Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081627
Daniel Wȩsierski, A. Jezierska
In visual tracking of surgical instruments, correlation filtering finds the best candidate with maximal correlation peak. However, most trackers only consider capturing target appearance but not target structure. In this paper we propose surgical instrument tracking approach that integrates prior knowledge related to rotation of both shaft and tool tips. To this end, we employ rigid parts mixtures model of an instrument. The rigidly composed parts encode diverse, pose-specific appearance mixtures of the tool. Tracking search space is confined to the neighbourhood of tool position, scale, and rotation with respect to previous best estimate such that the rotation constraint translates into querying subset of templates. Qualitative and quantitative evaluation on challenging benchmarks demonstrate state-of-the-art results.
{"title":"Surgical tool tracking by on-line selection of structural correlation filters","authors":"Daniel Wȩsierski, A. Jezierska","doi":"10.23919/EUSIPCO.2017.8081627","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081627","url":null,"abstract":"In visual tracking of surgical instruments, correlation filtering finds the best candidate with maximal correlation peak. However, most trackers only consider capturing target appearance but not target structure. In this paper we propose surgical instrument tracking approach that integrates prior knowledge related to rotation of both shaft and tool tips. To this end, we employ rigid parts mixtures model of an instrument. The rigidly composed parts encode diverse, pose-specific appearance mixtures of the tool. Tracking search space is confined to the neighbourhood of tool position, scale, and rotation with respect to previous best estimate such that the rotation constraint translates into querying subset of templates. Qualitative and quantitative evaluation on challenging benchmarks demonstrate state-of-the-art results.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127239905","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081372
Somayeh Danafar, M. Piórkowski, Krzysztof Krysczcuk
Understanding human mobility patterns is of great importance for planning urban and extra-urban spaces and communication infrastructures. The omnipresence of mobile telephony in today's society opens new avenues of discovering the patterns of human mobility by means of analyzing cellular network data. Of particular interest is analyzing passively collected Network Events (NEs) due to their scalability. However, mobility pattern analysis based on network events is challenging because of the coarse granularity of NEs. In this paper, we propose network event-based Bayesian approaches for mobility pattern recognition and reconstruction, mode of transport recognition and modeling the frequent trajectories.
{"title":"Bayesian framework for mobility pattern discovery using mobile network events","authors":"Somayeh Danafar, M. Piórkowski, Krzysztof Krysczcuk","doi":"10.23919/EUSIPCO.2017.8081372","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081372","url":null,"abstract":"Understanding human mobility patterns is of great importance for planning urban and extra-urban spaces and communication infrastructures. The omnipresence of mobile telephony in today's society opens new avenues of discovering the patterns of human mobility by means of analyzing cellular network data. Of particular interest is analyzing passively collected Network Events (NEs) due to their scalability. However, mobility pattern analysis based on network events is challenging because of the coarse granularity of NEs. In this paper, we propose network event-based Bayesian approaches for mobility pattern recognition and reconstruction, mode of transport recognition and modeling the frequent trajectories.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127401927","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081337
Jani Saloranta, G. Destino
In addition to high data rate, millimeter-wave technology has great potential to provide extremely high localization accuracy. In this paper, we outline the benefits of this technology for positioning and their main applications, which are no longer confined to services only but also to improve communication. We shall focus on the trade-off between data communication and positioning looking the reconfiguration mechanisms of the radio interface. Specifically, in this paper we investigate a trade-off between achievable data rate and positioning capability via position and rotation error bound analysis, with the aim of achieving an optimal trade-off.
{"title":"Reconfiguration of 5G radio interface for positioning and communication","authors":"Jani Saloranta, G. Destino","doi":"10.23919/EUSIPCO.2017.8081337","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081337","url":null,"abstract":"In addition to high data rate, millimeter-wave technology has great potential to provide extremely high localization accuracy. In this paper, we outline the benefits of this technology for positioning and their main applications, which are no longer confined to services only but also to improve communication. We shall focus on the trade-off between data communication and positioning looking the reconfiguration mechanisms of the radio interface. Specifically, in this paper we investigate a trade-off between achievable data rate and positioning capability via position and rotation error bound analysis, with the aim of achieving an optimal trade-off.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115475658","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081588
A. Shamsabadi, M. Babaie-zadeh, Seyyede Zohreh Seyyedsalehi, H. Rabiee, C. Jutten
Data representation plays an important role in performance of machine learning algorithms. Since data usually lacks the desired quality, many efforts have been made to provide a more desirable representation of data. Among many different approaches, sparse data representation has gained popularity in recent years. In this paper, we propose a new sparse autoencoder by imposing the power two of smoothed L0 norm of data representation on the hidden layer of regular autoencoder. The square of smoothed L0 norm increases the tendency that each data representation is "individually" sparse. Moreover, by using the proposed sparse autoencoder, once the model parameters are learned, the sparse representation of any new data is obtained simply by a matrix-vector multiplication without performing any optimization. When applied to the MNIST, CIFAR-10, and OPTDIGITS datasets, the results show that the proposed model guarantees a sparse representation for each input data which leads to better classification results.
{"title":"A new algorithm for training sparse autoencoders","authors":"A. Shamsabadi, M. Babaie-zadeh, Seyyede Zohreh Seyyedsalehi, H. Rabiee, C. Jutten","doi":"10.23919/EUSIPCO.2017.8081588","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081588","url":null,"abstract":"Data representation plays an important role in performance of machine learning algorithms. Since data usually lacks the desired quality, many efforts have been made to provide a more desirable representation of data. Among many different approaches, sparse data representation has gained popularity in recent years. In this paper, we propose a new sparse autoencoder by imposing the power two of smoothed L0 norm of data representation on the hidden layer of regular autoencoder. The square of smoothed L0 norm increases the tendency that each data representation is \"individually\" sparse. Moreover, by using the proposed sparse autoencoder, once the model parameters are learned, the sparse representation of any new data is obtained simply by a matrix-vector multiplication without performing any optimization. When applied to the MNIST, CIFAR-10, and OPTDIGITS datasets, the results show that the proposed model guarantees a sparse representation for each input data which leads to better classification results.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115616647","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081390
Neetha Das, Simon Van Eyndhoven, T. Francart, A. Bertrand
Hearing prostheses have built-in algorithms to perform acoustic noise reduction and improve speech intelligibility. However, in a multi-speaker scenario the noise reduction algorithm has to determine which speaker the listener is focusing on, in order to enhance it while suppressing the other interfering sources. Recently, it has been demonstrated that it is possible to detect auditory attention using electroencephalography (EEG). In this paper, we use multi-channel Wiener filters (MWFs), to filter out each speech stream from the speech mixtures in the micro-phones of a binaural hearing aid, while also reducing background noise. From the demixed and denoised speech streams, we extract envelopes for an EEG-based auditory attention detection (AAD) algorithm. The AAD module can then select the output of the MWF corresponding to the attended speaker. We evaluate our algorithm in a two-speaker scenario in the presence of babble noise and compare it to a previously proposed algorithm. Our algorithm is observed to provide speech envelopes that yield better AAD accuracies, and is more robust to variations in speaker positions and diffuse background noise.
{"title":"EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters","authors":"Neetha Das, Simon Van Eyndhoven, T. Francart, A. Bertrand","doi":"10.23919/EUSIPCO.2017.8081390","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081390","url":null,"abstract":"Hearing prostheses have built-in algorithms to perform acoustic noise reduction and improve speech intelligibility. However, in a multi-speaker scenario the noise reduction algorithm has to determine which speaker the listener is focusing on, in order to enhance it while suppressing the other interfering sources. Recently, it has been demonstrated that it is possible to detect auditory attention using electroencephalography (EEG). In this paper, we use multi-channel Wiener filters (MWFs), to filter out each speech stream from the speech mixtures in the micro-phones of a binaural hearing aid, while also reducing background noise. From the demixed and denoised speech streams, we extract envelopes for an EEG-based auditory attention detection (AAD) algorithm. The AAD module can then select the output of the MWF corresponding to the attended speaker. We evaluate our algorithm in a two-speaker scenario in the presence of babble noise and compare it to a previously proposed algorithm. Our algorithm is observed to provide speech envelopes that yield better AAD accuracies, and is more robust to variations in speaker positions and diffuse background noise.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"394 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115916050","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081163
Shuo Jiang, Xingchen Wang, Maria Kyrarini, A. Gräser
In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating the missing peaks. The variance of segmented cycles' lengths is computed as a criterion for evaluating the performance of segmentation. The proposed algorithm is tested on gait signals of patients diagnosed with Parkinson's disease collected from three databases. The segmentation results on three types of gait signals demonstrate the capability of the proposed algorithm to segment gait cycles accurately, and have achieved better performance than the original peak detection methods.
{"title":"A robust algorithm for gait cycle segmentation","authors":"Shuo Jiang, Xingchen Wang, Maria Kyrarini, A. Gräser","doi":"10.23919/EUSIPCO.2017.8081163","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081163","url":null,"abstract":"In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating the missing peaks. The variance of segmented cycles' lengths is computed as a criterion for evaluating the performance of segmentation. The proposed algorithm is tested on gait signals of patients diagnosed with Parkinson's disease collected from three databases. The segmentation results on three types of gait signals demonstrate the capability of the proposed algorithm to segment gait cycles accurately, and have achieved better performance than the original peak detection methods.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124209268","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081332
Wence Zhang, Xu Bao, Jisheng Dai
In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce the order of Landweber Method by introducing a set of coefficients and propose reduced order Landweber Method (ROLM) algorithm. A analysis on the convergence and the complexity is provided. Numerical results show that the proposed algorithms outperform the existing algorithm significantly when the system scale is large.
{"title":"Low-complexity detection based on landweber method in the uplink of Massive MIMO systems","authors":"Wence Zhang, Xu Bao, Jisheng Dai","doi":"10.23919/EUSIPCO.2017.8081332","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081332","url":null,"abstract":"In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce the order of Landweber Method by introducing a set of coefficients and propose reduced order Landweber Method (ROLM) algorithm. A analysis on the convergence and the complexity is provided. Numerical results show that the proposed algorithms outperform the existing algorithm significantly when the system scale is large.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124303981","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081551
Michael Ulrich, Y. Yang, Bin Yang
Multi-carrier (MC) multiple-input multiple-output (MIMO) radar offers an additional degree of freedom in the array optimization through the carrier frequencies. In this paper, we study the MC-MIMO array optimization with respect to the direction of arrival (DOA) estimation based on the Cramer-Rao bound (CRB). In particular, we choose the transmit and receive antenna positions as well as the carrier frequencies to minimize the single-target CRB subject to a constraint of the peak sidelobe level. A genetic algorithm is used to solve the problem and numerical examples demonstrate the superiority of our approach over both single-carrier MIMO radar and existing design rules.
{"title":"Design of multi-carrier MIMO radar array for DOA estimation","authors":"Michael Ulrich, Y. Yang, Bin Yang","doi":"10.23919/EUSIPCO.2017.8081551","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081551","url":null,"abstract":"Multi-carrier (MC) multiple-input multiple-output (MIMO) radar offers an additional degree of freedom in the array optimization through the carrier frequencies. In this paper, we study the MC-MIMO array optimization with respect to the direction of arrival (DOA) estimation based on the Cramer-Rao bound (CRB). In particular, we choose the transmit and receive antenna positions as well as the carrier frequencies to minimize the single-target CRB subject to a constraint of the peak sidelobe level. A genetic algorithm is used to solve the problem and numerical examples demonstrate the superiority of our approach over both single-carrier MIMO radar and existing design rules.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114405033","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081378
Aymeric Thibault, P. Bondon
Interest in risk measurement for high-frequency data has increased since the volume of high-frequency trading stepped up over the two last decades. This paper proposes a multimodal extension of the Exponential Power Distribution (EPD), called the Multimodal Asymmetric Exponential Power Distribution (MAEPD). We derive moments and we propose a convenient stochastic representation of the MAEPD. We establish consistency, asymptotic normality and efficiency of the maximum likelihood estimators (MLE). An application to risk measurement for high-frequency data is presented. An autoregressive moving average multiplicative component generalized autoregressive conditional heteroskedastic (ARMA-mcsGARCH) model is fitted to Financial Times Stock Exchange (FTSE) 100 intraday returns. Performances for Value-at-Risk (VaR) and Expected Shortfall (ES) estimation are evaluated. We show that the MAEPD outperforms commonly used distributions in risk measurement.
{"title":"A multimodal asymmetric exponential power distribution: Application to risk measurement for financial high-frequency data","authors":"Aymeric Thibault, P. Bondon","doi":"10.23919/EUSIPCO.2017.8081378","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081378","url":null,"abstract":"Interest in risk measurement for high-frequency data has increased since the volume of high-frequency trading stepped up over the two last decades. This paper proposes a multimodal extension of the Exponential Power Distribution (EPD), called the Multimodal Asymmetric Exponential Power Distribution (MAEPD). We derive moments and we propose a convenient stochastic representation of the MAEPD. We establish consistency, asymptotic normality and efficiency of the maximum likelihood estimators (MLE). An application to risk measurement for high-frequency data is presented. An autoregressive moving average multiplicative component generalized autoregressive conditional heteroskedastic (ARMA-mcsGARCH) model is fitted to Financial Times Stock Exchange (FTSE) 100 intraday returns. Performances for Value-at-Risk (VaR) and Expected Shortfall (ES) estimation are evaluated. We show that the MAEPD outperforms commonly used distributions in risk measurement.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114468955","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 : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081663
Avraam Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, Alexandros Iosifidis
Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The introduction of electronic trading and the availability of large amounts of data allow for developing novel machine learning techniques that address some of the difficulties faced by the aforementioned methods. In this work we propose a deep learning methodology, based on recurrent neural networks, that can be used for predicting future price movements from large-scale high-frequency time-series data on Limit Order Books. The proposed method is evaluated using a large-scale dataset of limit order book events.
{"title":"Using deep learning to detect price change indications in financial markets","authors":"Avraam Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, Alexandros Iosifidis","doi":"10.23919/EUSIPCO.2017.8081663","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081663","url":null,"abstract":"Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The introduction of electronic trading and the availability of large amounts of data allow for developing novel machine learning techniques that address some of the difficulties faced by the aforementioned methods. In this work we propose a deep learning methodology, based on recurrent neural networks, that can be used for predicting future price movements from large-scale high-frequency time-series data on Limit Order Books. The proposed method is evaluated using a large-scale dataset of limit order book events.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116891482","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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