IEEE Open Journal of Vehicular Technology最新文献

{"title":"Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts","authors":"Aamir Ullah Khan;Saw James Mint;Syed Najaf Haider Shah;Christian Schneider;Joerg Robert","doi":"10.1109/OJVT.2025.3554365","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3554365","url":null,"abstract":"Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"948-968"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830467","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":"Model-Based Analysis of Transient Currents to Dimension a Vehicular Power System With Electronic Fuses Regarding Short Circuit Selectivity","authors":"Christoph Mayer;Martin Baumann;Luis Finkl;Leo T. Peters;Hans-Georg Herzog","doi":"10.1109/OJVT.2025.3573836","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3573836","url":null,"abstract":"A fail-operational vehicular power system requires the selective tripping of electronic fuses (eFuses) in case of a fault event, e.g. short circuit, impermissible wire temperature. As a result of a short circuit and the associated interruption of the fault current by an eFuse, transient currents within the vehicular power system commutate into neighboring paths and potentially cause an unintended tripping of other eFuses. In this paper, modeling of the vehicular power system and an analytical methodology using state-space systems are proposed to calculate the transient currents during a short circuit and switch-off process. Comparing the results of the new approach with data obtained from simulation and measurement revealed a sufficient accuracy to represent the current trajectories. Furthermore, the analytical methodology enables a significant runtime reduction compared to simulations. Using the methodology, a parameter study is done to examine the influencing parameters on the transient currents in a <inline-formula><tex-math>$12 ,mathrm{V}$</tex-math></inline-formula> or <inline-formula><tex-math>$48 ,mathrm{V}$</tex-math></inline-formula> vehicular power system, and to derive guidelines for accomplishing selectivity. Based on this knowledge, an exemplary dimensioning process of a vehicular power system with regard to selectivity is shown and the effect of the different parameters on the critical currents regarding selectivity is discussed. The fault isolation time and the current threshold of the tripping eFuse have shown to be crucial parameters to reduce the transient currents efficiently.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1399-1425"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11015788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308287","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":"Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework","authors":"Tianfeng Long;Pengcheng Zhang;Xiaoqi Liu;Huaqing Shang;Meiling Yue;Xuesong Shen;Jianwen Meng","doi":"10.1109/OJVT.2025.3573705","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3573705","url":null,"abstract":"Data-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nature of battery performance changes. To address these limitations, this paper presents a novel Transformer-based approach for accurate SOH prediction. The correlation between various measured and computed features extracted from battery charge/discharge curves and their impact on battery performance degradation are investigated using Pearson correlation coefficients. Three strongly correlated features are identified as multiple input variables for the Transformer framework. The effectiveness of this Transformer-based SOH prediction method is demonstrated using public datasets, revealing that predictions for internal resistance and capacity closely align with actual values, with most RMSE values falling below 0.01. Furthermore, validation with an additional laboratory dataset confirms the accuracy and adaptability of our proposed approach, highlighting its potential to enhance SOH prediction in real-world applications.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1363-1379"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11015565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255559","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":"Clustered Federated Reinforcement Learning for Autonomous UAV Control in Air Corridors","authors":"Meng Xiang Xuan;Liangkun Yu;Xiang Sun;Sudharman K. Jayaweera","doi":"10.1109/OJVT.2025.3573647","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3573647","url":null,"abstract":"Advanced Air Mobility (AAM) aims to integrate unmanned aerial vehicles (UAVs) into urban airspace for efficient cargo and passenger transport, relying on autonomous navigation within designated 3D air corridors. Deep reinforcement learning (DRL) has demonstrated significant potential for autonomous UAV control in complex environments, particularly when trained with sufficient flight data. However, the performance of DRL-based models can degrade when real-world conditions differ from their training environments, leading to increased collision risks and boundary violations. To address this challenge, we propose CLustered fEderAted Reinforcement Learning (CLEAR), a novel approach that enables UAVs to collaboratively fine-tune their DRL models in real time using flight data from their operational environment. Unlike traditional federated reinforcement learning (FRL) frameworks that assume clients have pre-existing local datasets, CLEAR organizes UAVs into clusters, where each cluster head aggregates flight data from its members to perform local training before contributing to a global model. This adaptive learning process enhances UAV control in dynamic airspace while maintaining decentralized autonomy. Simulation results show that CLEAR significantly outperforms HTransRL—which lacks model fine-tuning—in terms of arrival rates and scalability as the number of UAVs increases. These findings underscore CLEAR's effectiveness in enabling real-time DRL adaptation, positioning it as a promising solution for robust UAV navigation in AAM ecosystems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1582-1592"},"PeriodicalIF":5.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11015557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502883","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":"A Survey on Reconfigurable Intelligent Surface-Assisted Orthogonal Time Frequency Space Systems","authors":"Qin Tao;Zhongjie Li;Kangda Zhi;Shuangyang Li;Weijie Yuan;Lorenzo Zaniboni;Slawomir Stanczak;Emanuele Viterbo;Xianbin Wang","doi":"10.1109/OJVT.2025.3573208","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3573208","url":null,"abstract":"The vision for 6th-generation (6G) wireless communication systems emphasizes the need for robust and reliable communication in extremely high-mobility scenarios, while also addressing critical demands for energy and spectral efficiency. Under such scenarios, doubly time-frequency selective fading channels often significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM) based systems due to the impact of large delay and Doppler shifts. Recently, orthogonal time frequency space (OTFS) modulation has emerged as a promising alternative. By processing signals in the delay-Doppler (DD) domain, OTFS offers several advantages, including quasi-static channel characteristics, full-time-frequency diversity, and low peak-to-average power ratio (PAPR), making it a promising candidate for high-mobility communications. Reconfigurable intelligent surfaces (RIS) are being further integrated to enhance the performance of OTFS systems cost-effectively. With their ability to dynamically reconfigure the wireless environment, the integration of RIS can offer significant performance improvements for OTFS systems. This survey offers a comprehensive review of RIS-assisted OTFS systems, including the fundamental principles, recent advances, and future research directions. Specifically, we first introduce the background of RIS-assisted OTFS systems, outlining the opportunities and challenges of their integration. To ensure the survey is self-contained, we provide a brief overview of the fundamental principles of OTFS and RIS technologies. Building on these foundations, we present a general input-output relationship and capacity characterization for RIS-assited MIMO-OTFS systems. Then, this survey further explores cutting-edge research in areas such as input-output analysis, RIS phase shift design, channel estimation, detection techniques, RIS-assisted integrated sensing and communication (ISAC), and other novel technologies. Finally, we outline some future research directions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1881-1909"},"PeriodicalIF":5.3,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11011681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671235","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":"LDPC-Hadamard Code-Assisted OTFS in High-Mobility Scenarios","authors":"Yi Gong;Zehao Li;Lichun Yang;Lu Tian;Jun Miao;Desheng Zhang;Zhan Xu","doi":"10.1109/OJVT.2025.3573073","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3573073","url":null,"abstract":"In high-speed mobile scenarios, Orthogonal Time Frequency Space (OTFS) effectively addresses the Doppler frequency shift. Channel coding enhances the performance of OTFS systems by adding redundancy and error correction capabilities. However, existing coding schemes still face limitations when dealing with complex conditions such as highly dynamic channel variations, delay spread, and Doppler effects. This paper proposes a Low-Density Parity-Check (LDPC)-Hadamard code to assist OTFS in combating complex channel conditions, thereby improving data transmission efficiency. In particular, the LDPC-Hadamard code leverages the sparsity of LDPC and the orthogonality of Hadamard codes to address the issue of rapidly varying channels in OTFS systems. Additionally, a low-complexity quasi-cyclic LDPC code construction algorithm is proposed to decrease the coding complexity. Otherwise, a Log-Likelihood Ratio Belief Propagation (LLR-BP) decoding algorithm is introduced, which reduces the computational complexity. Therefore, the proposed coding and decoding methods enable the OTFS to achieve better performance in low SNR conditions. Compared to conventional quasi-cyclic LDPC schemes, the proposed LDPC-Hadamard code-assisted OTFS system exhibits a 0.2 dB SNR gain and achieves a lower BER of <inline-formula><tex-math>$10^{-6}$</tex-math></inline-formula> at 1.1 dB SNR, demonstrating enhanced robustness against Doppler-induced impairments in highly dynamic channels.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1849-1860"},"PeriodicalIF":5.3,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11012692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671198","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":"Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation","authors":"Taoyu Xie;Siyao Li","doi":"10.1109/OJVT.2025.3572387","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3572387","url":null,"abstract":"Symbiotic radio (SR) holds promise for large-scale Internet of Things (IoT) networks by allowing both primary and secondary systems to share spectrum resources for mutually beneficial transmission. However, in high-mobility scenarios, rapidly changing channels pose significant challenges for reliable and low-latency communications. To enable SR transmission over high-mobility channels, this paper explores the secondary transmission and equivalent channel estimation problems in intelligent reflecting surface (IRS)-SR systems utilizing orthogonal time frequency space (OTFS) modulation. The IRS is divided into multiple groups, each attached with informative frequencies, for secondary transmission. Exploiting the spectrum structure induced by the secondary information, a deep learning (DL)-based secondary information detector is proposed. Moreover, after transforming the channel estimation problem into a compressed sensing (CS) problem, a DL-sparse Bayesian learning (SBL) network is proposed to improve the estimation accuracy and ease the computational afford by avoiding the large amount of iterations in conventional CS algorithms, e.g. the SBL algorithm. Finally, numerical results are provided to illustrate the superiority of the proposed detector and estimator over several benchmarks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1872-1880"},"PeriodicalIF":5.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671117","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":"A Novel Performance Evaluation Framework for OTFS System","authors":"Zhenduo Wang;Yijin Wang;Yong Yu;Zihang Zhang;Chunpeng Liu","doi":"10.1109/OJVT.2025.3572601","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3572601","url":null,"abstract":"Orthogonal Time Frequency Space (OTFS) systems have been widely investigated to achieve better performances in high-speed mobile scenarios. Bit Error Rate (BER) is always considered as the core indicator to measure communication performance in different conditions. However, the main issues of BER assessments are limited evaluation indicators and long computation time. Therefore, in this paper, we propose a novel communication performance evaluation framework for OTFS systems, which complements and extends traditional communication performance evaluation based on BER, and thus faster evaluation as well as parameter selections can be obtained. Specifically, the proposed evaluation framework is constituted by three main components, which includes indicator selection, weight calculation, and performance ranking. Firstly, waveform parameters, channel information and receiving algorithm of OTFS systems are considered as evaluation characteristics, and thus four key indicators are integrated and extracted for subsequent assessments. Secondly, subjective and objective weighting algorithms are respectively employed to determine the weights of different indicators, and then game theory is considered to obtain a joint weight. The Analytic Hierarchy Process (AHP) is enhanced through cloud model and optimal transfer matrix, to address the issue of multi-expert collaboration and the repeated consistency checks. In addition, the correlation coefficient matrix is applied in the coefficient of variation (CV) to solve the problem of indicator independence. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is selected as the ranking algorithm to judge performances of different schemes, and thus evaluation results can be obtained. In simulation results, eleven different schemes with different configurations are chosen for performance assessments, which demonstrates that the proposed evaluation framework effectively compares communication performances of different schemes. Additionally, the evaluation results can also point out the optimal solution, and thus the optimal parameter selections can be guided.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1837-1848"},"PeriodicalIF":5.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671257","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":"Analysis of the Impact of Rain on Perception in Automated Vehicle Applications","authors":"Tim Brophy;Darragh Mullins;Ashkan Parsi;Jonathan Horgan;Enda Ward;Patrick Denny;Ciarán Eising;Brian Deegan;Martin Glavin;Edward Jones","doi":"10.1109/OJVT.2025.3553718","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3553718","url":null,"abstract":"The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. Visible-spectrum cameras provide a rich source of information and are cost-effective compared with other sensors; however, their performance can degrade under adverse environmental conditions. Despite the general consensus that the object detection performance in computer vision is adversely affected by rain, there is a relative lack of research investigating this relationship in detail. This study investigates the performance of object detection under rain conditions, focusing on algorithm performance and low-level object characteristics. Using the publicly available BDD100 k dataset, this study examines object detection performance across multiple deep-learning object detection architectures, analyzing error types and image characteristics under rain and no rain conditions. In addition, statistical methods were used to compare image-level metrics to determine statistical significance. The results reveal that rain is not detrimental to object detection performance, and in some cases, better performance is observed. For some models, medium-sized objects experience improved detection and classification under rain conditions, while large objects experience a slight decline in performance. The error analysis shows an increase in localization errors and a decrease in classification errors. The object-level analysis revealed statistically significant changes in the contrast-to-noise ratio, entropy, mean pixel value, pixel variance, hue, saturation, and value, with hue and saturation experiencing the most significant changes. This study highlights the need for more detailed weather labeling in datasets to fully understand the nuances of the relationship between rain and object detection.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1018-1032"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856345","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":"Sensor Switching-Based Automatic Misalignment Detection and Correction System for Wireless Power Transfer","authors":"Ali Younis Al Dahhan;Shayok Mukhopadhyay;Mohamed S. Hassan;Ahmed H. Osman","doi":"10.1109/OJVT.2025.3572413","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3572413","url":null,"abstract":"Misalignment between the transmitting and receiving coils is an inevitable problem for electric vehicle (EV) wireless power transfer (WPT) systems. Regardless of the WPT system being static or dynamic, coil misalignment reduces the efficiency of the charging system. This paper, focuses on using a combination of computer vision and one of two different misalignment sensors to detect, and further correct lateral misalignment between the EV receiving (Rx) coil and the segmented transmitting (Tx) coils in a charging lane. The vision-based component uses a camera for lane detection and is primarily responsible for detecting larger deviations and making coarse compensations by estimating the lateral shift of the EV, relative to the center of the charging lane. The sensor-based approach relies on Hall effect sensors or detection coils to detect the misalignment in a smaller range, and perform finer corrections. A one-dimensional (1D) actuator moves the receiving coil to correct the coil misalignment, independent of vehicle movements. The vision-based approach showed a wide detection range for misalignment spanning [<inline-formula><tex-math>$-$</tex-math></inline-formula>15,15] cm, with a correction accuracy of <inline-formula><tex-math>$approx pm$</tex-math></inline-formula>2 cm. This is juxtaposed with the sensor-based approach which operates on a misalignment range of [<inline-formula><tex-math>$-$</tex-math></inline-formula>3,3] cm, but outperforms the vision-based approach with a correction accuracy of less than <inline-formula><tex-math>$pm$</tex-math></inline-formula>1 mm. The proposed sensor switching-based approach combines the advantages of the above individual techniques. An experimental setup is developed and tests are performed to evaluate the proposed approach while transferring 108 W of power wirelessly.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1380-1398"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308217","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}