IEEE Open Journal of Vehicular Technology最新文献

{"title":"Terahertz Band for High-Altitude Platform Station Communications: Performance Analysis, Potential, and Challenges","authors":"Mikail Erdem;Khalid A. Qaraqe;Mohamed-Slim Alouini","doi":"10.1109/OJVT.2026.3696983","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3696983","url":null,"abstract":"The Terahertz (THz) band (0.1–10 THz) attracts attention due to its massive bandwidth, potentially enabling ultra-high data rate with ultra-low latency. High-altitude platform stations (HAPSs) are envisioned as nodes of a non-terrestrial network (NTN) that complements terrestrial networks, particularly for connecting the unconnected and addressing temporary needs, such as disaster relief. THz HAPS communication is a promising solution for NTN, since HAPS is quasi-stationary in nature, located in the stratosphere, thereby avoiding beam misalignment and molecular absorption loss issues in the THz band. In this paper, we provide an overview of THz band HAPS communication, considering HAPS-to-ground, HAPS-to-HAPS, HAPS-to-satellite, and HAPS-to-uncrewed aerial vehicle links. We present simulation results of outage probability, ergodic capacity, and spectral efficiency under beam misalignment fading and turbulence fading in comparison to millimeter wave and sub-6 GHz bands. In addition, we summarize existing THz HAPS studies to reveal ongoing efforts. Moreover, we discuss open issues and research directions for THz band HAPS-related links. Overall, this paper draws a clear picture of THz HAPS communication by reporting its potential and challenges.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1548-1558"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11535301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measured Energy Consumption for AI-Based Precoding","authors":"Pengyu Cong;Chenyang Yang;Shengqian Han;Nan Li;Qi Sun","doi":"10.1109/OJVT.2026.3692717","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3692717","url":null,"abstract":"This paper presents the first measurement-based study on the energy consumption of artificial intelligence (AI) algorithms for radio access networks (RAN), aiming to investigate whether the integration of AI leads to higher energy consumption than traditional non-AI algorithms. We use the multiuser precoding problem as a representative example, where AI is widely recognized for its potential in supporting the growing deployment of antennas for 6G systems. Our assessment examines three AI algorithms using the convolutional neural network (CNN), edge graph neural network (EGNN), and communication model-based graph neural network (MGNN), in comparison with two non-AI algorithms, the weighted minimum mean square error (WMMSE) and regularized zero-forcing (RZF) algorithms. Through extensive measurements of energy and power consumption, we examine how efficient architecture designs of deep neural networks (DNNs) affect energy consumptions, and provide a quantitative comparison between measured results and estimates from existing power estimation models. The results indicate that the MGNN, which is the most efficient architecture among the three DNNs, consumes substantially less energy than the non-AI algorithms for large-scale systems, while the CNN consumes significantly more energy for both training and inference compared to the MGNN and EGNN. Moreover, GPUs do not always consume more power than CPUs; the power consumption of CPUs should be taken into account when GPUs are used, and DRAM contributes little to total power consumption. We demonstrate the shortcomings of existing utilization-based and FLOPs-based models in estimating energy consumption, highlighting the need for more accurate models to support green AI designs for RAN.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1559-1575"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11516189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical Characterization and DRT-Guided ECM Modeling of Commercial Sodium-Ion 18650 Cells","authors":"Corentin Piat;Ali Sari;Christophe Viton","doi":"10.1109/OJVT.2026.3700666","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3700666","url":null,"abstract":"Sodium-ion batteries are attracting increasing interest for stationary energy storage, yet their aging behavior and modeling remain less mature than for lithium-ion systems. This paper presents an initial experimental and modeling study of commercial 18650 sodium-ion cells (0.9 Ah) based on capacity measurements, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS), and distribution of relaxation times (DRT). Cycling tests were performed under selected operating conditions, and the early aging response was monitored through repeated electrochemical characterization. The impedance spectra were interpreted using a DRT-guided equivalent circuit model (ECM), which was then used to reconstruct the cell voltage in the time domain under a representative current profile. The results show good alignment between measured and simulated voltage under moderate dynamic conditions, while also highlighting the limitations of the model under sharp current transients. In addition, the study reveals inter-cell variability and temperature-dependent degradation trends that deserve further statistical and mechanistic investigation. Overall, this work establishes a physically constrained modeling framework that links frequency-domain diagnostics to time-domain behavioral modeling and provides a basis for future state-of-health prediction studies on commercial sodium-ion cells.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1715-1723"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11551993","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient DRL-Based 3D UAV Deployment for Optimizing Mobile User Monitoring Under Wireless Service-Reliability Constraints","authors":"Dil Nashin Anwar;Haythem Bany Salameh;Moussa Ayyash;Hany Elgala;Malik Umar","doi":"10.1109/OJVT.2026.3705949","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3705949","url":null,"abstract":"Indoor flying networks (IFNs) can provide flexible monitoring support in crowded indoor environments, but practical deployment must jointly account for user mobility, limited UAV energy, motion boundaries, and reliable UAV-to-access-point (AP) communication. This paper studies energy- and service-reliability-constrained three-dimensional (3D) UAV deployment in a hybrid LiFi-WiFi IFN. The optimization objective is to maximize the number of mobile users monitored by each UAV, while energy, boundary, speed, and AP-connectivity requirements are imposed as operational constraints. To solve the resulting sequential decision problem, we use a coverage-driven deep reinforcement learning (DRL) framework whose main state keeps the UAV position, residual energy, and current coverage. The paper also implements an aggregate density-aware state variant based on coarse cell-level occupancy, density centroid, and density-centroid displacement to quantify the benefit of explicit mobility-context information without requiring exact user localization. The UAV operates in a continuous physical space, yet the DQL controller selects actions from a discrete set of local 3D motion primitives. This discretization is motivated by the limited onboard computational resources of the indoor UAV and the need to make rapid local positioning decisions. Collision avoidance is provided structurally by non-overlapping sub-region assignment, while boundary and wireless-service feasibility are enforced through action screening and reward penalties. The proposed policy is evaluated against several practical schemes (random selection, centroid-based selection, greedy heuristics, particle swarm optimization, and genetic algorithms), as well as against an upper-bound benchmark based on finite-grid integer linear programming (ILP). The results show that the proposed DRL method substantially outperforms practical baselines in terms of the average number of monitored users, while still achieving 93.7% of the performance of the finite-grid ILP benchmark. The evaluation also reports multi-seed robustness, QoS-violation rates, reward-threshold sensitivity, scalability, UAV-speed effects, and LiFi/WiFi service-reliability behavior.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1948-1963"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11573675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148482090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rotor Electromagnetic Structure Optimization for Improving the Efficiency of Permanent-Magnet Synchronous Motors in New Energy Vehicles","authors":"Hucheng Chen;Peiyuan Ma;Yunchong Wang;Bensheng Xu;Hui Jing;Guo Ma;Weizhong Mo","doi":"10.1109/OJVT.2026.3701458","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3701458","url":null,"abstract":"Rotor electromagnetic structure optimization (RESO) has emerged as a key approach for improving the efficiency of permanent-magnet synchronous motors (PMSMs), resulting in a substantial volume of published studies. This paper systematically reviews the major RESO strategies targeting efficiency improvement. The rotor electromagnetic-structure characteristics and efficiency evaluation indicators are identified. Then, according to the different optimization mechanisms of the rotor structure, RESO techniques are classified into several classic categories. The most popular optimization methods are summarized in terms of their mechanisms, characteristics, and complexities. Finally, current research limitations and future directions are highlighted. This review indicates that RESO is an important pathway for improving efficiency, but its effectiveness depends on motor topology, operating conditions, performance requirements, manufacturing feasibility, and cost constraints. It is expected that this review will provide useful guidelines for designers and researchers seeking to enhance PMSM efficiency and offer insightful prospects, indicating where extra efforts are needed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1884-1901"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11554238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VAGNet: Vision-Based Accident Anticipation With Global Features","authors":"Vipooshan Vipulananthan;Charith D. Chitraranjan","doi":"10.1109/OJVT.2026.3704953","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3704953","url":null,"abstract":"Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers, forming a key component of advanced driver assistance systems. In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover when required. Using dash-cam video as input offers a cost-effective solution, but it is challenging due to the complexity of real-world driving scenes. Accident anticipation systems need to operate in real-time. However, current methods involve extracting features from each detected object, which is computationally intensive. We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes without requiring explicit object-level features. The network consists of transformer and graph modules, and we use the vision foundation model VideoMAE-V2 for global feature extraction. Experiments on four benchmark datasets (DAD, DoTA, DADA, and Nexar) show that our method anticipates accidents with higher average precision while being computationally more efficient compared to existing methods. It also achieves higher mean time-to-accident scores in most cases, while being competitive in others.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1873-1883"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11574005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Simulation and Image Quality for Automotive Semantic Segmentation","authors":"Daniel Jakab;Joel Herrera Vázquez;Mahendar Kumbham;Brian Deegan;Tim Brophy;Reenu Mohandas;Anthony Scanlan;Enda Ward;Fiachra Collins;Ciarán Eising","doi":"10.1109/OJVT.2026.3702262","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3702262","url":null,"abstract":"Cameras are often deployed in automated driving systems for multiple perception tasks, including object recognition. In previous work, a novel optical simulation framework, Superposition of Optical Lenses in Automotive Simulation (SOLAS), was proposed to simulate wide-angle and fisheye cameras on automotive data for 2D object detection. The main focus of this work is to assess semantic segmentation networks in relation to camera quality, mainly used for collision avoidance, lane marking detection, and enhanced road navigation. Characterising semantic segmentation performance across the full range of camera production tolerances is essential, given that image quality may vary across units. This study further develops the novel optical simulation framework, SOLAS, where a Pythonic ray-tracing library, Kraken Optical Simulator (KrakenOS), is adapted to simulate wide-angle surround-view optical systems across a range of optical defocus, directly in automotive scenes. Optical defocus is achieved through sensor displacement. In this paper, we propose extending SOLAS with Automated Reference-Free Defocus characterisation for Automotive Near-Field Cameras (ARDÁN), a pipeline for measuring image quality in automotive scenes. Furthermore, we demonstrate that our framework, utilising a diverse set of cameras and dense grids of Point Spread Functions (Point Spread Functions (PSFs)), is scalable in semantic segmentation, achieving consistent results across various datasets supported by statistical metrics. The primary metric used for performance evaluation is mean Intersection over Union (mean Intersection over Union (mIoU)). Based on our results, the Segformer network with 24.73 M parameters was the most robust across wide-angle automotive datasets, namely, nutonomy Scenes (nuScenes) and Virtual Karlsruhe Institute and Toyota Technological Institute (KITTI) 2.0. In nuScenes, image sharpness <inline-formula><tex-math>$(cycles/pixel)$</tex-math></inline-formula> dropped by 20.5% from a nominal focus, with a small Segformer performance drop from 55.21% mIoU by 0.37%. In Virtual KITTI 2.0, image sharpness dropped by 44.66% from a nominal focus, with a Segformer performance drop by 37.42%. However, for fisheye, the Proportional-Integral-Derivative (PID)Net model with 28.76 M parameters was the most robust model across the defocus range. In Woodscape, image sharpness dropped by 53.45% from a nominal focus, with a PIDNet performance drop by 8.55%. In Parallel Domain Woodscape, image sharpness dropped by 34.46% from a nominal focus, with a PIDNet performance drop by 7.28%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1788-1803"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11557188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modulated Charging Current-Based Charger-EV Pairing for Plug-and-Charge Service Using ISO 15118-Incompatible AC Chargers","authors":"Masaki Imanaka;Takashi Ikegami;Hiroyuki Baba","doi":"10.1109/OJVT.2026.3708877","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3708877","url":null,"abstract":"Plug-and-Charge (PnC), an identification mode in which the customer only needs to connect an electric vehicle (EV) and all aspects of energy transfer are handled without further driver intervention, can improve the convenience of AC normal charging. ISO 15118 supports PnC. However, the deployment of ISO 15118 compliant EVs and chargers remains limited. Therefore, this study investigates Modulated Charging Current Technology (MCCT), a transitional pairing method for ISO 15118-incompatible AC chargers and EVs. MCCT actively modulates the charging current and achieves charger–EV pairing by comparing charging-current measurements obtained on the charger and EV sides, without requiring additional hardware. The objective of this study is to propose a pairing procedure and matching algorithm for MCCT and evaluate their initial feasibility as a proof-of-concept. A practical pairing procedure applicable to heterogeneous charging systems with different architectures is developed and evaluated using real charger–EV pairs. In addition to cases where both the charger and EV participate in MCCT, scenarios involving non-target chargers or EVs are also examined. The results demonstrate that MCCT can correctly distinguish charger–EV pairs within its scope from those outside its scope. Practical challenges affecting pairing accuracy and scalability are identified, including time alignment for representative-value selection and the development of distinguishable modulation-pattern sets using refined similarity scores and charging current analysis.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2042-2054"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11592438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Precise Pad-to-Coil Alignment in EV Inductive Charging With UWB","authors":"Ryotaro Hada;Shunsuke Saruwatari;Kenichi Koga;Masahisa Doko;Koji Inafune;Toshinori Matsuura;Takuya Fujihashi;Dinesh Bharadia","doi":"10.1109/OJVT.2026.3725689","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3725689","url":null,"abstract":"In inductive wireless power transfer (WPT) for electric vehicles (EVs), final pad-to-coil alignment is critical because centimeter-class lateral displacement or yaw misalignment can degrade magnetic coupling, reduce transfer efficiency, and increase charging time. This motivates millimeter-class planar-position accuracy and sub-degree yaw estimation. This paper presents a calibrated ultra-wideband (UWB) relative pose estimation framework that fuses time-of-flight (ToF) and phase-difference-of-arrival (PDoA) measurements. The system consists of a four-element UWB anchor unit integrated with the transmitter pad and four synchronized UWB modules rigidly mounted around the receiver-coil region on the vehicle. ToF measurements provide a robust initialization in the correct geometric basin, while wrapped PDoA measurements supply high-resolution local constraints for pose refinement. Angle- and distance-dependent biases in both ToF and carrier-phase measurements are calibrated offline and compensated online. Because phase wrapping makes the joint ToF+PDoA objective highly nonconvex, the pose is estimated in two stages: gradient-based ToF initialization followed by particle-filter-based joint refinement under rigid-body constraints. In a static pose-grid simulation, the proposed method achieves p50/p90 planar-position errors of <inline-formula><tex-math>$10.97/43.46$</tex-math></inline-formula> mm and p50/p90 yaw errors of <inline-formula><tex-math>$0.38^circ /1.21^circ$</tex-math></inline-formula>. Hardware experiments with a scaled vehicle mockup further validate the sensing chain at the final stopping pose. Across 200 repeated UWB updates, the proposed method achieves p50/p90 planar alignment errors of 2.32/4.23 mm and p50/p90 yaw errors of <inline-formula><tex-math>$0.25^circ /0.72^circ$</tex-math></inline-formula> after bias compensation. These results demonstrate millimeter-class final stopping-pose alignment in a controlled scaled testbed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2668-2684"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11667381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monocular Ground Normal Prediction for the Road Ahead","authors":"Norbert Markó;Zoltán Rózsa;Áron Ballagi;Tamás Szirányi","doi":"10.1109/OJVT.2026.3676610","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3676610","url":null,"abstract":"Robust fusion of monocular and inertial data has the potential to offer a low-cost alternative for ground surface normal prediction ahead, compared to more expensive sensors, such as LiDAR. Yet robust camera-based prediction remains challenging, particularly for steep grades and texture-poor, homogeneous road surfaces. To address these issues, we propose an enhanced monocular camera-IMU fusion pipeline incorporating a lightweight transformer-based feature matcher for improved correspondence accuracy, and robust temporal filtering, using a spherical linear interpolation (SLERP) filter, to enhance consistency and reduce drift. To enable rigorous benchmarking and reproducibility, we also standardize the evaluation protocol and release a novel dataset containing synchronized camera, LiDAR, and IMU-derived pose data, specifically captured across diverse incline and decline scenarios. Extensive continuous validation demonstrates that our method significantly improves both accuracy and temporal stability over existing approaches, setting a new state of the art for robust, continuous ground normal estimation ahead.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1066-1080"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11450408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}