Search Results (254)

Broadband antenna, frequency and channel selection requires efficient prioritisation of finite candidate configurations, yet strong surrogate performance within a simplified analytical model does not ensure that the learned ordering will transfer to another electromagnetic representation. This study developed a validation-aware surrogate-shortlisting framework using a controlled breast-mimetic benchmark comprising 120 scenarios and 80 antenna–frequency–channel candidates per scenario. Surrogate models were developed using grouped scenario-level validation, and the model and five-candidate shortlist policy were frozen before external evaluation. Random forest produced the lowest-regret analytical-domain shortlist and remained stable across model-initialisation seeds. The frozen ranking was then challenged on held-out scenarios using a separately implemented restricted two-dimensional transverse-magnetic finite-difference time-domain model. Although numerical-reference checks supported shortlist-level use of the operational grid, candidate ordering did not transfer reliably: optimum inclusion, shortlist agreement and rank association were weak, although a qualified candidate within 2 mm of the finite-library FDTD optimum was retained in 58.3% of cases. Transfer failure varied by frequency band and channel family, while incomplete alignment between the analytical and FDTD candidate libraries prevented attribution of the discrepancy to electromagnetic-model shift alone. The framework therefore positions analytical surrogates as auditable shortlisting tools that reduce downstream candidate-level assessment while retaining independent electromagnetic evaluation before final design selection. Full article

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions. Full article

To address the challenge of achieving a unified dynamic evaluation of in-service performance for reflector antennas subjected to coupled wind disturbances, structural flexibility, and servo control, an end-to-end performance evaluation method based on integrated modeling is proposed. A disturbance–structure–electromagnetic–control integrated modeling framework is constructed, in which the fluctuating wind load, structural dynamics model, cascaded servo control, and end-to-end performance mapping model are unified within a state-space closed-loop system, thereby enabling time-domain dynamic evaluation from environmental excitation inputs to performance index outputs. The Davenport spectrum and harmonic superposition method are adopted to establish a stochastic fluctuating wind model, and structural disturbance inputs are formed through wind pressure linearisation and modal projection. A low-order flexible dynamic model of the reflector antenna is developed using finite element modal condensation, and a main-axis closed-loop control model is formulated by incorporating fuzzy active disturbance rejection control and notch filtering. By combining the best-fit parabolic surface, the weighted half-path-length difference, and the Ruze formula, an end-to-end mapping model that relates structural nodal displacements to electromagnetic performance degradation is established. The research demonstrates that the proposed method can effectively reveal the influence of wind speed, elevation angle, and flexible mode coupling on antenna performance. Furthermore, a performance evaluation system developed based on this method integrates parameter input, simulation computation, and result output, providing an effective tool for antenna design optimization and performance assurance. Full article

Gustatory perception in honeybees is a key determinant of foraging decisions and pollen source selection. However, the molecular mechanisms underlying this sensory discrimination remain poorly understood. To investigate these mechanisms during the collection of pollen from different floral sources, this study utilized antennae from worker bees foraging on pear and rapeseed pollen, and non-pollen-foraging workers as controls. Illumina high-throughput transcriptome sequencing was employed to identify differentially expressed genes (DEGs), perform functional annotation, and characterize gustatory receptor (GR) genes. Compared with the control group, 583 DEGs and 516 DEGs were identified in pear-pollen and rapeseed-pollen foragers, respectively, whereas only 73 DEGs were detected between the two pollen-foraging groups. Several DEGs were associated with chemosensory perception, signal transduction, energy metabolism, and immune responses. Notably, genes involved in membrane-associated signaling and stimulus response exhibited differential expression patterns among foraging groups, suggesting adaptive molecular responses to distinct floral resources. Gene Ontology (GO) analysis indicated that DEGs were primarily associated with cellular processes, membrane components, and binding functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment highlighted significant involvement in phagosome, phosphatidylinositol signaling system, oxidative phosphorylation, and extracellular matrix–receptor interaction. Notably, seven GR-related genes were identified in the antennal transcriptome, including five known GR genes and two novel candidates, all with complete open reading frames. Four of these genes featured the canonical seven-transmembrane domain structure of insect GRs. Phylogenetic analysis, in addition to the known sugar receptors AmelGR43a, AmelGR64f, and AmelGR64f-X1, based on GRs from Apis mellifera and Drosophila melanogaster suggested that AmelGR28b, AmelGR10, AmelGR12, and AmelGR13 may belong to the bitter taste receptor family. Quantitative real-time PCR (qRT-PCR) validation demonstrated that the expression patterns of the selected seven DEGs were consistent with the RNA-seq results. This study reveals differential expression patterns and potential functional divergence of gustatory receptor genes in Apis mellifera during pollen collection from different floral sources. It provides important molecular evidence for understanding how honeybees accurately recognize and preferentially forage specific pollen sources via gustatory perception, and offers valuable theoretical and practical insights for honeybee behavioral ecology and crop pollination management. Full article

This paper addresses the challenge of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices compromises the capability of these methods to satisfy stringent accuracy requirements. To overcome this limitation, we propose a positioning framework based on uplink Angle-of-Arrival (AoA) measurements. By performing AoA estimation at the Transmission and Reception Point (TRP), the proposed approach maintains hardware simplicity, requiring only a single antenna at the UE. The framework incorporates a computationally efficient AoA estimation algorithm derived from the analysis of the spatial covariance matrix, eliminating the need for the exhaustive beam scanning typically required for angular grid search. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The localization algorithm employs a Weighted Least Squares (WLS) estimator to provide a unified approach to UE positioning in both 2D and 3D physical spaces. The framework’s efficacy is confirmed via numerical simulations under the dense multipath conditions defined by standard 5G Indoor Factory (InF) environments. Full article

This paper presents the design of an inverted-F antenna intended for integration into a smartwatch operating in the 2.4 GHz band. The antenna design addresses spatial constraints imposed by the device’s miniaturized form factor and the proximity of electronic components, including the printed circuit board, display, and battery. The influence of the user’s body on the antenna’s performance characteristics was considered during the design phase through numerical simulations employing the Finite-Difference Time-Domain (FDTD) method with a heterogeneous human body model. Simulation results and measurements of a fabricated prototype antenna are presented, demonstrating satisfactory performance in terms of impedance matching with VSWR below 1.5 in the whole band and gain of −1 dBi. Full article

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

In microwave tomography (MWT), reconstruction accuracy is challenged by modeling error, namely the mismatch between the numerical representation and the actual experiment. Accurate antenna modeling is perceived as an important step toward reducing this error, but the actual benefit of increasing antenna model complexity has not been analyzed in the literature. This work fills this gap by conducting a rigorous numerical analysis of the issue using two popular algorithms for its study: the finite-difference time-domain (FDTD) method for antenna and forward-problem modeling, and the distorted Born iterative method (DBIM) for implementing the iterative inversion algorithm. We consider various FDTD tools of increasing complexity to improve the agreement between the FDTD forward solver and an accurate numerical model implemented in commercial software. After validating these models for different antennas, we perform reconstructions for a stroke-detection scenario. Our results show that in a practical setting, sophisticated antenna modeling in the forward solver does not necessarily improve reconstruction accuracy for monopole-type antennas widely used in MWT. Our model-error analysis confirms that calibration is always necessary in practice and that its impact supersedes efforts to model the antenna more faithfully. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N$ = 50–300 samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>$99.5% accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>$97% under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mu$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

With the rapid development of software-defined radio (SDR) technology, a digital, software-reconfigurable, and flexible solution is provided for microwave radiometers, particularly suitable for atmospheric water vapor and oxygen detection with wideband, multi-channel requirements, significantly improving system efficiency. Meanwhile, digitization helps improve channel consistency and address nonlinearity issues, while the digital zero-balancing mechanism implemented through adaptive integration is more suitable for digital platforms. This paper proposes a digital Dicke-type radiometer system based on an SDR platform, using Xilinx RFSoC XCZU47DR (AMD, San Jose, CA, USA) as the core hardware to achieve single-chip integration of RF signal sampling, digital local oscillator generation, and signal processing. The system implements a 46-channel channelized receiver (23 channels each for K-band and V-band) on an FPGA using a polyphase filter bank. The prototype filters achieve 70 dB stopband attenuation and 0.5 dB passband ripple, with each polyphase branch requiring only 25 coefficients, significantly reducing hardware resource consumption. An adaptive integration method is proposed, where an adaptive switch controller dynamically adjusts the hot source injection time ratio by calculating the power difference between adjacent integration periods, enabling the Dicke zero-balancing mechanism to operate entirely in the digital domain. Furthermore, a complete hardware transfer model is established for three signal branches (antenna, hot source, and matched load), and full-chain calibration of all 46 channels is performed using a liquid nitrogen cold source, with calibration reliability verified through blackbody measurements. Experimental results demonstrate brightness temperature consistency better than 0.7 K, with a sensitivity of less than 0.15 K for the K-band and less than 0.21 K for the V-band at 1 s integration time. Full article

The design of stretchable energy harvesting systems entails complex multiphysics coupling between electromagnetic and mechanical domains, typically requiring engineers to proficiently use disparate simulation tools and optimization algorithms. This steep learning curve, combined with the absence of integrated workflows, poses a substantial obstacle to efficient design. To overcome these challenges, we present StretchCopilot, a multi-agent collaborative framework driven by Large Language Models (LLMs) for the generative design of stretchable radio frequency (RF) energy harvesters operating in the 2.45 GHz band. In contrast to conventional approaches dependent on manual iteration or isolated algorithmic methods, our framework utilizes a graph-based state machine architecture (LangGraph) to coordinate specialized agents. It interprets high-level user instructions, such as “design a robust energy harvester capable of withstanding 15% strain”, and autonomously manages domain-specific solvers, including inverse design networks and rectifier circuit synthesis tools, through a unified interface. Experimental evaluations indicate that the framework effectively streamlines the design workflow, allowing users to produce desired rectenna (rectifying antenna) systems via natural language interactions. Case studies confirm that, once the underlying surrogate models are fully trained, the proposed approach compresses the marginal design time from several hours to within minutes, while ensuring consistent energy harvesting performance under mechanical deformation. Full article

This article concerns the issue of contactless estimation of the complex electrical permittivity of masonry materials by means of a microwave technique in the frequency domain. The main aim of the study was to develop a method enabling the determination of the real part of relative permittivity and the electrical conductivity of ceramic building materials using microwave reflection measurements, as well as to assess the applicability of the proposed approach for moisture diagnostics in porous media. The research was performed using a reflection-mode measuring setup comprising a vector network analyser and a broadband horn antenna, while measurements were carried out in the frequency range from 1 to 6 GHz on samples of solid ceramic brick with six gravimetric moisture levels. A one-dimensional model of electromagnetic wave propagation in the material was developed, considering complex permittivity, impedance transformation, and a calibration procedure compensating for the influence of the antenna and free-space propagation. Based on the fitting of the magnitude and phase characteristics of the reflection coefficient, the electrical parameters of the tested samples were estimated. The results obtained showed an increase in both permittivity and conductivity with increasing moisture content and revealed very good agreement with the reference values determined using the time-domain method. It can be concluded that the frequency-domain microwave approach may be effectively applied for contactless and non-destructive diagnostics and estimation of the dielectric properties and moisture content in ceramic materials. Full article

Aiming at solving the detection problems caused by weak partial discharge signals of underground cable joints and random and variable spatial electromagnetic wave interference, a non-contact detection technology based on the dynamic multi-notch method is proposed. This technology synchronously collects pure interference signals and mixed signals containing partial discharge through a dual-position detection antenna. After converting to the frequency domain via Fast Fourier Transform (FFT), the notch frequency bands are dynamically determined based on the real-time interference spectrum, and interference suppression is achieved by frequency domain zeroing filtering. Finally, the partial discharge pulse signal is restored through Inverse Fast Fourier Transform (IFFT). A simulation experiment platform for 10 kV XLPE cable joints was built to verify the detection of typical defects such as metal debris, insulation scratches, and conductor burrs. Experimental results show that the average extraction success rate of this method for weak partial discharge signals reaches 94.7%, and the detection accuracy is ≥92.3% in a normal environment without strong interference, which is significantly better than the traditional ultra-high frequency (UHF) detection method (45.8%) and the fixed notch method (68.3%). This technology realizes the accurate detection of weak partial discharge signals in complex environments, provides a reliable solution for the early warning of insulation defects in underground cable intermediate joints, and has important engineering application value. Full article

Non-destructive characterization of electromagnetic (EM) wave propagation properties in drill cores is gaining prominence as a foundation for reliable geophysical inversion, improved rock-physics modeling, and increasingly data-driven mineral exploration workflows. Lab-based rock characterization requires benchmarks that link the density, elastic, electrical, magnetic, and EM properties of studied cores to lithology and mineralization, enabling more accurate interpretation of geophysical data. This study develops a robust high-frequency EM (HFEM) wave velocity measurement technique and incorporates it within a standardized non-destructive framework validated across multiple mineral systems in Newfoundland and Labrador, Canada. The developed method derives EM velocities from two-way travel time through drill cores positioned above a metallic reflector, supported by finite-difference time-domain simulations to optimize antenna frequency and test geometry. A repeatable signal-processing workflow was implemented to enhance reflection picking. Results reveal systematic EM velocity contrasts among host rocks and oxide or sulfide-bearing systems, with oxide-rich and massive sulfide intervals exhibiting higher density, elevated conductivity and susceptibility with strong EM attenuation. The integrated dataset shows that conductivity and magnetic susceptibility significantly influence EM velocity response and detectability limits. The proposed multi-parameter benchmark enables enhanced discrimination of lithological and mineralization controls in mineral exploration workflows and supports more accurate time–depth conversion in HFEM geophysical and ground-penetrating radar (GPR) methods. Full article