this paper, a four-port Multiple-Input Multiple-Output (MIMO) antenna array is designed and analyzed for millimeter-wave (mm-wave) 5G applications. The configuration comprises a slotted microstrip patch array antenna excited through a T-junction power divider/combiner-based feed network. The set of truncated square slots are arranged on a rectangular patch to act as radiating surfaces. Similarly, the ground plane is a combination of rectangular and square-shaped slots used in a ground plane to enhance impedance bandwidth and radiation characteristics. The proposed fourport antenna is fabricated and the prototype is experimentally characterized for the S-parameters (S11,S21,S31, S41, S32, S42, S43), radiation pattern, and gain. Typical dimensions of the antenna are 28 & times; 28 & times; 0.254 mm3. Measured results are in excellent agreement with simulated results. The four-port MIMO antenna operates effectively over the 27.45-28.55 GHz frequency range and is suitable for emerging 5G applications with a gain of 10.02 dBi. To introduce the isolation between the adjacent elements in the array, the technique of polarization diversity has been employed. The corresponding enveloper correlation coefficient (ECC) suppression has been noticed. Based on the results, the proposed antenna confirms excellent diversity performance, and hence the design can be a promising solution for mm-wave and 5G applications.
In recent years, high-power millimeter waves (HPMMW) have emerged as a severe electromagnetic disturbance that endangers telecommunication networks significantly, as the intense electromagnetic interference induced by HPMMW can damage metallic electronic circuits and front-end devices. To address this issue, this paper proposes an all-dielectric resonant metasurfacebased front-end system that enables millimeter-wave components to resist HPMMW-induced damage. The system comprises a laser, an all-dielectric metasurface, an electro-optic resonator, a photodetector, and electronic circuitry. A 3 & times;3 cell-array metasurface antenna is used to capture and transmit a 65 GHz millimeter-wave signal to an electro-optic field sensor. Subsequently, the photodetector converts the optical signal into a demodulated radio-frequency signal, which can be further processed by the subsequent electronic circuitry. With a compact footprint of 7.7 & times; 7.7 mm2 and a high receiver sensitivity of-52 dBm, the proposed system can be integrated with other electronic circuits, facilitating the miniaturization of telecommunication equipment.
paper develops a novel Mesh-Based Generated Reluctance NetWork (MBGRN) model, which is based on the lumped parameter modeling method. The mesh-based approach automates network generation, replacing the manual flux path defini-tions required in traditional magnetic equivalent circuit (MEC). In this approach, the computational domain is represented in a polar coordinate system where the mesh elements are defined as isosceles trapezoids. The model utilizes a rotation simulation to bypass the remeshing processes common in the finite element method (FEM). A key advantage of the proposed MBGRN method is that the number of computational elements is reduced by half compared to the conventional FEM. This leads to a significant reduction in computation time, ranging from 10 to 15 times faster than traditional FEM, while maintaining a calculation error of less than 1% rela-tive to the FEM. The development of this method is validated through a practical benchmark problem: the surface-mounted permanent magnet synchronous motor under no-load condition. The results obtained from the MBGRN model are thoroughly compared with those from the 2D FEM.
This paper presents an evolutionary design process, fabrication, and verification of a substrateloaded Vivaldi antenna (VA) design for the pulse-type high-power microwave (HPM) solid-state T/R module. The antenna design utilizes a substrate with high permittivity to improve its power handling capacity (PHC), a snowflake-like metasurface (SFL-MS) lens on the front of the VA to enhance the directivity and impedance characteristics in the middle and high frequency bands, and rectangular slits on the radiating brims of the VA for further broadening its bandwidth and realized gain at low frequencies. After HPM measurement, these functional methods are proven to be effective for jointly contributing to optimizing antenna performances. The proposed prototype shows an operating band of 2.28-6.54 GHz (voltage standing wave ratio [VSWR] <2) and the PHC values are over 8000 W in this band. The measured realized gain and the maximum gain enhancement can reach 6.56-8.15 dBi and 5.96 dBi. The measured results are reasonable and agree well with simulations.
- In this paper, two classes of impedance transformers with wide operating bandwidth are presented. based on the specified impedance transformation ratio, fractional bandwidth (FBW), and in-band return loss an impedance transformer is designed at the center frequency (f0) of 2.4 GHz and an impedance-transforming ratio (r) of 0.5. The filtering impedance transformer with FBW = 90% and RL = 20 dB is fabricated and more, to demonstrate its application potential, the second impedance transformer is employed to realize a and enhanced isolation bandwidth.
this study, a deep learning-based beam-forming comparative study for anti-jamming applications in 2D-planar phased arrays is presented. For better array architecture benchmarking, three different geometries (circular, rectangular, hexagonal) are considered. Convolutional Neural Network (CNN) is employed to translate a target radiation pattern, generated as an image, directly into the optimal antenna currents. Adaptive antenna array beamforming weights can be estimated efficiently by the deep learning-based MATLAB code according to the desired beam steering angle and the null direction of the jammer. This approach establishes a smart, non-iterative mapping that bypasses traditional optimization algorithms, reducing computation time by up to 260x. Once trained, the model delivers optimal currents and weights in a single and efficient forward pass.
In Unmanned Aerial Vehicle (UAV) communications, Air-to-Ground (A2G) channel modeling is complex due to high mobility and environmental dynamics. While Machine Learning (ML) and Deep Learning (DL) techniques have been adopted to improve prediction accuracy over traditional empirical models, their performance remains highly dependent on hyperparameter configuration. Recent techniques such as Random Search and Bayesian Search are commonly used for hyperparameter tuning; however, they often struggle with convergence efficiency and prediction stability. To address these challenges, this study aims to develop a hyperparameter tuning framework based on the Gravitational Search Algorithm (GSA) to enhance the predictive performance of ML-based A2G models. The framework is applied to K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Long Short-Term Memory (LSTM) models at 1 GHz, 2 GHz, and 5.8 GHz. Experimental results demonstrate that GSA-optimized models demonstrate improved predictive stability and competitive accuracy, with GSA-LSTM and GSA-RF achieving an Root Mean Square Error (RMSE) of 5.46 dB, representing a 56% improvement over the free-space model. The proposed approach demonstrates improved robustness compared to conventional search strategies.
In recent years, Applied Computational Electromagnetics Society (ACES) Journal has highlighted the work of women in applied and computational electromagnetics. These articles aim not only to showcase the contributions of outstanding female researchers and educators but also to inspire young students and professionals as they pursue their careers in the field. This year, we were fortunate to learn from Professor Natalia Nikolova, who shared her insights about her professional journey, as well as her advice and guidance for the next generation of scientists and engineers.
recent years, artificial intelligence has been widely introduced into the design of electromagnetic devices. Traditional designs of DC-5.2 GHz wideband rectangular transverse electromagnetic (TEM) devices depend on complex formulas and electromagnetic simulation software such as HFSS and CST Microwave Studio Suite TM 2013. This paper proposes a DC-5.2 GHz rectangular TEM device optimized by genetic algorithms (GAs). The main innovation is the comparison between AI-based optimization and traditional design methods while ensuring excellent wideband transmission performance. The GA-optimized TEM device presents favorable performance and is suitable for cellular radiation experiments in wireless communication systems.
Nonlinear frequency diverse arrays have attracted increasing attention because of their unique point-like focusing characteristics in the joint angle-range domain. However, the quantitative relationship between the focusing pattern and key design parameters, as well as systematic performance evaluation tools, have not been fully established. In this paper, we propose the gain-focusing area as a quantitative metric for assessing the focusing efficiency of nonlinear frequency diverse arrays (NFDA). Unlike conventional one-dimensional beamwidth measures, the gain-focusing area captures the joint angle-range behavior of the array and provides a basis for performance evaluation and design optimization. Using this metric, we systematically study single- and dual-region focusing, and quantify the influence of focusing location (angle, range) and array design parameters (number of elements, frequency-offset coefficient) on the gain-focusing performance. Numerical simulations demonstrate that the proposed gain-focusing-area based evaluation method provides a more reliable performance metric; compared with existing approaches, it nearly doubles the valid angular range while maintaining accurate characterization of the focusing behavior. This enables NFDA spatial focusing capability and parameter sensitivity to be robustly quantified over a wider field of view, offering a more dependable tool for shaping the electromagnetic environment in and around the target region.
this paper, an electronically continuous tunable phased array antenna is proposed, which integrates a 1-port to 4-ports unequal power divider, four electronically continuous tunable phase shifters, and a 4 & times;3 right hand circularly polarized patch array antenna. The unequal power divider is designed with a power ratio of 1:2.25:2.25:1 to achieve a side-lobe level suppression of 20 dB. The phase shifters provide electronically linear and continuously tunable phase control within +/- 180 degrees. Their phase difference can be easily controlled by adjusting the direct current voltage. The array antenna consists of 12 hexagon patches, providing right hand circular polarization within the operating bandwidth and achieving a high gain of 13.68 dB. Furthermore, the proposed phased array antenna is capable of continuously steering the main beam over a range of-50 degrees to 55 degrees in the Y-Z plane at 3 GHz. Finally, the measurement results show good agreement with the simulations, confirming that the proposed electronically continuous tunable phased array antenna exhibits excellent performance.
In electromagnetic imaging applications, acquiring labeled data for supervised learning poses a significant challenge due to the high cost and time-consuming annotation processes. To address this limitation, we propose a semi-supervised electromagnetic imaging algorithm leveraging generative adversarial networks (GANs), which effectively integrates limited labeled data with abundant unlabeled measurements. Unlike conventional approaches that directly learn from raw scattered data, our method employs diffraction tomography (DT)-generated images as network inputs, thereby embedding spatial prior knowledge of scatterers to mitigate inherent artifacts such as boundary blurring and speckle noise. The framework features a modified U-Net architecture augmented with convolutional block attention modules (CBAMs) and residual blocks, enhancing feature extraction and segmentation robustness. Furthermore, adversarial training is introduced to refine the segmentation network using pseudo-labels generated from unlabeled DT images, enabling the discriminator to enforce physical consistency between labeled and unlabeled domains. Extensive simulations demonstrate the superiority of our method: when trained with only 100 labeled samples and 1,000 unlabeled samples, the proposed algorithm achieves a 23.0% reduction in mean squared error (MSE) compared to purely supervised counterparts. Additional validation on the handwritten digits and the “Austria” profile highlights its strong generalization capability for reconstructing unseen targets. This work bridges the gap between data-driven deep learning and physical priors, offering a practical solution for high-precision electromagnetic imaging under limited supervision.
paper presents a hybrid optimization strategy for wideband antenna design that leverages the strengths of both Random Forest (RF) and Differential Evolution (DE) algorithms. The strategy employs DE for iteratively updating antenna parameters and RF for feature selection in the process of antenna performance optimization. Initially, DE is applied to update antenna parameters for a predetermined number of iterations, generating a dataset of antenna performance metrics. This dataset is then used to train an RF model, which identifies the importance of each design variable. Feature selection, guided by the RF-derived importance, is applied to reduce the dimensionality of the search space. DE subsequently continues the optimization process within this reduced parameter space. Validation of this hybrid approach is performed through the design of a wideband slot antenna and compared against standalone DE, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Results demonstrate that the proposed strategy significantly accelerates convergence, achieving the target reflection coefficient and gain with substantially fewer iterations than the other methods (reductions of 75.56%, 45%, 42.11%, and 50% compared to DE, GA, SA, and PSO, respectively). Furthermore, the hybrid strategy consistently finds superior solutions exhibiting lower loss values compared to the benchmark algorithms. The method offers a computationally efficient, interpretable, and effective approach to antenna optimization.
A high-selectivity filtering magnetoelectric (ME) dipole antenna based on rectangular micro-coaxial lines (RMCLs) is presented, fabricated using micro-metal additive manufacturing (M-MAM) for V-band operation. The structure integrates two )1/4 resonators, one )1/2 resonator, and an ME dipole antenna, coupled through J/K-inverters realized as RMCL gaps and shortcircuited stubs. Notably, while a standalone ME dipole inherently supports an impedance bandwidth over 30%, this design achieves a 5.04% operating bandwidth centered at 59.5 GHz after integrating filtering functionality. Simulations confirm a peak gain of 4.53 dBi within the passband, with cross-polarization consistently below-20 dB. A sharp gain roll-off to-10 dBi at 1.048 f0 and 40 dB out-of-band suppression demonstrates exceptional frequency selectivity. Owing to inherent miniaturization, lightweight construction, and low-loss characteristics, the antenna exhibits significant potential for low-earth-orbit (LEO) satellite internet systems.
A two-layer filtering antenna based on a multi-lobe dipole structure is presented in this paper. Parasitic substrates and vertical copper elements are incorporated between the upper and lower substrates, with impedance matching improved by overcoming the closed magnetic flux limitation. Filtering performance is achieved through interaction with semi-circular-rectangular dual-mode matching structures and vertical metal, the composite structure can generate reverse current distribution. As a result, high-roll-off radiation nulls are formed and frequency selectivity is enhanced. In order to simultaneously enhance out-of-band suppression, high-current etching technology is employed to reconstruct the current path, etching semi-circular-rectangular dual-shape composite matching structures on the radiation patch, thus a significant improvement in gain stability is achieved. Distributed current control technology is utilized to decompose the dipole into multiple lobes, ensuring uniform current distribution and reducing concentration effects. Etched rectangular holes in the surrounding electromagnetic shielding isolation walls help reduce cross-polarization by suppressing surface waves and edge diffraction. The design achieves an impedance bandwidth exceeding 36%, out-of-band suppression exceeding 32 dB, a peak gain of 8.9 dBi, with cross-polarization levels below -30dB and -26dB in the E- and H-planes, respectively.
Quantum Interference Devices Time-Domain Electromagnetic Method (SQUID TEM) is currently the most accurate electromagnetic detection technology used in geophysics. However, SQUID is highly susceptible to electromagnetic interference in outdoor spaces, so it needs to work continuously and stably in a Dewar bucket wrapped with a metal shielding material. Therefore, the influence of the metal shielding thin layer on the observation signal cannot be ignored. We propose a vector finite element method based on unstructured grids to spatially model the sleeve formed by the metal shielding thin layer wrapped around the SQUID and analyze the influence of the metal shielding sleeve on the SQUID TEM observation signal. Firstly, we derive the governing equations from Maxwell's equations. Secondly, the Galerkin method is used for finite element discretization of the control equations, and unstructured mesh discretization is applied to the metal shielding sleeve and other computational areas. By solving the interpolation basis functions of tetrahedral vector elements, the local equations of each element are obtained and combined into a global large sparse matrix. Finally, the direct solution method is used to calculate the electromagnetic response at the observation points inside the metal shielding sleeve. The effectiveness and universality of the proposed method are verified through numerical simulations. Furthermore, through field experiments in the Da Hinggan Ling area, the necessity of metal shielding sleeves in field experiments and the reliability of the calculation results proposed have been demonstrated.
This paper proposes a 3 dB coupler with high-power handling capability feeds applied to high-power intelligent metasurface, based on loosely coupled structures and defected ground structure (DGS). The proposed coupler structure consists of two tandem coupled couplers with a coupling coefficient of 8.34 dB and a DGS, a design that significantly enhances the couplers' ability to handle high power levels. The measurement results are in good agreement with the simulation results: within the 3.5 to 4.5 GHz range, the return loss exceeds 21.4 dB, the isolation is at least 20.8 dB, the insertion loss is less than 0.3 dB, and the phase difference between output ports is 93-94.5 degrees. Furthermore, the coupler can handle high power exceeding 1.5 kW with a 10% duty cycle. The proposed 3 dB coupler features low insertion loss, high isolation, low return loss, high-power capability, and can improve the power capability of intelligent metasurface systems.
the issues of electromagnetic exposure safety in the application of an electric vehicle's wireless power transmission (WPT), this study proposes a surrounding active shield coils structure, laying on the four sides of the WPT system, which effectively reduces the lateral magnetic leakage field while supplementing the magnetic field inside the transmission channel. At the same time, this study proposes a ferrite groove structure as the passive shielding, achieving reduction of the vertical magnetic leakage field. On this basis, the paper takes system transfer efficiency and surrounding magnetic leakage field density as the optimization objectives, combining the extreme learning machine (ELM) surrogate model with multi-objective optimization algorithm for hybrid shielding structural design, realizing the further improvement of power transfer and electromagnetic shielding capability. A numerical simulation test is carried out and the results show that the proposed shielding scheme can ensure the system transfer efficiency, meanwhile reducing the magnetic leakage from all directions, and providing effective electromagnetic exposure safety protection for the human body.
superconducting quantum interference device time-domain electromagnetic (SQUID TEM) method has been widely used for the exploration of geological and mineral resources. Extracting resistivity and polarizability from TEM data aids in delineating subsurface metallic mineralization. However, traditional inversion methods are computationally intensive and slow. We propose an inversion method based on a convolutional neural network and bidirectional long short-term memory with attention (CNN-BiLSTM-Attention) to extract resistivity and polarizability of polarizable media from SQUID TEM data acquired with a magnetic source. The method combines the advantages of CNN for automatic feature extraction with the capabilities of BiLSTM for processing temporal data. Additionally, it incorporates an attention mechanism that emphasizes the extraction of key polarization features, thereby optimizing the parameters extraction process. The method can effectively extract resistivity and polarizability from SQUID TEM data. It is validated by the TEM data of theoretical models, and the errors of CNN-BiLSTM-Attention inversion results are smaller than that of the BiLSTM and CNN-LSTM methods.
paper proposes a high-precision cosecant square beamforming reflectarray using an improved hybrid Particle Swarm Optimization and Genetic Algorithm (PSO-GA) and low-coupling square ring element. Firstly, a novel hybrid PSO-GA algorithm is carried out to optimize the phase distribution of the high-precision beamforming reflectarray. Then, an element is presented whose reflection phase is insensitive to different incident angles and reflection amplitude stable with variations of element size. By using the above-mentioned methods, a high-precision beamforming reflectarray is designed and analyzed. The experimental results show well-defined cosecant squared beams in the predefined direction are achieved in the frequency range from 13.1 to 14.3 GHz and have low loss.
