Calibrating roadside camera is essential and indispensable for intelligent traffic surveillance systems. Due to the characteristics of the traffic scenes, the traditional camera calibration methods based on calibration patterns are no longer suitable, since there are generally no calibration patterns (e.g. checkerboard) in traffic scenes. In this paper, we propose a simple and practical calibration method for roadside camera, where the vanishing point in the traffic road direction and the vertical vanishing point are employed that can be easily obtained from most traffic scenes. By making full use of video information, the multiple observations of two vanishing points are available. In order to obtain more accurate calibration results, we present a dynamic calibration method that employs these observations to correct camera parameters and substitutes least squares optimization for closed-form computation. The experimental results on real traffic images demonstrate the effectiveness and practicability of the proposed calibration method.
{"title":"An efficient and practical calibration method for roadside camera using two vanishing points","authors":"Yuan Zheng, Zhenyu He, Wei-Guo Yang, Xiaofeng Zhang","doi":"10.1109/SPAC.2014.6982658","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982658","url":null,"abstract":"Calibrating roadside camera is essential and indispensable for intelligent traffic surveillance systems. Due to the characteristics of the traffic scenes, the traditional camera calibration methods based on calibration patterns are no longer suitable, since there are generally no calibration patterns (e.g. checkerboard) in traffic scenes. In this paper, we propose a simple and practical calibration method for roadside camera, where the vanishing point in the traffic road direction and the vertical vanishing point are employed that can be easily obtained from most traffic scenes. By making full use of video information, the multiple observations of two vanishing points are available. In order to obtain more accurate calibration results, we present a dynamic calibration method that employs these observations to correct camera parameters and substitutes least squares optimization for closed-form computation. The experimental results on real traffic images demonstrate the effectiveness and practicability of the proposed calibration method.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127113585","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982675
Xu Dahua, Wang Jun
The signal-acquisition-control-modules of existing well-logging tools have poor universality, low integration and poor stability. A general-purpose signal-acquisition-control module for versatile well-logging tool is designed which is based on SM320F28335 DSP chip and A3P250 FPGA chip. The composition of the hardware circuit is given. The acquisition on analog signals, pulse signals, as well as waves can be completed by this module, which can control both relay and analog switch. The module can provide versatile bus interfaces. On different well-logging tools, no change is needed for the hardware circuit, what's only needed is to download the corresponding procedure. Tests show that this module works stably and has high performance and low failure rate, which shows that it is suitable for the mal-condition of high temperature and pressure underground.
{"title":"Design of general-purpose acquisition-control module for well-logging signal based on DSP and FPGA","authors":"Xu Dahua, Wang Jun","doi":"10.1109/SPAC.2014.6982675","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982675","url":null,"abstract":"The signal-acquisition-control-modules of existing well-logging tools have poor universality, low integration and poor stability. A general-purpose signal-acquisition-control module for versatile well-logging tool is designed which is based on SM320F28335 DSP chip and A3P250 FPGA chip. The composition of the hardware circuit is given. The acquisition on analog signals, pulse signals, as well as waves can be completed by this module, which can control both relay and analog switch. The module can provide versatile bus interfaces. On different well-logging tools, no change is needed for the hardware circuit, what's only needed is to download the corresponding procedure. Tests show that this module works stably and has high performance and low failure rate, which shows that it is suitable for the mal-condition of high temperature and pressure underground.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128777936","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982694
Hualei Shen, Yongwang Zhao, Dian-fu Ma, Yong Guan
Multiple features have been employed for content-based medical image retrieval. To reduce curse of dimensionality, subspace learning techniques have been applied to learn a low-dimensional subspace from multiple features. Most of the existing methods have two drawbacks: first, they ignore the fact that multiple features have complementary properties, and thus have different contributions to construct the final subspace; second, they construct the optimal subspace without considering user's query preference, i.e., for a same query example, different users want different query results. In this paper, we propose a new method termed Query Dependent Multiview Features Fusion (QDMFF) for content-based medical image retrieval. Inspired by ideas of multiview subspace learning and relevance feedback, QDMFF iteratively learns an optimal subspace by fusing multiple features obtained from user feedback examples. The method operates in the following four stages: first, in local patch construction, local patch is constructed for each feedback example in different feature space; second, in patches combination, all patches within different feature spaces are assigned different weights and unified as a whole one; third, in linear approximation, the projection between original high dimensional feature spaces and the final low-dimensional subspace is approximated by a linear projection; finally, in alternating optimization, the alternating optimization trick is utilized to solve the optimal subspace. Experimental results on IRMA medical image data set demonstrate the effectiveness of QDMFF.
{"title":"Query dependent multiview features fusion for effective medical image retrieval","authors":"Hualei Shen, Yongwang Zhao, Dian-fu Ma, Yong Guan","doi":"10.1109/SPAC.2014.6982694","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982694","url":null,"abstract":"Multiple features have been employed for content-based medical image retrieval. To reduce curse of dimensionality, subspace learning techniques have been applied to learn a low-dimensional subspace from multiple features. Most of the existing methods have two drawbacks: first, they ignore the fact that multiple features have complementary properties, and thus have different contributions to construct the final subspace; second, they construct the optimal subspace without considering user's query preference, i.e., for a same query example, different users want different query results. In this paper, we propose a new method termed Query Dependent Multiview Features Fusion (QDMFF) for content-based medical image retrieval. Inspired by ideas of multiview subspace learning and relevance feedback, QDMFF iteratively learns an optimal subspace by fusing multiple features obtained from user feedback examples. The method operates in the following four stages: first, in local patch construction, local patch is constructed for each feedback example in different feature space; second, in patches combination, all patches within different feature spaces are assigned different weights and unified as a whole one; third, in linear approximation, the projection between original high dimensional feature spaces and the final low-dimensional subspace is approximated by a linear projection; finally, in alternating optimization, the alternating optimization trick is utilized to solve the optimal subspace. Experimental results on IRMA medical image data set demonstrate the effectiveness of QDMFF.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115487719","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982714
Xiubao Jiang, Xinge You, Yi Mou, Shujian Yu, W. Zeng
Variable selection has been extensively studied in linear regression and classification models. Most of these models assume that the input variables are noise free, the response variables are corrupted by Gaussian noise. In this paper, we discuss the variable selection problem assuming that both input variables and response variables are corrupted by Gaussian noise. We analyze the prediction error when augment one related noise variable. We show that the prediction error always decrease when more variable were employed for prediction when the joint distribution of variables are known. Based on this analysis, in sense of mean square error, the optimal variable selection can be obtained. We found that the results is very different from the matching pursuit algorithm(MP), which is widely used in variable selection problems.
{"title":"Gaussian latent variable models for variable selection","authors":"Xiubao Jiang, Xinge You, Yi Mou, Shujian Yu, W. Zeng","doi":"10.1109/SPAC.2014.6982714","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982714","url":null,"abstract":"Variable selection has been extensively studied in linear regression and classification models. Most of these models assume that the input variables are noise free, the response variables are corrupted by Gaussian noise. In this paper, we discuss the variable selection problem assuming that both input variables and response variables are corrupted by Gaussian noise. We analyze the prediction error when augment one related noise variable. We show that the prediction error always decrease when more variable were employed for prediction when the joint distribution of variables are known. Based on this analysis, in sense of mean square error, the optimal variable selection can be obtained. We found that the results is very different from the matching pursuit algorithm(MP), which is widely used in variable selection problems.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131153261","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982722
Wei Huang, Peng Zhang, Minmin Shen
Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.
{"title":"Medical social media analytics via ranking and big learning: An image-based disease prediction study","authors":"Wei Huang, Peng Zhang, Minmin Shen","doi":"10.1109/SPAC.2014.6982722","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982722","url":null,"abstract":"Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116952717","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982659
Xinjian Fan, Xuelin Wang, Yongfei Xiao
Binocular stereo vision is an important branch of the research area in computer vision. Stereo matching is the most important process in binocular vision. In this paper, a new stereo matching scheme using shape-based matching (SBM) is presented to improve the depth reconstruction method of binocular stereo vision systems. The method works in two steps. First, an operator registers the pattern including the key features of an object to be measured. Then during the operation stage, the stereo camera snaps stereo images and finds the patterns in right and left images separately by means of the SBM. The 3D positions of the object are calculated by using the corresponding points of the stereo images and the projection matrices of the stereo camera. Since we apply robust image processing algorithms, such as the SBM, the proposed method becomes more reliable than the conventional stereo vision systems.
{"title":"A shape-based stereo matching algorithm for binocular vision","authors":"Xinjian Fan, Xuelin Wang, Yongfei Xiao","doi":"10.1109/SPAC.2014.6982659","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982659","url":null,"abstract":"Binocular stereo vision is an important branch of the research area in computer vision. Stereo matching is the most important process in binocular vision. In this paper, a new stereo matching scheme using shape-based matching (SBM) is presented to improve the depth reconstruction method of binocular stereo vision systems. The method works in two steps. First, an operator registers the pattern including the key features of an object to be measured. Then during the operation stage, the stereo camera snaps stereo images and finds the patterns in right and left images separately by means of the SBM. The 3D positions of the object are calculated by using the corresponding points of the stereo images and the projection matrices of the stereo camera. Since we apply robust image processing algorithms, such as the SBM, the proposed method becomes more reliable than the conventional stereo vision systems.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023809","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982702
Jian Zhang, Jinxiang Zhang, Rui Sun
Face recognition has found its usage in various domains like video surveillance and human computer interaction. Current face recognition technique is enslaved to unknown pose of the given face image. This paper proposes a novel approach to pose-invariant face recognition. In the training phase, the SIFT feature descriptors of the sample images are extracted, then an image manifold is constructed using Laplacian Eigenmaps based on Hausdorff distance metric to model the low-dimensional embeddings of the sample images. In recognition phase, the SIFT feature descriptors of the given face image are similarly extracted, and the image is embedded into the existed manifold based on Hausdorff distance metric, the recognition is finally achieved by a K-nearest-neighbor classifier in the low-dimensional subspace. Experimental results on multiple datasets demonstrate the superiority of the proposed approach to existing methods in recognition accuracy rate.
{"title":"Pose-invariant face recognition via SIFT feature extraction and manifold projection with Hausdorff distance metric","authors":"Jian Zhang, Jinxiang Zhang, Rui Sun","doi":"10.1109/SPAC.2014.6982702","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982702","url":null,"abstract":"Face recognition has found its usage in various domains like video surveillance and human computer interaction. Current face recognition technique is enslaved to unknown pose of the given face image. This paper proposes a novel approach to pose-invariant face recognition. In the training phase, the SIFT feature descriptors of the sample images are extracted, then an image manifold is constructed using Laplacian Eigenmaps based on Hausdorff distance metric to model the low-dimensional embeddings of the sample images. In recognition phase, the SIFT feature descriptors of the given face image are similarly extracted, and the image is embedded into the existed manifold based on Hausdorff distance metric, the recognition is finally achieved by a K-nearest-neighbor classifier in the low-dimensional subspace. Experimental results on multiple datasets demonstrate the superiority of the proposed approach to existing methods in recognition accuracy rate.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124805807","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982669
Gang Liu, Zhimeng Li, Zhenshi Zhang
To solve tasking capability evaluation of satellite information application chain, the basic process of satellite information application chain is analyzed, and a network model of the process was build. The model consider common tasks and emergency tasks, each task has a priority and deadline. The solving process of model is given.
{"title":"Evaluation of satellite information tasks processing capacity","authors":"Gang Liu, Zhimeng Li, Zhenshi Zhang","doi":"10.1109/SPAC.2014.6982669","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982669","url":null,"abstract":"To solve tasking capability evaluation of satellite information application chain, the basic process of satellite information application chain is analyzed, and a network model of the process was build. The model consider common tasks and emergency tasks, each task has a priority and deadline. The solving process of model is given.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126430245","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}
In this paper, an abnormal event detection system inspired by the saliency attention mechanism of human visual system is presented. Conventionally, training-based methods assume that anomalies are events with rare appearance, which suffer from visual scale, complexity of normal events and insufficiency of training data. Instead, we make the assumption that anomalies are events that attract human attentions. Temporal and spatial anomaly saliency are considered consistently by representing the value of each pixel in each frame as a quaternion composed of intensity, contour, motion-speed and motion-direction feature. For each quaternion frame, Quaternion Discrete Cosine Transformation (QDCT) and signature operation are applied. The spatio-temporal anomaly saliency map is developed by inverse QDCT and Gaussian smoothing. Abnormal events appear at those areas with high saliency values. Experiments on typical datasets show that our method can achieve high accuracy results.
{"title":"Abnormal event detection in crowd scenes using quaternion discrete cosine transformation signature","authors":"Huiwen Guo, Xinyu Wu, Nannan Li, Huan Wang, Yen-Lun Chen","doi":"10.1109/SPAC.2014.6982654","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982654","url":null,"abstract":"In this paper, an abnormal event detection system inspired by the saliency attention mechanism of human visual system is presented. Conventionally, training-based methods assume that anomalies are events with rare appearance, which suffer from visual scale, complexity of normal events and insufficiency of training data. Instead, we make the assumption that anomalies are events that attract human attentions. Temporal and spatial anomaly saliency are considered consistently by representing the value of each pixel in each frame as a quaternion composed of intensity, contour, motion-speed and motion-direction feature. For each quaternion frame, Quaternion Discrete Cosine Transformation (QDCT) and signature operation are applied. The spatio-temporal anomaly saliency map is developed by inverse QDCT and Gaussian smoothing. Abnormal events appear at those areas with high saliency values. Experiments on typical datasets show that our method can achieve high accuracy results.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126690608","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982691
Dongsheng Wang, Xin Niu, Y. Dou
In this paper, we propose an efficient automatic contrast enhancement algorithm for low lighting video. The algorithm is based on a piecewise stretch on the brightness component extracted with Retinex theory in HSV space to improve the visuality of the image. By dividing the brightness component into dark and bright part, nonlinear transformations with different distribution assumption were performed respectively. All the model parameters were estimated automatically according to the illumination conditions. We use two methods to estimate the brightness. The one is global illumination estimation and the other is local illumination estimation. In comparison with global estimation, a local illumination estimation method is proposed for the further improvement. Experiments show that the algorithm can achieve satisfactory effect for nighttime image or video enhancement by comparing with some state-of-the-art approaches.
{"title":"A piecewise-based contrast enhancement framework for low lighting video","authors":"Dongsheng Wang, Xin Niu, Y. Dou","doi":"10.1109/SPAC.2014.6982691","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982691","url":null,"abstract":"In this paper, we propose an efficient automatic contrast enhancement algorithm for low lighting video. The algorithm is based on a piecewise stretch on the brightness component extracted with Retinex theory in HSV space to improve the visuality of the image. By dividing the brightness component into dark and bright part, nonlinear transformations with different distribution assumption were performed respectively. All the model parameters were estimated automatically according to the illumination conditions. We use two methods to estimate the brightness. The one is global illumination estimation and the other is local illumination estimation. In comparison with global estimation, a local illumination estimation method is proposed for the further improvement. Experiments show that the algorithm can achieve satisfactory effect for nighttime image or video enhancement by comparing with some state-of-the-art approaches.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128142059","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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