Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646562
Hongbin Zhu, Haifeng Wang, Xiliang Luo, H. Qian
Fog computing extends cloud computing and services to the edge of networks, bringing advantages of the cloud closer to where data is created and acted upon. To support real time applications, latency performance is a crucial metric in fog computing. In this paper, we consider a sequential decision-making problem for computation offloading with unknown dynamics in which a mobile user offloads its arrival tasks to associated fog nodes (FNs) at each time slot. The queue of arrival tasks at each FN is modeled as a Markov chain. In order to provide satisfactory quality of experience, the network latency, which is directly associated with the queue condition, needs to be minimized. Taking advantage of reinforcement learning, the sequential decision-making problem is formulated as a restless multi-armed bandit problem. We construct a policy with interleaved exploration and exploitation stages, which achieves a regret with sub-linear order. Both analytical and simulation results validate the effectiveness of the proposed method in dealing with sequential decision-making problem.
{"title":"AN ONLINE LEARNING APPROACH TO WIRELESS COMPUTATION OFFLOADING","authors":"Hongbin Zhu, Haifeng Wang, Xiliang Luo, H. Qian","doi":"10.1109/GlobalSIP.2018.8646562","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646562","url":null,"abstract":"Fog computing extends cloud computing and services to the edge of networks, bringing advantages of the cloud closer to where data is created and acted upon. To support real time applications, latency performance is a crucial metric in fog computing. In this paper, we consider a sequential decision-making problem for computation offloading with unknown dynamics in which a mobile user offloads its arrival tasks to associated fog nodes (FNs) at each time slot. The queue of arrival tasks at each FN is modeled as a Markov chain. In order to provide satisfactory quality of experience, the network latency, which is directly associated with the queue condition, needs to be minimized. Taking advantage of reinforcement learning, the sequential decision-making problem is formulated as a restless multi-armed bandit problem. We construct a policy with interleaved exploration and exploitation stages, which achieves a regret with sub-linear order. Both analytical and simulation results validate the effectiveness of the proposed method in dealing with sequential decision-making problem.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131091923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646534
Harry Sevi, G. Rilling, P. Borgnat
In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.
{"title":"MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING","authors":"Harry Sevi, G. Rilling, P. Borgnat","doi":"10.1109/GLOBALSIP.2018.8646534","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646534","url":null,"abstract":"In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129345142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646378
Ekhi Uranga, Á. Llorente, A. D. L. Fuente
The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission operates in the 1400-1427 MHz frequency band, which is allocated to the EESS (passive) service in the ITU Radio-Regulations. The measurements of SMOS radiometer are perturbed by radio frequency interference (RFI) that jeopardize part of its scientific retrieval in certain areas of the World.The strategies initiated by the European Space Agency to mitigate the impact of RFI includes the detection, monitoring, and reporting of the interference cases. Due to the large number of sources detected, their temporal variability, and the fluid contacts with some National Administrations, it was necessary to automate the RFI mitigation process.This paper presents the database created for the classification of the RFI sources and their details, including a website for queries and reports using the stored data. In addition, the algorithms developed to automate the detections that populate the database are explained.
{"title":"DATABASE OF SMOS RFI SOURCES IN THE 1400-1427MHZ PASSIVE BAND","authors":"Ekhi Uranga, Á. Llorente, A. D. L. Fuente","doi":"10.1109/GlobalSIP.2018.8646378","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646378","url":null,"abstract":"The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission operates in the 1400-1427 MHz frequency band, which is allocated to the EESS (passive) service in the ITU Radio-Regulations. The measurements of SMOS radiometer are perturbed by radio frequency interference (RFI) that jeopardize part of its scientific retrieval in certain areas of the World.The strategies initiated by the European Space Agency to mitigate the impact of RFI includes the detection, monitoring, and reporting of the interference cases. Due to the large number of sources detected, their temporal variability, and the fluid contacts with some National Administrations, it was necessary to automate the RFI mitigation process.This paper presents the database created for the classification of the RFI sources and their details, including a website for queries and reports using the stored data. In addition, the algorithms developed to automate the detections that populate the database are explained.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124642042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646372
Sylvain Cluzel, M. Dervin, J. Radzik, Sonia Cazalens, C. Baudoin, D. Dragomirescu
One of the main issues in using a Low Earth Orbit (LEO) satellite constellation to extend a Low-Powered Wide Area Network is the frequency synchronization. Using a link based on random access solves this concern, but also prevents delivery guarantees, and implies less predictable performance. This paper concerns the estimation of Bit Error Rate (BER) and Packet Error Rate (PER) using physical layer abstractions under a time and frequency random scheme, namely Time and Frequency Aloha. We first derive a BER calculation for noncoded QPSK transmission with one collision. Then, we use the 3GPP LTE NB-IoT coding scheme. We analyze the interference that could be induced by repetition coding scheme and propose an efficient summation to improve the decoder performance. Finally, to estimate a PER for any collided scenario, we propose a physical layer abstraction, which relies on an equivalent Signal-to-Noise Ratio (SNR) calculation based on Mutual Information.
利用低地球轨道(LEO)卫星星座扩展低功率广域网的主要问题之一是频率同步。使用基于随机访问的链接解决了这个问题，但也阻止了交付保证，并且意味着更不可预测的性能。本文研究了在时间和频率随机方案(time and frequency Aloha)下，利用物理层抽象来估计误码率(BER)和包错误率(PER)。我们首先推导了具有一次碰撞的非编码QPSK传输的误码率计算。然后，我们使用3GPP LTE NB-IoT编码方案。我们分析了重复编码方案可能引起的干扰，并提出了一种有效的求和方法来提高解码器的性能。最后，为了估计任何碰撞场景的PER，我们提出了一种物理层抽象，它依赖于基于互信息的等效信噪比(SNR)计算。
{"title":"PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEMS FOR IOT USING TIME-FREQUENCY ALOHA SCHEME","authors":"Sylvain Cluzel, M. Dervin, J. Radzik, Sonia Cazalens, C. Baudoin, D. Dragomirescu","doi":"10.1109/GLOBALSIP.2018.8646372","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646372","url":null,"abstract":"One of the main issues in using a Low Earth Orbit (LEO) satellite constellation to extend a Low-Powered Wide Area Network is the frequency synchronization. Using a link based on random access solves this concern, but also prevents delivery guarantees, and implies less predictable performance. This paper concerns the estimation of Bit Error Rate (BER) and Packet Error Rate (PER) using physical layer abstractions under a time and frequency random scheme, namely Time and Frequency Aloha. We first derive a BER calculation for noncoded QPSK transmission with one collision. Then, we use the 3GPP LTE NB-IoT coding scheme. We analyze the interference that could be induced by repetition coding scheme and propose an efficient summation to improve the decoder performance. Finally, to estimate a PER for any collided scenario, we propose a physical layer abstraction, which relies on an equivalent Signal-to-Noise Ratio (SNR) calculation based on Mutual Information.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125605759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/globalsip.2018.8646458
{"title":"GlobalSIP 2018 Committees","authors":"","doi":"10.1109/globalsip.2018.8646458","DOIUrl":"https://doi.org/10.1109/globalsip.2018.8646458","url":null,"abstract":"","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125915028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646691
A. Koochakzadeh, P. Pal
This paper considers canonical polyadic (CP) decomposition of symmetric even order tensors. In earlier work, we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank exceeds the dimensions of the tensor (overcomplete tensors).
{"title":"Simplified Algorithms for Canonical Polyadic Decomposition for Over-Complete Even Order Tensors (Ongoing Work)","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/GlobalSIP.2018.8646691","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646691","url":null,"abstract":"This paper considers canonical polyadic (CP) decomposition of symmetric even order tensors. In earlier work, we showed that decomposition of such tensors is equivalent to solving a system of quadratic equations. As part of ongoing work, we further show that for almost all tensors, singular value decomposition of a certain matrix can uniquely obtain the solution to the system of quadratic equations. Our proposed algorithm is able to find the CP-decomposition, even in the regime where the CP-rank exceeds the dimensions of the tensor (overcomplete tensors).","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646419
J. Baur, G. Dobler, F. Bianco, Mohit S. Sharma, A. Karpf
We present the persistent hyperspectral imaging of the New York City urban lightscape, with ~ 7.2 ×10−4 μm spectral resolution, surveyed over 25 consecutive summer nights over a 6 minute time resolution. We train a supervised classifier to automatically determine the location of light sources in each hyperspectral image. This work issues the first urban lightscape combined hyperspectral - multitemporal survey of its kind.
{"title":"Persistent Hyperspectral Observations of the Urban Lightscape","authors":"J. Baur, G. Dobler, F. Bianco, Mohit S. Sharma, A. Karpf","doi":"10.1109/GlobalSIP.2018.8646419","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646419","url":null,"abstract":"We present the persistent hyperspectral imaging of the New York City urban lightscape, with ~ 7.2 ×10−4 μm spectral resolution, surveyed over 25 consecutive summer nights over a 6 minute time resolution. We train a supervised classifier to automatically determine the location of light sources in each hyperspectral image. This work issues the first urban lightscape combined hyperspectral - multitemporal survey of its kind.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127607737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646500
Xueyuan Wang, M. C. Gursoy
In this paper, we consider simultaneous wireless information and power transfer in millimeter wave (mmWave) cellular networks with user-centric base station deployments. The distinguishing features of mmWave communications are incorporated into the system model. Moreover, the locations of user equipments (UEs) are modeled as a Thomas cluster process. First, the association probability is investigated. Subsequently, using tools from stochastic geometry, we analyze the energy coverage and signal-to-interference-plus-noise ratio (SINR) coverage of the network and provide general expressions. Through numerical results, we draw insights on how to model the system to improve the coverage performance.
{"title":"JOINT ENERGY AND SINR COVERAGE IN ENERGY HARVESTING MMWAVE CELLULAR NETWORKS WITH USER-CENTRIC BASE STATION DEPLOYMENTS","authors":"Xueyuan Wang, M. C. Gursoy","doi":"10.1109/GlobalSIP.2018.8646500","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646500","url":null,"abstract":"In this paper, we consider simultaneous wireless information and power transfer in millimeter wave (mmWave) cellular networks with user-centric base station deployments. The distinguishing features of mmWave communications are incorporated into the system model. Moreover, the locations of user equipments (UEs) are modeled as a Thomas cluster process. First, the association probability is investigated. Subsequently, using tools from stochastic geometry, we analyze the energy coverage and signal-to-interference-plus-noise ratio (SINR) coverage of the network and provide general expressions. Through numerical results, we draw insights on how to model the system to improve the coverage performance.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646367
Yantao Lu, Senem Velipasalar
Many methods have been proposed for human activity classification, which rely either on Inertial Measurement Unit (IMU) data or data from static cameras watching subjects. There have been relatively less work using egocentric videos, and even fewer approaches combining egocentric video and IMU data. Systems relying only on IMU data are limited in the complexity of the activities that they can detect. In this paper, we present a robust and autonomous method, for fine-grained activity classification, that leverages data from multiple wearable sensor modalities to differentiate between activities, which are similar in nature, with a level of accuracy that would be impossible by each sensor alone. We use both egocentric videos and IMU sensors on the body. We employ Capsule Networks together with Convolutional Long Short Term Memory (LSTM) to analyze egocentric videos, and an LSTM framework to analyze IMU data, and capture temporal aspect of actions. We performed experiments on the CMU-MMAC dataset achieving overall recall and precision rates of 85.8% and 86.2%, respectively. We also present results of using each sensor modality alone, which show that the proposed approach provides 19.47% and 39.34% increase in accuracy compared to using only ego-vision data and only IMU data, respectively.
{"title":"HUMAN ACTIVITY CLASSIFICATION INCORPORATING EGOCENTRIC VIDEO AND INERTIAL MEASUREMENT UNIT DATA","authors":"Yantao Lu, Senem Velipasalar","doi":"10.1109/GlobalSIP.2018.8646367","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646367","url":null,"abstract":"Many methods have been proposed for human activity classification, which rely either on Inertial Measurement Unit (IMU) data or data from static cameras watching subjects. There have been relatively less work using egocentric videos, and even fewer approaches combining egocentric video and IMU data. Systems relying only on IMU data are limited in the complexity of the activities that they can detect. In this paper, we present a robust and autonomous method, for fine-grained activity classification, that leverages data from multiple wearable sensor modalities to differentiate between activities, which are similar in nature, with a level of accuracy that would be impossible by each sensor alone. We use both egocentric videos and IMU sensors on the body. We employ Capsule Networks together with Convolutional Long Short Term Memory (LSTM) to analyze egocentric videos, and an LSTM framework to analyze IMU data, and capture temporal aspect of actions. We performed experiments on the CMU-MMAC dataset achieving overall recall and precision rates of 85.8% and 86.2%, respectively. We also present results of using each sensor modality alone, which show that the proposed approach provides 19.47% and 39.34% increase in accuracy compared to using only ego-vision data and only IMU data, respectively.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127115042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646433
Xiaoyu Zhang, Xuanfeng Li, Yong Zhou, H. Qian, Xiliang Luo
To tackle the uplink pilot contamination problem in massive multiple-input multiple-output (MIMO) systems, current researches only relied on the angle of arrival at the base station. However, this information is insufficient when the users share the same scattering environment. In this paper, we propose a novel strategy by exploiting the user mobility. Due to limited scatterers around the users, we first investigate the channel sparsity and derive the corresponding angle-Doppler frequency domain channel power spectrum (AD-CPS). We then propose a method to mitigate the pilot contamination through aligning the AD-CPSs. Compared with the existing works, we further demonstrate the effectiveness of the proposed scheme in supporting more orthogonal pilots when the interfering users exhibit different moving patterns. Simulations verify the superior performance and show that the proposed scheme can serve as an additional decontamination mechanism for the UL pilots in massive MIMO systems.
{"title":"HOW TO EXPLOIT MOBILITY TO MITIGATE PILOT CONTAMINATION?","authors":"Xiaoyu Zhang, Xuanfeng Li, Yong Zhou, H. Qian, Xiliang Luo","doi":"10.1109/GlobalSIP.2018.8646433","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646433","url":null,"abstract":"To tackle the uplink pilot contamination problem in massive multiple-input multiple-output (MIMO) systems, current researches only relied on the angle of arrival at the base station. However, this information is insufficient when the users share the same scattering environment. In this paper, we propose a novel strategy by exploiting the user mobility. Due to limited scatterers around the users, we first investigate the channel sparsity and derive the corresponding angle-Doppler frequency domain channel power spectrum (AD-CPS). We then propose a method to mitigate the pilot contamination through aligning the AD-CPSs. Compared with the existing works, we further demonstrate the effectiveness of the proposed scheme in supporting more orthogonal pilots when the interfering users exhibit different moving patterns. Simulations verify the superior performance and show that the proposed scheme can serve as an additional decontamination mechanism for the UL pilots in massive MIMO systems.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127606314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：