Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631860
Xiaofeng Shu, Yi Zhou, Yin Cao
In this paper, a speech enhancement method based on the framework of progressive deep neural networks (PDNNs) is proposed for low signal-to-noise ratio (SNR) and highly reverberant environments. It aims at assisting the complicated regression task of mapping noisy and reverberant speech to clean speech by utilizing two independent tasks, which suppress reverberation and noises respectively. Furthermore, a progressive learning approach is used for each task, which brings intermediate learning targets to enhance system performances. Experimental results reveal that the proposed method can achieve improvements in both objective and subjective evaluations in low SNR and high reverberation time 60 (RT60) environments when compared with the conventional deep neural network-based method.
{"title":"A Progressive Enhancement Method for Noisy and Reverberant Speech","authors":"Xiaofeng Shu, Yi Zhou, Yin Cao","doi":"10.1109/ICDSP.2018.8631860","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631860","url":null,"abstract":"In this paper, a speech enhancement method based on the framework of progressive deep neural networks (PDNNs) is proposed for low signal-to-noise ratio (SNR) and highly reverberant environments. It aims at assisting the complicated regression task of mapping noisy and reverberant speech to clean speech by utilizing two independent tasks, which suppress reverberation and noises respectively. Furthermore, a progressive learning approach is used for each task, which brings intermediate learning targets to enhance system performances. Experimental results reveal that the proposed method can achieve improvements in both objective and subjective evaluations in low SNR and high reverberation time 60 (RT60) environments when compared with the conventional deep neural network-based method.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131509297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631592
Xu Cheng, D. Ciuonzo, P. Rossi
We consider decentralized detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via wireless sensor networks (WSNs). To cope with energy and/or bandwidth constraints, we assume that sensors adopt multilevel quantization. The data are then transmitted through binary symmetric channels to a fusion center (FC), where a Rao test is proposed as a simpler alternative to the generalized likelihood ratio test (GLRT). The asymptotic performance analysis of the multi-bit Rao test is provided and exploited to propose a (signal-independent) quantizer design. Numerical results show the effectiveness of Rao test in comparison to GLRT and the performance gain obtained by threshold optimization.
{"title":"Multi-bit Decentralized Detection of a Weak Signal in Wireless Sensor Networks with a Rao test","authors":"Xu Cheng, D. Ciuonzo, P. Rossi","doi":"10.1109/ICDSP.2018.8631592","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631592","url":null,"abstract":"We consider decentralized detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via wireless sensor networks (WSNs). To cope with energy and/or bandwidth constraints, we assume that sensors adopt multilevel quantization. The data are then transmitted through binary symmetric channels to a fusion center (FC), where a Rao test is proposed as a simpler alternative to the generalized likelihood ratio test (GLRT). The asymptotic performance analysis of the multi-bit Rao test is provided and exploited to propose a (signal-independent) quantizer design. Numerical results show the effectiveness of Rao test in comparison to GLRT and the performance gain obtained by threshold optimization.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131557056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631813
Bingyi Zhang, Xin Li, Jun Han, Xiaoyang Zeng
Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system–-MiniTracker, integration of algorithm and hardware–-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.
{"title":"MiniTracker: A Lightweight CNN-based System for Visual Object Tracking on Embedded Device","authors":"Bingyi Zhang, Xin Li, Jun Han, Xiaoyang Zeng","doi":"10.1109/ICDSP.2018.8631813","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631813","url":null,"abstract":"Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system–-MiniTracker, integration of algorithm and hardware–-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631644
Himanshu Kumar, Sumana Gupta, K. Venkatesh
Focal Plane Ambiguity (FPA) is a fundamental limitation of the Depth from Defocus (DFD) technique and refers to ambiguity of two possible distances corresponding to a single defocus blur value. Since, mix-sided scenes exist frequently in images and image-sequences, the assumption of a one sided focused scene often does not hold true. This leads to errors in the estimated defocus map. However, the inherent ordering of defocus blurs at the edges due to chromatic aberration in the R, G and B color planes can be used to correct this ambiguity. But, in highly defocused regions the ordering of defocus blurs becomes unreliable as the detection of edges becomes erroneous. In this paper, we propose a novel region based method using Color Uniformity Principle (CUP) for detecting the ordering of defocus blurs in R, G and B color planes to resolve the FPA.
焦平面模糊(Focal Plane Ambiguity, FPA)是离焦深度(Depth from Defocus, DFD)技术的一个基本缺陷，它是指一个离焦模糊值对应的两个可能距离的模糊度。由于混合面场景经常存在于图像和图像序列中，因此单面聚焦场景的假设往往不成立。这将导致估计散焦图中的错误。然而，由于R、G和B色平面的色差导致的边缘离焦模糊的固有顺序可以用来纠正这种模糊性。但是，在高度散焦区域，由于边缘检测错误，散焦模糊的排序变得不可靠。本文提出了一种新的基于区域的方法，利用颜色均匀性原理(CUP)来检测R、G和B颜色平面上离焦模糊的顺序，以解决FPA问题。
{"title":"Resolving Focal Plane Ambiguity using Chromatic Aberration and Color Uniformity Principle","authors":"Himanshu Kumar, Sumana Gupta, K. Venkatesh","doi":"10.1109/ICDSP.2018.8631644","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631644","url":null,"abstract":"Focal Plane Ambiguity (FPA) is a fundamental limitation of the Depth from Defocus (DFD) technique and refers to ambiguity of two possible distances corresponding to a single defocus blur value. Since, mix-sided scenes exist frequently in images and image-sequences, the assumption of a one sided focused scene often does not hold true. This leads to errors in the estimated defocus map. However, the inherent ordering of defocus blurs at the edges due to chromatic aberration in the R, G and B color planes can be used to correct this ambiguity. But, in highly defocused regions the ordering of defocus blurs becomes unreliable as the detection of edges becomes erroneous. In this paper, we propose a novel region based method using Color Uniformity Principle (CUP) for detecting the ordering of defocus blurs in R, G and B color planes to resolve the FPA.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631649
S. Chan, H. C. Wu, Jianqiang Lin, Z. G. Zhang
Conventional procedures for preliminary diagnosis of Alzheimer's disease (AD) are invasive and painful. It is important to devise noninvasive biomarker which can provide conclusive diagnosis of early onset of AD and mild cognitive impairment (MCI). Recent attention has been drawn recently to gene microarray analysis for understanding disease onset and progression. In this paper, we extend our previous work to develop a new large-scale partial least squares based multivariate regression approach for the identification of putative interacting partners of gene markers for high-throughput gene microarray and other related data. Preliminary analysis of the interacting gene partners of a marker gene of frontotemporal dementia show that the identified genes are significantly enriched in innate immune and inflammatory response processes, which align well with the nature of the disease. These suggest that the proposed approach may serve as a valuable tool for inferring putative gene interacting partners in biological studies involving gene microarray data and other related datasets.
{"title":"A Partial least squares-based regression approach for analysis of frontotemporal dementia gene markers in human brain gene microarray data","authors":"S. Chan, H. C. Wu, Jianqiang Lin, Z. G. Zhang","doi":"10.1109/ICDSP.2018.8631649","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631649","url":null,"abstract":"Conventional procedures for preliminary diagnosis of Alzheimer's disease (AD) are invasive and painful. It is important to devise noninvasive biomarker which can provide conclusive diagnosis of early onset of AD and mild cognitive impairment (MCI). Recent attention has been drawn recently to gene microarray analysis for understanding disease onset and progression. In this paper, we extend our previous work to develop a new large-scale partial least squares based multivariate regression approach for the identification of putative interacting partners of gene markers for high-throughput gene microarray and other related data. Preliminary analysis of the interacting gene partners of a marker gene of frontotemporal dementia show that the identified genes are significantly enriched in innate immune and inflammatory response processes, which align well with the nature of the disease. These suggest that the proposed approach may serve as a valuable tool for inferring putative gene interacting partners in biological studies involving gene microarray data and other related datasets.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130320293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631839
Chaosheng Tang, Elizabeth Lee
Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.
{"title":"Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization","authors":"Chaosheng Tang, Elizabeth Lee","doi":"10.1109/ICDSP.2018.8631839","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631839","url":null,"abstract":"Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114523914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631691
Feng-yan Xu, Linfu Duan, Xiansheng Guo, Lin Li, F. Hu
The existing WiFi and geomagnetism based positioning methods using single classifier show low accuracy because they are sensitive to changing environments. In this paper, we propose a global dynamic fusion location algorithm for multiple classifiers based on WiFi and geomagnetic fingerprints. In the offline phase, we first divide a positioning environment into some grid points and construct RSS and geomagnetic fingerprints for each grid point. Then, we train multiple classifiers by using the constructed fingerprints. Second, we derive a global dynamic fusion weight training method for each grid point through the global supervised optimization learning. In the online phase, given an RSS testing sample, we select the matching weights for fusion by using K-nearest neighbor (KNN). Our proposed multiple classifiers global dynamic fusion algorithm can make full use of the intrinsic complementarity of multiple classifiers, thus effectively improving the positioning accuracy of RSS and geomagnetic fingerprints. Experimental results show that the proposed algorithm outperforms some existing methods in complex indoor environments.
{"title":"Multiple Classifiers Global Dynamic Fusion Location System based on WiFi and Geomagnetism","authors":"Feng-yan Xu, Linfu Duan, Xiansheng Guo, Lin Li, F. Hu","doi":"10.1109/ICDSP.2018.8631691","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631691","url":null,"abstract":"The existing WiFi and geomagnetism based positioning methods using single classifier show low accuracy because they are sensitive to changing environments. In this paper, we propose a global dynamic fusion location algorithm for multiple classifiers based on WiFi and geomagnetic fingerprints. In the offline phase, we first divide a positioning environment into some grid points and construct RSS and geomagnetic fingerprints for each grid point. Then, we train multiple classifiers by using the constructed fingerprints. Second, we derive a global dynamic fusion weight training method for each grid point through the global supervised optimization learning. In the online phase, given an RSS testing sample, we select the matching weights for fusion by using K-nearest neighbor (KNN). Our proposed multiple classifiers global dynamic fusion algorithm can make full use of the intrinsic complementarity of multiple classifiers, thus effectively improving the positioning accuracy of RSS and geomagnetic fingerprints. Experimental results show that the proposed algorithm outperforms some existing methods in complex indoor environments.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115114361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631608
Yang Chen, Tong Ma, Ying Wei
A design of variable filters is proposed based on frequency response masking technique and frequency warping. Instead of using traditional masking filters, the masking filters in the proposed method are obtained by nonlinear transformation to a prototype filter using frequency wrapping. The design process is given and the mapping between the final filters and the control parameters are deduced. Experiments illustrate the effectiveness of the proposed method.
{"title":"A Design of Variable Digital Filters Based on FRM Technique and Frequency Warping","authors":"Yang Chen, Tong Ma, Ying Wei","doi":"10.1109/ICDSP.2018.8631608","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631608","url":null,"abstract":"A design of variable filters is proposed based on frequency response masking technique and frequency warping. Instead of using traditional masking filters, the masking filters in the proposed method are obtained by nonlinear transformation to a prototype filter using frequency wrapping. The design process is given and the mapping between the final filters and the control parameters are deduced. Experiments illustrate the effectiveness of the proposed method.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115244324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631542
Cheen-Hau Tan, Jie Chen, Yun Ni, Lap-Pui Chau, L. M. Soh
In this paper we develop a vision-based rain intensity measurement method for dynamic scenes. The method first measures the area density of rain by analyzing temporal changes in pixel values in the video input. The area density, represented as a binary rain map, is then mapped to a rain intensity value using linear regression. To ensure temporal consistency of scene content across frames in dynamic scenes, we applied superpixel-based content alignment. Potential false detections in the binary rain map are removed using directional morphological opening. Experiments show that both superpixel-based content alignment and morphological opening are important for good rain map generation and rain intensity estimation
{"title":"Vision-Based Rain Gauge for Dynamic Scenes","authors":"Cheen-Hau Tan, Jie Chen, Yun Ni, Lap-Pui Chau, L. M. Soh","doi":"10.1109/ICDSP.2018.8631542","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631542","url":null,"abstract":"In this paper we develop a vision-based rain intensity measurement method for dynamic scenes. The method first measures the area density of rain by analyzing temporal changes in pixel values in the video input. The area density, represented as a binary rain map, is then mapped to a rain intensity value using linear regression. To ensure temporal consistency of scene content across frames in dynamic scenes, we applied superpixel-based content alignment. Potential false detections in the binary rain map are removed using directional morphological opening. Experiments show that both superpixel-based content alignment and morphological opening are important for good rain map generation and rain intensity estimation","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631672
F. Bai, Ruijie Liu
In this paper, we present an improved nonparallel hyperplanes classifier for multi-class classification, termed as INHCMC. As in the nonparallel support vector machine (NPSVM) for binary classification, the ε-insensitive loss function is adopted in the primal problems of multi-class classification to improve the sparseness associated with the nonparallel hyperplanes classifier for multi-class classification (NHCMC) where the quadratic loss function is used. Experimental results on some benchmark datasets are reported to show the effectiveness of our method in terms of sparseness and classification accuracy.
{"title":"Improved Nonparallel Hyperplanes Support Vector Machines for Multi-class Classification","authors":"F. Bai, Ruijie Liu","doi":"10.1109/ICDSP.2018.8631672","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631672","url":null,"abstract":"In this paper, we present an improved nonparallel hyperplanes classifier for multi-class classification, termed as INHCMC. As in the nonparallel support vector machine (NPSVM) for binary classification, the ε-insensitive loss function is adopted in the primal problems of multi-class classification to improve the sparseness associated with the nonparallel hyperplanes classifier for multi-class classification (NHCMC) where the quadratic loss function is used. Experimental results on some benchmark datasets are reported to show the effectiveness of our method in terms of sparseness and classification accuracy.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122052358","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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