Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472665
Peng Song, S. Ou, Wenming Zheng, Yun Jin, Li Zhao
In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-corpus speech emotion recognition. First, the NMF algorithm is adopted to learn a latent common feature space for the source and target datasets. Then, the discrepancies between the feature distributions of different corpora are considered, and the maximum mean discrepancy (MMD) algorithm is used for the similarity measurement. Finally, the TNMF approach, which integrates the NMF and MMD algorithms, is proposed. Experiments are carried out on two popular datasets, and the results verify that the TNMF method can significantly outperform the automatic and competitive methods for cross-corpus speech emotion recognition.
{"title":"Speech emotion recognition using transfer non-negative matrix factorization","authors":"Peng Song, S. Ou, Wenming Zheng, Yun Jin, Li Zhao","doi":"10.1109/ICASSP.2016.7472665","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472665","url":null,"abstract":"In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-corpus speech emotion recognition. First, the NMF algorithm is adopted to learn a latent common feature space for the source and target datasets. Then, the discrepancies between the feature distributions of different corpora are considered, and the maximum mean discrepancy (MMD) algorithm is used for the similarity measurement. Finally, the TNMF approach, which integrates the NMF and MMD algorithms, is proposed. Experiments are carried out on two popular datasets, and the results verify that the TNMF method can significantly outperform the automatic and competitive methods for cross-corpus speech emotion recognition.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410835","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472239
Cheng-Yu Hung, M. Kaveh
Estimation of directions-of-arrival (DoA) in the spatial co-variance model is studied. Unlike the compressed sensing methods which discretize the search domain into possible directions on a grid, the theory of super resolution is applied to estimate DoAs in the continuous domain. We reformulate the spatial spectral covariance model into a Multiple Measurement Vector (MMV)-like model, and propose a block total variation norm minimization approach, which is the analog of Group Lasso in the super-resolution framework and that promotes the group-sparsity. The DoAs can be estimated by solving its dual problem via semidefinite programming. This gridless recovery approach is verified by simulation results for both uncorrelated and correlated source signals.
{"title":"Super-resolution DOA estimation via continuous group sparsity in the covariance domain","authors":"Cheng-Yu Hung, M. Kaveh","doi":"10.1109/ICASSP.2016.7472239","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472239","url":null,"abstract":"Estimation of directions-of-arrival (DoA) in the spatial co-variance model is studied. Unlike the compressed sensing methods which discretize the search domain into possible directions on a grid, the theory of super resolution is applied to estimate DoAs in the continuous domain. We reformulate the spatial spectral covariance model into a Multiple Measurement Vector (MMV)-like model, and propose a block total variation norm minimization approach, which is the analog of Group Lasso in the super-resolution framework and that promotes the group-sparsity. The DoAs can be estimated by solving its dual problem via semidefinite programming. This gridless recovery approach is verified by simulation results for both uncorrelated and correlated source signals.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701250","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472132
Lei Sun, Badong Chen, K. Toh, Zhiping Lin
Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.
{"title":"A parameter-free Cauchy-Schwartz information measure for independent component analysis","authors":"Lei Sun, Badong Chen, K. Toh, Zhiping Lin","doi":"10.1109/ICASSP.2016.7472132","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472132","url":null,"abstract":"Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128272695","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472939
Junmei Yang, Chuan Zhang, Shugong Xu, X. You
In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with simple logic structures. Several approaches which can potentially improve the detection performance are exploited. Simulation results have demonstrated that the stochastic BP detector can achieve similar detection performance compared with deterministic one for 32 × 32 MIMO system with 4-quadrature amplitude modulation (4-QAM). With the increase of antenna number, the detection performance improves at the linear expense of complexity and latency. Therefore, the proposed stochastic BP detector is suitable for large-scale MIMO system applications with good balance of detection performance and implementation complexity.
{"title":"Efficient stochastic detector for large-scale MIMO","authors":"Junmei Yang, Chuan Zhang, Shugong Xu, X. You","doi":"10.1109/ICASSP.2016.7472939","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472939","url":null,"abstract":"In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with simple logic structures. Several approaches which can potentially improve the detection performance are exploited. Simulation results have demonstrated that the stochastic BP detector can achieve similar detection performance compared with deterministic one for 32 × 32 MIMO system with 4-quadrature amplitude modulation (4-QAM). With the increase of antenna number, the detection performance improves at the linear expense of complexity and latency. Therefore, the proposed stochastic BP detector is suitable for large-scale MIMO system applications with good balance of detection performance and implementation complexity.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128381289","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7471963
Shengxin Zha, T. Pappas
We propose a family of cutset sampling schemes and a generalized k-level image reconstruction approach formulated under a minimum mean squared error (MMSE) framework. The k-level reconstruction approach is a direct generalization of the recently proposed pattern-based approach, and can be applied to periodic samples either on a cutset or on a grid. Our experimental results indicate that the generalization of the k-level reconstruction approach results in only a small performance loss. For rectangular cutsets, we show that the proposed approach outperforms the cutset-MRF approach as well as two inpainting approaches. Moreover, we show that combining the cutset sampling with an additional point sample inside the periodic structure outperforms k-level reconstruction from cutset sampling and point sampling under comparable sampling densities.
{"title":"Generalized k-level cutset sampling and reconstruction","authors":"Shengxin Zha, T. Pappas","doi":"10.1109/ICASSP.2016.7471963","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471963","url":null,"abstract":"We propose a family of cutset sampling schemes and a generalized k-level image reconstruction approach formulated under a minimum mean squared error (MMSE) framework. The k-level reconstruction approach is a direct generalization of the recently proposed pattern-based approach, and can be applied to periodic samples either on a cutset or on a grid. Our experimental results indicate that the generalization of the k-level reconstruction approach results in only a small performance loss. For rectangular cutsets, we show that the proposed approach outperforms the cutset-MRF approach as well as two inpainting approaches. Moreover, we show that combining the cutset sampling with an additional point sample inside the periodic structure outperforms k-level reconstruction from cutset sampling and point sampling under comparable sampling densities.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587887","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472324
A. Kalantari, Mojtaba Soltanalian, S. Maleki, S. Chatzinotas, B. Ottersten
In this work, a directional modulation-based technique is devised to enhance the security of a multi-antenna wireless communication system employing M-PSK modulation to convey information. The directional modulation method operates by steering the array beam in such a way that the phase of the received signal at the receiver matches that of the intended M-PSK symbol. Due to the difference between the channels of the legitimate receiver and the eavesdropper, the signals received by the eavesdropper generally encompass a phase component different than the actual symbols. As a result, the transceiver which employs directional modulation can impose a high symbol error rate on the eavesdropper without requiring to know the eavesdropper's channel. The optimal directional modulation beamformer is designed to minimize the consumed power subject to satisfying a specific resulting phase and minimal signal amplitude at each antenna of the legitimate receiver. The simulation results show that the directional modulation results in a much higher symbol error rate at the eavesdropper compared to the conventional benchmark scheme, i.e., zero-forcing precoding at the transmitter.
{"title":"Secure M-PSK communication via directional modulation","authors":"A. Kalantari, Mojtaba Soltanalian, S. Maleki, S. Chatzinotas, B. Ottersten","doi":"10.1109/ICASSP.2016.7472324","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472324","url":null,"abstract":"In this work, a directional modulation-based technique is devised to enhance the security of a multi-antenna wireless communication system employing M-PSK modulation to convey information. The directional modulation method operates by steering the array beam in such a way that the phase of the received signal at the receiver matches that of the intended M-PSK symbol. Due to the difference between the channels of the legitimate receiver and the eavesdropper, the signals received by the eavesdropper generally encompass a phase component different than the actual symbols. As a result, the transceiver which employs directional modulation can impose a high symbol error rate on the eavesdropper without requiring to know the eavesdropper's channel. The optimal directional modulation beamformer is designed to minimize the consumed power subject to satisfying a specific resulting phase and minimal signal amplitude at each antenna of the legitimate receiver. The simulation results show that the directional modulation results in a much higher symbol error rate at the eavesdropper compared to the conventional benchmark scheme, i.e., zero-forcing precoding at the transmitter.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324362","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472429
John Murray-Bruce, P. Dragotti
We present a framework for estimating non-localized sources of diffusion fields using spatiotemporal measurements of the field. Specifically in this contribution, we consider two non-localized source types: straight line and polygonal sources and assume that the induced field is monitored using a sensor network. Given the sensor measurements, we demonstrate, for each non-point source parameterization, how to reduce the source estimation problem to a system governed by a power series expansion that can then be efficiently solved using Prony's method, in order to reconstruct the source. We then evaluate the proposed algorithms by performing some numerical simulations using both noiseless and noisy spatiotemporal sensor measurements of the field.
{"title":"Reconstructing non-point sources of diffusion fields using sensor measurements","authors":"John Murray-Bruce, P. Dragotti","doi":"10.1109/ICASSP.2016.7472429","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472429","url":null,"abstract":"We present a framework for estimating non-localized sources of diffusion fields using spatiotemporal measurements of the field. Specifically in this contribution, we consider two non-localized source types: straight line and polygonal sources and assume that the induced field is monitored using a sensor network. Given the sensor measurements, we demonstrate, for each non-point source parameterization, how to reduce the source estimation problem to a system governed by a power series expansion that can then be efficiently solved using Prony's method, in order to reconstruct the source. We then evaluate the proposed algorithms by performing some numerical simulations using both noiseless and noisy spatiotemporal sensor measurements of the field.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129424200","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472754
I-Bin Liao, Chen-Yu Chiang, Sin-Horng Chen
In this paper, a structural maximum a posterior speaker adaptation method to adjust the existing speaking rate (SR) dependent hierarchical prosodic model (SR-HPM) to a new speaker's data for realizing a new voice of any given SR is discussed. The adaptive SR-HPM is formulated based on MAP estimation with a reference SR-HPM serving as an informative prior. The prior information provided by the reference SR-HPM is hierarchically organized by decision trees. The results of objective and subjective evaluations showed that the proposed method not only performed slightly better than the maximum likelihood-based model in the observed SR range of the target speaker's data, but also was much better in the unseen SR range.
{"title":"Structural maximum a posteriori speaker adaptation of speaking rate-dependent hierarchical prosodic model for Mandarin TTS","authors":"I-Bin Liao, Chen-Yu Chiang, Sin-Horng Chen","doi":"10.1109/ICASSP.2016.7472754","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472754","url":null,"abstract":"In this paper, a structural maximum a posterior speaker adaptation method to adjust the existing speaking rate (SR) dependent hierarchical prosodic model (SR-HPM) to a new speaker's data for realizing a new voice of any given SR is discussed. The adaptive SR-HPM is formulated based on MAP estimation with a reference SR-HPM serving as an informative prior. The prior information provided by the reference SR-HPM is hierarchically organized by decision trees. The results of objective and subjective evaluations showed that the proposed method not only performed slightly better than the maximum likelihood-based model in the observed SR range of the target speaker's data, but also was much better in the unseen SR range.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129667919","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7471979
Qin Zou, Zhongwen Hu, Long Chen, Qian Wang, Qingquan Li
Shadows often incur uneven illumination to pavement images, which brings great challenges to image-based pavement crack detection. Thus, it is desired to remove pavement shadows before detecting pavement cracks. However, due to the large penumbras cast by trees, light poles, etc., it is difficult to locate shadows in a pavement image. In this paper, an automatic pavement shadow removal method is proposed based on geodesic analysis. First, a geodesic shadow model is used to partition a pavement shadow into a number of geodesic regions. Then, an optimal background region is selected for reference by statistic analysis. Finally, a texture-balanced illuminance compensation is applied on all geodesic regions over the image. Experiments demonstrate the effectiveness of the proposed method.
{"title":"Geodesic-based pavement shadow removal revisited","authors":"Qin Zou, Zhongwen Hu, Long Chen, Qian Wang, Qingquan Li","doi":"10.1109/ICASSP.2016.7471979","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471979","url":null,"abstract":"Shadows often incur uneven illumination to pavement images, which brings great challenges to image-based pavement crack detection. Thus, it is desired to remove pavement shadows before detecting pavement cracks. However, due to the large penumbras cast by trees, light poles, etc., it is difficult to locate shadows in a pavement image. In this paper, an automatic pavement shadow removal method is proposed based on geodesic analysis. First, a geodesic shadow model is used to partition a pavement shadow into a number of geodesic regions. Then, an optimal background region is selected for reference by statistic analysis. Finally, a texture-balanced illuminance compensation is applied on all geodesic regions over the image. Experiments demonstrate the effectiveness of the proposed method.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899152","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472177
Qiang Huang
Deep architecture based Deep Brief Nets (DBNs) has shown its data modelling power by stacking up several Restricted Boltzmann Machines (RBMs). However, the multiple-layer structure used in DBN brings expensive computation, and furthermore leads to slow convergence. This is because the pretraining stage is usually implemented in a data-driven way, and class information attached to the training data is only used for fine-tuning. In this paper, we aim to simplify a multiple-layer DBN to a one-layer structure. We use class information as a constraint to the hidden layer during pre-training. For each training instance and its corresponding class, a binary sequence will be generated in order to adapt the output of hidden layer. We test our approaches on four data sets: basic, MNIST, basic negative MNIST, rotation MNIST and rectangle (tall vs. wide rectangles). The obtained results show that the adapted one-layer structure can compete with a three-layer, DBN.
{"title":"Simplified learning with binary orthogonal constraints","authors":"Qiang Huang","doi":"10.1109/ICASSP.2016.7472177","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472177","url":null,"abstract":"Deep architecture based Deep Brief Nets (DBNs) has shown its data modelling power by stacking up several Restricted Boltzmann Machines (RBMs). However, the multiple-layer structure used in DBN brings expensive computation, and furthermore leads to slow convergence. This is because the pretraining stage is usually implemented in a data-driven way, and class information attached to the training data is only used for fine-tuning. In this paper, we aim to simplify a multiple-layer DBN to a one-layer structure. We use class information as a constraint to the hidden layer during pre-training. For each training instance and its corresponding class, a binary sequence will be generated in order to adapt the output of hidden layer. We test our approaches on four data sets: basic, MNIST, basic negative MNIST, rotation MNIST and rectangle (tall vs. wide rectangles). The obtained results show that the adapted one-layer structure can compete with a three-layer, DBN.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130484607","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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