IEEE Open Journal of Vehicular Technology最新文献

{"title":"Energy Management and Optimal Operation of Light Rail Trains on Jerusalem's Proposed Green Line","authors":"Maoz Eitan;Sebastián de la Torre","doi":"10.1109/OJVT.2026.3676227","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3676227","url":null,"abstract":"This work presents an integrated optimization framework for the operation of urban light rail systems, incorporating detailed traction dynamics, energy exchanges, battery behavior, and realistic infrastructure constraints into a unified mathematical model. Notably, the model accounts for the energy recovered through regenerative braking, significantly reducing operating costs. The formulation results in a non-linear problem solved efficiently with commercial solvers, and its flexible structure allows adaptation to different railway technologies, grid configurations, and operating strategies. The model was applied to the Jerusalem Light Rail Green Line using detailed topographic data and over 2,000 track segments covering a 32.7 km round trip, enabling full-cycle energy and operational assessment under realistic service conditions. Sixteen realistic operational scenarios were simulated, featuring cost or time minimization, onboard energy storage system (ESS) availability, regenerative braking, and different maximum speeds. Additionally, sensitivity analyses were carried out to quantify the influence of key parameters on operational performance and costs. Results show that time minimization produces small travel time reductions, while considerably increasing energy cost. In contrast, cost minimization yields a more balanced and sustainable trade-off between performance and efficiency. The best configuration corresponds to cost minimization with both regenerative braking and ESS. Detailed analysis reveals how the ESS smooths power fluctuations, enhances energy recovery, and limits purchases from the grid. Finally, the model was extended to full-day operation under realistic timetables with 5-minute frequencies; in this configuration, daily operating costs reach €8,273.52, with €270.14 saved thanks to regenerative braking. These results demonstrate the practical applicability of the framework and offer actionable insights and practical guidance for railway operators.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1137-1150"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665236","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":"Quantum-Native Resource Allocation for NOMA-Enabled NTNs: A Neutral Atom Approach","authors":"Remon Polus;Patatchona Keyela;Soumaya Cherkaoui;Ola Ahmad","doi":"10.1109/OJVT.2026.3726179","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3726179","url":null,"abstract":"Resource management in next-generation wireless communication networks poses substantial computational challenges due to its combinatorial structure and mixed discrete–continuous nature. Many such problems are NP-hard (non-deterministic polynomial-time hard), rendering conventional optimization techniques computationally demanding and often impractical for large-scale deployments. Recent advances in neutral atom quantum computing have demonstrated innovative potential for solving combinatorial optimization problems through graph-based formulations. However, their application to wireless resource management remains largely unexplored. This paper investigates the use of neutral atom quantum processors to address the maximum access problem (MAP) in uplink non-orthogonal multiple access (NOMA)-enabled non-terrestrial networks (NTNs). The MAP is first formulated as a large-scale mixed-integer optimization problem that jointly captures admission control, user clustering, channel assignment, and power allocation under signal-to-interference-plus-noise ratio (SINR) constraints, while accounting for imperfect successive interference cancellation (SIC) at the receiver. To enable hardware-aligned quantum implementation, the problem is transformed into a maximum independent set (MIS) problem over a conflict graph. The resulting graph structure is compatible with the operational principles of neutral atom quantum hardware, allowing direct mapping onto Rydberg atom arrays via blockade-induced interactions. Numerical evaluations assess admission performance, graph complexity, and computational behavior, highlighting the effectiveness of the proposed neutral atom-based framework for solving such graph-theoretic problems. By integrating quantum combinatorial optimization into wireless resource management, this work demonstrates a hardware-aligned mapping for NOMA-enabled NTN resource allocation and provides proof-of-concept validation on a current neutral atom processor, showcasing the potential of neutral atom quantum processors for addressing optimization problems in future 6G and beyond networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2685-2699"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11661688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033863","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":"Average-Reward Baseline Centering: A Plug-In Stabilizer for DDPG and MADDPG in C-V2X Platooning","authors":"Vasimbhai Vohra;Vinay Thumar;Hiren Mewada;Sagarkumar B. Patel","doi":"10.1109/OJVT.2026.3700393","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3700393","url":null,"abstract":"Fast and reliable data exchange is essential for connected and autonomous vehicles, particularly in platooning scenarios where vehicles travel in close formation and depend on timely updates for safety and traffic efficiency. Actor–critic reinforcement learning can exhibit unstable learning signals and suboptimal policies under dynamic vehicular network conditions, especially in continuing (non-episodic) resource-allocation tasks, where slot-level fast fading, bursty CAM arrivals, and mode-switching between V2I and V2V links amplify reward variance. We propose a plug-and-play Baseline-Centered Target (BCT) update for DDPG-family methods. The core idea is: before forming each temporal-difference target, a sliding-window estimate of the long-run average reward is subtracted from the sampled reward, with no other change to the training loop. Theoretically, BCT preserves the fixed point of the Bellman equation and reduces TD target variance without biasing the actor update. BCT is evaluated across four coordination paradigms—centralized DDPG independent-learning DDPG (IL-DDPG), CTDE MADDPG, and Hybrid MADDPG—each paired with its BCT variant, with TD3 as a single-agent reference. BCT variants consistently improve steady-state global reward across all paradigms (<inline-formula><tex-math>$+11.5%$</tex-math></inline-formula> to <inline-formula><tex-math>$+26.0%$</tex-math></inline-formula>) and produce more stable training curves. In the Centralized paradigm, BCT and TD3 achieve comparable gains (<inline-formula><tex-math>$+17.9%$</tex-math></inline-formula> and <inline-formula><tex-math>$+19.0%$</tex-math></inline-formula> respectively), confirming complementary. In the Independent paradigm, IL-TD3 degrades below its base while BCT-IL-DDPG still improves by <inline-formula><tex-math>$+11.5%$</tex-math></inline-formula>, demonstrating BCT’s robustness under multi-agent non-stationarity. Results are validated across multiple random seeds and window sizes (<inline-formula><tex-math>$W in lbrace 5,ldots,20rbrace$</tex-math></inline-formula>), and an <inline-formula><tex-math>$N_{text{RB}}$</tex-math></inline-formula> ablation (<inline-formula><tex-math>$lbrace 3, 6rbrace$</tex-math></inline-formula>) confirms BCT-Hybrid retains its advantage, with mean AoI dropping 83% at <inline-formula><tex-math>$N_{text{RB}}=6$</tex-math></inline-formula>.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1770-1787"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11550746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443263","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":"Composite Inertia Weighted APSO for Autonomous Vehicle Path Planning","authors":"Muhammad Haris;Yifang Shi;Haewoon Nam","doi":"10.1109/OJVT.2026.3697528","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3697528","url":null,"abstract":"Efficient path planning is essential for autonomous vehicle navigation. This paper proposes a Composite Inertia Weighted Adaptive Particle Swarm Optimization (APSO) algorithm to improve convergence reliability and solution quality. The proposed method introduces a non linear inertia weight update mechanism that combines exponential decay with a quadratic distance based component to dynamically balance global exploration and local exploitation. This adaptive strategy enables the algorithm to effectively navigate complex environments and generate collision free, smooth, and efficient trajectories. The performance of the proposed algorithm is evaluated using benchmark functions and autonomous path planning simulations. Results demonstrate that the composite approach significantly outperforms traditional metaheuristics, achieving a reduction in path length of up to 6.05% over standard PSO, 8.90% over ABC, and 2.48% over Dijkstra. Furthermore, the algorithm increases convergence speed by 34.9% and 20.8% compared to PSO and Dijkstra, respectively, proving its efficacy for high-dimensional, real-time optimization in complex environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1627-1640"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11535698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433187","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":"TFE-Fusion: Tri-Modality Feature Enhanced Fusion for Robust Object Detection","authors":"Muhammad Usama;Imad Ali Shah;Roshan George;Enda Ward;Fiachra Collins;William O'Grady;Martin Glavin;Brian Deegan;Edward Jones","doi":"10.1109/OJVT.2026.3726173","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3726173","url":null,"abstract":"Robust and reliable object detection under adverse conditions remains a critical challenge for automated driving systems (ADS). The performance of RGB-based (visible-spectrum) cameras degrades in poor lighting conditions, whereas the performance of RGB-Long-Wave Infrared (LWIR) fusion architectures remains limited in adverse weather and low thermal contrast scenarios due to partial spectral coverage. At the same time, Short-Wave Infrared (SWIR) has the potential to address some of these gaps but remains under-utilised in ADS. To address these limitations, we propose a novel tri-modality fusion architecture, TFE-Fusion, that simultaneously leverages information from RGB, SWIR, and LWIR modalities. Our architecture employs a feature-level fusion strategy that incorporates pixel-level weighting and a spectral attention mechanism, enabling dynamic fusion of complementary features from all three modalities. A YOLOv8-based detection head, modified for multi-modal input streams, is used for efficient and robust inference. Extensive experiments on the Multispectral Object Detection (MOD) dataset demonstrate that the proposed method outperforms the RGB-LWIR baseline by 2.48 percentage points in mAP@0.5:0.95. The most significant performance gains are observed for the vehicle class, where SWIR features compensate for low thermal contrast in LWIR imagery by capturing distinct material reflectance properties. These results validate the effectiveness of the proposed TFE-Fusion architecture for enhancing detection performance, particularly under low-light and low-thermal-contrast conditions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2593-2605"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11661795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925333","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":"UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios","authors":"Mohamed Amine Ferrag;Abderrahmane Lakas;Merouane Debbah","doi":"10.1109/OJVT.2026.3721777","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3721777","url":null,"abstract":"Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making; yet, the lack of standardized, physically grounded benchmarks limits systematic evaluation of their reasoning capabilities. To address this gap, we introduce UAVBench, an open benchmark dataset comprising <italic>50,000 validated UAV flight scenarios</i> generated through taxonomy-guided LLM prompting and multi-stage safety validation. Each scenario is encoded in a structured JSON schema encompassing mission objectives, vehicle configuration, environmental conditions, and quantitative risk labels, providing a unified representation of UAV operations across diverse domains. Building on this foundation, we present <monospace>UAVBench_MCQ</monospace>, a reasoning-oriented extension containing <italic>50,000 multiple-choice questions</i> spanning ten cognitive and ethical reasoning styles—from aerodynamics and navigation to multi-agent coordination and hybrid integrated reasoning. This framework enables interpretable, machine-checkable assessment of UAV-specific cognition under realistic operational contexts. We evaluate 32 state-of-the-art LLMs, including GPT-5, ChatGPT 4o, Gemini 2.5 Flash, DeepSeek V3, Qwen3 235B, and ERNIE 4.5 300B, and find strong performance in perception and policy reasoning but persistent challenges in ethics-aware and resource-constrained decision-making. <monospace>UAVBench</monospace> establishes a reproducible, physically grounded foundation for benchmarking agentic AI in autonomous aerial systems and advancing next-generation UAV reasoning intelligence.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2542-2558"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925340","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":"Fault Search Strategies Including Vehicle Log Files","authors":"Nikodemus Sperlich;Philipp Schmidt;Sidney Körper;Thomas Bachmann","doi":"10.1109/OJVT.2026.3721907","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3721907","url":null,"abstract":"The use of high-performance computers (HPCs) in Software Defined Vehicles (SDVs) provides an additional source of in-vehicle data from future vehicles. Today, troubleshooting is mainly focused on diagnostic trouble codes (DTCs) in electronic control units (ECUs) which can be retrieved via Unified Diagnostic Services (UDS). Log files from HPCs can provide important information about the processes in the vehicle and are therefore also of interest when analyzing the fault memory and repairing the vehicle. In order to use this information, it is necessary to match DTCs and log files. The Retrospective Event Correlation Engine (RECE) introduces a suitable procedure for matching DTCs and log files by using correlation identifiers (IDs) in the vehicle. Creating a proof of concept within a limited controlled test environment offers the chance to demonstrate the function and use of statistical log correlation and anomaly detection. An implementation of this procedure in future architectures may enable workshops to include log files and their correlation into their troubleshooting strategies.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2531-2541"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925371","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}

{"title":"Selective and Noise-Resistant Virtual Point Cloud Fusion for Long-Range 3D Object Detection","authors":"Chih-Ming Hsu;Yuan-Ting Fu;Bo-Yun Wang","doi":"10.1109/OJVT.2026.3689631","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3689631","url":null,"abstract":"Virtual point clouds alleviate long-range sparsity in LiDAR-based 3D object detection by back-projecting image-guided depth into 3D space. However, depth uncertainties near object boundaries and occlusions introduce boundary-aligned artifacts, which can degrade detection if virtual points are fused without selection and can also increase runtime overhead due to excessive point density. This paper presents a selective and noise-resistant virtual point cloud fusion framework that explicitly suppresses boundary artifacts and improves cross-modal feature integration under practical efficiency constraints. The proposed method consists of three components. First, Dilated Noise-Resistant Convolution (DNrC) performs projection-domain context aggregation to mitigate boundary artifacts before cross-modal interaction. Second, Multiscale Cross Multi-Head Attention (MCMA) selectively injects multiscale image semantics into sparse 3D voxel features under calibration-consistent alignment. Third, a lightweight Cascade Attention Module (CasA) progressively refines predictions to improve localization for long-range and hard samples. Experiments on the KITTI validation set demonstrate consistent improvements over a strong virtual point baseline, with clear gains on challenging settings while maintaining practical inference speed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1399-1412"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11503424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171522","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":"SIDC-GAN: Single Image De-Rain Cascade UNet GAN","authors":"Saifuddin Sk;Bibek Das;Ahmad Sami Al-Shamayleh;Asfak Ali;Suvojit Acharjee;Sheli Sinha Chaudhuri;Adnan Akhunzada","doi":"10.1109/OJVT.2026.3669163","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3669163","url":null,"abstract":"Single image de-raining focuses on removing rain effects from an image while preserving important details, making it one of the challenging tasks in computer vision. This article introduces the Single Image De-Rain Cascade-UNet Generative Adversarial Network (SIDC-GAN). The framework consists of a lightweight UNet-based generator and a discriminator working adversarially to enhance the de-raining process. The generator, a lightweight UNet structure that consists of a convolution block, inverted residual block, and inverted residual block with squeeze connection, aims to produce realistic, rain-free images, while the discriminator differentiates between real and generated images. The inverted residual block utilizes depth-wise convolutions to significantly reduce computational cost compared to regular convolution, thereby enabling a lightweight UNet-based generator architecture. The adversarial interaction drives continuous improvement, resulting in higher-quality, de-rained outputs. SIDC-GAN, trained on the extensive Rain13 K dataset, demonstrates robust performance in diverse and complex rainy scenarios due to its exposure to a wide range of rain conditions. Evaluation of five benchmark data sets—Rain100 L, Rain100H, Test100, Rain1200, and Test2800—shows that SIDC-GAN achieves significant improvements in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The SIDC-GAN model shows a 2.6% increase in PSNR compared to state-of-the-art methods. The SIDC-GAN model further evaluates the effect of cascading multiple UNet networks to progressively refine the results in the generator. The result shows that two consecutive UNet layers have achieved an improvement of 3.45% in PSNR and 4.066% in SSIM compared to a single UNet layer in the generator. However, increasing the number of UNet layers in the generator from two to four observed a diminishing return in output.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"917-929"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11417701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557397","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}