Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313218
Lingqing Gan, P. Djurić
Models of growing networks have attracted a lot of interest during the past few years. An important question about these models is to decide which model explains an observed network formation most accurately. In this work, we propose a Bayesian model selection scheme which chooses the best model based on predictive distributions. The procedure was investigated on three types of models, including the random model, the preferential attachment model and the hybrid model. With the hybrid model, we leverage results on imperfect Bernoulli trial experiments to obtain the posterior distribution of the weight parameter, which is characterized as a polynomial function on the interval [0,1]. A Beta distribution is used to approximate the posterior in order to reduce the growing computational and representation complexity. Simulations in accordance with the proposed scheme are carried out. They demonstrate validity of the proposed approach.
{"title":"Bayesian selection of models of network formation","authors":"Lingqing Gan, P. Djurić","doi":"10.1109/CAMSAP.2017.8313218","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313218","url":null,"abstract":"Models of growing networks have attracted a lot of interest during the past few years. An important question about these models is to decide which model explains an observed network formation most accurately. In this work, we propose a Bayesian model selection scheme which chooses the best model based on predictive distributions. The procedure was investigated on three types of models, including the random model, the preferential attachment model and the hybrid model. With the hybrid model, we leverage results on imperfect Bernoulli trial experiments to obtain the posterior distribution of the weight parameter, which is characterized as a polynomial function on the interval [0,1]. A Beta distribution is used to approximate the posterior in order to reduce the growing computational and representation complexity. Simulations in accordance with the proposed scheme are carried out. They demonstrate validity of the proposed approach.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116893750","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313121
Dimitris G. Chachlakis, Panos P. Markopoulos, F. Ahmad
Standard direction-of-arrival estimation using coprime arrays samples the entries of the estimated physical-array autocorrelation matrix, organizes them in a matrix structure, and conducts multiple-signal classification (MUSIC) with singular vectors of the resulting matrix. A majority of the existing literature samples the physical-array autocorrelations by selection, retaining only one of the samples that correspond to each element of the difference coarray. Other more recent works conduct averaging of all samples that relate to each coarray element. Even though the two methods coincide when applied on the nominal/true physical-array autocorrelations, their performance differs significantly when applied on finite-snapshot estimates. In this paper, we present for the first time in closed form the mean-squared-error of both selection and averaging autocorrelation sampling and clarify/establish the superiority of the latter.
{"title":"The Mean-Squared-Error of autocorrelation sampling in coprime arrays","authors":"Dimitris G. Chachlakis, Panos P. Markopoulos, F. Ahmad","doi":"10.1109/CAMSAP.2017.8313121","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313121","url":null,"abstract":"Standard direction-of-arrival estimation using coprime arrays samples the entries of the estimated physical-array autocorrelation matrix, organizes them in a matrix structure, and conducts multiple-signal classification (MUSIC) with singular vectors of the resulting matrix. A majority of the existing literature samples the physical-array autocorrelations by selection, retaining only one of the samples that correspond to each element of the difference coarray. Other more recent works conduct averaging of all samples that relate to each coarray element. Even though the two methods coincide when applied on the nominal/true physical-array autocorrelations, their performance differs significantly when applied on finite-snapshot estimates. In this paper, we present for the first time in closed form the mean-squared-error of both selection and averaging autocorrelation sampling and clarify/establish the superiority of the latter.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"501 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900695","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313160
Fei Hua, Roula Nassif, C. Richard, Haiyan Wang
We consider distributed estimation problems over multitask networks where the parameter vectors at distinct agents are coupled via a set of linear equality constraints. Unlike previous existing works, the current work assumes that each constraint involves agents that are not necessarily one-hop neighbors. At each time instant, we assume that each agent has access to the instantaneous estimates of its one-hop neighbors and to the past estimates of its multi-hop neighbors through a multi-hop relay protocol. A distributed penalty-based algorithm is then derived and its performance analyses in the mean and in the mean-square-error sense are provided. Simulation results show the effectiveness of the strategy and validate the theoretical models.
{"title":"Penalty-Based multitask estimation with non-local linear equality constraints","authors":"Fei Hua, Roula Nassif, C. Richard, Haiyan Wang","doi":"10.1109/CAMSAP.2017.8313160","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313160","url":null,"abstract":"We consider distributed estimation problems over multitask networks where the parameter vectors at distinct agents are coupled via a set of linear equality constraints. Unlike previous existing works, the current work assumes that each constraint involves agents that are not necessarily one-hop neighbors. At each time instant, we assume that each agent has access to the instantaneous estimates of its one-hop neighbors and to the past estimates of its multi-hop neighbors through a multi-hop relay protocol. A distributed penalty-based algorithm is then derived and its performance analyses in the mean and in the mean-square-error sense are provided. Simulation results show the effectiveness of the strategy and validate the theoretical models.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121187065","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313141
Martijn Boussé, L. D. Lathauwer
The canonical polyadic decomposition (CPD) is an important tensor tool in signal processing with various applications in blind source separation and sensor array processing. Many algorithms have been developed for the computation of a CPD using a least squares cost function. Standard least-squares methods assumes that the residuals are uncorrelated and have equal variances which is often not true in practice, rendering the approach suboptimal. Weighted least squares allows one to explicitly accommodate for general (co)variances in the cost function. In this paper, we develop a new nonlinear least-squares algorithm for the computation of a CPD using low-rank weights which enables efficient weighting of the residuals. We briefly illustrate our algorithm for direction-of-arrival estimation using an array of sensors with varying quality.
{"title":"Nonlinear least squares algorithm for canonical polyadic decomposition using low-rank weights","authors":"Martijn Boussé, L. D. Lathauwer","doi":"10.1109/CAMSAP.2017.8313141","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313141","url":null,"abstract":"The canonical polyadic decomposition (CPD) is an important tensor tool in signal processing with various applications in blind source separation and sensor array processing. Many algorithms have been developed for the computation of a CPD using a least squares cost function. Standard least-squares methods assumes that the residuals are uncorrelated and have equal variances which is often not true in practice, rendering the approach suboptimal. Weighted least squares allows one to explicitly accommodate for general (co)variances in the cost function. In this paper, we develop a new nonlinear least-squares algorithm for the computation of a CPD using low-rank weights which enables efficient weighting of the residuals. We briefly illustrate our algorithm for direction-of-arrival estimation using an array of sensors with varying quality.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262550","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313153
O. Musa, Gabor Hannak, N. Goertz
Compressed sensing (CS) is a novel technique that allows for stable reconstruction with sampling rate lower than Nyquist rate if the unknown vector is sparse. In many practical applications CS measurements are first scalar quantized and later corrupted in different ways. Reconstruction by conventional techniques on such highly distorted measurements will result in poor accuracy. To address this problem, we use the well established generalized approximate message passing (GAMP) algorithm and tailor it for quantized CS measurements corrupted with noise. We provide the necessary expressions for the nonlinear updates for different noise models, namely the symmetric discrete memoryless channel (SDMC) and the additive white Gaussian noise (AWGN) channel. Numerical results show superiority of the GAMP algorithm compared to conventional reconstruction algorithms in both SDMC and AWGN channels.
{"title":"Efficient recovery from noisy quantized compressed sensing using generalized approximate message passing","authors":"O. Musa, Gabor Hannak, N. Goertz","doi":"10.1109/CAMSAP.2017.8313153","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313153","url":null,"abstract":"Compressed sensing (CS) is a novel technique that allows for stable reconstruction with sampling rate lower than Nyquist rate if the unknown vector is sparse. In many practical applications CS measurements are first scalar quantized and later corrupted in different ways. Reconstruction by conventional techniques on such highly distorted measurements will result in poor accuracy. To address this problem, we use the well established generalized approximate message passing (GAMP) algorithm and tailor it for quantized CS measurements corrupted with noise. We provide the necessary expressions for the nonlinear updates for different noise models, namely the symmetric discrete memoryless channel (SDMC) and the additive white Gaussian noise (AWGN) channel. Numerical results show superiority of the GAMP algorithm compared to conventional reconstruction algorithms in both SDMC and AWGN channels.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115458219","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313110
F. Tivive, A. Bouzerdoum
Through-the-wall radar uses electromagnetic waves to detect and discern targets behind opaque obstacles, such as doors and walls. Wall clutter mitigation and scene reconstruction are performed to produce the image of the behind-the-wall scene. These two problems, however, are often addressed separately, which may result in a suboptimal solution. In this paper, the wall clutter removal and image formation are unified as a joint low-rank and sparsity constrained optimization problem, which is solved using augmented Lagrange multiplier method. Experimental results shows that the proposed method produces clearer images than the existing method that uses a wall clutter mitigation method in conjunction with backprojection method for imaging.
{"title":"Joint low-rank and sparse based image reconstruction for through-the-wall radar imaging","authors":"F. Tivive, A. Bouzerdoum","doi":"10.1109/CAMSAP.2017.8313110","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313110","url":null,"abstract":"Through-the-wall radar uses electromagnetic waves to detect and discern targets behind opaque obstacles, such as doors and walls. Wall clutter mitigation and scene reconstruction are performed to produce the image of the behind-the-wall scene. These two problems, however, are often addressed separately, which may result in a suboptimal solution. In this paper, the wall clutter removal and image formation are unified as a joint low-rank and sparsity constrained optimization problem, which is solved using augmented Lagrange multiplier method. Experimental results shows that the proposed method produces clearer images than the existing method that uses a wall clutter mitigation method in conjunction with backprojection method for imaging.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114893932","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313115
K. Slavakis, Gaurav N. Shetty, Abhishek Bose, Ukash Nakarmi, L. Ying
This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application to dynamic magnetic resonance imaging (dMRI). The framework comprises several modules: First, a set of landmark points is identified to describe concisely a data cloud formed by highly under-sampled dMRI data, and second, low-dimensional renditions of the landmark points are computed. Searching for the linear operator that decompresses low-dimensional data to high-dimensional ones, and for those combinations of landmark points which approximate the manifold data by affine patches, leads to a bi-linear model of the dMRI data, cognizant of the intrinsic data geometry. Preliminary numerical tests on synthetically generated dMRI phantoms, and comparisons with state-of-the-art reconstruction techniques, underline the rich potential of the proposed method for the recovery of highly under-sampled dMRI data.
{"title":"Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery","authors":"K. Slavakis, Gaurav N. Shetty, Abhishek Bose, Ukash Nakarmi, L. Ying","doi":"10.1109/CAMSAP.2017.8313115","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313115","url":null,"abstract":"This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application to dynamic magnetic resonance imaging (dMRI). The framework comprises several modules: First, a set of landmark points is identified to describe concisely a data cloud formed by highly under-sampled dMRI data, and second, low-dimensional renditions of the landmark points are computed. Searching for the linear operator that decompresses low-dimensional data to high-dimensional ones, and for those combinations of landmark points which approximate the manifold data by affine patches, leads to a bi-linear model of the dMRI data, cognizant of the intrinsic data geometry. Preliminary numerical tests on synthetically generated dMRI phantoms, and comparisons with state-of-the-art reconstruction techniques, underline the rich potential of the proposed method for the recovery of highly under-sampled dMRI data.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"IM-30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624419","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313192
B. Mark, Garvesh Raskutti, R. Willett
Multivariate Poisson autoregressive models are a common way of capturing self-exciting point processes, where cascading series of events from nodes in a network either stimulate or inhibit events from other nodes. These models can be used to learn the structure of social or biological neural networks. An important problem associated with these multivariate network models is determining how different nodes influence each other. This problem presents a number of technical challenges since the number of nodes is typically large relative to the number of observed events. This paper addresses these challenges and provides learning rates for a class of multivariate self-exciting Poisson autoregressive models. Importantly, the derived learning rates apply in the high-dimensional setting when our network is sparse. We also provide a real data example to support our methodology and main results.
{"title":"Network estimation via poisson autoregressive models","authors":"B. Mark, Garvesh Raskutti, R. Willett","doi":"10.1109/CAMSAP.2017.8313192","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313192","url":null,"abstract":"Multivariate Poisson autoregressive models are a common way of capturing self-exciting point processes, where cascading series of events from nodes in a network either stimulate or inhibit events from other nodes. These models can be used to learn the structure of social or biological neural networks. An important problem associated with these multivariate network models is determining how different nodes influence each other. This problem presents a number of technical challenges since the number of nodes is typically large relative to the number of observed events. This paper addresses these challenges and provides learning rates for a class of multivariate self-exciting Poisson autoregressive models. Importantly, the derived learning rates apply in the high-dimensional setting when our network is sparse. We also provide a real data example to support our methodology and main results.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"2674 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126131585","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313074
Rong Yang, Y. Bar-Shalom
It is desirable for a sensor to keep silent to avoid being detected. Passive tracking is therefore preferred as it estimates target trajectories through “listening” to the signals emitted by others without any emission. The multistatic concept can be used for this application, where the receiver (or the listener) is considered as own sensor, and the transmitters can be emitters deployed on stationary or moving platforms. Such a multistatic system requires the positions of the transmitters to be known by the receiver. Unfortunately, this is not always true for non-cooperative transmitters (especially for moving transmitters), who do not inform the receiver their positions timely. This paper proposes a multistatic configuration with a receiver and two transmitters with unknown position. This configuration can provide good observability for the trajectories of the transmitters and targets based on the measured bearings and the time-difference-of-arrival (TDOA) of the direct and indirect path signals. A two-stage unscented Kalman filter (UKF) is developed to track the transmitters and target simultaneously. Unlike the algorithms from the literature which assume known transmitter positions, the algorithm of this paper estimates the state of the target while adapting itself to the moving transmitters' locations. Simulation tests are conducted to show the filter performance.
{"title":"Adaptive target tracking using multistatic sensor with unknown moving transmitter positions","authors":"Rong Yang, Y. Bar-Shalom","doi":"10.1109/CAMSAP.2017.8313074","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313074","url":null,"abstract":"It is desirable for a sensor to keep silent to avoid being detected. Passive tracking is therefore preferred as it estimates target trajectories through “listening” to the signals emitted by others without any emission. The multistatic concept can be used for this application, where the receiver (or the listener) is considered as own sensor, and the transmitters can be emitters deployed on stationary or moving platforms. Such a multistatic system requires the positions of the transmitters to be known by the receiver. Unfortunately, this is not always true for non-cooperative transmitters (especially for moving transmitters), who do not inform the receiver their positions timely. This paper proposes a multistatic configuration with a receiver and two transmitters with unknown position. This configuration can provide good observability for the trajectories of the transmitters and targets based on the measured bearings and the time-difference-of-arrival (TDOA) of the direct and indirect path signals. A two-stage unscented Kalman filter (UKF) is developed to track the transmitters and target simultaneously. Unlike the algorithms from the literature which assume known transmitter positions, the algorithm of this paper estimates the state of the target while adapting itself to the moving transmitters' locations. Simulation tests are conducted to show the filter performance.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126207964","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313151
Y. Abramovich, G. S. Antonio, Stephen T. Mondschein
Accelerating targets need to be detected, estimated, and (if strong enough) removed from input data for weak target detection. In this paper, we derive analytical expressions for probability of false alarm and detection of the non-linear detector (suggested in [1]) for accelerating targets based on Wigner-Ville transformation. Simulation results validate the derived formulas and demonstrate relatively high performance of the Winger-Ville detector.
{"title":"Operational characteristics of wigner-ville accelerating target detector","authors":"Y. Abramovich, G. S. Antonio, Stephen T. Mondschein","doi":"10.1109/CAMSAP.2017.8313151","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313151","url":null,"abstract":"Accelerating targets need to be detected, estimated, and (if strong enough) removed from input data for weak target detection. In this paper, we derive analytical expressions for probability of false alarm and detection of the non-linear detector (suggested in [1]) for accelerating targets based on Wigner-Ville transformation. Simulation results validate the derived formulas and demonstrate relatively high performance of the Winger-Ville detector.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130520028","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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