Pub Date : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553299
K. Yamaoka, Andreas Brendel, Nobutaka Ono, S. Makino, M. Buerger, Takeshi Yamada, Walter Kellermann
In this paper, we present a speech enhancement method using two microphones for underdetermined situations. A conventional speech enhancement method for underdetermined situations is time-frequency masking, where speech is enhanced by multiplying zero or one to each time-frequency component appropriately. Extending this method, we switch multiple preconstructed beamformers at each time-frequency bin, each of which suppresses a particular interferer. This method can suppress an interferer even when both the target and an interferer are simultaneously active at a given time-frequency bin. As a switching criterion, selection of minimum value of the outputs of the all beamformers at each time-frequency bin is investigated. Additionally, another method using direction of arrival estimation is also investigated. In experiments, we confirmed that the proposed methods were superior to conventional time-frequency masking and fixed beamforming in the performance of speech enhancement.
{"title":"Time-Frequency-Bin-Wise Beamformer Selection and Masking for Speech Enhancement in Underdetermined Noisy Scenarios","authors":"K. Yamaoka, Andreas Brendel, Nobutaka Ono, S. Makino, M. Buerger, Takeshi Yamada, Walter Kellermann","doi":"10.23919/EUSIPCO.2018.8553299","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553299","url":null,"abstract":"In this paper, we present a speech enhancement method using two microphones for underdetermined situations. A conventional speech enhancement method for underdetermined situations is time-frequency masking, where speech is enhanced by multiplying zero or one to each time-frequency component appropriately. Extending this method, we switch multiple preconstructed beamformers at each time-frequency bin, each of which suppresses a particular interferer. This method can suppress an interferer even when both the target and an interferer are simultaneously active at a given time-frequency bin. As a switching criterion, selection of minimum value of the outputs of the all beamformers at each time-frequency bin is investigated. Additionally, another method using direction of arrival estimation is also investigated. In experiments, we confirmed that the proposed methods were superior to conventional time-frequency masking and fixed beamforming in the performance of speech enhancement.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132166895","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553209
Gyanajyoti Routray, R. Hegde
In this paper, a novel sparsity based framework is proposed for accurate spatial sound field reproduction in spherical harmonic domain. The proposed framework can effectively reduce the number of loudspeakers required to reproduce the desired sound field using higher order ambisonics (HOA) over a fixed listening area. Although H OA provides accurate reproduction of spatial sound, it has a disadvantage in terms of the restriction on the area of sound reproduction. This area can be increased with the increase in the number of loudspeakers during reproduction. In order to limit the use of a large number of loudspeakers the sparse nature of the weight vector in the HOA signal model is utilized in this work. The problem of obtaining the weight vector is first formulated as a constrained optimization problem which is difficult to solve due to orthogonality property of the spherical harmonic matrix. This problem is therefore reformulated to exploit the sparse nature of the weight vector. The solution is then obtained by using the Bregman iteration method. Experiments on sound field reproduction in free space using the proposed sparsity based method are conducted using loudspeaker arrays. Performance improvements are noted when compared to least squares and compressed sensing methods in terms of sound field reproduction accuracy, subjective, and objective evaluations.
{"title":"Sparsity Based Framework for Spatial Sound Reproduction in Spherical Harmonic Domain","authors":"Gyanajyoti Routray, R. Hegde","doi":"10.23919/EUSIPCO.2018.8553209","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553209","url":null,"abstract":"In this paper, a novel sparsity based framework is proposed for accurate spatial sound field reproduction in spherical harmonic domain. The proposed framework can effectively reduce the number of loudspeakers required to reproduce the desired sound field using higher order ambisonics (HOA) over a fixed listening area. Although H OA provides accurate reproduction of spatial sound, it has a disadvantage in terms of the restriction on the area of sound reproduction. This area can be increased with the increase in the number of loudspeakers during reproduction. In order to limit the use of a large number of loudspeakers the sparse nature of the weight vector in the HOA signal model is utilized in this work. The problem of obtaining the weight vector is first formulated as a constrained optimization problem which is difficult to solve due to orthogonality property of the spherical harmonic matrix. This problem is therefore reformulated to exploit the sparse nature of the weight vector. The solution is then obtained by using the Bregman iteration method. Experiments on sound field reproduction in free space using the proposed sparsity based method are conducted using loudspeaker arrays. Performance improvements are noted when compared to least squares and compressed sensing methods in terms of sound field reproduction accuracy, subjective, and objective evaluations.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396687","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553497
T. Emerson, T. Doster, C. Olson
We introduce a path-augmentation step to the standard orthogonal matching pursuit algorithm. Our augmentation may be applied to any algorithm that relies on the selection and sorting of high-correlation atoms during an analysis or identification phase by generating a “path” between the two highest-correlation atoms. Here we investigate two types of path: a linear combination (Euclidean geodesic) and a construction relying on an optimal transport map (2-Wasserstein geodesic). We test our extension by generating k-sparse reconstructions of faces using an eigen-face dictionary learned from a subset of the data. We show that our method achieves lower reconstruction error for fixed sparsity levels than either orthogonal matching pursuit or generalized orthogonal matching pursuit.
{"title":"Path Orthogonal Matching Pursuit for k-Sparse Image Reconstruction","authors":"T. Emerson, T. Doster, C. Olson","doi":"10.23919/EUSIPCO.2018.8553497","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553497","url":null,"abstract":"We introduce a path-augmentation step to the standard orthogonal matching pursuit algorithm. Our augmentation may be applied to any algorithm that relies on the selection and sorting of high-correlation atoms during an analysis or identification phase by generating a “path” between the two highest-correlation atoms. Here we investigate two types of path: a linear combination (Euclidean geodesic) and a construction relying on an optimal transport map (2-Wasserstein geodesic). We test our extension by generating k-sparse reconstructions of faces using an eigen-face dictionary learned from a subset of the data. We show that our method achieves lower reconstruction error for fixed sparsity levels than either orthogonal matching pursuit or generalized orthogonal matching pursuit.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132661763","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553206
P. Dawson, E. D. Sena, P. Naylor
The image-source method models the specular reflection from a plane by means of a secondary source positioned at the source's reflected image. The method has been widely used in acoustics to model the reverberant field of rectangular rooms, but can also be used for general-shaped rooms and non-flat reflectors. This paper explores the relationship between the physical properties of a non-flat reflector and the statistical properties of the associated cloud of image-sources. It is shown here that the standard deviation of the image-sources is strongly correlated with the ratio between depth and width of the reflector's spatial features.
{"title":"An acoustic image-source characterisation of surface profiles","authors":"P. Dawson, E. D. Sena, P. Naylor","doi":"10.23919/EUSIPCO.2018.8553206","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553206","url":null,"abstract":"The image-source method models the specular reflection from a plane by means of a secondary source positioned at the source's reflected image. The method has been widely used in acoustics to model the reverberant field of rectangular rooms, but can also be used for general-shaped rooms and non-flat reflectors. This paper explores the relationship between the physical properties of a non-flat reflector and the statistical properties of the associated cloud of image-sources. It is shown here that the standard deviation of the image-sources is strongly correlated with the ratio between depth and width of the reflector's spatial features.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134500192","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553237
Adarsh Kumar, N. Narendra, P. Balamuralidhar, M. Chandra
This paper considers the problem of super-resolution (SR) image reconstruction from a set of totally aliased low resolution (LR) images with different unknown sub-pixel offsets. By assuming the translational motion model, a linear compact representation between the LR image spectrums and SR image spectrum, based on multi-coset sampling is provided. Based on this model, we formulate the joint estimation of the unknown shifts and SR image spectrum as a dictionary learning problem and alternating minimization approach is employed to solve this joint estimation. Two different approaches for obtaining the SR image; one based on estimated shifts and another based on estimate SR spectrum are described. The significant advantage of the proposed approach is the smaller matrix sizes to be handled during the computation; typically on the order of number of images and enhancement factors, and is completely independent on the actual dimensions of LR and SR images, hence requiring significantly lesser resources than the current state of the art approaches. Brief simulation results are also provided to demonstrate the efficacy of this approach.
{"title":"Computationally Efficient Image Super Resolution from Totally Aliased Low Resolution Images","authors":"Adarsh Kumar, N. Narendra, P. Balamuralidhar, M. Chandra","doi":"10.23919/EUSIPCO.2018.8553237","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553237","url":null,"abstract":"This paper considers the problem of super-resolution (SR) image reconstruction from a set of totally aliased low resolution (LR) images with different unknown sub-pixel offsets. By assuming the translational motion model, a linear compact representation between the LR image spectrums and SR image spectrum, based on multi-coset sampling is provided. Based on this model, we formulate the joint estimation of the unknown shifts and SR image spectrum as a dictionary learning problem and alternating minimization approach is employed to solve this joint estimation. Two different approaches for obtaining the SR image; one based on estimated shifts and another based on estimate SR spectrum are described. The significant advantage of the proposed approach is the smaller matrix sizes to be handled during the computation; typically on the order of number of images and enhancement factors, and is completely independent on the actual dimensions of LR and SR images, hence requiring significantly lesser resources than the current state of the art approaches. Brief simulation results are also provided to demonstrate the efficacy of this approach.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822811","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553605
G. G. Calvi, I. Kisil, D. Mandic
Tensor networks (TNs) have been earning considerable attention as multiway data analysis tools owing to their ability to tackle the curse of dimensionality through the representation of large-scale tensors via smaller-scale interconnections of their intrinsic features. However, despite the obvious benefits, the current treatment of TNs as stand-alone entities does not take full advantage of their underlying structure and the associated feature localization. To this end, we exploit the analogy with feature fusion to propose a rigorous framework for the combination of TNs, with a particular focus on their summation as a natural way of their combination. The proposed framework is shown to allow for feature combination of any number of tensors, as long as their TN representation topologies are isomorphic. Simulations involving multi-class classification of an image dataset show the benefits of the proposed framework.
{"title":"Feature Fusion via Tensor Network Summation","authors":"G. G. Calvi, I. Kisil, D. Mandic","doi":"10.23919/EUSIPCO.2018.8553605","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553605","url":null,"abstract":"Tensor networks (TNs) have been earning considerable attention as multiway data analysis tools owing to their ability to tackle the curse of dimensionality through the representation of large-scale tensors via smaller-scale interconnections of their intrinsic features. However, despite the obvious benefits, the current treatment of TNs as stand-alone entities does not take full advantage of their underlying structure and the associated feature localization. To this end, we exploit the analogy with feature fusion to propose a rigorous framework for the combination of TNs, with a particular focus on their summation as a natural way of their combination. The proposed framework is shown to allow for feature combination of any number of tensors, as long as their TN representation topologies are isomorphic. Simulations involving multi-class classification of an image dataset show the benefits of the proposed framework.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"525 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133733408","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553169
Francisco Hawas, P. Djurić
We present a novel approach of graph representation based on mutual information of a random walk in a graph. This representation, as any global metric of a graph, can be used to identify the model generator of the observed network. In this study, we use our graph representation combined with Random Forest (RF) to discriminate between Erdos-Renyi (ER), Stochastic Block Model (SBM) and Planted Clique (PC) models. We also combine our graph representation with a Squared Mahalanobis Distance (SMD)-based test to reject a model given an observed network. We test the proposed method with computer simulations.
{"title":"Graph representation using mutual information for graph model discrimination","authors":"Francisco Hawas, P. Djurić","doi":"10.23919/EUSIPCO.2018.8553169","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553169","url":null,"abstract":"We present a novel approach of graph representation based on mutual information of a random walk in a graph. This representation, as any global metric of a graph, can be used to identify the model generator of the observed network. In this study, we use our graph representation combined with Random Forest (RF) to discriminate between Erdos-Renyi (ER), Stochastic Block Model (SBM) and Planted Clique (PC) models. We also combine our graph representation with a Squared Mahalanobis Distance (SMD)-based test to reject a model given an observed network. We test the proposed method with computer simulations.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208731","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553545
Sina Ghassemi, C. Sandu, A. Fiandrotti, F. G. Tonolo, P. Boccardo, Gianluca Francini, E. Magli
We address the problem of training a convolutional neural network for satellite images segmentation in emergency situations, where response time constraints prevent training the network from scratch. Such case is particularly challenging due to the large intra-class statistics variations between training images and images to be segmented captured at different locations by different sensors. We propose a convolutional encoder-decoder network architecture where the encoder builds upon a residual architecture. We show that our proposed architecture enables learning features suitable to generalize the learning process across images with different statistics. Our architecture can accurately segment images that have no reference in the training set, whereas a minimal refinement of the trained network significantly boosts the segmentation accuracy.
{"title":"Satellite Image Segmentation with Deep Residual Architectures for Time-Critical Applications","authors":"Sina Ghassemi, C. Sandu, A. Fiandrotti, F. G. Tonolo, P. Boccardo, Gianluca Francini, E. Magli","doi":"10.23919/EUSIPCO.2018.8553545","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553545","url":null,"abstract":"We address the problem of training a convolutional neural network for satellite images segmentation in emergency situations, where response time constraints prevent training the network from scratch. Such case is particularly challenging due to the large intra-class statistics variations between training images and images to be segmented captured at different locations by different sensors. We propose a convolutional encoder-decoder network architecture where the encoder builds upon a residual architecture. We show that our proposed architecture enables learning features suitable to generalize the learning process across images with different statistics. Our architecture can accurately segment images that have no reference in the training set, whereas a minimal refinement of the trained network significantly boosts the segmentation accuracy.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115398721","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553582
Y. Buchris, I. Cohen, J. Benesty
We present a joint-diagonalization based approach for a closed-form solution of the asymmetric supercardioid, implemented with circular differential microphone arrays. These arrays are characterized as compact frequency-invariant su-perdirective beamformers, allowing perfect steering for all azimuthal directions. Experimental results show that the asymmetric supercardioid yields superior performance in terms of white noise gain, directivity factor, and front-to-back ratio, when additional directional attenuation constraints are imposed in order to suppress interfering signals.
{"title":"Asymmetric Supercardioid Beamforming Using Circular Microphone Arrays","authors":"Y. Buchris, I. Cohen, J. Benesty","doi":"10.23919/EUSIPCO.2018.8553582","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553582","url":null,"abstract":"We present a joint-diagonalization based approach for a closed-form solution of the asymmetric supercardioid, implemented with circular differential microphone arrays. These arrays are characterized as compact frequency-invariant su-perdirective beamformers, allowing perfect steering for all azimuthal directions. Experimental results show that the asymmetric supercardioid yields superior performance in terms of white noise gain, directivity factor, and front-to-back ratio, when additional directional attenuation constraints are imposed in order to suppress interfering signals.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124162704","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553566
L. Eren, Y. Çekiç, M. Devaney
Bearing faults are by far the biggest single source of motor failures. Both fast Fourier (frequency based) and wavelet (time-scale based) transforms are used commonly in analyzing raw vibration or current data to detect bearing faults. A hybrid method, Empirical Wavelet Transform (EWT), is used in this study to provide better accuracy in detecting faults from bearing vibration data. In the proposed method, the raw vibration data is processed by fast Fourier transform. Then, the Fourier spectrum of the vibration signal is divided into segments adaptively with each segment containing part of the frequency band. Next, the wavelet transform is applied to all segments. Finally, inverse Fourier transform is utilized to obtain time domain signal with the frequency band of interest from EWT coefficients to detect bearing faults. The bearing fault related segments are identified by comparing rms values of healthy bearing vibration signal segments with the same segments of faulty bearing. The main advantage of the proposed method is the possibility of extracting the segments of interest from the original vibration data for determining both fault type and severity.
{"title":"Motor Condition Monitoring by Empirical Wavelet Transform","authors":"L. Eren, Y. Çekiç, M. Devaney","doi":"10.23919/EUSIPCO.2018.8553566","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553566","url":null,"abstract":"Bearing faults are by far the biggest single source of motor failures. Both fast Fourier (frequency based) and wavelet (time-scale based) transforms are used commonly in analyzing raw vibration or current data to detect bearing faults. A hybrid method, Empirical Wavelet Transform (EWT), is used in this study to provide better accuracy in detecting faults from bearing vibration data. In the proposed method, the raw vibration data is processed by fast Fourier transform. Then, the Fourier spectrum of the vibration signal is divided into segments adaptively with each segment containing part of the frequency band. Next, the wavelet transform is applied to all segments. Finally, inverse Fourier transform is utilized to obtain time domain signal with the frequency band of interest from EWT coefficients to detect bearing faults. The bearing fault related segments are identified by comparing rms values of healthy bearing vibration signal segments with the same segments of faulty bearing. The main advantage of the proposed method is the possibility of extracting the segments of interest from the original vibration data for determining both fault type and severity.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114360911","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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