flying targets in the presence of EM clutter. Full article

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a deep learning-based passive UAV detection and identification system leveraging radio frequency (RF) spectrograms. The system employs a high-resolution RF front-end comprising a multi-beam directional antenna and a wideband spectrum analyzer to scan the target airspace and capture UAV signals with enhanced spatial and spectral granularity. A YOLO-based detection module is then used to extract frequency hopping signal (FHS) regions from the spectrogram, which are subsequently classified by a convolutional neural network (CNN) to identify specific UAV models. Extensive measurements are carried out in both line-of-sight (LoS) and non-line-of-sight (NLoS) urban environments. The proposed system achieves over 96% accuracy in both detection and identification under LoS conditions. In NLoS conditions, it improves the identification accuracy by more than 15% compared with conventional full-spectrum CNN-based methods. These results validate the system’s robustness, real-time responsiveness, and strong practical applicability. Full article

The suspension system can adapt to different road excitations by adjusting its own stiffness or damping, or outputting active driving force, thereby improving the comprehensive dynamic performance of the vehicle, including ride comfort and vehicle handling. As the automotive industry’s requirements for “intelligence, comfort, and safety” continue to increase, the intelligence of suspension systems has become a research hotspot for scientific research institutions and enterprises, with broad development prospects. This article reviews the current development status of automotive suspensions and introduces the working principles and research status of different types of suspension systems, such as passive suspensions, semi-active suspensions, active suspensions, and electromagnetic suspensions. In addition, it summarizes the control methods of vehicle intelligent suspensions, including classical control, modern control, and intelligent control, and expounds the advantages and disadvantages of each control strategy. Finally, it summarizes the challenges and development trends faced by suspension systems. This review can provide technical reference for researchers engaged in the study of intelligent suspension under the modern chassis architecture and offer direction guidance for the development of key suspension technologies. Full article

Comprehensive maritime domain awareness is crucial for navigation safety, traffic management, and security surveillance. In the context of an increasingly complex modern electromagnetic environment, the disadvantages of traditional active single-station radars, such as their high cost and susceptibility to interference, have started to surface. Due to their unique advantages, such as low cost, environmental sustainability (by reusing existing signals), and resilience in congested spectral environments, non-cooperative passive multistatic radar (PMR) systems have gained significant interest in maritime monitoring. This paper presents the research background of non-cooperative passive multistatic radar systems, performs a fundamental analysis of the detection performance of multistatic radar systems, and suggests an optimization method for the transceiver configuration of non-cooperative passive multistatic radar systems based on geometric coverage theory and a signal-to-noise ratio model. A multi-objective optimization model is developed, considering both detection coverage and positioning error, and is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization aims to find the optimal receiver location relative to a fixed configuration of four transmitters, representing common maritime traffic patterns. According to the simulation results, the multi-target genetic algorithm can be utilized to optimize the receiver position under the S-band radar settings used in this work. Compared to a random placement baseline, this can reduce the positioning error by about 8.9% and extend the detection range by about 15.8%. Furthermore, for the specific four-transmitter configuration and S-band radar parameters considered in this study, it is found that the best detection performance is more likely to be obtained when the receiver is placed within 15 km of the transmitters’ geometric center. Full article

This study presents a novel, fully passive radiofrequency (RF)-based localization system designed to detect the position of a robotic hand on a flat surface within its tactile range, particularly in scenarios where other sensing systems may face limitations. The system employs U-shaped, chipless resonator tags printed on the surface using a customized conductive ink, together with a coplanar RF probe integrated into the robotic hand, to determine position through impedance variations. Unlike conventional approaches, the proposed method provides a compact, low-cost, and robust solution that is resilient to variations in lighting, dust, and other environmental conditions. The resonator tags are arranged in a structured grid inspired by a Sudoku pattern, enabling both position and orientation detection in the near-field region. The system is fabricated on 3D-printed flexible substrates using a flexible and stretchable conductive ink, and its performance is validated through both electromagnetic simulations and experimental measurements. The results confirm that the proposed approach enables accurate and repeatable two-dimensional localization of the robotic hand under various configurations. This work introduces a scalable, high-precision, and vision-independent sensing platform with strong potential for robotic manipulation in challenging environments. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation. Full article

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications. Full article

The optimal design of electromagnetic interference (EMI) filters relies on accurate characterization of noise source impedance. The conventional insertion loss method involves integrating two distinct passive two-port networks between the linear impedance stabilization network (LISN) and the equipment under test (EUT). The utilization of the insertion loss to formulate a system of binary quadratic equations concerning the real and imaginary components of the impedance of the noise source enables the precise extraction of the magnitude and phase of the noise source impedance in theory. However, inherent inaccuracies in the insertion loss method during extraction can compromise impedance accuracy or even cause extraction failure. This work employs a series inductance method to overcome these limitations. Exact analytical expressions are derived for the magnitude and phase of the noise source impedance. Subsequently, the application scope of the series insertion loss method is analyzed, and the impact of insertion loss measurement error on noise source impedance extraction accuracy is quantified. Requirements for improving extraction accuracy are discussed, and method optimization strategies are proposed. The permissible range of insertion loss error ensuring a solution exists is deduced. Finally, simulation and experimental results validate the proposed approach in a buck converter. Full article

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness. Full article