Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104300
Jun Tao, Fengzhong Qu, Hongta Zhang
For single-carrier underwater acoustic (UWA) communications, phase correction is critical to the symbol detection on the receiver side. Existing receiver schemes either run a phase- locked loop (PLL) in parallel with an equalizer or perform the phase correction at the output of an equalizer. Both parallel and serial phase correction methods suffer limitations in practical use though. In this work, we propose to introduce a carrier frequency offset (CFO) pre-compensation module for existing receivers, with the CFO estimated with an m-sequence. The so-obtained receiver scheme was tested by real data collected in an at-sea UWA communication trial. Experimental results verified the extra performance gain brought by the CFO precompensation. In particular, when the CFO is the main source of phase rotation, conventional CFO correction modules like the PLL can be dropped without performance degradation.
{"title":"Direct Adaptive Equalization with CFO Pre-compensation for Single-Carrier Underwater Acoustic Communications","authors":"Jun Tao, Fengzhong Qu, Hongta Zhang","doi":"10.1109/SAM48682.2020.9104300","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104300","url":null,"abstract":"For single-carrier underwater acoustic (UWA) communications, phase correction is critical to the symbol detection on the receiver side. Existing receiver schemes either run a phase- locked loop (PLL) in parallel with an equalizer or perform the phase correction at the output of an equalizer. Both parallel and serial phase correction methods suffer limitations in practical use though. In this work, we propose to introduce a carrier frequency offset (CFO) pre-compensation module for existing receivers, with the CFO estimated with an m-sequence. The so-obtained receiver scheme was tested by real data collected in an at-sea UWA communication trial. Experimental results verified the extra performance gain brought by the CFO precompensation. In particular, when the CFO is the main source of phase rotation, conventional CFO correction modules like the PLL can be dropped without performance degradation.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"33 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74744197","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104310
Hua Chen, Yonghong Liu, Qing Wang, Wei Liu, Zongju Peng, Gang Wang
In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property of the signal is utilized to establish an extended generalized array manifold (GAM) model based on the first-order Taylor series approximation. Then, the central DOA of source signals is obtained based on the generalized shift invariance property of the array manifold and the reduced-rank principle. Compared with the algorithm without exploiting the noncircularity information, the proposed algorithm can achieve a higher accuracy and handle more sources. Simulation results are provided to demonstrate the performance of the proposed algorithm.
{"title":"A general ESPRIT method for noncircularity-based incoherently distributed sources","authors":"Hua Chen, Yonghong Liu, Qing Wang, Wei Liu, Zongju Peng, Gang Wang","doi":"10.1109/SAM48682.2020.9104310","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104310","url":null,"abstract":"In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property of the signal is utilized to establish an extended generalized array manifold (GAM) model based on the first-order Taylor series approximation. Then, the central DOA of source signals is obtained based on the generalized shift invariance property of the array manifold and the reduced-rank principle. Compared with the algorithm without exploiting the noncircularity information, the proposed algorithm can achieve a higher accuracy and handle more sources. Simulation results are provided to demonstrate the performance of the proposed algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74818499","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104339
Weize Sun, Chuanshan Xu, Yingying Huang, Lei Huang
This paper address the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals based on uniform linear array with malfunctioning sensors. By utilizing the subspace structures of the local second-order statistics of quasi-stationary signals, a Khatri-Rao subspace approach is developed. Our scheme first collects the local covariance matrices of the source signals and then transfers them into a new virtual linear array which can identify at least twice as much DOAs as to the original physical one. It is also shown that the coprime configuration is a special case of the proposed model therefore the same techniques can be applied directly. Simulations are also carried out for the comparison of the proposed algorithm and state-of-the-art approaches.
{"title":"Underdetermined DOA Estimation of Quasi-Stationary Signals in the Presence of Malfunctioning Sensors","authors":"Weize Sun, Chuanshan Xu, Yingying Huang, Lei Huang","doi":"10.1109/SAM48682.2020.9104339","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104339","url":null,"abstract":"This paper address the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals based on uniform linear array with malfunctioning sensors. By utilizing the subspace structures of the local second-order statistics of quasi-stationary signals, a Khatri-Rao subspace approach is developed. Our scheme first collects the local covariance matrices of the source signals and then transfers them into a new virtual linear array which can identify at least twice as much DOAs as to the original physical one. It is also shown that the coprime configuration is a special case of the proposed model therefore the same techniques can be applied directly. Simulations are also carried out for the comparison of the proposed algorithm and state-of-the-art approaches.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"7 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78365934","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104264
Chenyu Wu, Yangyang Xu
The coordinate descent (CD) method has recently become popular for solving very large-scale problems, partly due to its simple update, low memory requirement, and fast convergence. In this paper, we explore the greedy CD on solving non-negative quadratic programming (NQP). The greedy CD generally has much more expensive per-update complexity than its cyclic and randomized counterparts. However, on the NQP, these three CDs have almost the same per-update cost, while the greedy CD can have significantly faster overall convergence speed. We also apply the proposed greedy CD as a subroutine to solve linearly constrained NQP and the non-negative matrix factorization. Promising numerical results on both problems are observed on instances with synthetic data and also image data.
{"title":"Greedy coordinate descent method on non-negative quadratic programming","authors":"Chenyu Wu, Yangyang Xu","doi":"10.1109/SAM48682.2020.9104264","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104264","url":null,"abstract":"The coordinate descent (CD) method has recently become popular for solving very large-scale problems, partly due to its simple update, low memory requirement, and fast convergence. In this paper, we explore the greedy CD on solving non-negative quadratic programming (NQP). The greedy CD generally has much more expensive per-update complexity than its cyclic and randomized counterparts. However, on the NQP, these three CDs have almost the same per-update cost, while the greedy CD can have significantly faster overall convergence speed. We also apply the proposed greedy CD as a subroutine to solve linearly constrained NQP and the non-negative matrix factorization. Promising numerical results on both problems are observed on instances with synthetic data and also image data.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76973856","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}
For the coexistent radar and communications system, the radar can receive the communications-path signal, i.e., the target return due to the transmission from the communications. The communications-path signal can be exploited to improve the radar detection performance when it has high signal to noise ratio (SNR). To show this, the statistical signal model at the radar receiver is developed, and then the generalized likelihood ratio test (GLRT) for the coexistent radar and communications system is derived in this paper. The derived GLRT has constant false alarm rate (CFAR). Finally, several numerical examples are presented to verify the performance gain by exploiting the communications-path signal with high SNR.
{"title":"Performance Improvement in a Coexistent Radar and Communications System*","authors":"Yongjun Liu, G. Liao, Shengqi Zhu, Zhiwei Yang, Yufeng Chen, Xiaowen Zhang","doi":"10.1109/SAM48682.2020.9104303","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104303","url":null,"abstract":"For the coexistent radar and communications system, the radar can receive the communications-path signal, i.e., the target return due to the transmission from the communications. The communications-path signal can be exploited to improve the radar detection performance when it has high signal to noise ratio (SNR). To show this, the statistical signal model at the radar receiver is developed, and then the generalized likelihood ratio test (GLRT) for the coexistent radar and communications system is derived in this paper. The derived GLRT has constant false alarm rate (CFAR). Finally, several numerical examples are presented to verify the performance gain by exploiting the communications-path signal with high SNR.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"638 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82984236","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104254
Qingyuan Zhao, Xin Du, Yao-bing Lu
In this paper, we applied the idea of deep learning to aircraft targets recognition based on time-frequency diagram. Firstly we introduced application of Convolutional Neural Network (CNN), and the methods of radar target recognition. Secondly, Short Time Fourier Transformation (STFT) was introduced. Thirdly, the structure of improved LeNet CNN was described, considering the character of radar echo wave. Fourthly, 4 kinds of aircraft targets were introduced. Then, the algorithm based on CNN and STFT was validated based on measured data, and was compared with Support Vector Machine (SVM). The accuracy rate could reaches up to 99.98%, 25% higher than SVM. Finally, we summarized advantages of the method proposed in this paper and give the suggestion in engineering application.
{"title":"Aircraft Target Classification Based on CNN","authors":"Qingyuan Zhao, Xin Du, Yao-bing Lu","doi":"10.1109/SAM48682.2020.9104254","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104254","url":null,"abstract":"In this paper, we applied the idea of deep learning to aircraft targets recognition based on time-frequency diagram. Firstly we introduced application of Convolutional Neural Network (CNN), and the methods of radar target recognition. Secondly, Short Time Fourier Transformation (STFT) was introduced. Thirdly, the structure of improved LeNet CNN was described, considering the character of radar echo wave. Fourthly, 4 kinds of aircraft targets were introduced. Then, the algorithm based on CNN and STFT was validated based on measured data, and was compared with Support Vector Machine (SVM). The accuracy rate could reaches up to 99.98%, 25% higher than SVM. Finally, we summarized advantages of the method proposed in this paper and give the suggestion in engineering application.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"44 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91393335","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104291
Jinqing Shen, Jianfeng Li, Beizuo Zhu, Changbo Ye
Generally, nested array (NA) is susceptible to mutual coupling due to the dense subarray, which seriously degrades the performance. To address this issue, we design an array switching-based scheme to achieve the blind direction of arrival (DOA) and mutual coupling estimation in this paper. Specifically, by exploiting the inherent sparse structural characteristics of NA, we first switch the sparse subarray on to perform initial DOA estimation, which enables to offer the well-performed estimates free from the severe mutual coupling effect. Subsequently, the unambiguous angles are determined with low complexity by utilizing the received signal of the whole NA. Furthermore, the contaminated steering vector is reconstructed and a quadratic optimization problem is established to estimate the mutual coupling coefficients. Finally, re-estimation is conducted to obtain the refined estimates. Numerical simulations demonstrate the superiority of the proposed scheme.
{"title":"A Blind Direction of Arrival and Mutual Coupling Estimation Scheme for Nested Array","authors":"Jinqing Shen, Jianfeng Li, Beizuo Zhu, Changbo Ye","doi":"10.1109/SAM48682.2020.9104291","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104291","url":null,"abstract":"Generally, nested array (NA) is susceptible to mutual coupling due to the dense subarray, which seriously degrades the performance. To address this issue, we design an array switching-based scheme to achieve the blind direction of arrival (DOA) and mutual coupling estimation in this paper. Specifically, by exploiting the inherent sparse structural characteristics of NA, we first switch the sparse subarray on to perform initial DOA estimation, which enables to offer the well-performed estimates free from the severe mutual coupling effect. Subsequently, the unambiguous angles are determined with low complexity by utilizing the received signal of the whole NA. Furthermore, the contaminated steering vector is reconstructed and a quadratic optimization problem is established to estimate the mutual coupling coefficients. Finally, re-estimation is conducted to obtain the refined estimates. Numerical simulations demonstrate the superiority of the proposed scheme.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"19 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84227458","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104284
M. S. Ibrahim, N. Sidiropoulos
Reliable detection and accurate estimation of weak targets and their Doppler frequencies is a challenging problem in MIMO radar systems. Reflections from such targets are often overpowered by those from stronger nearby targets and clutter. Considering a 3-D data model where the coherent processing interval comprises multiple pulses, a novel weak target detection and estimation approach is proposed in this paper. The proposed method is based on creating partially overlapping spatial beams, and performing canonical correlation analysis (CCA) in the resulting beamspace. It is shown that if a target is present in the overlap sector, then its Doppler profile can be reliably estimated via beamspace CCA, even if hidden under much stronger interference from nearby targets and clutter. Numerical results are included to validate this theoretical claim, demonstrating that the proposed Beamspace Canonical Correlation (BCC) method yields considerable performance improvement over existing approaches.
{"title":"Weak Target Detection in MIMO Radar via Beamspace Canonical Correlation","authors":"M. S. Ibrahim, N. Sidiropoulos","doi":"10.1109/SAM48682.2020.9104284","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104284","url":null,"abstract":"Reliable detection and accurate estimation of weak targets and their Doppler frequencies is a challenging problem in MIMO radar systems. Reflections from such targets are often overpowered by those from stronger nearby targets and clutter. Considering a 3-D data model where the coherent processing interval comprises multiple pulses, a novel weak target detection and estimation approach is proposed in this paper. The proposed method is based on creating partially overlapping spatial beams, and performing canonical correlation analysis (CCA) in the resulting beamspace. It is shown that if a target is present in the overlap sector, then its Doppler profile can be reliably estimated via beamspace CCA, even if hidden under much stronger interference from nearby targets and clutter. Numerical results are included to validate this theoretical claim, demonstrating that the proposed Beamspace Canonical Correlation (BCC) method yields considerable performance improvement over existing approaches.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"62 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82065440","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104374
Sudan Han, L. Pallotta, G. Giunta, Wanli Ma, D. Orlando
This paper devises a tunable detection architecture to deal with mismatched signals embedded in Gaussian interference with unknown covariance matrix based on a sparse recovery technique. Specifically, a sparse learning method is exploited to estimate the amplitude and angle of arrival of the possible targets, which are then employed to design detectors relying on the two-stage detection paradigm. Remarkably, the new decision scheme exhibits a bounded-constant false alarm rate property. The performance assessment, carried out through Monte Carlo simulations, shows that the new detectors can outperform classic counterparts in terms of rejecting mismatched signals, while retaining reasonable detection performance for matched signals.
{"title":"A Sparse Learning Based Detector with Enhanced Mismatched Signals Rejection Capabilities","authors":"Sudan Han, L. Pallotta, G. Giunta, Wanli Ma, D. Orlando","doi":"10.1109/SAM48682.2020.9104374","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104374","url":null,"abstract":"This paper devises a tunable detection architecture to deal with mismatched signals embedded in Gaussian interference with unknown covariance matrix based on a sparse recovery technique. Specifically, a sparse learning method is exploited to estimate the amplitude and angle of arrival of the possible targets, which are then employed to design detectors relying on the two-stage detection paradigm. Remarkably, the new decision scheme exhibits a bounded-constant false alarm rate property. The performance assessment, carried out through Monte Carlo simulations, shows that the new detectors can outperform classic counterparts in terms of rejecting mismatched signals, while retaining reasonable detection performance for matched signals.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"55 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84463412","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104391
Chen Huang, Hongqing Liu, Lu Gan, Zhen Luo, Yi Zhou
This work studies the effects of different waveform designs on the clutter removal and target localization in through-the-wall radar (TWR) system. To removal the wall clutter, its low-rank property is utilized, whereas the sparse property of the target returns is exploited to perform target reconstruction. As a result, a joint low-rank and sparse model is developed where alternating direction method of multipliers (ADMM) is utilized to solve the corresponding optimization. Moreover, to demonstrate the performances of different waveforms, three well-known signals including mono-frequency, stepped-frequency, and frequency-modulated continuous-wave (FMCW) waveforms have been selected for transmit. The experimental results show advantages and disadvantages of each waveform.
{"title":"Signal waveform design for high resolution target localization in through-the-wall radar","authors":"Chen Huang, Hongqing Liu, Lu Gan, Zhen Luo, Yi Zhou","doi":"10.1109/SAM48682.2020.9104391","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104391","url":null,"abstract":"This work studies the effects of different waveform designs on the clutter removal and target localization in through-the-wall radar (TWR) system. To removal the wall clutter, its low-rank property is utilized, whereas the sparse property of the target returns is exploited to perform target reconstruction. As a result, a joint low-rank and sparse model is developed where alternating direction method of multipliers (ADMM) is utilized to solve the corresponding optimization. Moreover, to demonstrate the performances of different waveforms, three well-known signals including mono-frequency, stepped-frequency, and frequency-modulated continuous-wave (FMCW) waveforms have been selected for transmit. The experimental results show advantages and disadvantages of each waveform.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"7 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83629362","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}
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