This article investigates the conducted electromagnetic susceptibility (EMS) mechanism of commercial Gigabit Ethernet switches and its relationship with port-to-port conducted electromagnetic interference (EMI) transfer. The EMS results reveal the susceptible frequency band from 70 to 100 MHz, with the critical threshold occurring around 90 MHz. This frequency-dependent behavior is explained by the combined effects of frequency-selective cable response associated with reflection and standing-wave enhancement, high-frequency parasitic coupling through the Ethernet isolation transformer, and the effective baseband response and filtering characteristics of the PHY receiver. The agreement between the explained resonance-related sensitive band and the measured minimum-threshold frequency supports the proposed EMS mechanism. Furthermore, a high-frequency equivalent model is developed to quantify port-to-port EMI transfer by incorporating transformer parasitic capacitance, inter-port capacitive crosstalk, and common-ground transfer impedance. The simulated transfer characteristics are validated by measurements, confirming that the model captures the dominant frequency-dependent inter-port coupling behavior. A comparative analysis of the EMS thresholds and port-to-port transfer characteristics provides frequency-domain evidence for identifying the dominant coupling paths in multiport Ethernet switches. The proposed mechanism-based analysis and validated model offer a reference for EMC assessment and robustness optimization of multiport Ethernet equipment.

Phased array antennas are widely used in modern wireless systems, where rapid and accurate radiation pattern prediction is essential for array analysis, design, and optimization. However, pattern prediction remains challenging due to diverse array configurations and mutual coupling effects. This paper proposes an efficient graph neural network-based method for array pattern prediction. The proposed approach predicts active element patterns to includes coupling effects. By representing array elements as graph nodes and their spatial interactions as graph edges, multi-layer graph convolutions are used to aggregate neighborhood information and learn coupling-induced variations in element patterns. A direction-weighted and symmetry-aware loss function is further introduced to improve accuracy and symmetry. Validation is conducted on two fabricated and measured arrays, where the proposed method achieves median element-level prediction errors of 0.3-1.2 dB, with 96.5% of errors below 3 dB. The average array-level prediction errors is significantly reduced compared with the case where coupling is ignored. The scalability is further verified on a numerically simulated 64-element array, for which an average prediction error of 1.57 dB is obtained. In addition, the proposed model maintains microsecond-level inference speed, providing an efficient and practical complement to repeated full-wave simulations for array antenna pattern analysis.

To address unnecessary axillary lymph node (ALN) dissection and long intraoperative waiting time in breast cancer treatment, a miniaturized symmetrical rhombic planar microstrip antenna $(27 \text{mm} \times 29 \text{mm} \times 1.6 \text{mm})$ is proposed. Operating in the $3.8-14 \text{GHz}$ broadband, it utilizes dielectric property differences between tissues for detection. Simulation results show the antenna can identify tumors $\geq 4\text{mm}$ in lymph nodes and achieve 8mm detection depth, providing rapid intraoperative guidance for ALN assessment.

A rotation-based synthetic concentric ring array (SCRA) is proposed for wide-angle scanning with minimal RF hardware. By rotating a linear wide-beam array, the SCRA forms virtual aperture samples supporting large-angle coverage. A two-way switch pairing scheme halves the number of RF channels, and a constrained two-archive evolutionary algorithm (CTAEA) jointly optimizes peak sidelobe level, directivity, and half-power beamwidth across a $\pm$70$<^>{\circ }$ range. A compact 5.71 GHz wide-beam patch antenna with $\pm$73$<^>{\circ }/\pm$70$<^>{\circ }$ E-/H-plane beamwidths enables stable element patterns for the synthesis. Simulations and measurements verify that CTAEA achieves robust convergence under strict constraints and superior sidelobe suppression and beam-shaping efficiency compared with conventional concentric ring arrays and dense RF-fed baselines.

Gallium nitride (GaN) Schottky barrier diodes (SBDs), as essential devices in power-electronic systems, are particularly vulnerable to high-power microwave (HPM) pulses. HPM pulses can induce electro-thermal multiphysics coupling effects in the GaN SBDs, potentially leading to device damage or failure. Dislocation density, as a critical parameter of GaN materials, has a significant effect on the performance of GaN SBDs. In this article, we present an electro-thermal multiphysics coupling analysis of GaN SBDs under HPM pulses, with particular emphasis on the influence of dislocation density on this coupling. Furthermore, by integrating deep learning techniques with technology computer-aided design (TCAD) simulations, we propose a deep multihead attention residual network (DMARN) model that enables the rapid and accurate prediction of maximum electric field strength and temperature within GaN SBDs under varying dislocation density configurations and HPM pulse parameters. Through the validation of ablation and comparative experiments, an additional test set, and hardware experiments, the DMARN model demonstrates outstanding performance and generalization ability.

This paper presents a deep learning approach for rapid correction of beam-control codes in radome-enclosed phased arrays. An end-to-end neural network predicts optimal phase-amplitude codes from sparse active element pattern (AEP) measurements using an Efficientunet with feature-wise linear modulation (FiLM) conditioning. A multi-task logarithmic domain loss term ensures main lobe fidelity, boresight error (BSE) and null depth (ND). Validated on an 8×8 Ka-band array with a multi-layer PCB radome, the 32-element sampling achieves 0° average BSE and optimal ND with millisecond-level prediction time per scanning angle.

The massive deployment of frequency modulated continuous wave (FMCW) radars in autonomous vehicles will lead to severe electromagnetic interference issues. Mutual interference mitigation is vital for ensuring the detection performance of automotive FMCW radars. Existing methods struggle to maintain a balance between interference mitigation performance and computational efficiency in complex scenarios with multiple interferences, which restricts the applicability in practical scenarios requiring rapid response. To address the predicament, this article proposes an efficient method for mutual interference mitigation among automotive FMCW radars using the short-time Fourier transform (STFT) spectrogram sliding cancellation. The interferences and target signals exhibit different time–frequency characteristics. After sliding the STFT spectrogram along the time dimension, the intensity of interference bins fluctuates dramatically while that of target bins remains virtually unchanged. Therefore, this intensity variation is employed to detect and cancel interference bins in the STFT domain. The effectiveness of the proposed method is validated through both simulations and experimental results in different interference scenarios. The results show that the proposed method can effectively eliminate the interferences and recover the target signals even if 100% of the samples are contaminated by the interference, and it is computationally efficient.

This paper presents rotated array radar (RAR), a new paradigm that innovatively leverages non-dedicated rotating structures as carrier platforms. Motivated by aerodynamic-induced fluctuations and the practical lack of high-fidelity trajectory monitors on such non-dedicated platforms, rotation is modeled with a periodic angular deviation with unknown parameters. A sharpness-driven autofocus approach is proposed to estimate these parameters to facilitate subsequent motion error compensation. Moreover, to efficiently process the resulting non-Cartesian array samples produced by RAR, a non-uniform fast Fourier transform (NUFFT) imaging algorithm is introduced. Extensive simulations over diverse parameter settings demonstrate that the recovered rotation trajectories closely approximate the true deviations, achieving estimation errors on the order of a few percent across the tested cases (mean error 2.5–5.1%) and supporting the robustness of the proposed pipeline. Simulations driven by real wind-turbine monitoring data support the practicality of the motion error model and demonstrate the effectiveness of the proposed methods under realistic trajectories. Finally, an application case is investigated with a pulsed RAR for range-velocity-angle estimation of targets.

Global navigation satellite system receivers are highly vulnerable to complex electromagnetic interference, while existing studies face difficulties in quantifying interference parameters and establishing a unified framework across different interference types. To address this challenge, this article proposes a receiver anti-interference performance evaluation method based on a power-sensitive characteristic exponential model. The approach first eliminates spectral amplitude deviations through L2-norm normalization, and then quantifies the similarity between measured and reference spectra using cosine similarity. A power penalty term is introduced to construct a weighted similarity measure, which is further combined with dynamically updated frequency weights to fuse multifrequency information and yield interference-sensitive feature scores. A joint loss function is designed to iteratively optimize the frequency weights and a unified decision threshold. Finally, discrete scores are fitted with an exponential model to form continuous power-sensitive characteristic exponential models. Experimental results demonstrate that the optimized unified threshold achieves reliable failure detection, with fitted threshold powers deviating within +/- 3 dB of measurements. Additional validation with signals of varying pulse duty cycles confirms the generality of the method.

To characterize local electromagnetic parameters in aircraft composite skins containing geometric irregularities, such as curved surfaces and slots, this paper proposes a partitioned equivalent modeling (PEM) method. The proposed method provides a parametric basis for low-frequency electromagnetic shielding effectiveness analysis of complex structures. Within the PEM method, an irregular structure is decomposed into homogeneous equivalent partitions; local equivalent electromagnetic parameters are then extracted through near-field scanning measurements, S-parameter-based inversion, and a physics-guided deep neural network. Numerical simulations show that the proposed method maintains low S-parameter reconstruction errors in the 10 MHz-20 MHz frequency range, with magnitude MSE values at the 10(-6) level. Experimental validation using a near-field measurement system further shows that more than 89% of the scanned regions have relative errors below 0.20%, with a maximum average error of 0.57%. These results indicate that the proposed method offers an effective approach for low-frequency near-field characterization and quantitative modeling of geometrically complex composite structures.

Inaccurate measurements of analog sensors caused by electromagnetic interference (EMI) signals threaten the security and reliability of sensor-based systems. This article proposes a self-check method for recognizing and correcting disturbed measurements of analog sensors under EMI signals. The core basis of this method involves the use of a dummy sensing element to make the sensor sensitive only to EMI signals, rather than the measurand. This configuration enables the sensor to switch between normal mode and self-check mode through switching devices. Then the sensor response to EMI signals in normal mode is characterized by that in self-check mode, which is computable. A detailed implementation framework is derived to ensure that the sensor responses to EMI signals in both modes are highly similar, involving the properties of the dummy sensing element, the input voltage of the operational amplifier (opamp), and the power supply rails. Owing to its low demand for additional components and computing resources, the proposed self-check method is cost-effective and convenient to implement. Its effectiveness and robustness are validated on a temperature sensor, an ambient light sensor, a linear variable differential transformer (LVDT) and a wireless temperature sensor node with EMI experiments using different signals parameters, temperatures and light intensities. The experimental results show that this method can enhance the measurement accuracy of analog sensors to help maintain the functioning of sensor-based systems under EMI signals.

This paper explores a novel emitter localization concept where a linear antenna array is conformed along the rotating blades of drones. As the blades rotate, the linear array synthesizes a two-dimensional radial array structure, enabling direction-ofarrival (DOA) estimation. A key challenge in this scenario is the presence of angular speed mismatch during rotation, which leads to phase misalignment and geometric distortion of the synthetic aperture. This work analytically derives the Cramer-Rao Lower Bound (CRLB) to quantify how angular speed errors affect DOA performance. Numerical simulations further validate the analysis, showing that angular speed errors could significantly degrade estimation accuracy, especially at moderate-to-high signal-tonoise ratios and in the undersampled regions. These findings underscore the need for precise rotational control or correction mechanisms in blade-mounted antenna systems.

The physical optics (PO) method is widely used to solve the electromagnetic (EM) scattering problems involving electrically large structures, in which each surface element is required to determine whether it is blocked by others. It may suffer from the computational efficiency issue due to elementwise shadowing testing. In this paper, an efficient Z-buffer based shadowing testing method is proposed to accelerate this procedure. In this method, all triangular facets are first mapped to a grid plane using the Z-buffer method, and for each grid cell, all projected triangles that intersect it are recorded. Then, rigorous shadowing testing is made for all facets recorded in the grid where the centroid of each triangle is projected. It can avoid a large number of redundant operations for pairs of triangles with no occlusion relation, which leads to the same accuracy as the traditional rigorous shadowing testing method while significantly inproving efficiency. Four numerical examples are carried out to validate its accuracy and efficiency.

A complex-frequency-shifted perfectly matched layer (CFS-PML) for the face-centered cubic finite-difference time-domain (FCC-FDTD) method with second-order in time and fourth-order in space, termed as FCC-FDTD(2,4), is proposed in this article. To apply the FCC-FDTD(2,4) method in solving complex electromagnetic problems, the CFS-PML is proposed through an auxiliary differential equation (ADE), which enhances the absorbing performance and substantially simplifies the derivation of the time-marching formulations. Due to the fourth-order sampling approaches of FCC grids in space, the computational domain-absorbing boundary interface is carefully analyzed. Moreover, the proposed method exhibits complying-divergence. Then the optimal ranges of three absorbing boundary constitutive parameters are found through the numerical sweeping method and verified by the genetic algorithm. Three numerical examples, the electromagnetic wave propagation, electromagnetic scattering of multiple targets with fine details and the electric field distribution of a large-scale airplane, are carried out to demonstrate the performance of the proposed method. The results indicate that the developed ADE-CFS-PML absorbing boundary for the FCC-FDTD(2,4) method shows good accuracy and stability.

High-resolution 3D visualization of dynamic environments is critical for applications such as remote sensing. Traditional 3D imaging systems, such as lidar, rely on avalanche photodiode (APD) arrays to determine the flight time of light for each scene pixel. In this context, we introduce and demonstrate a high-resolution 3D imaging approach leveraging an Electron Multiplying Charge Coupled Device (EMCCD). This sensor’s low bandwidth properties allow for the use of electro-optic modulators to achieve both temporal resolution and rapid shuttering at sub-nanosecond speeds. This enables range-gated 3D imaging, which significantly enhances the signal-to-noise ratio (SNR) within our proposed framework. By employing a dual EMCCD setup, it is possible to reconstruct both a depth image and a grayscale image from a single raw data frame, thereby improving dynamic imaging capabilities, irrespective of object or platform movement. Additionally, the adaptive gate-opening range technology can further refine the range resolution of specific scene objects to as low as 10 cm.

This paper systematically studies the impact of imbalances between adjacent lines and effects on crosstalk. A novel perspective of displacement current is introduced to analyze and explain the simulated observations. The imbalances caused by coupling between single-single, single-differential, and differential-differential lines are studied and analyzed by considering the near-field coupling through the generated displacement currents. Measurements are conducted for various cases of coupled adjacent lines. An equivalent model considering the variation of displacement current with geometrical parameters is also proposed, and the corresponding coupling coefficients are extracted based on simulations to characterize the impact of imbalances. The methods and results presented in this paper provide useful guidelines for designing high-speed circuit layouts with closely spaced transmission lines.

The localization and power estimation of radio frequency (RF) interference sources is increasingly becoming an important topic in RF sensing. This article proposes a novel two-step approach based on machine learning (ML), which employs a nonconvex sparse recovery (NCSR) algorithm and a fusion neural network (FNN) specifically designed for the localization and power estimation of multiple RF sources. In the first step, the improved NCSR algorithm for the direction of arrival (DOA) and received signal strength (RSS) joint estimation is proposed based on an iterative maximally sparse convex (IMSC) algorithm, which offers enhanced DOA and RSS estimation accuracy. In the second step, a DOA and RSS fusion artificial neural network is constructed to accurately determine the location and power of the sources by inputting the DOA and RSS estimated in the first step. Simulation results show that both the first step and the second step perform better when used alone or in combination. The performance of the proposed two-step approach is verified through a sensing experiment involving two sources and four sensor arrays with an uncrewed vehicle-based testing system.

Jamming attacks can severely disrupt the communications in wireless sensor networks (WSNs). Accurate jammer localization is crucial for the implementation of active anti-jamming strategies. Existing localization methods primarily target the proactive jammer equipped with an omnidirectional antenna within the WSN area, which limits their effectiveness in more complex scenarios. To bridge this capability gap, an accurate localization method based on signal strength map (ALSSM) is proposed to locate the jammer, regardless of whether it is proactive or reactive, and irrespective of its antenna type or location. The ALSSM method constructs a jamming signal strength (JSS) map using JSSs collected by sensor nodes via a deception strategy. This map is then utilized to formulate an evaluation metric to quantify localization estimation errors. Therefore, the localization task is transformed into an optimization problem. This method is convenient to implement, as it requires no additional dedicated hardware, and the collected JSS data can be integrated into the WSN’s normal data transmission process. Simulation and experimental results demonstrate that the ALSSM method can accurately locate the jammer, regardless of whether it is directional or omnidirectional, and irrespective of its deployment inside or outside the WSN area.

Planar sparse array synthesis plays a critical role in modern radar and wireless communication systems. However, it remains challenging to simultaneously minimize the number of antenna elements while maintaining a low peak sidelobe level. Inspired by typical electromagnetic compatibility (EMC) phenomena, we previously developed the Maxwell’s Equations Derived Optimization (MEDO) algorithm, which enables accurate and robust solutions for complex electromagnetic optimization problems. In this paper, we extend MEDO to a multi-objective version (MO-MEDO), by incorporating non-dominated sorting with elitism and an external archive mechanism for Pareto front preservation. The proposed MO-MEDO algorithm exhibits excellent performance in sparse planar array synthesis, effectively achieving a favorable trade-off between array sparsity and sidelobe suppression.

The rapid and accurate identification of biological tissue types in resected specimens is critical to ensure complete tumor excision during surgery. By leveraging inherent electromagnetic property variations among tissues, this study presents a novel dual-port electromagnetic method that employs two-port S-parameters for quantitative tissue discrimination. The proposed technology leverages differences in the broadband electromagnetic properties among biological tissues, which are manifested as distinct attenuation characteristics during signal transmission. This approach allows for the successful differentiation of various tissue types, such as skin, muscle, fat, and tumor tissues, in ex vivo tumor-bearing mouse models. Specifically designed for biological tissue detection, this dual-port framework is the first to achieve a calibration-free operation and facilitate the detection of tumors with a size as small as 0.1 mm. Experimental validation in tumor-bearing mouse models demonstrated robust differentiation among skin, fat, muscle, and tumor tissues. Consistent measurements across multiple orientations were achieved, with a specific absorption rate below 0.0091 W/kg confirming operational safety. The transmission characteristics reveal significant bioelectromagnetic interactions, providing physical insights into tissue dielectric properties. This method provides a promising platform for clinical diagnostics and precision surgical guidance.