Search Results (1,571)

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

This paper presents a multi-problem surrogate-assisted evolutionary multi-objective optimization approach for antenna design. By transforming the traditional antenna design optimization problem into expensive multi-objective optimization problems, this method employs a multi-problem surrogate (MPS) model to stack multiple antenna design problems. The MPS model is a knowledge-transfer framework that stacks multiple surrogate models (e.g., Gaussian Processes) trained on related antenna design problems (e.g., Yagi–Uda antennas with varying director configurations) to accelerate optimization. The parameters of Yagi–Uda antenna including radiation patterns and beamwidth—across various director configurations are considered as decision variables. The several surrogates are constructed based on the number of directors of Yagi–Uda antenna. The MPS algorithm identifies promising candidate solutions using an expected improvement strategy and refines them through true function evaluations, effectively balancing exploration with computational cost. Compared to benchmark algorithms assessed by hypervolume, our approach demonstrated superior average performance while requiring fewer function evaluations. Full article

Deeply implanted biomedical devices like leadless pacemakers require an antenna with minimal volume and high radiation efficiency to ensure reliable in-body communication and long operational time within the human body. This paper introduces a novel implantable antenna designed to significantly reduce the spatial requirements within an implantable capsule while maintaining high radiation efficiency in lossy media like heart tissue. The design principles of the proposed antenna are outlined, followed by antenna parameters and an equivalent circuit study that demonstrates how to fine-tune the antenna’s resonant frequency. The radiation characteristics of the antenna are thoroughly investigated, revealing a radiation efficiency of up to 28% at the Medical Implant Communication System (MICS) band and 56% at the 2.4 GHz ISM band. The transmission efficiency between two deeply implanted antennas within heart tissue has been improved by more than 15 dB compared to the current state of the art. The radiation and transmission performance of the proposed antennas has been validated through comprehensive simulations using anatomical human body models, phantom measurements, and in vivo animal experiments, confirming their superior radiation performance. Full article

This paper presents the design, manufacturing, testing, and validation of the MASKARA (Multiple Apertures for high-resolution SAR based on Ka-band Reflectarray) Breadboard Model (BBM), a large Ka-band reflectarray antenna developed for Synthetic Aperture Radar (SAR) applications. The BBM features a dual-offset antenna configuration intended for a high-resolution, wide-swath SAR instrument. At the core of the system is a 1.5 m × 0.55 m reflectarray operating between 35.5–36.0 GHz in the Ka-band. To our knowledge, this is the first demonstration of a reflectarray antenna designed to support two distinct modes of operation, exploiting the inherent advantages of reflectarrays—such as reduced cost and compact stowage—over traditional solutions. The antenna provides a high-resolution mode requiring a higher-gain beam in one polarization and a low-resolution mode covering a larger swath with broader beam coverage in the orthogonal polarization. The design process follows a holistic, multidisciplinary approach, integrating RF and thermomechanical considerations through iterative and concurrent design reviews. The BBM has been successfully manufactured and experimentally tested, and the measurement results show good agreement with simulations, confirming the validity of the proposed concept and demonstrating its potential for future high-performance SAR missions. Full article

This paper presents an intelligent design framework for a high-performance 26 GHz MIMO antenna array tailored to 5G applications, built upon a compact single-element patch. The 11.5 mm × 11.5 mm × 1.6 mm microstrip patch on FR4 exhibits near-unity electrical length, an ultra-deep return loss (S11 < −40 dB at 26 GHz), and a wide operational bandwidth from 24.4 to 31.2 GHz (6.8 GHz, ~26.2%). A two-element array, spaced at λ/2, is first augmented with a inspired metamaterial (MTM) unit cell whose dimensions are optimized via a Multi-Layer Perceptron (MLP) model to maximize gain (+2 dB) while preserving S11. In the second phase, a closed-square parasitic ring is introduced between the elements; its side length, thickness, and position are predicted by a Random Forest (RF) model with Bayesian optimization to minimize mutual coupling (S12) from −25 dB to −58 dB at 26 GHz without significantly degrading S11 (remains below −25 dB). Full-wave simulations and anechoic chamber measurements confirm the ML predictions. The close agreement among predicted, simulated, and measured S-parameters validates the efficacy of the proposed AI-assisted optimization methodology, offering a rapid and reliable route to next-generation millimeter-wave MIMO antenna systems. Full article

This study addresses the key technical challenges in monitoring hydraulic fracturing within unconventional reservoirs through an innovative wide-field electromagnetic (WEM) monitoring technique. The method employs a 5A AC-excited wellbore-fracturing fluid system to establish a conductor antenna effect, coupled with a surface electrode array (100–250 m offset) to detect millivolt-level time-lapse potential anomalies, enabling real-time dynamic monitoring of 142 fracturing stages. A line current source integral model was developed to achieve quantitative fracture network inversion with less than 12% error, attaining 10 m spatial resolution and dynamic updates every 10 min (80% faster than conventional methods). Optimal engineering parameters were identified, including fluid intensity ranges of 25–30 m³/m for tight sandstone and 30–35 m³/m for shale, with particulate diverters achieving 93.1% diversion efficiency (significantly outperforming chemical diverters at 35%). Application in deep reservoirs maintained signal attenuation rates below 5% per kilometer. Theoretically, a nonlinear relationship model between fluid intensity and stimulated area was established, while practical implementation through real-time adjustments in 142 stages enhanced single-well production by 15–20% and reduced diverter costs, advancing the paradigm shift from empirical to scientific fracturing in unconventional reservoir development. Full article

Kapton and Mylar film materials are used to manufacture space inflatable antenna reflectors; therefore, their mechanical characteristics are considered important parameters for the design of inflatable antenna reflectors. This paper mainly introduces a series of experiments on the mechanical properties of Kapton VN and Kapton HN, and Mylar I and II film specimens, including film tensile tests, film seam tests with tape bonding and glue bonding, and skirt edge joint tests. Therefore, failure modes, stress versus strain curves, ultimate tensile strength, and extension at break are obtained for these specimens of Kapton VN and Kapton HN and Mylar I and II films. Based on these measured data, stress conditions of models with 12 and 18 sections using ANASYS are compared to identify the effect of different sections and pressures on the force of inflatable antenna reflectors. Full article

With the rapid growth in satellite types and numbers in space information networks, accurate and fast inter-satellite link prediction has become a core requirement for topology modeling and capability evaluation. However, the current space information networks are characterized by large scales and the coexistence of multi-orbit satellites, posing dual challenges to inter-satellite link prediction. Link state prediction demands higher accuracy with limited computing power, while diverse satellite communication antenna loads require stronger generalization to adapt to different scenarios. To address these issues, this paper proposes a fully connected neural network model based on Bayesian optimization. By introducing a weighted loss function, the model effectively handles data imbalance in the link states. Combined with Bayesian optimization, the neural network hyperparameters and weighted loss function coefficients are fine-tuned, significantly improving the prediction accuracy and scene adaptability. Experimental results show that the BO-FCNN model exhibited an excellent performance on the test dataset, with an F1 score of 0.91 and an average accuracy of 93%. In addition, validation with actual satellite data from CelesTrak confirms the model’s real-world performance and its potential as a reliable solution for inter-satellite link prediction. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

A novel hierarchical sectorized neural network module for a fast direction of arrival (DoA) estimation (HSNN-DoA) of the signal received by a textile wearable antenna array (TWAA) under strong noise conditions is presented. The developed DoA module accounts for variations in antenna element gain, inter-element spacing, and resonant frequencies under the conditions of textile crumpling caused by the motion of the TWAA wearer. The proposed model consists of a sector identification phase, which aims to determine the spatial sector in which the radio gateway (RG) is currently located based on the elements of the spatial correlation matrix of the signal sampled by the TWAA, and a DoA estimation phase, which aims to accurately determine the angular position of the RG in the azimuthal plane. The architecture of the HSNN-DoA module, with different time window lengths in which angular position of RG is recorded, is investigated and compared with the DoA module based on a stand-alone MLP network and the corresponding Root-MUSIC DoA module in terms of accuracy and speed of DoA estimation under variable noise conditions. Full article

Antenna structure design constitutes a computationally expensive optimization problem due to the requirement for full-wave electromagnetic (EM) simulations. Surrogate-assisted evolutionary algorithms offer a promising approach for addressing such challenges. However, several challenges remain in solving expensive, highly constrained antenna design problems. This paper introduces a surrogate-assisted dynamic constrained multi-objective evolutionary algorithm framework to tackle expensive and highly constrained antenna design optimization tasks. A multi-layer perceptron (MLP) is employed as the surrogate model to approximate EM evaluations and alleviate the computational burden, while a dynamic scale-constrained boundary strategy is implemented to handle highly constraints. The effectiveness of the proposed method is validated on a set of constrained benchmark problems and two antenna design cases. Full article

With the increasing deployment of unmanned surface vessels (USVs) in complex marine operations such as ocean monitoring, search and rescue, and military reconnaissance, precise heading control under environmental disturbances and system delays has become a critical challenge. This paper presents an advanced robust heading control strategy for USVs operating under these demanding conditions. The proposed approach integrates three key innovations: (1) an enhanced Smith predictor for accurate time-delay compensation, (2) a variable-universe fuzzy PID controller with self-adaptive scaling domains that dynamically adjust to error magnitude and rate of change, and (3) a hybrid metaheuristic optimization algorithm combining beetle antennae search, harmony search, and genetic algorithm (BAS-HSA-GA) for optimal parameter tuning. Through comprehensive simulations using a Nomoto first-order time-delay model under combined white noise and second-order wave disturbances, the system demonstrates superior performance with over 90% reduction in steady-state heading error and ≈30% faster settling time compared to conventional PID and single-optimization fuzzy PID methods. Field trials under sea-state 4 conditions confirm 15–25% lower tracking error in realistic operating scenarios. The controller’s stability is rigorously verified through Lyapunov analysis, while comparative studies show significant improvements in S-shaped path tracking performance, achieving better IAE/ITAE metrics than DRL, ANFC, and ACO approaches. This work provides a comprehensive solution for high-precision, delay-resilient USV heading control in dynamic marine environments. Full article

With the continuous advancement of on-orbit services and space intelligence sensing technologies, the efficient and accurate identification of spacecraft components has become increasingly critical. However, complex lighting conditions, background interference, and limited onboard computing resources present significant challenges to existing segmentation algorithms. To address these challenges, this paper proposes a lightweight spacecraft component segmentation framework for on-orbit applications, termed M4MLF-YOLO. Based on the YOLOv5 architecture, we propose a refined lightweight design strategy that aims to balance segmentation accuracy and resource consumption in satellite-based scenarios. MobileNetV4 is adopted as the backbone network to minimize computational overhead. Additionally, a Multi-Scale Fourier Adaptive Calibration Module (MFAC) is designed to enhance multi-scale feature modeling and boundary discrimination capabilities in the frequency domain. We also introduce a Linear Deformable Convolution (LDConv) to explicitly control the spatial sampling span and distribution of the convolution kernel, thereby linearly adjusting the receptive field coverage range to improve feature extraction capabilities while effectively reducing computational costs. Furthermore, the efficient C3-Faster module is integrated to enhance channel interaction and feature fusion efficiency. A high-quality spacecraft image dataset, comprising both real and synthetic images, was constructed, covering various backgrounds and component types, including solar panels, antennas, payload instruments, thrusters, and optical payloads. Environment-aware preprocessing and enhancement strategies were applied to improve model robustness. Experimental results demonstrate that M4MLF-YOLO achieves excellent segmentation performance while maintaining low model complexity, with precision reaching 95.1% and recall reaching 88.3%, representing improvements of 1.9% and 3.9% over YOLOv5s, respectively. The mAP@0.5 also reached 93.4%. In terms of lightweight design, the model parameter count and computational complexity were reduced by 36.5% and 24.6%, respectively. These results validate that the proposed method significantly enhances deployment efficiency while preserving segmentation accuracy, showcasing promising potential for satellite-based visual perception applications. Full article

This paper presents a novel compact 4 × 4 Butler matrix (BM) employing a quasi-twisted branch line coupler (QBLC) as the unit cell to achieve enhanced bandwidth performance. The proposed BM integrates four QBLCs, a uniquely designed 0 dB crossover, and a 45° phase shifter, all fabricated on a double-layer Rogers RO4003C substrate with a thickness of 0.8 mm, dielectric constant (εr) of 3.3, and a loss tangent of 0.0027. A common ground plane is used to separate the layers. Both simulation and experimental results indicate a reflection coefficient of approximately −6.5 dB at the resonant frequency of 6.5 GHz and isolation levels better than −20 dB at all ports. The system achieves output phase differences of ±13°, ±41°, ±61°, ±89°, and ±120° (±10°) at the designated frequencies. The BM occupies a compact area of 13.8 mm × 38.8 mm, achieving a 92.5% size reduction compared to conventional T-shaped BM structures. The design was modeled and simulated using CST Microwave Studio, with a strong correlation observed between simulated and measured results, validating the design’s reliability and effectiveness. Furthermore, the BM’s beamforming performance is evaluated by integrating it with a 1 × 4 microstrip antenna array. The measured return loss at all ports is below −10 dB at 6.5 GHz, and the system successfully achieves switched beam steering toward four distinct angles: −5°, +6°, +26°, −24°, +43, and −43 with antenna gains ranging from 7 to 10 dBi. Full article