Electromagnetic susceptibility testing often involves a large number of repeated tests across various excitation input combinations (e.g., frequencies, waveforms) to identify sparsely distributed characteristics in the excitation domain that contribute to product failure. Group testing provides a cost-effective approach for large-scale fault detection in such sparsely distributed scenarios. However, obtaining an optimal grouping in multi-stage group testing remains challenging due to the transcendental nature of the equations involved in existing analytical solutions. This paper introduces a novel multi-stage group testing strategy that maximizes the number of groups by assigning each fault sample to as many distinct groups as possible. At each stage, the strategy estimates the maximum number of fault groups based on known prior probabilities, enabling the calculation of optimal groupings even in worst-case scenarios where the number of groups exceeds the number of fault samples. Through theoretical analysis and simulation, we demonstrate the strategy significantly reduces costs compared to traditional group testing, particularly with low prior probabilities and sufficient sample sizes. Our selection framework guides practitioners in choosing efficient testing strategies based on probability and sample size. Validation through simulations and practical implementation confirms substantial efficiency improvements over conventional methods.

Evaluating the internal specific absorption rate (SAR) in heterogeneous biological tissues is important for realistic electromagnetic exposure assessment. Classical SAR assessment relies on robotic scanning in standardized phantoms or surface-level evaluation, and is therefore unsuitable for real-time internal exposure estimation. This letter proposes a material-embedded physics-informed neural network (PINN) for reconstructing internal SAR directly from sparse boundary electric-field samples. By embedding spatially varying tissue properties into the normalized physics residual, the proposed method alleviates gradient imbalance in high-contrast media. Numerical results on two-dimensional human arm and torso models show that the proposed framework achieves accurate SAR reconstruction even under extremely sparse boundary sampling, outperforming existing PINN and finite-difference methods. These results demonstrate the potential of the proposed framework for noninvasive internal SAR evaluation in realistic electromagnetic exposure scenarios.

Understanding the electromagnetic compatibility of power modules in complex electromagnetic environments is critical for the safety of integrated modular avionics. However, fully testing the module against various ElectroMagnetic Interference (EMI) waveforms is time-consuming and labor-intensive. To address this challenge, we propose a deep-learning-based approach, termed Multi-waveform Transfer Learning (MWTL), building a unified model to predict module responses across multiple interference waveforms. MWTL utilizes a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture to effectively extract the temporal features and build the relation between the interference signals and the response signals. In addition, by leveraging shared features across different scenarios, a Transfer Learning (TL) strategy is applied, generalizing the model into unseen interference waveforms, thereby reducing the need for extensive training data in new tasks. The experimental results show that the proposed method delivers excellent predictive performance across various types of interference, maintaining high accuracy even with limited data. In particular, by transferring shared features from multitask learning to new tasks, the approach significantly reduces data requirements for new scenarios while preserving prediction accuracy.

Electromagnetic environment monitoring is crucial to ensure safety under electromagnetic exposure. Yet commonly adopted antenna design methods are often inadequate for distributed sensing arrays. This paper presents a theoretical framework for optimizing antenna array configurations to enhance spatial resolution in EM monitoring. The framework defines spatial resolution based on source distinguishability and links it via analytical models to antenna radiation patterns, physical parameters, and inter-element spacing. Key design constraints—feed density, physical layout, far-field operation, and mutual coupling—are systematically incorporated. Simulations using an ideal source model validated theoretical predictions for spatial resolution and feed density, showing good agreement. This research provides a structured approach to antenna array design, prioritizing overall environmental sensing performance over individual element optimization.

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.

Existing approaches regarding the modeling of shielding effectiveness (SE) of planar carbon fiber composites are predominantly focused on normal incident scenarios, despite the common presence of obliquely incident waves in practical applications. This study proposes a modified homogenization model derived from the Maxwell-Garnett framework, by introducing an angular correction factor, specifically tailored for oblique incidence. The proposed model enables the computation of the equivalent permittivity and SE of carbon fiber composites under varying angles of incidence. Comparison with full-wave simulations demonstrates that the proposed angular correction factor significantly enhances prediction accuracy, particularly for transverse electric (TE) polarization.

This paper proposes a novel quantitative evaluation framework for electromagnetic radiation from floating systems adopting characteristic mode theory (CMT). Floating systems such as aircraft and spacecraft face unique challenges due to their isolation from Earth, where injected energy can only dissipate via radiation or resistive losses. By establishing explicit mathematical relationships between mode weighting coefficients, eigenvalues and power components (radiated, reactive and dissipated), this work enables precise prediction of energy distribution. The methodology integrates CMT-based spatial energy decomposition with full-wave simulations and experimental validation in a GTEM cell. Results show good alignment between predicted and measured radiated power. This study offers critical insights for the evaluation and mitigation of potential electromagnetic interference in aerospace to facilitate high-integrity electronic system design.

This article introduces a novel approach to construct dipole equivalence models based on near-field measurements, aiming to improve the source localization toward the analysis of electromagnetic interference in the near-field region among circuit boards. In contrast to conventional methods that simultaneously optimize dipole amplitudes and positions, the proposed method decouples these tasks by determining optimal dipole positions prior to dipole amplitude optimization. This strategy leverages the principle that current hotspots align with paths of minimum cumulative impedance, simplifying the search process. Through the decoupled search of dipole amplitudes and positions, the proposed method achieves efficient and accurate dipole equivalence construction with the knowledge of input/output port locations. Case studies on two-port/four-port microstrip line boards and practical mixing circuit boards are demonstrated. It is proved that the proposed method significantly reduces the computational time by more than seven times while improving the source reconstruction accuracy compared to the classical iterative approach.

PIN diode limiters protect RF systems from high-power microwave threats but suffer from transient spike leakage, risking device damage. This paper systematically explores how carrier lifetime τ, junction capacitance Cj package inductance Lp, I-layer width w, and the carrier frequency fc jointly govern leakage via ADS-based simulations. It is revealed that spike leakage is minimized at the Cj – Lp resonant frequency ${f_{\text{0}}} = 1/\left( {2\pi \sqrt {{C_j}{L_p}} } \right)$. while deviations from f0 signify dependencies on τ and w. These findings potentially provide guidelines for designing robust and frequency-tailored protection circuits.

The spatial resolution of near-field magnetic probes is a crucial performance metric for conducting spatial field measurements in practical applications. However, the commonly adopted approaches for determining the spatial resolution of the probe face substantial ambiguity and limitations. Addressing this issue, a generalized definition of magnetic probe spatial resolution is proposed, which correlates directly with the effective size of the probe's sensing front end. Following this definition, a novel calibration method is introduced, utilizing the evanescent field within a rectangular waveguide structure. This calibration method is implemented on the basis of a custom-designed transverse electromagnetic (TEM) cell, whose performance has been validated both in simulations and in experiments. The proposed method effectively mitigates uncertainties arising from the measurement position and field distribution, enabling a universal and accurate assessment of the spatial resolution of the probe. The efficacy of the calibration method is validated on a commercial magnetic probe. The comparative analysis demonstrates that the proposed calibration technique yields significantly improved accuracy compared to the existing method for determining the spatial resolution of the probe.

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.

This paper focuses on the conformal antenna modeling for breast-shaped surface, aiming to address the challenges in constructing strict conformal antennas for complex and irregular shapes. By employing the cage-based deformation algorithm and Green Coordinates, we develop a method to achieve conformal antenna modeling on the breast-shaped surface. The performance analysis of the conformal antennas is conducted. The results demonstrate the feasibility and effectiveness of our proposed approach, providing insights for the design and application of conformal antennas in related fields.

The accurate estimation of quiet zones in anechoic chambers is critical for electromagnetic compatibility testing. Waveguide testing of absorbers limits the reflectivity measurement to normal incidence, while full-wave simulations of pyramidal absorbers are computationally intensive and impractical for large chambers. This paper introduces a novel, efficient modeling approach that overcomes these challenges by combining full-wave simulations with a Quasi-Newton optimization method to extract the relative permittivity and angular-dependent reflectivity of absorbers. By replacing complex pyramidal structures with planar equivalents via Fresnel Boundaries, the proposed method achieves over 95% reduction in simulation time while maintaining high accuracy (errors in field distribution < 3dB). Validation is performed against traditional simulations and experimental data, proving the proposed approach enables rapid evaluation of quiet zones at 1 GHz and demonstrates good agreement in reflection levels. The proposed modeling approach offers practical value for instances requiring fast, reliable performance assessments of RF absorbers and anechoic chambers.

In modern RF receiving systems, the escalating demand for wider bandwidths and complex signal handling necessitates precise characterization of nonlinear effects in RF amplifiers. The widely adopted Volterra series models suffer from excessive parameters and limited physical interpretability. In this paper, we address the challenge of nonlinearity modeling by proposing a novel behavioral model for predicting third-order intermodulation (IM3) based on the mechanism of second-order intermodulation (IM2) feedback. The proposed behavioral model accurately predicts the asymmetry between high/low third-order intermodulation components of a representative amplifier circuit, with an error within ± 3.05 dBm. The proposed model explicitly links asymmetry in high/low IM3 products to IM2 feedback interactions, offering critical physical insights while significantly reducing parameter complexity.

We have developed an implantable biopotential monitoring system-on-board (SoB) for electrocardiographic sensing, with performance validated through ECG signal simulator test. The integrated system incorporates BLE 5.0 wireless communication, enabling real-time graphical signal visualization through a dedicated PC interface. To improve patient comfort and minimize surgical interventions, a wireless charging module is implemented to eliminate the need for battery replacement procedures. The prototype demonstrates miniaturization, achieving complete system integration within a circular form factor of 14 mm radius (equivalent to 615 mm2 footprint) with a total component-inclusive profile thickness of 2.67 mm. Electrical characterization reveals an operational current consumption of approximately 90 mA during active mode with Bluetooth communication, while maintaining a maximum unobstructed transmission range of 4.5 meters under free-space conditions.In the electromagnetic safety simulation of the implanted device, we model the implanted device and evaluate the radiation of the antenna inside.The results are in line with the ICNIRP standard.

Acoustically actuated multiferroic antennas offer a significant advantage in terms of miniaturization. For multiferroic antennas operating in the kHz range, the near-field radiation is typically measured to assess directionality. However, the feeding structure introduced between the multiferroic antenna and the instrument may induce strong radiated leakage, which can compromise the accuracy of the measurement results. This letter conducts detailed analysis of the radiation leakage issue and presents the first evaluation of radiated leakage generated by the feedline in measurements for several laminated composite kHz multiferroic antennas. The results indicate that the radiated leakage even exceeds the radiation produced by magnetoelectric coupling of multiferroic antenna. A feeding structure is proposed to suppress radiation leakage by 17.58 dB and 16.62 dB in the case studies, respectively, thereby effectively mitigating its impact on measurement. The proposed method will serve as a valuable reference for near-field radiation measurement of kHz-range multiferroic antennas.

In this article, we introduce and investigate a hybridization algorithm based on particle swarm optimization (PSO) and brainstorm optimization (BSO). The hybrid BSO–PSO (HBPSO) technique adopts PSO that is initialized by BSO within the starting iterations. The performance of HBPSO is significantly enhanced compared to single BSO or PSO when applied to high-dimensional optimization problems with local minima. The hybrid procedure is validated by showing appropriate convergence curves when applied to six benchmark functions. Guidelines regarding the selection of the inertial factor and switching iteration are investigated and presented accordingly. The proposed HBPSO is then validated using practical optimization tasks. It is demonstrated that HBPSO can outperform single PSO or BSO techniques in addressing representative antenna-related problems, including patch antenna circuit model extraction, conformal antenna array synthesis, and full-wave antenna design problems.

This paper presents the design of a patch electrode and a data-driven recognition algorithm for early-stage skin cancer detection. The skin and cancerous tissues exhibit skin depths of 0.35–0.87 mm and 0.45–1.1 mm, respectively, within the 15–40 GHz frequency range, meeting skin cancer detection requirements. A dataset was constructed by dividing the skin into 7×7 grids and randomly populating it with cancerous tissue. The dataset was augmented using geometric symmetry and used to train a deep residual network for cancer tissue recognition, achieving an accuracy of 95.3%. This study provides a promising pathway toward accurate, low-cost hardware and algorithms for early-stage skin cancer detection.

This paper presents a simulation-based analysis method for interferences in wireless channel among multiple functionalities in distributed electromagnetic(EM) monitoring systems. The system consists of three functional modules: EM data acquisition, UHF data transmission, and UWB localization. The performance of each module depends on the coupling between the module transceivers. By simulating the coupling between the transceivers corresponding to these three modules and applying the link budget formula, we analyze the system’s node deployment strategy. We aim for this study to provide a foundation for the system deployment of distributed electromagnetic monitoring systems, thus improving the overall system efficiency.

In radio frequency (RF) receiving units, predistortion technology is an effective means to address nonlinear distortion. This paper focuses on the in-band nonlinear response of single modulated signals in RF receiving units and uses the error vector magnitude (EVM) to evaluate the nonlinear effects. Regarding the limitation of traditional EVM testing methods and the problems in parameter acquisition in the spectrum-correlation-based method, this paper simplifies the testing process and introduces a convenient approach to accurately estimate the transfer function. Simulations show that the proposed method generates consistent results with the traditional method without prior knowledge of the transfer function of the device-under-test, which verifies its effectiveness.