Search Results (14,623)

We present a circular complementary split ring resonator (CCSRR) flexible antenna operating in the 1.4 GHz radio-astronomy quiet frequency band. The antenna is designed for microwave non-invasive brain temperature sensing of an infant’s head to aid in the therapeutic hypothermia treatment of hypoxic–ischemic encephalopathy (HIE) and traumatic brain injury (TBI). The proposed metamaterial-inspired antenna is designed on a flexible Kapton substrate with a biocompatible Polydimethylsiloxane (PDMS) protective superstrate layer. For brain temperature measurement, the flexible antenna is placed directly on the scalp to collect thermal noise power from the underlying tissue layers. The received thermal power is to be delivered to a sensitive microwave radiometer. The CCSRR antenna exhibits sharp frequency selectivity at 1.4 GHz with inherent filtering capability, strong field confinement, and excellent suppression of out-of-tissue (external) electromagnetic interference and thermal noise contributions. To closely match the realistic scenario, the CCSRR antenna, initially designed in a planar multi-layer configuration, is investigated in various bending configurations (cylindrical and spherical) with a curvature radius of 55 mm. The results indicate stable performance under bending. Good agreement between simulated and on-body measured results is observed in the desired frequency band. Full article

This work investigates the feasibility of identifying steel reinforcing bars in concrete using a fully contactless radar system operating in the X-band. High-frequency electromagnetic inspection is particularly challenging due to attenuation and strong reflections at the air–concrete interface. This study combines numerical simulations and laboratory experiments to assess the sensitivity of microwave scattering measurements to the presence of reinforcement. Ad hoc mini reinforced-concrete pillars, both reinforced and unreinforced, were designed and built as benchmark specimens. Measurements were performed in a bistatic configuration using X-band horn antennas and a vector network analyzer, and were compared with finite-difference time-domain simulations reproducing the experimental setup. The qualitative results, comprising a processing strategy to detect the bars, show a clear agreement between numerical and experimental data and confirm that the scattered field remains sensitive to the presence of reinforcing bars despite unfavorable propagation conditions. Full article

4D millimeter-wave radar provides a promising solution for robust perception in adverse weather. Existing detectors still struggle with sparse and noisy point clouds, and maintaining real-time inference while achieving competitive accuracy remains challenging. We propose SGE-Flow, a streamlined PointPillars-based 4D radar 3D detector that embeds lightweight spatiotemporal geometric enhancements into the voxelization front-end. Velocity Displacement Compensation (VDC) leverages compensated radial velocity to align accumulated points in physical space and improve geometric consistency. Distribution-Aware Density (DAD) enables fast density feature extraction by estimating per-pillar density from simple statistical moments, which also restores vertical distribution cues lost during pillarization. To compensate for the absence of tangential velocity measurements, a Transformer-based Inter-frame Flow (IFF) module infers latent motion from frame-to-frame pillar occupancy changes. Evaluations on the View-of-Delft (VoD) dataset show that SGE-Flow achieves 53.23% 3D mean Average Precision (mAP) while running at 72 frames per second (FPS) on an NVIDIA RTX 3090. The proposed modules are plug-and-play and can also improve strong baselines such as MAFF-Net. Full article

Recently, the demand for electric motors that can achieve high performance while ensuring stable magnet supply has continued to increase across various industrial sectors. Although rare-earth permanent magnets, such as neodymium and samarium cobalt, enable superior electromagnetic performance, their high cost and supply instability have motivated growing interest in motors employing non-rare-earth permanent magnets, such as ferrite magnets. Due to the relatively low remanent flux density and coercivity of non-rare-earth magnets, spoke-type rotor structures are commonly adopted to enhance flux concentration. However, spoke-type configurations inherently suffer from axial leakage flux, in which a portion of the magnetic flux generated by the permanent magnets fails to link with the stator and instead leaks along the axial direction. This axial leakage flux reduces the effective air-gap flux density, leading to a degradation of back electromotive force (back-EMF) and overall motor performance. In this study, a double-spoke-type motor employing asymmetric permanent magnet geometry is investigated. Finite element analysis (FEA) is performed to identify an effective rotor structure that reduces axial leakage flux without increasing magnet usage, demonstrating the feasibility of performance improvement in non-rare-earth permanent magnet motors. Full article

The optimization of the MOSFET gate drive parameters is crucial for the trade-off between switching loss and electromagnetic interference (EMI). However, the nonlinear coupling among gate drive parameters, board-level parasitic, and switching performance limits the effectiveness of traditional MOSFET drive design methods. This paper proposes an adaptive tuning framework based on the proximal policy optimization (PPO) algorithm. An analytical switching model incorporating board-level parasitics is first derived to analyze the coupling between drive parameters and switching performance. The optimization problem is then formulated as a Markov decision process (MDP). Within this framework, domain randomization is applied during training. This enables the agent to learn a generalizable optimization strategy that remains robust across the varying parasitic inductances encountered in different PCB layouts. Compared to the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II), the proposed method uses the trained policy for direct inference. This reduces computation time by $98.7\%$ while maintaining a multi-objective performance difference within $10.06\%$. In addition, hardware verification shows a 10.7% average deviation between the measured and simulated results. These results demonstrate that the proposed method provides an efficient and scalable solution for MOSFET gate drive optimization. Full article

A support vector machine based on AdaBoost algorithm (SVM-AB) is proposed for complicated underground mine tunnel modeling. This method accurately predicts the nonlinear propagation characteristics of electromagnetic waves in complex environments in the case of small samples. Firstly, an electromagnetic wave propagation loss model is established by analyzing complex factors including tunnel geometry, wall roughness, tilt, dielectric properties, and multipath effects. Secondly, the complex factors and measured signal strength serve as inputs of the SVM model to establish a nonlinear mapping for preliminary prediction. Furthermore, the AdaBoost algorithm is applied to dynamically correct the SVM prediction errors, further enhancing accuracy. Finally, the measured experiments are carried out in complex underground mine tunnels to verify the proposed theoretical model. The experimental results demonstrate that the proposed SVM-AB model achieves a fitting accuracy of over 99.92%. In addition, compared with the traditional support vector machine, its Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by about 84.76% and 92.61%, respectively. The proposed tunnel model has important application value for optimizing the layout of communication system of underground mine tunnel. Full article

Increased alloying content in advanced Ni-based superalloys for large disc forgings intensifies microsegregation and promotes the formation of detrimental secondary phases, challenging the cast-and-wrought processing route. This study investigates the effects of Ta addition on the solidification and homogenization behaviors of a high-alloyed GH4065A superalloy by comparing the base alloy with a variant containing 5 wt.% Ta (5Ta alloy). As-cast and homogenized microstructures were characterized using SEM and EPMA, solidification behavior was analyzed via DSC, and homogenization kinetics were modeled. Results demonstrate that Ta addition stabilizes the η phase, increasing its solidification temperature and fraction in the as-cast microstructure, but does not alter the solidification sequence. During homogenization, Nb remained the most segregated element and governed the homogenization kinetics, whereas Ta preferentially partitioned into MC carbides and the η phase. The diffusion activation energy for Nb in the 5Ta alloy was determined, and a diffusion model was established to describe the elimination of microsegregation. Optimum homogenization parameters were determined to completely dissolve the η phase and eliminate microsegregation. The results indicate that strategic Ta addition for enhanced performance does not compromise ingot manufacturability, providing valuable guidance for the processing and composition design of advanced disc superalloys. Full article

With the increasing complexity of the space electromagnetic environment, traditional omnidirectional antenna-aided communication and networking techniques can no longer meet the collaboration requirements of aircraft clusters. To achieve goals such as anti-jamming, anti-interception, and enhanced spatial multiplexing, an increasing number of aircraft are being equipped with high-gain directional antennas. However, modeling of directional antenna-constrained Flying Ad Hoc Networks (FANETs) is far more complex than modeling of omnidirectional antenna-aided networks. The former task is highly dependent on the real-time flight state and the spatial topology of the network. In response to the communication challenges posed by directional networking of highly-dynamic aircraft clusters, this study proposes an antenna selection and beam pointing control algorithm, which is deeply integrated with the aircraft’s Guidance, Navigation, and Control (GNC) system. By introducing parameters that characterize dynamic flight state, such as position and attitude information, and combining them with high-precision multi-coordinate system transformations and spatial geometric analysis methods, the proposed algorithm enables the real-time optimization of antenna selection and beam pointing under the relative motion trends of aircraft. It effectively maintains high-quality connections between flying nodes. Digital simulation and physical experiment results demonstrate that the proposed method can accurately calculate the appropriate antenna selection and determine precise beam pointing directions based on the position data of flying nodes. This provides an important reference for the design of optimized communication strategies used in directional networking of highly-dynamic aircraft clusters. Full article

A capsule endoscope (CE) provides noninvasive access to the gastrointestinal tract, offering diagnostic information that cannot be obtained through external imaging alone. However, during the examination inside the stomach, the CE’s posture may change rapidly as it moves within a dynamically deforming organ, making it difficult to determine its orientation using only the onboard camera feedback. To address this problem, this study proposes a method that employs an external array of Hall Effect Sensors (HES) to estimate the capsule’s position and orientation in real time, based on the magnetic field generated by a permanent magnet (PM) embedded inside the capsule, without the need for any additional internal sensors. This approach introduces a unified magnetic actuation and localization framework that enables real-time 5-degree-of-freedom posture estimation using only the internal PM of the capsule. Furthermore, the proposed system features an integrated architecture capable of simultaneous actuation and localization. To enhance system practicality, the sensor module and communication board were combined into a single unit that employs a digital serial communication scheme, eliminating the need for analog to digital conversion of sensing signals. By avoiding additional onboard sensors and employing a PM-based actuation system, the proposed system simplifies hardware configuration by preserving capsule miniaturization and by eliminating the high power consumption and thermal issues associated with electromagnet-based actuation, while maintaining accurate real-time tracking performance. Through an optimization process, the system achieved a position error of less than 2 mm and an angular error within 2° over a sensing range of up to 60 mm. Repeated experiments further validated the system’s effectiveness and reliability under realistic operating conditions, demonstrating its feasibility for compact and clinically applicable active capsule endoscopy systems. Full article

With the rise of telecommunication systems in recent decades, the implications for human health have prompted a search for ways to reduce the impact of electromagnetic waves in buildings when necessary. A viable and promising solution to realize electromagnetic shielding could be the use of drywall panels coated with a biochar paste, as proposed in this study. Biochar (bio-charcoal), a low-cost and carbon-based material, can be obtained by the thermochemical conversion of different biomass sources. A commercial wood-based biochar thermally treated at 750 °C is considered in this work. Transmission coefficients of several gypsum board elements with a biochar coating are measured in the frequency K-band (18–27 GHz). In addition, the SE of a double panel configuration, obtained by joining two coated boards to form a multilayer structure, is evaluated. The results show that the biochar coating significantly enhances the SE compared to uncoated drywall. At the highest biochar loading investigated (0.20 g/cm²), the shielding effectiveness consistently exceeds 27 dB for single panels and 46 dB for double panels across the entire frequency band. These findings indicate that biochar-coated drywall systems offer a practical and sustainable solution for integrating electromagnetic shielding into building envelopes, paving the way for innovative applications in indoor exposure control. Full article

Quasi-Static Electromagnetic Forming (QSEF) technology utilizes stable magnetic fields generated by long-pulse flat-top currents to achieve non-contact, high-precision forming of large-scale integral aerospace components. To meet the immense energy demands of large-scale component forming, the drive system requires instantaneous power output capabilities at the Gigawatt level. Consequently, the precise regulation of ultra-high flat-top current waveforms becomes a critical challenge for ensuring forming quality. However, traditional meta-heuristic methods, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), exhibit limited adaptability and robustness when addressing strong geometric nonlinearities induced by workpiece deformation and the performance degradation of pulsed power modules. To address engineering challenges such as capacitor degradation, inductance drift, and module failures, this paper proposes a Staged Deep Reinforcement Learning (Staged-DQN) adaptive current control framework. This framework decouples the discharge scheduling into “heuristic rapid rise” and “DQN fine compensation” stages, adaptively optimizing triggering timing to suppress plateau oscillations and compensate for energy deficits caused by faults. Simulation results demonstrate that under typical high-energy operating conditions, the proposed method achieves superior tracking accuracy compared to traditional PSO in fault-free scenarios. In extreme scenarios involving 25 faulty modules, the Mean Absolute Percentage Error (MAPE) is maintained between 1.13% and 1.80%, significantly lower than the 2.65–3.52% of the baseline DQN. This study validates the effectiveness of the proposed method in enhancing waveform quality and system fault tolerance, offering a reliable intelligent control solution for large-scale electromagnetic manufacturing equipment. Full article

This paper proposes a flexible and polarization-insensitive metasurface (MS) operating at the 5.8 GHz band for electromagnetic energy harvesting. The proposed MS unit features a top-layer dual-ring resonator with a T-shaped gap and a bottom cross-shaped coplanar waveguide (CPW), fabricated on a flexible polyimide substrate. To elucidate the physical mechanism of energy capture, an equivalent circuit model is established based on transmission line theory. Expressions for the total input impedance are derived, revealing the quantitative relationship between the structural parameters and the impedance-matching condition. The simulation results validate this theoretical model and show that the structure achieves an absorption efficiency of 97.5% and a harvesting efficiency (HE) of 86.6% at 5.72 GHz. The conversion efficiency remains above 50% over a wide range of incident angles, and the HE exhibits minimal variation within a polarization angle range of 0–90°. Experimental results indicate that the MS reaches a maximum HE of 73.2%, maintains over 40% efficiency under large-angle incidence, and achieves more than 65% HE across various curved surfaces. With its mechanical flexibility, polarization insensitivity, and simplified manufacturing, this MS harvester provides a reliable and scalable power solution for wireless power transfer applications. Full article

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection. Full article

Background/Objectives: High-voltage, low-current electric shocks inflict superficial second-degree burns on the skin, accompanied by a vicious cycle of excessive oxidative stress and inflammation. As efficient treatment of such electrical burns remains a clinical challenge, we explored the efficacy of an injectable thermosensitive chitosan hydrogel engineered with an antioxidant agent (astaxanthin) and an anti-inflammatory agent (ibuprofen) for the treatment of high-voltage, low-current electrical burn injuries. Methods: The proposed CS/AST/IBU hydrogel was prepared and its thermosensitivity was characterized. Subsequently, the hydrogel was injected into the wounds of male Sprague–Dawley (SD) rats subjected to electrical burn injury (20 kV, 3 mA). Finally, a series of experiments were performed to elucidate the dynamics of wound healing and the mechanisms by which the hydrogel promotes wound repair. Results: The injectable hydrogel, through its thermally responsive gelation effect at 37 °C, adapts to the complex irregularities of the wound surface. This facilitates the release of astaxanthin and ibuprofen throughout the wound, which collectively diminish the formation of reactive oxygen species and MDA. Furthermore, it enhances the synthesis of endogenous antioxidants such as SOD, CAT, and GSH; encourages collagen deposition; stimulates the development of dermal appendages; and fosters neovascularization. It interrupts the deleterious cycle of oxidative stress and inflammation mediated by the NF-κB signaling pathway, thereby suppressing the expression of pro-inflammatory markers such as TNF-α, CD11b, and IL-1β while upregulating CD163, an anti-inflammatory receptor. Conclusions: The use of this multipronged, contour-adaptive hydrogel represents an effective strategy for complex wound management and demonstrates broad therapeutic potential for superficial second-degree electrical burns caused by high-voltage, low-current discharge. Full article