Pub Date : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590353
Qinghua Ma, Luxi Yang, Zhenya He
The single antenna user nodes at the transmitters and at the receivers share their antennas to form a virtual multiple antenna system and to realize the cooperative diversity. It can enlarge the systempsilas capacity and enhance the service quality of the ad hoc network and improve the systempsilas performance. Applying the orthogonal property of the space-time coding, we transform the vector signal jointly maximum likelihood (ML) decoding into single symbol decoding, and we provide the single symbol decoding methods for the situations in decode and forward mode (DF) and in amplify and forward (AF) separately. After smoothing the noisepsilas influence, we can enhance the systempsilas performance. Given the total power at the transmitter, adaptively adjust the target signal power and the cooperative signal power according to the channel quality information, the performance in AF mode approaches that of DF mode. Simulation demonstrates that the application of userpsilas cooperation diversity can effetely improve the systempsilas performance. If the channel quality information is known at the transmitter, we can adaptively adjust the target signalpsilas transmit power and the cooperative signalpsilas transmit power to get much more performance improvement. Otherwise space-time coding in AF mode is a better choice instead of that in DF mode.
{"title":"Adaptive power adjusting for space-time coding based on two-transmitter and two-receiver cooperation","authors":"Qinghua Ma, Luxi Yang, Zhenya He","doi":"10.1109/ICNNSP.2008.4590353","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590353","url":null,"abstract":"The single antenna user nodes at the transmitters and at the receivers share their antennas to form a virtual multiple antenna system and to realize the cooperative diversity. It can enlarge the systempsilas capacity and enhance the service quality of the ad hoc network and improve the systempsilas performance. Applying the orthogonal property of the space-time coding, we transform the vector signal jointly maximum likelihood (ML) decoding into single symbol decoding, and we provide the single symbol decoding methods for the situations in decode and forward mode (DF) and in amplify and forward (AF) separately. After smoothing the noisepsilas influence, we can enhance the systempsilas performance. Given the total power at the transmitter, adaptively adjust the target signal power and the cooperative signal power according to the channel quality information, the performance in AF mode approaches that of DF mode. Simulation demonstrates that the application of userpsilas cooperation diversity can effetely improve the systempsilas performance. If the channel quality information is known at the transmitter, we can adaptively adjust the target signalpsilas transmit power and the cooperative signalpsilas transmit power to get much more performance improvement. Otherwise space-time coding in AF mode is a better choice instead of that in DF mode.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995731","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590397
Daming Zhang, Hui Guo, B. Luo
The expectation-maximization (EM) algorithm is a popular approach for parameter estimation of finite mixture model (FMM). A drawback of this approach is that the number of components of the finite mixture model is not known in advance, nevertheless, it is a key issue for EM algorithms. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Under the framework of Greedy EM, a fast and accurate algorithm for estimating the number of components of the Gaussian mixture model (GMM) is proposed. The performance of this algorithm is validated via simulative experiments of univariate and bivariate Gaussian mixture models.
{"title":"An algorithm for estimating number of components of Gaussian mixture model based on penalized distance","authors":"Daming Zhang, Hui Guo, B. Luo","doi":"10.1109/ICNNSP.2008.4590397","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590397","url":null,"abstract":"The expectation-maximization (EM) algorithm is a popular approach for parameter estimation of finite mixture model (FMM). A drawback of this approach is that the number of components of the finite mixture model is not known in advance, nevertheless, it is a key issue for EM algorithms. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Under the framework of Greedy EM, a fast and accurate algorithm for estimating the number of components of the Gaussian mixture model (GMM) is proposed. The performance of this algorithm is validated via simulative experiments of univariate and bivariate Gaussian mixture models.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125443141","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590352
Jianguo Liu, Luxi Yang, Z. He
In order to exploit multiuser diversity gain and spatial multiplexing gain for multi-user MIMO system with spatial correlation Ricean fading channel, a joint multi-user precoding and scheduling algorithm is proposed based on partial channel state information (CSI). Utilizing partial instantaneous CSI and statistical CSI for all users, the base station (BS) estimates the channel for each user using constrained maximum likelihood (CML) approach, and then schedules a group of users with optimal precoding using the estimated channels. Simulation results demonstrate that the proposed scheme greatly improves system throughput with a bit of feedback overhead.
{"title":"Multiuser precoding and scheduling algorithm for spatial correlation-aided Ricean fading channel","authors":"Jianguo Liu, Luxi Yang, Z. He","doi":"10.1109/ICNNSP.2008.4590352","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590352","url":null,"abstract":"In order to exploit multiuser diversity gain and spatial multiplexing gain for multi-user MIMO system with spatial correlation Ricean fading channel, a joint multi-user precoding and scheduling algorithm is proposed based on partial channel state information (CSI). Utilizing partial instantaneous CSI and statistical CSI for all users, the base station (BS) estimates the channel for each user using constrained maximum likelihood (CML) approach, and then schedules a group of users with optimal precoding using the estimated channels. Simulation results demonstrate that the proposed scheme greatly improves system throughput with a bit of feedback overhead.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"1520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128053973","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590359
Gao Yunchao, Sang Enfang, Liu Baifeng, Sheng Zhengyan
On the basis of analysis of processing a signal from a single vector sensor using Hilbert-Huang transform (HHT) with empirical mode decomposition (EMD), complex empirical mode decomposition (CEMD) has been introduced to improve it. As an extension of EMD in complex, CEMD is a powerful tool for complex data. Its characteristic analyzing the complex white Gaussian noise has been studied. It is proved that CEMD is a dyadic filter bank and the real parts and the imaginary parts of complex IMF is with same frequency feature. Experiment has been carried with simulated signal from a single vector sensor with multiple targets, and the signals have been combined in different forms. The results show that CEMD is better in using the information between the correlative signals. Founded on different mechanism in direction estimation, it has been showed that the analytic signal is beneficial to direction estimation with different targets.
{"title":"Application of Complex Empirical Mode Decomposition in separation of multiple targets using a single vector sensor","authors":"Gao Yunchao, Sang Enfang, Liu Baifeng, Sheng Zhengyan","doi":"10.1109/ICNNSP.2008.4590359","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590359","url":null,"abstract":"On the basis of analysis of processing a signal from a single vector sensor using Hilbert-Huang transform (HHT) with empirical mode decomposition (EMD), complex empirical mode decomposition (CEMD) has been introduced to improve it. As an extension of EMD in complex, CEMD is a powerful tool for complex data. Its characteristic analyzing the complex white Gaussian noise has been studied. It is proved that CEMD is a dyadic filter bank and the real parts and the imaginary parts of complex IMF is with same frequency feature. Experiment has been carried with simulated signal from a single vector sensor with multiple targets, and the signals have been combined in different forms. The results show that CEMD is better in using the information between the correlative signals. Founded on different mechanism in direction estimation, it has been showed that the analytic signal is beneficial to direction estimation with different targets.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129415364","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590417
Qi Sumin, Huang Xianwu
Mean shift algorithm is an iterative procedure that shifts each data point to the average of data points in its neighborhood. It been applied to object tracking. But traditional mean shift tracker by isotropic kernel often loses the object with the changing structure of object in video sequences, especially when object structure varies fast. This paper proposes a non-rigid object tracker with anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the changing object structure. The proposed tracker is used for hand tracking in video. Gesture recognition is implemented simultaneously with orientation histograms. Experimental results show that the new algorithm ensures the robust and real-time hand tracking and and accurate gesture recognition.
{"title":"Hand tracking and gesture gecogniton by anisotropic kernel mean shift","authors":"Qi Sumin, Huang Xianwu","doi":"10.1109/ICNNSP.2008.4590417","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590417","url":null,"abstract":"Mean shift algorithm is an iterative procedure that shifts each data point to the average of data points in its neighborhood. It been applied to object tracking. But traditional mean shift tracker by isotropic kernel often loses the object with the changing structure of object in video sequences, especially when object structure varies fast. This paper proposes a non-rigid object tracker with anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the changing object structure. The proposed tracker is used for hand tracking in video. Gesture recognition is implemented simultaneously with orientation histograms. Experimental results show that the new algorithm ensures the robust and real-time hand tracking and and accurate gesture recognition.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121236228","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590442
Rui Ding, Xiqi Gao, X. You
A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.
{"title":"A low-complexity implementation of sampling-based MIMO detection","authors":"Rui Ding, Xiqi Gao, X. You","doi":"10.1109/ICNNSP.2008.4590442","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590442","url":null,"abstract":"A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063822","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590439
Boon-Seng Chew, Lap-Pui Chau, Kim-Hui Yap
This study presents a novel approach of progressive transmission of 3D animated mesh for different level of details. A typical 3D animation consists of large coherence between frames which can be effectively tapped to achieve compression and by coupling it to the proposed bit plane encoding scheme in the transform domain, efficient generation of progressive 3D animated model can be achieved. Our method is lossless in nature as it does not require addition quantization. In addition, the algorithm can be directly applied on the floating numbers of the mesh. From the experimental result, it can be shown that progressive quality of the animated mesh measured by SNR (dB) can be realized given more bit stream received at the decoder.
{"title":"Bitplane coding technique for 3-D animated meshes","authors":"Boon-Seng Chew, Lap-Pui Chau, Kim-Hui Yap","doi":"10.1109/ICNNSP.2008.4590439","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590439","url":null,"abstract":"This study presents a novel approach of progressive transmission of 3D animated mesh for different level of details. A typical 3D animation consists of large coherence between frames which can be effectively tapped to achieve compression and by coupling it to the proposed bit plane encoding scheme in the transform domain, efficient generation of progressive 3D animated model can be achieved. Our method is lossless in nature as it does not require addition quantization. In addition, the algorithm can be directly applied on the floating numbers of the mesh. From the experimental result, it can be shown that progressive quality of the animated mesh measured by SNR (dB) can be realized given more bit stream received at the decoder.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115262936","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590395
Xinfeng Zhang, Zhuo Li, Dagan Feng
Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier's generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.
{"title":"A hyper-sphere SVM introduced the margin","authors":"Xinfeng Zhang, Zhuo Li, Dagan Feng","doi":"10.1109/ICNNSP.2008.4590395","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590395","url":null,"abstract":"Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier's generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115518050","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590377
Guangming Lin, Jihong Zhang, Yongsheng Liang, Lishan Kang
In this paper a multi-phase evolutionary algorithm (MPEA) for solving general non-linear programming problems (NLP) is proposed. It uses population decomposition, elite multi-parent crossover, better of Gauss and Cauchy mutation and population hill-climbing strategies for adaptive search and particle swarm optimization (PSO). Comparing with other algorithms, it has the following advantages. (1) It can be used for solving non-linear optimization problems with or without constraints, real NLP, integer NLP (including 0-1 NLP) and real-integer mixed NLP. (2) It can be used for solving multi-modal function optimization problems. It means that it can be used to get multiple solutions in one run if the NLP has many global optimal solutions. (3) It is not needed to continuity, convexity and derivative information. In this paper, numerical experiment results show that this evolutionary algorithm is very effective in generality, reliability, precision, robustness and intelligence.
{"title":"Multi-phase evolutionary algorithm for non-linear programming problems with multiple solutions","authors":"Guangming Lin, Jihong Zhang, Yongsheng Liang, Lishan Kang","doi":"10.1109/ICNNSP.2008.4590377","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590377","url":null,"abstract":"In this paper a multi-phase evolutionary algorithm (MPEA) for solving general non-linear programming problems (NLP) is proposed. It uses population decomposition, elite multi-parent crossover, better of Gauss and Cauchy mutation and population hill-climbing strategies for adaptive search and particle swarm optimization (PSO). Comparing with other algorithms, it has the following advantages. (1) It can be used for solving non-linear optimization problems with or without constraints, real NLP, integer NLP (including 0-1 NLP) and real-integer mixed NLP. (2) It can be used for solving multi-modal function optimization problems. It means that it can be used to get multiple solutions in one run if the NLP has many global optimal solutions. (3) It is not needed to continuity, convexity and derivative information. In this paper, numerical experiment results show that this evolutionary algorithm is very effective in generality, reliability, precision, robustness and intelligence.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131043043","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 : 2008-06-07DOI: 10.1109/ICNNSP.2008.4590435
Ling Houy, O.C. Auy, Mengyao Maz, Liwei Guoy, Xiaopeng Fan
Ray-Space interpolation is one of the key technologies to generate arbitrary viewpoint images so as to implement Ray-Space based FTV (Free Viewpoint Television) system. Since Ray-Space data is composed of straight lines with different slopes, how to find the direction of the lines is the problem to be solved in Ray-Space interpolation. In this paper, a radon transform based free view generation algorithm in Ray-Space is proposed. First, feature points of epipolar plane image (EPI) are extracted to form a feature EPI (FEPI). Then, Radon Transform is applied to each FEPI to find the possible interpolation direction. Finally, the optimal interpolation direction is determined by an improved block-based matching algorithm. Experimental results show that the virtual view point image generated by the proposed algorithm has much higher quality than that generated by the traditional pixel matching based interpolation (PMI) and block matching based interpolation (BMI).
{"title":"Free view generation in Ray-Space via the radon transform","authors":"Ling Houy, O.C. Auy, Mengyao Maz, Liwei Guoy, Xiaopeng Fan","doi":"10.1109/ICNNSP.2008.4590435","DOIUrl":"https://doi.org/10.1109/ICNNSP.2008.4590435","url":null,"abstract":"Ray-Space interpolation is one of the key technologies to generate arbitrary viewpoint images so as to implement Ray-Space based FTV (Free Viewpoint Television) system. Since Ray-Space data is composed of straight lines with different slopes, how to find the direction of the lines is the problem to be solved in Ray-Space interpolation. In this paper, a radon transform based free view generation algorithm in Ray-Space is proposed. First, feature points of epipolar plane image (EPI) are extracted to form a feature EPI (FEPI). Then, Radon Transform is applied to each FEPI to find the possible interpolation direction. Finally, the optimal interpolation direction is determined by an improved block-based matching algorithm. Experimental results show that the virtual view point image generated by the proposed algorithm has much higher quality than that generated by the traditional pixel matching based interpolation (PMI) and block matching based interpolation (BMI).","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127217987","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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