IEEE Open Journal of Vehicular Technology最新文献

{"title":"2024 Index IEEE Open Journal of Vehicular Technology Vol. 5","authors":"","doi":"10.1109/OJVT.2025.3536782","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3536782","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1766-1790"},"PeriodicalIF":5.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105977","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":"Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments","authors":"Samuel Thornton;Nithin Santhanam;Rajeev Chhajer;Sujit Dey","doi":"10.1109/OJVT.2025.3533368","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3533368","url":null,"abstract":"Vehicular sensing has reached new heights due to advances in external perception systems enabled by the increasing number and type of sensors in vehicles, as well as the availability of on-board computing. These changes have led to improvements in driver safety and have also created a highly heterogeneous environment of vehicles on the road today in terms of sensing and computing. Using collaborative perception, the information obtained by vehicles with sensing capabilities can be expanded and improved, and older vehicles that lack external sensors and computing capabilities can be informed of potential hazards, opening the opportunity to improve traffic efficiency and safety on the roads. However, achieving real-time collaborative perception is a difficult task due to the dynamic availability of vehicular sensing and computing and the highly variable nature of vehicular communications. To address these challenges, we propose a Heterogeneous Adaptive Collaborative Perception (HAdCoP) framework which utilizes a Context-aware Latency Prediction Network (CaLPeN) to intelligently select which vehicles should transmit their sensor data, the specific individual and collaborative perception tasks, and the amount of computational offloading that should be utilized given information about the current state of the environment. Additionally, we propose an Adaptive Perception Frequency (APF) model to determine the optimal end-to-end latency requirement according to the current state of the environment. The proposed CaLPeN model outperforms six implemented comparison models in terms of effective mean average precision (EmAP), beating the next best model's performance by 5.5% on average when tested on the OPV2V perception dataset using two different combinations of wireless communication conditions and vehicular sensor/computing distributions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"471-486"},"PeriodicalIF":5.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388613","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":"Coverage Probability of RIS-Assisted Wireless Communication Systems With Random User Deployment Over Nakagami-$m$ Fading Channel","authors":"Ashraf Al-Rimawi;Faeik T Al Rabee;Arafat Al-Dweik","doi":"10.1109/OJVT.2025.3533081","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3533081","url":null,"abstract":"In beyond 5G (B5G), the higher directivity and attenuation make millimeter-wave (mmWave) very vulnerable to blockages that degrades the system performance. However, reconfigurable intelligent surface (RIS) is considered as a key enabler for B5G applications to avoid the blockages effect. In this paper, to accurately model real-world behavior, we investigate a new analytical framework model for a RIS-aided wireless communication system with a random user deployment over Nakagami-<inline-formula><tex-math>$m$</tex-math></inline-formula> fading channel where the user's position distributes according to random waypoint (RWP) model, to characterize the performance of the system, considering direct and indirect links. As a result, new expressions for end-to-end signal-to-noise ratio (SNR), coverage probability, and ergodic capacity (EC) are derived. The impact of different metrics such as: blockages density (<inline-formula><tex-math>$lambda _{b}$</tex-math></inline-formula>), number of RIS reflecting elements (<inline-formula><tex-math>$N$</tex-math></inline-formula>), fading parameter at the indirect link (<inline-formula><tex-math>$m_{R}$</tex-math></inline-formula>), and path loss parameter (<inline-formula><tex-math>$alpha$</tex-math></inline-formula>) has been studied to evaluate the system performance. The results provide valuable insights into the performance of the system under these metrics. The coverage probability is degraded by increasing the blockage density and path loss parameter as they hinder the signal propagation and limit the signal strength at the MU. For example, at <inline-formula><tex-math>$-10$</tex-math></inline-formula> dB, the coverage probability is degrading from <inline-formula><tex-math>$8times 10^{-2}$</tex-math></inline-formula> for blockage density <inline-formula><tex-math>$lambda _{b}=3$</tex-math></inline-formula> Blockes/<inline-formula><tex-math>$km^{2}$</tex-math></inline-formula> to <inline-formula><tex-math>$5times 10^{-5}$</tex-math></inline-formula> at <inline-formula><tex-math>$lambda _{b}=11$</tex-math></inline-formula> Blockes/<inline-formula><tex-math>$km^{2}$</tex-math></inline-formula>. On the other hand, increasing the number of RIS reflecting elements (<inline-formula><tex-math>$N$</tex-math></inline-formula>) and fading parameter (<inline-formula><tex-math>$m_{R}$</tex-math></inline-formula>) at the indirect link, improves the coverage probability by enhancing the signal strength, reducing the effects of fading, and compensating for environmental challenges such as blockages. For example, the coverage probability, at <inline-formula><tex-math>$-10$</tex-math></inline-formula> dB, increases from <inline-formula><tex-math>$3times 10^{-1}$</tex-math></inline-formula> at number of reflecting elements <inline-formula><tex-math>$N = 15$</tex-math></inline-formula> to <inline-formula><tex-math>$8times 10^{-1}$</tex-math></inline-formula> at <inline-formula><tex-math>$N=40$</tex-math></inline-formula>. As ","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"596-606"},"PeriodicalIF":5.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430595","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":"CDMA/OTFS Sensing Outperforms Pure OTFS at the Same Communication Throughput","authors":"Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo","doi":"10.1109/OJVT.2025.3532848","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3532848","url":null,"abstract":"There is a dearth of publications on the subject of spreading-aided Orthogonal Time Frequency Space (OTFS) solutions, especially for Integrated Sensing and Communication (ISAC), even though Code Division Multiple Access (CDMA) assisted multi-user OTFS (CDMA/OTFS) exhibits tangible benefits. Hence, this work characterises both the communication Bit Error Rate (BER) and sensing Root Mean Square Error (RMSE) performance of Code Division Multiple Access OTFS (CDMA/OTFS), and contrasts them to pure OTFS. Three CDMA/OTFS configurations are considered: Delay Code Division Multiple Access OTFS (Dl-CDMA/OTFS), Doppler Code Division Multiple Access OTFS (Dp-CDMA/OTFS), and Delay Doppler Code Division Multiple Access OTFS (DD-CDMA/OTFS), which harness direct sequence spreading along the delay axis, Doppler axis, and DD domains respectively. For each configuration, the performance of Gold, Hadamard, and Zadoff-Chu sequences is investigated. The results demonstrate that Zadoff-Chu Dl-CDMA/OTFS and DD-CDMA/OTFS consistently outperform pure OTFS sensing, whilst maintaining a similar communication performance at the same throughput. The extra modulation complexity of CDMA/OTFS is similar to that of other OTFS multi-user methodologies, but the demodulation complexity of CDMA/OTFS is lower than that of some other OTFS multi-user methodologies. CDMA/OTFS sensing can also consistently outperform OTFS sensing whilst not requiring any additional complexity for target parameter estimation. Therefore, CDMA/OTFS is an appealing candidate for implementing multi-user OTFS ISAC.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"502-519"},"PeriodicalIF":5.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10849597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388542","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":"Cellular Uplink Impairments in Vehicular Repeater Deployments","authors":"Martin Lerch;Philipp Svoboda;Josef Resch;Markus Rupp","doi":"10.1109/OJVT.2025.3531916","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3531916","url":null,"abstract":"Vehicular repeater systems improve the mobile coverage inside railroad cars by amplifying the signals received by a pick-up antenna on the roof and distributing the amplified signals inside the car. Uplink signals are received accordingly in the cars, amplified and transmitted via the roof antenna. At the same time, amplified noise is also transmitted. In uplink direction, this can lead to impairments of mobile communication in the entire cell. However, in vehicular repeater systems there are other sources of uplink interference that could be mistakenly be interpreted as additive noise. In addition to the influence of additive noise, in this paper we investigate the influence of inter-symbol interference due to direct propagation through the windows, interference due to passive intermodulation that can occur in the indoor antenna, and interference due to limited isolation between the indoor and outdoor antenna. We introduce a pathloss model for a vehicle repeater system. Based on this model, we investigate the influence of these different sources of interference on the uplink experimentally. Depending on the kind of interference, we conduct our investigations over different system parameters, such as the penetration loss of the windows, isolation between the indoor and outdoor antenna, and the gain settings of the repeater. The findings presented in this study provide valuable insights for network operators and researchers, facilitating the design of robust and efficient vehicular repeater systems that enhance connectivity and user experience in cellular wireless networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"487-501"},"PeriodicalIF":5.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10848174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388662","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":"Comparative Analysis of a Low-Voltage CHB Inverter Without PWM and Two-Level IGBT/SiC Inverters for Electric Vehicles on Driving Cycles","authors":"Gaël Pongnot;Anatole Desreveaux;Clément Mayet;Denis Labrousse;Francis Roy","doi":"10.1109/OJVT.2025.3531652","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3531652","url":null,"abstract":"Electric Vehicles (EVs) based on Cascaded H Bridge (CHB) promise reduced consumption and improved modularity, repairability, resilience, and versatility. This study focuses on evaluating the efficiency of CHB inverters utilizing low-voltage Si MOSFETs to improve EV performance and range. Through a comprehensive system-level approach and modeling, a simulation of the CHB-based powertrain is developed and experimentally validated. Electrical and mechanical simulations are conducted separately and finally combined to streamline computation times. Subsequently, CHB-based EV is compared with standard two-level inverters (2LI) across different driving cycles, considering multiple sources of losses from the battery to the road. Despite increased battery losses, CHB proves reduction of consumption during urban driving cycles, making it a compelling choice for sustainable commuter vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"542-553"},"PeriodicalIF":5.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403849","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":"Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion","authors":"Mehmood Nawaz;Sheheryar Khan;Muhammad Daud;Muhammad Asim;Ghazanfar Ali Anwar;Ali Raza Shahid;Ho Pui Aaron HO;Tom Chan;Daniel Pak Kong;Wu Yuan","doi":"10.1109/OJVT.2025.3529495","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3529495","url":null,"abstract":"In autonomous vehicles (AV), sensor fusion methods have proven to be effective in merging data from multiple sensors and enhancing their perception capabilities. In the context of sensor fusion, the distinct strengths of multi-sensors, such as LiDAR, RGB, Thermal sensors, etc., can be leveraged to mitigate the impact of challenges imposed by extreme weather conditions. In this paper, we address multi-sensor fusion in AVs and present a comprehensive integration of a thermal sensor aimed at enhancing the cognitive robustness of AVs. Thermal sensors possess an impressive capability to detect objects and hazards that may be imperceptible to traditional visible light sensors. When integrated with RGB and LiDAR sensors, the thermal sensor becomes highly beneficial for detecting and locating objects in adverse weather conditions. The proposed deep learning-assisted multi-sensor fusion technique consists of two parts: (1) visual information fusion and (2) object detection using LiDAR, RGB, and Thermal sensors. The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. The object detection framework uses the modified version of the YoloV8 model, which exhibits high accuracy in real-time detection. In the YoloV8 model, we adjusted the network architecture to incorporate additional convolutional layers and altered the loss function to enhance detection accuracy in foggy and rainy conditions. The proposed technique is effective and adaptable in challenging conditions, such as night or dark mode, smoke, and heavy rain. The experimental results of the proposed method demonstrate enhanced efficiency and cognitive robustness compared to state-of-the-art fusion and detection techniques. This is evident from tests conducted on two public datasets (FLIR and TarDAL) and one private dataset (CUHK).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"426-441"},"PeriodicalIF":5.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106230","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":"Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates","authors":"Mamady Delamou;Ahmed Naeem;Hüseyin Arslan;El Mehdi Amhoud","doi":"10.1109/OJVT.2025.3530008","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3530008","url":null,"abstract":"Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number of frames in a communication slot are already predefined. However, two facts oblige us to redefine the problem, 1) the environment is often dynamic and uncertain, and 2) mmWave is severely impacted by wireless environments. A striking example where this challenge is very prominent is autonomous vehicle (AV). Such a system leverages integrated sensing and communication (ISAC) using mmWave to manage data transmission and the dynamism of the environment. In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing. This enables the AV to achieve two primary objectives: establishing a stable communication link with other AVs and accurately estimating the velocities of surrounding objects with high resolution. The communication performance is therefore evaluated based on the queue state, the effective data rate, and the discarded packets rate. In contrast, the effectiveness of the sensing is assessed using the velocity resolution. In addition, we exploit adaptive OFDM techniques for dynamic modulation, and we suggest a reward function that leverages the age of updates to handle the communication buffer and improve sensing. The system is validated using advantage actor-critic (A2C) and proximal policy optimization (PPO). Furthermore, we compare our solution with the existing design and demonstrate its superior performance by computer simulations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"455-470"},"PeriodicalIF":5.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184213","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":"Time Complexity of Training DNNs With Parallel Computing for Wireless Communications","authors":"Pengyu Cong;Chenyang Yang;Shengqian Han;Shuangfeng Han;Xiaoyun Wang","doi":"10.1109/OJVT.2025.3526847","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3526847","url":null,"abstract":"Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. While DNNs have demonstrated the ability to reduce the time complexity of inference, their training often incurs a high computational cost. Since practical wireless systems require retraining due to operating in open and dynamic environments, it is crucial to analyze the factors affecting the training complexity, which can guide the DNN architecture selection and the hyper-parameter tuning for efficient policy learning. As a metric of time complexity, the number of floating-point operations (FLOPs) for inference has been analyzed in the literature. However, the time complexity of training DNNs for learning wireless communication policies has only been evaluated in terms of runtime. In this paper, we introduce the number of serial FLOPs (se-FLOPs) as a new metric of time complexity, accounting for the ability of parallel computing. The se-FLOPs metric is consistent with actual runtime, making it suitable for measuring the time complexity of training DNNs. Since graph neural networks (GNNs) can learn a multitude of wireless communication policies efficiently and their architectures depend on specific policies, no universal GNN architecture is available for analyzing complexities across different policies. Thus, we first use precoder learning as an example to demonstrate the derivation of the numbers of se-FLOPs required to train several DNNs. Then, we compare the results with the se-FLOPs for inference of the DNNs and for executing a popular numerical algorithm, and provide the scaling laws of these complexities with respect to the numbers of antennas and users. Finally, we extend the analyses to the learning of general wireless communication policies. We use simulations to validate the analyses and compare the time complexity of each DNN trained for achieving the best learning performance and achieving an expected performance.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"359-384"},"PeriodicalIF":5.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106231","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":"Enabling Super-Resolution for Automotive Imaging Radars in the Presence of Antenna Calibration Errors","authors":"Ionela-Cristina Voicu;Filip Rosu","doi":"10.1109/OJVT.2025.3526133","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3526133","url":null,"abstract":"Radar is an essential technology for Advanced Driving Assistance Systems (ADAS), used to accurately localize objects even in unfavorable weather conditions. Most radar systems that are now being produced for ADAS provide either 3D or 4D point clouds, containing range, Doppler, azimuth, and elevation information for every detected point target. Out of all dimensions, the azimuth and elevation are estimated using more advanced algorithms than the ones generally used for range and Doppler. This is due to the restricted size of the aperture that can be safely mounted on a vehicle, hence the resolution must be enhanced digitally. When using advanced algorithms challenges such as precise antenna manufacturing are of significant importance, to avoid phase and gain mismatch between the antenna elements along with their inherent coupling. These negative effects lead to a significant degradation in the Direction of Arrival estimation. Super-resolution techniques such as MUSIC and CAPON are widely referenced, however their performance throughout prior work is evaluated in ideal environments and generally with multiple available data acquisition snapshots. In this paper we address the issues faced when applying such algorithms in a radar application and offer a solution based on linear prediction and spatial smoothing to enhance the performance of such algorithms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"385-395"},"PeriodicalIF":5.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106232","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}