Search Results (199)

This study investigates the effects and mechanisms of high-power microwave on GaN HEMTs. By injecting high-power microwave from the gate into the device and employing techniques such as DC characteristics, gate-lag effect analysis, low-frequency noise measurement, and focused ion beam (FIB) cross-sectional inspection, a systematic investigation was conducted on GaN HEMT degradation and failure behaviors under conditions of a low duty cycle and narrow pulse width. Experimental results indicate that under relatively low-power HPM stress, GaN HEMT exhibits only a slight threshold voltage shift and a modest increase in transconductance, attributed to the passivation of donor-like defects near the gate. However, when the injected power exceeds 43 dBm, the electric field beneath the gate triggers avalanche breakdown, forming a leakage path and causing localized heat accumulation, which ultimately leads to permanent device failure. This study reveals the physical failure mechanisms of GaN HEMTs under low-duty-cycle HPM stress and provides important guidance for the reliability design and hardening protection of RF devices. Full article

Accurate estimation of State of Health (SOH) is pivotal for managing the lifecycle of lithium-ion batteries (LIBs) and ensuring safe and reliable operation in electric vehicles (EVs) and energy storage systems. While feature fusion methods show promise for battery health assessment, they often suffer from suboptimal integration strategies and limited utilization of complementary health indicators (HIs). In this study, we propose a Feature Accretion Method (FAM) that systematically integrates four carefully selected health indicators–voltage profiles, incremental capacity (IC), and polynomial coefficients derived from IC–voltage and capacity–voltage curves—via a progressive three-phase pipeline. Unlike single-indicator baselines or naïve feature concatenation methods, FAM couples’ progressive accretion with tuned ensemble learners to maximize predictive fidelity. Comprehensive validation using Gaussian Process Regression (GPR) and Random Forest (RF) on the CALCE and Oxford datasets yields state-of-the-art accuracy: on CALCE, RMSE = 0.09%, MAE = 0.07%, and R² = 0.9999; on Oxford, RMSE = 0.33%, MAE = 0.24%, and R² = 0.9962. These results represent significant improvements over existing feature fusion approaches, with up to 87% reduction in RMSE compared to state-of-the-art methods. These results indicate a practical pathway to deployable SOH estimation in battery management systems (BMS) for EV and energy storage applications. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. 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

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article

Background/Objectives: Peak force (PF) reflects maximal strength, while early rate of force development (RFD; 0–100 ms) indicates explosive neuromuscular output. The Athletic Shoulder (ASH) test is gaining popularity in overhead athlete profiling, but its use for assessing explosive strength in various shoulder positions is underexplored. This study compared PF and RFD at shoulder abductions of 180° (ASH-I), 135° (ASH-Y), and 90° (ASH-T) in baseball players. Methods: Seventeen male athletes (age 22.7 ± 4.2 years; height 186.3 ± 7.3 cm; body mass 83.9 ± 10.1 kg) performed isometric ASH tests with the dominant arm. PF, PF relative to body mass (PF/BM), and early RFD were analysed. Results: ASH I showed 25% significantly higher PF (182 ± 41 N), PF/BM (2.15 ± 0.39 N/kg), and 40% higher RFD (545 N/s) than ASH Y or T (all p < 0.001), which did not differ significantly. PF showed excellent reliability (ICC = 0.86–0.93); RFD showed moderate-to-good reliability (ICC = 0.75–0.81). Smallest worthwhile changes were ~5% for PF and ~15% for RFD. Conclusions: Maximal isometric shoulder strength and explosiveness were highest at 180° abduction in baseball athletes, with no significant difference between 135° and 90°. PF demonstrated excellent reliability, while early RFD showed moderate to good reliability and higher variability, highlighting the need for repeated measures. These findings provide specific position reference values and support the inclusion of multiple abduction angles in shoulder strength assessment to detect neuromuscular deficits and monitor training adaptations in baseball athletes. Full article

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily life, their susceptibility to cyber threats such as replay, jamming, spoofing, and denial-of-service (DoS) attacks necessitates the development of robust cybersecurity measures. Additionally, EV-specific threats, including battery management system (BMS) exploitation and compromised charging interfaces, introduce distinct vulnerabilities requiring specialized attention. This research proposes a comprehensive and integrated cybersecurity framework that rigorously examines current V2V, vehicle-to-everything (V2X), and EV-specific systems through systematic threat assessments, vulnerability analyses, and the deployment of advanced security controls. Unlike previous state-of-the-art approaches, which primarily focus on isolated threats or specific components such as V2V protocols, the proposed framework provides a holistic cybersecurity strategy addressing the entire communication stack, EV subsystems, and incorporates AI-driven threat detection mechanisms. This comprehensive and integrated approach addresses critical gaps found in the existing literature, making it significantly more adaptable and resilient against evolving cyber-attacks. Our framework aligns with industry standards and regulatory requirements, significantly enhancing the security, safety, and reliability of modern transportation systems. By incorporating specialized cryptographic techniques, secure protocols, and continuous monitoring mechanisms, the proposed approach ensures robust protection against sophisticated cyber threats, thereby safeguarding vehicle operations and user privacy. Full article

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

Organic photodetectors (OPDs) offer considerable promise for low-power, solution-processable biosensing and imaging applications; however, their performance remains limited by spectral mismatch and interfacial trap states. In this study, a highly sensitive polymer photodiode was developed via trace incorporation (0.8 wt%) of InP/ZnSe/ZnS quantum dots (QDs) into a PTB7-Th:PC₇₁BM bulk heterojunction (BHJ) matrix. This QD doping approach enhanced the external quantum efficiency (EQE) across the 540–660 nm range and suppressed the dark current density at −2 V by passivating interface trap states. Despite a slight decrease in optical absorption at the optimized composition, the internal quantum efficiency (IQE) increased significantly from ~80% to nearly 95% resulting in a net EQE improvement. This suggests that QD incorporation improved charge transport without compromising charge separation efficiency. As a result, the device achieved a specific detectivity (D*) of 1.8 × 10¹³ Jones, representing a 93% improvement over binary BHJs, along with an ultra-low dark current density of 7.76 × 10⁻¹⁰ A/cm². Excessive QD loading, however, led to optical losses and increased dark current, underscoring the need for precise compositional control. Furthermore, the enhanced detectivity led to a 4 dB improvement in the signal-to-noise ratio (SNR) of photoplethysmography (PPG) signals in the target wavelength range, enabling more reliable biophotonic sensing without increased power consumption. This work demonstrates that QD-based spectral and interfacial engineering offers an effective and scalable route for advancing the performance of OPDs, with broad applicability to low-power biosensors and high-resolution polymer–QD imaging systems. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

Glanders is a highly contagious and often fatal zoonotic disease of equids caused by Burkholderia mallei, a pathogen of significant concern due to its potential for bioterrorism. In Brazil, glanders remains endemic, particularly among working equids in the Northeast region. Diagnostic confirmation typically involves serology, culture, and polymerase chain reaction (PCR), although false-negative PCR results have been increasingly reported. This study aimed to evaluate the diagnostic performance and analytical sensitivity of four B. mallei-specific PCR primer sets using samples from 30 seropositive equids. Microbiological cultures were obtained from various organs and swabs, followed by PCR targeting four genomic regions: fliP-IS407A(a), fliP-IS407A(b), Burk457, and Bm17. All animals were confirmed positive for B. mallei via culture, but PCR detection rates varied significantly across primer sets. The fliP-IS407A(b) primer set showed the highest sensitivity, detecting 86% of samples, while the WOAH-recommended fliP-IS407A(a) set had the lowest performance (13.4%). Analytical sensitivity assays confirmed that fliP-IS407A(b) and Bm17 primers detected DNA concentrations as low as 0.007 ng, outperforming the others. These findings suggest that certain widely used primer sets may lack sufficient sensitivity for reliable detection of B. mallei, especially in chronically infected animals with low bacterial loads. The study underscores the need for ongoing validation of molecular diagnostics to improve the detection and control of glanders in endemic regions. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article