Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646569
L. N. Theagarajan
The problem of recovering a sparse matrix X from its sketch AXBT is referred to as the matrix sketching problem. Typically, the sketch is a lower dimensional matrix compared to X, and the sketching matrices A and B are known. Matrix sketching algorithms have been developed in the past to recover matrices from a continuous valued vectorspace (e.g., ℝN×N). However, employing such algorithms to recover discrete valued matrices may not be optimal. In this paper, we propose two novel algorithms that can efficiently recover a discrete valued sparse matrix from its sketch. We consider sparse matrices whose non-zero entries belong to a finite set. First, using the well known orthogonal matching pursuit (OMP), we present a matrix sketching algorithm. Second, we present a low-complexity message passing based recovery algorithm which exploits any sparsity structure that is present in X. Our simulation results verify that the proposed algorithms outperform the state-of-art matrix sketching algorithms in recovering discrete valued sparse matrices.
{"title":"SKETCHING DISCRETE VALUED SPARSE MATRICES","authors":"L. N. Theagarajan","doi":"10.1109/GlobalSIP.2018.8646569","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646569","url":null,"abstract":"The problem of recovering a sparse matrix X from its sketch AXBT is referred to as the matrix sketching problem. Typically, the sketch is a lower dimensional matrix compared to X, and the sketching matrices A and B are known. Matrix sketching algorithms have been developed in the past to recover matrices from a continuous valued vectorspace (e.g., ℝN×N). However, employing such algorithms to recover discrete valued matrices may not be optimal. In this paper, we propose two novel algorithms that can efficiently recover a discrete valued sparse matrix from its sketch. We consider sparse matrices whose non-zero entries belong to a finite set. First, using the well known orthogonal matching pursuit (OMP), we present a matrix sketching algorithm. Second, we present a low-complexity message passing based recovery algorithm which exploits any sparsity structure that is present in X. Our simulation results verify that the proposed algorithms outperform the state-of-art matrix sketching algorithms in recovering discrete valued sparse matrices.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"20 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":"126393412","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/GlobalSIP.2018.8645970
P. Stinco, A. Tesei, A. Maguer, Fabrizio Ferraioli, V. Latini, Luca Pesa
This paper addresses the problem of Port-Starboard (PS) beamforming for low-frequency active sonar (LFAS) with a triplet receiver array. The paper presents a new algorithm for sub-bands beam-space adaptive beamforming with twist compensation and evaluates its performance with experimental data collected at sea. The results show that the algorithm provides the ability to solve the PS ambiguity with a strong PS rejection even at end-fire where ordinary triplet beamformers have poor performance, allowing to unmask targets in the presence of strong coastal reverberation and/or traffic noise.
{"title":"Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays","authors":"P. Stinco, A. Tesei, A. Maguer, Fabrizio Ferraioli, V. Latini, Luca Pesa","doi":"10.1109/GlobalSIP.2018.8645970","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8645970","url":null,"abstract":"This paper addresses the problem of Port-Starboard (PS) beamforming for low-frequency active sonar (LFAS) with a triplet receiver array. The paper presents a new algorithm for sub-bands beam-space adaptive beamforming with twist compensation and evaluates its performance with experimental data collected at sea. The results show that the algorithm provides the ability to solve the PS ambiguity with a strong PS rejection even at end-fire where ordinary triplet beamformers have poor performance, allowing to unmask targets in the presence of strong coastal reverberation and/or traffic noise.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"17 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":"126517839","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/GlobalSIP.2018.8646451
Z. Xiong, Xiaoming Tao, Nan Zhao, Baihong Lin
Single image super-resolution (SR), which intends to recover a high-resolution (HR) image from a single low-resolution (LR) image, has attracted increasing attentions with a wide range of applications. In this paper, we propose a novel non-local scheme based on a 3D convolutional neural network (3DCNN) for image super-resolution. Different from most previous methods, our scheme takes the inherent non-local self-similarity property of natural images into account. Specifically, the non-local similar patches are searched and extracted from low-resolution images. Then a 3DCNN is constructed to jointly sharpen these non-local patches, which can make full use of the non-local similarity in natural images. Finally, the super-resolved image is reconstructed from the sharpened patches. Experiments show that the proposed non-local method achieves the superior reconstruction accuracy compared with several state-of-the-art methods.
{"title":"SINGLE IMAGE SUPER-RESOLUTION USING A NON-LOCAL 3D CONVOLUTIONAL NEURAL NETWORK","authors":"Z. Xiong, Xiaoming Tao, Nan Zhao, Baihong Lin","doi":"10.1109/GlobalSIP.2018.8646451","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646451","url":null,"abstract":"Single image super-resolution (SR), which intends to recover a high-resolution (HR) image from a single low-resolution (LR) image, has attracted increasing attentions with a wide range of applications. In this paper, we propose a novel non-local scheme based on a 3D convolutional neural network (3DCNN) for image super-resolution. Different from most previous methods, our scheme takes the inherent non-local self-similarity property of natural images into account. Specifically, the non-local similar patches are searched and extracted from low-resolution images. Then a 3DCNN is constructed to jointly sharpen these non-local patches, which can make full use of the non-local similarity in natural images. Finally, the super-resolved image is reconstructed from the sharpened patches. Experiments show that the proposed non-local method achieves the superior reconstruction accuracy compared with several state-of-the-art methods.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"27 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":"128324466","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/GlobalSIP.2018.8646579
A. Choudhury, S. Daly
We present a Full-Reference Image Quality Assessment (FR-IQA) approach to improve High Dynamic Range (HDR) IQA by combining results from various quality metrics (HDR-CQM). To combine these results, we apply linear regression and various machine learning techniques such as multilayer perceptron, random forest, random trees, radial basis function network and support vector machine (SVM) regression. We found that using a non-linear combination of scores from different quality metrics using SVM is better at prediction than the other techniques. We use the Sequential Forward Floating Selection technique to select a subset of metrics from a list of quality metrics to improve performance and reduce complexity. We demonstrate improved performance using HDR-CQM as compared to a number of existing IQA metrics. We find that our HDR-CQM metric comprised of only four metrics can obtain statistically significant improvement over HDR video quality measure (HDR-VQM), the best performing individual IQA metric for HDR still images.
{"title":"HDR IMAGE QUALITY ASSESSMENT USING MACHINE-LEARNING BASED COMBINATION OF QUALITY METRICS","authors":"A. Choudhury, S. Daly","doi":"10.1109/GlobalSIP.2018.8646579","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646579","url":null,"abstract":"We present a Full-Reference Image Quality Assessment (FR-IQA) approach to improve High Dynamic Range (HDR) IQA by combining results from various quality metrics (HDR-CQM). To combine these results, we apply linear regression and various machine learning techniques such as multilayer perceptron, random forest, random trees, radial basis function network and support vector machine (SVM) regression. We found that using a non-linear combination of scores from different quality metrics using SVM is better at prediction than the other techniques. We use the Sequential Forward Floating Selection technique to select a subset of metrics from a list of quality metrics to improve performance and reduce complexity. We demonstrate improved performance using HDR-CQM as compared to a number of existing IQA metrics. We find that our HDR-CQM metric comprised of only four metrics can obtain statistically significant improvement over HDR video quality measure (HDR-VQM), the best performing individual IQA metric for HDR still images.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"17 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":"120943275","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/GlobalSIP.2018.8646354
A. Zeineddine, S. Paquelet, M. Kanj, C. Moy, A. Nafkha, P. Jezequel
In this paper, a novel method for designing reconfigurable Newton structure is proposed. The Newton structure is a variable fractional delay filter that implements Lagrange interpolation only. This structure has been extended later in the literature to implement other types of interpolation, such as Spline and Hermite interpolations. Our proposed method develops dynamically reconfigurable Newton structures that allow implementing different interpolation methods using the same fixed hardware implementation. This method allows reconfigurability through one input variable. Moreover, we present two sample rate conversion examples where we show how our method allows adapting the interpolation method to the processed signal, maximizing thereby the filtering performance.
{"title":"RECONFIGURABLE NEWTON STRUCTURE FOR SAMPLE RATE CONVERSION","authors":"A. Zeineddine, S. Paquelet, M. Kanj, C. Moy, A. Nafkha, P. Jezequel","doi":"10.1109/GlobalSIP.2018.8646354","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646354","url":null,"abstract":"In this paper, a novel method for designing reconfigurable Newton structure is proposed. The Newton structure is a variable fractional delay filter that implements Lagrange interpolation only. This structure has been extended later in the literature to implement other types of interpolation, such as Spline and Hermite interpolations. Our proposed method develops dynamically reconfigurable Newton structures that allow implementing different interpolation methods using the same fixed hardware implementation. This method allows reconfigurability through one input variable. Moreover, we present two sample rate conversion examples where we show how our method allows adapting the interpolation method to the processed signal, maximizing thereby the filtering performance.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"250 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":"123343202","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/GLOBALSIP.2018.8646574
Gregory H. Canal, S. Manivasagam, Shaoheng Liang, C. Rozell
We consider the problem of interactively specifying an object segment in an image in an efficient and robust manner via binary inputs corrupted by noise. Our method frames interactive segmentation as a communications system with feedback and leverages a simple channel coding scheme to allow a user to select a segment from an ordered lexicon of segments for a given image. We propose an intuitive lexicon based on ellipses (EllipseLex) and evaluate its ability to specify desired object segments over increasing numbers of inputs at various levels of input noise, and compare it to a baseline algorithm. After evaluating the performance of each method on the Microsoft Common Objects in Context (MS-COCO) dataset using several metrics, we find that our method exhibits competitive performance when specifying real-world objects in images.
{"title":"INTERACTIVE OBJECT SEGMENTATION WITH NOISY BINARY INPUTS","authors":"Gregory H. Canal, S. Manivasagam, Shaoheng Liang, C. Rozell","doi":"10.1109/GLOBALSIP.2018.8646574","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646574","url":null,"abstract":"We consider the problem of interactively specifying an object segment in an image in an efficient and robust manner via binary inputs corrupted by noise. Our method frames interactive segmentation as a communications system with feedback and leverages a simple channel coding scheme to allow a user to select a segment from an ordered lexicon of segments for a given image. We propose an intuitive lexicon based on ellipses (EllipseLex) and evaluate its ability to specify desired object segments over increasing numbers of inputs at various levels of input noise, and compare it to a baseline algorithm. After evaluating the performance of each method on the Microsoft Common Objects in Context (MS-COCO) dataset using several metrics, we find that our method exhibits competitive performance when specifying real-world objects in images.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"58 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":"129546375","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/GLOBALSIP.2018.8646630
K. Wu, P. Cosman, L. Milstein
The impact of joint partial-band, partial-time jamming on a multi-carrier asynchronous DS-CDMA system in a Rayleigh fading environment is studied. Two types of partial-time jamming are studied: equal probability jamming per symbol, and burst jamming. An easy to evaluate upper bound using the Chernoff bound is provided and compared to simulation results.
{"title":"JOINT PARTIAL-TIME PARTIAL-BAND JAMMING OF A MULTICARRIER DS-CDMA SYSTEM IN A FADING ENVIRONMENT","authors":"K. Wu, P. Cosman, L. Milstein","doi":"10.1109/GLOBALSIP.2018.8646630","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646630","url":null,"abstract":"The impact of joint partial-band, partial-time jamming on a multi-carrier asynchronous DS-CDMA system in a Rayleigh fading environment is studied. Two types of partial-time jamming are studied: equal probability jamming per symbol, and burst jamming. An easy to evaluate upper bound using the Chernoff bound is provided and compared to simulation results.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"124 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":"131180059","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/GlobalSIP.2018.8645976
G. Abreu, Alireza Ghods
We introduce a complex-domain1 reformulation of the super multidimensional scaling (SMDS) wireless localization framework, obtaining from it an entirely new method to extract accurate location information from hybrid (angles and distances) information. Specifically, under this reformulation, the SMDS edge kernel is complex-valued and its block structure exposes clear relationships between anchor-to-anchor, anchor-to-target and target-to-target information dependencies. Exploiting these features, several new SMDS algorithms are designed which not only eliminate the need for eigen-decompositions in favor of much simpler vector multiplication operations similar to maximum ratio combining, but also are suited to particular data erasure structures emerging from typical and practical conditions faced by wireless localization systems. It is shown that these new algorithms offer different complexity/performance improvements, culminating with a new iterative design which is both faster and more accurate than the original SMDS method.
{"title":"Turbo MRC-SMDS: Low-complexity Cooperative Localization from Hybrid Information","authors":"G. Abreu, Alireza Ghods","doi":"10.1109/GlobalSIP.2018.8645976","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8645976","url":null,"abstract":"We introduce a complex-domain1 reformulation of the super multidimensional scaling (SMDS) wireless localization framework, obtaining from it an entirely new method to extract accurate location information from hybrid (angles and distances) information. Specifically, under this reformulation, the SMDS edge kernel is complex-valued and its block structure exposes clear relationships between anchor-to-anchor, anchor-to-target and target-to-target information dependencies. Exploiting these features, several new SMDS algorithms are designed which not only eliminate the need for eigen-decompositions in favor of much simpler vector multiplication operations similar to maximum ratio combining, but also are suited to particular data erasure structures emerging from typical and practical conditions faced by wireless localization systems. It is shown that these new algorithms offer different complexity/performance improvements, culminating with a new iterative design which is both faster and more accurate than the original SMDS method.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"373 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":"132552906","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/GLOBALSIP.2018.8646600
M. AlHajri, N. Ali, R. Shubair
Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements. Results obtained demonstrate that the combination of received signal strength (RSS) and channel transfer function (CTF) yields a classification accuracy of 83.0% for identifying the type of the indoor environment.
{"title":"A Machine Learning Approach for the Classification of Indoor Environments Using RF Signatures","authors":"M. AlHajri, N. Ali, R. Shubair","doi":"10.1109/GLOBALSIP.2018.8646600","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646600","url":null,"abstract":"Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements. Results obtained demonstrate that the combination of received signal strength (RSS) and channel transfer function (CTF) yields a classification accuracy of 83.0% for identifying the type of the indoor environment.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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":"130089294","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/GLOBALSIP.2018.8646640
R. Sohrabi, Y. Hua
This study investigates a secure wireless communication scheme which combines two of the most effective strategies to combat (passive) eavesdropping, namely mixing information with artificial noise at the transmitter and jamming from a full-duplex receiver. All nodes are assumed to possess multiple antennas, which is known as a MIMOME network. The channel state information (CSI) of Eve is known to Eve but not to Alice and Bob. While such setup has been investigated in related works, new and important insights are revealed in this work. We investigate the design of optimal jamming parameters to achieve higher secrecy, and in particular we focus on two important cases corresponding to Bob using either a simple jamming or a smart jamming. Furthermore, simulations are presented to highlight the effectiveness of the proposed strategies.
{"title":"A NEW LOOK AT SECRECY CAPACITY OF MIMOME USING ARTIFICIAL NOISE FROM ALICE AND BOB WITHOUT KNOWLEDGE OF EVE’S CSI","authors":"R. Sohrabi, Y. Hua","doi":"10.1109/GLOBALSIP.2018.8646640","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646640","url":null,"abstract":"This study investigates a secure wireless communication scheme which combines two of the most effective strategies to combat (passive) eavesdropping, namely mixing information with artificial noise at the transmitter and jamming from a full-duplex receiver. All nodes are assumed to possess multiple antennas, which is known as a MIMOME network. The channel state information (CSI) of Eve is known to Eve but not to Alice and Bob. While such setup has been investigated in related works, new and important insights are revealed in this work. We investigate the design of optimal jamming parameters to achieve higher secrecy, and in particular we focus on two important cases corresponding to Bob using either a simple jamming or a smart jamming. Furthermore, simulations are presented to highlight the effectiveness of the proposed strategies.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"14 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":"114376717","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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