To enhance robustness and dynamic performance of permanent magnet synchronous motor (PMSM) drives at high speeds, a deadbeat predictive current control method based on a predictive disturbance suppression model (DPCC-PDSM) is proposed. First, the mathematical model of the PMSM and the principle of traditional deadbeat predictive current control (DPCC) are presented. Second, to estimate and compensate disturbance effects caused by external uncertainties, a predictive disturbance suppression model is designed by integrating the recursive least squares (RLS) algorithm with an extended state observer (ESO). Furthermore, leading angle flux weakening control strategy is incorporated into the predictive control framework to overcome voltage and current limitations in high-speed operation. Finally, the stability and effectiveness of the proposed method are validated through experiments. The results demonstrate that the DPCC-PDSM significantly improves robustness and ensures stable and reliable performance of PMSM drives in high-speed flux weakening operation.

This paper examines the optimisation of antenna parameters for wire monopole, vertical trapezoidal monopole, and circular disc monopole antennas with the Enhanced Optimizable Neural Network Classifier (EONNC) and Sugeno Fuzzy Inference System (SFIS). This study includes both quantitative and conventional antenna design techniques, offering comprehensive insights into antenna optimisation tactics. An advanced antenna selection algorithm identifies the ideal antenna by a comprehensive examination of performance metrics with the EONNC, hence reinforcing the rigour of our research process. The geometric parameters of the antennas are delineated, with SFIS proficiently ascertaining the appropriate dimensions. The EONNC categorises antennas into three classifications, whereas the SFIS determines optimal parameters for estimating antenna size. Accuracy measures assess the EONNC performance, whereas the SFIS performance is measured using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our suggested technique demonstrates remarkable precision in parameter prediction and antenna classification, with a mean absolute percentage error (MAPE) of less than 4% and an accuracy exceeding 99.3%. The research examines the circular disc monopole antenna due to limitations in simulation duration for SAR measurements, resulting in SAR values of 0.978 W/kg for arm measurements and 0.985 W/kg for hand measurements. The proposed techniques are very relevant to actual antenna designs, especially for wearable applications.

In this paper, a radiator employing the Cherenkov mechanism for electromagnetic energy transfer from an optically less dense medium into a more dense one is developed and studied using a two-dimensional numerical model. The radiator’s principal components are a dielectric prism and an open dielectric waveguide, where the phase velocity of eigenwaves exceeds that within the prism. For two linear field polarizations in the 24 GHz to 64 GHz range, this radiator exhibits high efficiency (over 93%) and radiation patterns with main lobes that closely coincide in both direction and width. The direction of radiation demonstrates strong agreement with predictions from the Cherenkov wave theory and shows weak dependence on frequency. These characteristics make the developed antenna suitable for directional emission and reception of electromagnetic pulses of various polarizations with spectral bandwidths of up to one octave or more. It is demonstrated that the radiation patterns of such antennas can be electrically controlled by altering the permittivity of the dielectric waveguide using an external control signal. The proposed antenna design avoids expensive fabrication processes and can be scaled to sub-millimeter wave ranges without significant modifications.

A hybrid machine-learning-based optimization method is proposed for quick optimization of antenna shape design. The hybrid optimization method combines self-supervised learning and transfer learning. The application of self-supervised learning avoids the requirement to obtain labeled simulation data for electromagnetic samples, thereby reducing the difficulty of sample construction. The introduction of transfer learning further improves the sample utilization and optimizes efficiency in electromagnetic tasks. The proposed method enables rapid and high-degree-of-freedom optimization of antennas. To validate its effectiveness, a reflectarray antenna design incorporating distinct elements is employed as a case study. Simulation results indicate that the designed antenna exhibits a realized gain of 26.3 dBi and 46% aperture efficiency at the center frequency, and each element has a highly flexible independent structural design. During the optimization process, the proposed hybrid method demonstrates higher optimization efficiency than traditional methods, while significantly reducing sample construction time.

Resonant slot cut microstrip antenna is a single patch solution to achieve circularly polarized response, but it does not offer tunability in the center frequency of axial ratio bandwidth. This paper presents reconfigurable designs of shorting post loaded U-slot cut circular and equilateral triangular microstrip antennas that offer tunable circularly polarized response. Shorting posts positions alter the excitation of resonant modes on the U-slot cut patch that achieves tuning in the circularly polarized frequency. On substrate thickness of ~0.05λ_cAR, using the circular patch, tuning in the center frequency of axial ratio bandwidth by 253 MHz (28.26%) is obtained, whereas equilateral triangular patch design offers 319 MHz (32.68%) of frequency tuning. In both the designs, broadside radiation pattern with a peak gain of larger than 7 dBic is obtained across the axial ratio bandwidth. Design methodology is proposed that yields a similar configuration as per specific wireless application. With the obtained frequency tuning in axial ratio bandwidth, redesigned variations of the proposed configurations can cater to pairs of GPS L-band applications.

This paper introduces a novel method for designing a wideband tunable bandstop filter (BSF) with constant absolute bandwidth (ABW). The design uses a doublet configuration, where two varactor-tuned resonators are symmetrically coupled to a main transmission line. To maintain constant ABW during frequency tuning, a coupling scheme is proposed where coupling strength decreases as the frequency increases, eliminating the need for additional circuits. Theoretical analysis and closed-form equations are provided for designing the BSF with a wide tuning range. A BSF prototype is designed and tested, demonstrating a 10-dB ABW of approximately 190 MHz across a continuous stopband tuning range from 3.3 to 5.1 GHz, with a fractional tuning range of 42.9%.

Smart antennas provide a unique and viable solution to the problem of multipath effects on signal propagation, particularly in the millimetre wave band. Multiple-Input Multiple-Output (MIMO) technology has certain advantages that can prove instrumental in not just eliminating multipath but turning it into an advantage and using it to improve communication link quality. With the use of MIMO and its unique beamforming capabilities, path loss can be significantly reduced, and more efficient use of the communications frequency spectrum can be achieved. MIMO antenna technology consists of a smart antenna array with multiple transmitting inputs and multiple receiving outputs. In this review, we compare some of the latest developments in MIMO technology. It focuses on design techniques, performance parameters, and novel developments. Recent developments include improvements in UWB, multi-band, and smart wear.

Electromagnetic induction (EMI) is a crucial non-destructive testing (NDT) technique for reinforced concrete structures, particularly for detecting and evaluating rebar distribution. However, the presence of multiple factors - including electromagnetic coupling effects from dense rebar arrangements, nonlinear waveform distortion due to rebar height differences, and environmental interference - renders traditional feature extraction methods inadequate for accurately reconstructing the rebar distribution parameters within the concrete cover. To address these challenges, a Sliding Omni-Scale Attention Network (SOSANet) is proposed in this paper. Initially, adaptive sliding window segmentation processes variable-length signals, preventing information distortion from signal truncation or padding. Subsequently, a dual-scale OS-Block architecture is constructed, wherein local small-scale OS-Blocks perform multi-scale feature extraction on the signals within each window. Furthermore, a multi-head attention mechanism and a global large-scale OS-Block are employed to model cross-window feature correlations, enhancing the discrimination of signal aliasing features induced by electromagnetic coupling among rebars. To address complex working conditions, a dataset of 1,740 samples comprising varying rebar quantities, cover thicknesses, spacings, and height differences was constructed. An interval random truncation strategy was employed to simulate scenarios involving incomplete signals. Five-fold cross-validation demonstrated that SOSANet achieves an F1-score of 99.34% for rebar quantity classification under complex working conditions, significantly outperforming 1D-CNN, Transformer, and other mainstream methods. Moreover, SOSANet maintains a high robustness with an F1-score of 99.03% under signal occlusion conditions.

With the development of the photovoltaic industry, accurate power prediction is critical to grid stability. To address photovoltaic power's high sensitivity to meteorological conditions, nonlinearity, and non-stationarity, this paper develops a prediction model that integrates multi-scale features and intelligent optimization. First, correlation coefficients are used to screen key weather factors, and K-shape clustering is applied to classify operational scenarios into sunny, cloudy, and rainy types. For the power data of each scenario, multi-scale features are extracted via Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), sample entropy secondary clustering, and Variational Mode Decomposition (VMD)-based deep decomposition. After fusing these features with weather factors, the integrated data is input into a Convolutional Neural Network-Bidirectional Long Short-Term Memory Network (CNN-BiLSTM), with hyperparameters optimized using the Northern Goshawk Optimization (NGO) algorithm. Verification with actual datasets indicates that this model outperforms traditional counterparts. Specifically, compared with the traditional BiLSTM model, its Mean Absolute Error (MAE) is reduced by 70.8%, 20.7%, and 47.0% under sunny, cloudy, and rainy scenarios, respectively - providing effective support for efficient dispatching and stable operation of photovoltaic power grids.

Quantum repeaters are essential for long-distance quantum communication, surmounting challenges like signal attenuation and decoherence. Nonetheless, current quantum repeater networks are constrained by static cutoff times for low-fidelity connections, suboptimal resource allocation, and the absence of quantum-classical integration. This paper introduces a hybrid quantum-classical method to tackle these issues by employing dynamic cutoff times contingent upon real-time fidelity decay and decoherence rates. Markov Decision Process (MDP) is used to characterize the system with the aim to optimize the entanglement generation processes, waiting and swapping. In this study, the objective is to reduce the time which is needed to realize end-to-end entanglement while fulfilling the requirements of classical channel capacity. To manage constraints such as classical user demands, quantum memory limits, and network congestion, Lagrangian optimization has been applied. The combined approach improves the use of both classical resources and quantum, providing a simplified solution that is adaptable to different users needs and different network conditions. The effectiveness of the model is tested via simulations processes, along with the mathematical process. This demonstrated important gains in fidelity preservation, resource efficiency, and latency minimization compared to the state-of-the-art traditional methods. This study makes a valuable contribution tackling the development of quantum networks, providing a rigid establishment to build a quantum capable for internet to support the security in distributed quantum computing and global communications.

This paper proposes a stacked slotted microstrip antenna (MSA) using multiple meta-surfaces that offers high gain and stable radiation patterns for 5G applications. A metal plated suspended MSA (SMSA) in air is designed to enhance gain and band width (BW). However, impedance becomes inductive and cross-polarization level (CPL) increases with increase in probe feed length. To decrease the inductive impedance and increase the capacitance, a slot in SMSA is etched. A parasitic patch with meta-surfaces on a superstrate is placed above the slotted SMSA and a rectangular ring around the slotted MSA is designed to increase the inductance. To compensate it, substrate height is decreased. The decrease in probe feed length/substrate height, decreases the CPL. Parasitic patch, rectangular ring around slotted SMSA and meta-surfaces, electro-magnetically couple with SMSA and enhance the BW of antenna. The low-profile (0.979λ₀ × 1.03λ₀ × 0.064λ₀, λ₀ - wavelength in free-space at 3.3 GHz) antenna offers peak gain of 9.8 dBi, antenna efficiency > 80%; SLL and CPL are < -22 dB; and the gain variation is < 0.5 dB over the 3.3-3.6 GHz frequency band for 5G application. The substrate height of the proposed novel structure is 2.5 times less than SMSA, and it offers an improvement of 8.1 dB in CPL as compared to SMSA.

Aircraft target detection in synthetic aperture radar (SAR) images faces numerous challenges, primarily including weak contrast, diverse morphologies, and faint signals, which are even more pronounced in complex backgrounds. Meanwhile, practical deployment environments are constrained by limited computational resources and energy consumption, making it essential to balance detection accuracy with model lightweight design. To address this, this paper proposes a lightweight detection network that integrates multi-branch feature enhancement. First, a Parallel Aggregation and Calibration (PAC) module is designed to achieve collaborative modeling of local and global information through multi-scale dilated convolutions; second, a Moment Channel Attention (MCA) module based on higher-order statistical features is introduced to enhance the model's sensitivity to weak signals and target boundaries; finally, during the network fusion stage, the branch calibration connections in the PAC module are removed, and a frequency-domain-driven Efficient Discriminative Frequency domain-based FFN (EDFFN) module is incorporated to improve detailed representation of low-contrast and blurred targets. Experimental results on the SAR-Aircraft-1.0 dataset demonstrate that the proposed method achieves 93.94% mAP, while reducing model parameters by 56% and computational complexity by 36% compared to YOLOv12s, effectively balancing performance and lightweight requirements.

The feasibility of utilization of VHF radars, radiating at lower and just above the plasma frequency of the gas formation exhausts of jet engine aircrafts, is investigated as a means to propose anti-stealth detection method. In the first step, the scattering of electromagnetic waves by a plasma sphere is studied, and comparison with Physical Optics (P.O.) Radar Cross Section (RCS) computations is done. This shows the possibility of using P.O. to compute the RCS under the assumption of jet engine exhaust plume structured modelled as multilayer prolate spheroid. Also, in case of radiation frequencies just above the plasma resonance, under the condition of weak scattering - refractive index being close to unity - the Rayleigh-Gans approximation is used to compute the RCS. Furthermore, computations based on this model shows the possibility to enhance the RCS of aircrafts by combining the ``specular'' reflection of part of the exhaust with plasma resonance frequency being higher than the radar frequency and also the part of exhaust having plasma frequency just below the radar radiation frequency. The numerical results show promising mechanisms to compete to improve the detectability of aircrafts with RCS as low as 0,001 m².

In ultra-high-voltage alternating current (HVAC) transmission systems, switching and lightning transients pose major challenges to insulation coordination. Non-gap line arresters (NGLAs) offer a promising distributed protection solution, capable of suppressing both types of transients when installed along the transmission corridor. However, the differences in protection performance under varying configurations and installation strategies remain insufficiently understood. This paper establishes a 750 kV, 400 km transmission line model using an ATP-EMTP and MATLAB co-simulation framework to investigate the transient suppression performance of NGLAs with different rated voltages and installation positions. Simulation results show that for switching transients, effective suppression of the 2% statistical overvoltage level below 1.8 p.u. can be achieved when NGLA is installed around an optimal position. Meanwhile, energy absorption of all arresters remains well below the 6 MJ thermal design threshold, confirming both suppression effectiveness and thermal stability. On the other hand, lightning transients exhibit strong spatial locality. NGLA can effectively reduce the lightning transient peak at positions close to lighting strike point. Even slight spatial offsets (1-5 km) drastically reduce its effectiveness in limiting peak voltage. Under typical lightning currents of 30-40 kA, the maximum energy absorbed by arresters remains below 2.2 MJ, demonstrating robust energy endurance. This study highlights the fundamental differences in propagation and protection mechanisms between switching and lightning transients, and underscores the need for differentiated arrester deployment strategies. The findings provide theoretical insight and engineering guidance for optimized NGLA configuration and insulation coordination in HVAC systems.

Frequency-Selective Surfaces (FSSs) are structures designed to selectively transmit or reflect electromagnetic waves, making them essential for applications requiring precise control over frequency bands and wave propagation characteristics. However, traditional FSS designs face challenges such as fixed geometries, limited scalability, and poor bandwidth efficiency, often requiring compromises between size reduction and performance. To address these limitations, this work introduces the use of Stepped Impedance Resonators (SIRs) to synthesize miniaturized FSS structures with four-legged elements (FLEs). By combining transmission line theory, SIR equations, and parallel coplanar stripline models, an innovative synthesis method is proposed, enabling precise control over spurious frequencies and resulting in a 54% reduction in unit-cell size without sacrificing performance. This approach significantly enhances the feasibility of compact FSS applications. To further improve performance, an arrow-bending technique was introduced to reduce the coupling between adjacent cells, yielding a 30% improvement in isolation. Three distinct surface designs have been fabricated and tested under both normal incidence and oblique angles for TE and TM modes. These designs include the SIR-based FSS cell, an enhanced design featuring arrow bending, and a reverse arrow formation intended to reduce edge effects between adjacent cells. Additionally, measurements demonstrate excellent performance stability, with tolerance maintained for incident angles up to 60°. Experimental validation confirms effective blocking at 10 GHz and highlights the robustness of the design across varying incident angles. Prototypes fabricated from the miniaturized FSS elements show excellent agreement with simulations, underscoring the potential of this method for advanced applications in communications, radar, and electromagnetic shielding.

In RF front-end circuits, the miniaturization and high-performance integration of duplexer remain critical challenges for 5G communication and IoT devices. A microstrip duplexer design scheme is proposed based on electric and magnetic coupling path separation and dual-mode characteristics. Through the collaborative design of second-order uniform impedance resonators and dual-mode T-shaped resonators, the design achieves signal separation for dual frequency bands at 2.4 GHz and 3.6 GHz. The design forms the electric coupling path via edge-gap coupling of rectangular split-ring resonators, and realizes magnetic coupling path through vias. By independently regulating electric and magnetic coupling strengths, eight transmission zeros are introduced on both sides of the dual passbands, significantly enhancing out-of-band suppression and port isolation. The simulation results show that the passband insertion loss is less than or equal to 1.9 dB. Due to machining tolerances, the measured center frequencies shift to 2.04 and 3.48 GHz, while the out-of-band rejection remains better than 39 dB, validating the engineering adaptability of the design. This scheme achieves high-performance integration of RF front-ends in a compact architecture through the coordinated regulation of multiple transmission zeros and coupling path separation technology, providing a solution for wireless communication devices.

The novel amorphous alloy transformer featuring a closed three-dimensional coil core (CTDCC) represents an innovative approach to transformer structure. In contrast to the conventional three-phase five-column transformer equipped with a planar coil core (PCC), the CTDCC configuration displays a completely equal magnetic circuit, leading to improved short-circuit tolerance. Nevertheless, the design and manufacturing process of the core faces a notable engineering obstacle due to the amplified magnetostrictive coefficient of the amorphous alloy, resulting in vibration noise. In order to address this issue, a magnetic-mechanical coupling mathematical model is developed in this research to analyze the magnetostrictive effect of the amorphous alloy CTDCC. Three-dimensional finite element analysis (FEA) is utilized to compute the magnetic flux distribution and quivering dislocation dissipation of the CTDCC. Furthermore, a validation experiment is carried out on a 30 kVA amorphous alloy CTDCC model to confirm the precision of the model. Moreover, the CTDCC structure has been proven to effectively minimize surface vibrations compared to the PCC model. Additionally, it unveils the governing frequency law of vibration movement at various locations within the CTDCC structure. This revelation serves as a fundamental basis for developing strategies to mitigate vibrations and control noise during the CTDCC design.

This article presents a compact highly isolated two-port ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with triple band suppression features. The antenna measures 25 × 39 mm² and comprises two orthogonally arranged microstrip-fed square radiators to achieve high inter-element isolation. A T-shaped and L-shaped stubs were incorporated into the ground plane to enhance isolation and broaden the impedance bandwidth. Triple band notches targeting Satellite C-band downlink (3.6-4.6 GHz), WLAN (4.9-5.5 GHz), and Wi-Fi 6E (6.1-6.7 GHz) are realized using three U-shaped slots introduced on each radiating element. The antenna's operation is analyzed through Characteristic Mode Analysis (CMA) by evaluating modal significance, characteristic angle, modal currents and mode patterns. MIMO performance is validated using key diversity metrics, including envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), channel capacity loss (CCL), multiplexing efficiency (ME), and group delay. Results demonstrate an impedance bandwidth exceeding 2.9-10.6 GHz UWB range, with 90% radiation efficiency, peak gain of 6.2 dBi, ECC below 0.02, and mutual coupling under -20 dB. These outcomes confirm the efficacy of the proposed antenna in achieving compactness and high performance for UWB MIMO applications.

In this paper, the dielectric constant distribution of concrete was determined, which is consistent with the experimental values, and the complex dielectric constants obtained were evaluated. Numerical results are given by resulting complex dielectric constant distributions of four types, the time response waveforms and frequency spectra of a composite dispersive medium consisting of concrete using these dielectric constant distributions, and the time response waveforms and frequency spectra separated by each reflection component. A fast inversion of the Laplace transform method was used for the numerical analysis. Consequently, we were able to clarify the dielectric constant distribution suitable for the analysis by using these time response waveforms and frequency spectra.

Long-time coherent integration (LTCI) is an effective method for maneuvering target detection, as it accumulates signal energy over a long observation period, thereby enhancing the signal-to-noise ratio (SNR). However, as the observation duration increases, range migration (RM) and Doppler frequency migration (DFM) occur, which degrade the integration performance. To this end, a scaling factor is first introduced into the parameterized centroid frequency–chirp rate distribution (PCFCRD) algorithm, thereby yielding the scaled PCFCRD (SPCFCRD), which enables flexible adjustment of the chirp rate estimation range and resolution. Furthermore, SPCFCRD is combined with the keystone transform (KT) to form the proposed KT-SPCFCRD algorithm. The RM caused by unambiguous velocity is first corrected by KT, after which the residual RM and DFM are further compensated by SPCFCRD to achieve coherent integration. The effectiveness of the proposed algorithm is validated through simulations and real-data analysis. Compared with several representative algorithms, KT-SPCFCRD achieves superior detection performance while maintaining a balanced computational cost.