Pub Date : 2012-03-25DOI: 10.1109/ICASSP.2012.6288088
Takahiro Ogawa, M. Haseyama
This paper presents a perceptually optimized subspace estimation method for missing texture reconstruction. The proposed method calculates the optimal subspace of known patches within a target image based on structural similarity (SSIM) index instead of calculating mean square error (MSE)-based eigenspace. Furthermore, from the obtained subspace, missing texture reconstruction whose results maximize the SSIM index is performed. In this approach, the non-convex maximization problem is reformulated as a quasi convex problem, and the reconstruction of the missing textures becomes feasible. Experimental results show that our method overcomes previously reported MSE-based reconstruction methods.
{"title":"Perceptually optimized subspace estimation for missing texture reconstruction","authors":"Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICASSP.2012.6288088","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288088","url":null,"abstract":"This paper presents a perceptually optimized subspace estimation method for missing texture reconstruction. The proposed method calculates the optimal subspace of known patches within a target image based on structural similarity (SSIM) index instead of calculating mean square error (MSE)-based eigenspace. Furthermore, from the obtained subspace, missing texture reconstruction whose results maximize the SSIM index is performed. In this approach, the non-convex maximization problem is reformulated as a quasi convex problem, and the reconstruction of the missing textures becomes feasible. Experimental results show that our method overcomes previously reported MSE-based reconstruction methods.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"31 1","pages":"1141-1144"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74316886","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288716
Hwanjoon Kwon, B. Rao
We study the problem of support recovery of block-sparse signals, where nonzero entries occur in clusters, via random noisy measurements. By drawing analogy between the problem of block-sparse signal recovery and the problem of communication over Gaussian multi-input and single-output multiple access channel, we derive the sufficient and necessary condition under which exact support recovery is possible. Based on the results, we show that block-sparse signals can reduce the number of measurements required for exact support recovery, by at least `1/(block size)', compared to conventional or scalar-sparse signals. The minimum gain is guaranteed by increased signal to noise power ratio (SNR) and reduced effective number of entries (i.e., not individual elements but blocks) that are dominant at low SNR and at high SNR, respectively. When the correlation between the elements of each nonzero block is low, a larger gain than `1/(block size)' is expected due to, so called, diversity effect, especially in the moderate and low SNR regime.
{"title":"On the benefits of the block-sparsity structure in sparse signal recovery","authors":"Hwanjoon Kwon, B. Rao","doi":"10.1109/ICASSP.2012.6288716","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288716","url":null,"abstract":"We study the problem of support recovery of block-sparse signals, where nonzero entries occur in clusters, via random noisy measurements. By drawing analogy between the problem of block-sparse signal recovery and the problem of communication over Gaussian multi-input and single-output multiple access channel, we derive the sufficient and necessary condition under which exact support recovery is possible. Based on the results, we show that block-sparse signals can reduce the number of measurements required for exact support recovery, by at least `1/(block size)', compared to conventional or scalar-sparse signals. The minimum gain is guaranteed by increased signal to noise power ratio (SNR) and reduced effective number of entries (i.e., not individual elements but blocks) that are dominant at low SNR and at high SNR, respectively. When the correlation between the elements of each nonzero block is low, a larger gain than `1/(block size)' is expected due to, so called, diversity effect, especially in the moderate and low SNR regime.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"8 1","pages":"3685-3688"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75226328","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288257
R. Naini, P. Moulin
In this paper, decoding metrics are designed for statistical fingerprint-based content identification. A fairly general class of structured codes is considered, and a statistical model for the resulting fingerprints and their degraded versions (following miscellaneous content distortions) is proposed and validated. The Maximum-Likelihood fingerprint decoder derived from this model is shown to considerably improve upon previous decoders based on the Hamming metric. A GLRT test is also proposed and evaluated to deal with unknown distortion channels.
{"title":"Model-based decoding metrics for content identification","authors":"R. Naini, P. Moulin","doi":"10.1109/ICASSP.2012.6288257","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288257","url":null,"abstract":"In this paper, decoding metrics are designed for statistical fingerprint-based content identification. A fairly general class of structured codes is considered, and a statistical model for the resulting fingerprints and their degraded versions (following miscellaneous content distortions) is proposed and validated. The Maximum-Likelihood fingerprint decoder derived from this model is shown to considerably improve upon previous decoders based on the Hamming metric. A GLRT test is also proposed and evaluated to deal with unknown distortion channels.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"18 1","pages":"1829-1832"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75405201","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6289095
R. Mudumbai, P. Bidigare, Scott Pruessing, S. Dasgupta, Miguel Oyarzyn, David Raeman
We explore a class of techniques for distributed transmit beamforming where the beamforming target sends cumulative feedback that is broadcast to all of the beamforming nodes. The simplest technique in this class is a 1-bit RSS feedback algorithm that has been studied in detail in the literature. Under this 1-bit algorithm, transmitters make random phase perturbations and the receiver periodically sends 1 bit of feedback indicating whether the received signal strength has increased or not compared to what was observed in the past. While this simple algorithm has very attractive properties such as dynamic tracking of time-varying phases, robustness to noise and other disturbances and is also simple to implement, we show in this paper that it also has serious limitations such as slow convergence and poor tracking performance in the presence of frequency offsets between the transmitters. We then show that enhanced feedback algorithms where the receiver sends as feedback several bits of feedback indicating the amplitude and phase of the received signal over time, are able to achieve beamforming in the presence of frequency offsets and large feedback channel latencies, while retaining the scalability and robustness of the 1-bit algorithm.
{"title":"Scalable feedback algorithms for distributed transmit beamforming in wireless networks","authors":"R. Mudumbai, P. Bidigare, Scott Pruessing, S. Dasgupta, Miguel Oyarzyn, David Raeman","doi":"10.1109/ICASSP.2012.6289095","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6289095","url":null,"abstract":"We explore a class of techniques for distributed transmit beamforming where the beamforming target sends cumulative feedback that is broadcast to all of the beamforming nodes. The simplest technique in this class is a 1-bit RSS feedback algorithm that has been studied in detail in the literature. Under this 1-bit algorithm, transmitters make random phase perturbations and the receiver periodically sends 1 bit of feedback indicating whether the received signal strength has increased or not compared to what was observed in the past. While this simple algorithm has very attractive properties such as dynamic tracking of time-varying phases, robustness to noise and other disturbances and is also simple to implement, we show in this paper that it also has serious limitations such as slow convergence and poor tracking performance in the presence of frequency offsets between the transmitters. We then show that enhanced feedback algorithms where the receiver sends as feedback several bits of feedback indicating the amplitude and phase of the received signal over time, are able to achieve beamforming in the presence of frequency offsets and large feedback channel latencies, while retaining the scalability and robustness of the 1-bit algorithm.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"57 1","pages":"5213-5216"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74693428","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6289040
Lucie Daubigney, M. Geist, O. Pietquin
Reinforcement learning (RL) is now part of the state of the art in the domain of spoken dialogue systems (SDS) optimisation. Most performant RL methods, such as those based on Gaussian Processes, require to test small changes in the policy to assess them as improvements or degradations. This process is called on policy learning. Nevertheless, it can result in system behaviours that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. Such methods usually fail to scale up and are thus not suited for real-world systems. In this contribution, a sample-efficient, online and off-policy RL algorithm is proposed to learn an optimal policy. This algorithm is combined to a compact non-linear value function representation (namely a multi-layers perceptron) enabling to handle large scale systems.
{"title":"Off-policy learning in large-scale POMDP-based dialogue systems","authors":"Lucie Daubigney, M. Geist, O. Pietquin","doi":"10.1109/ICASSP.2012.6289040","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6289040","url":null,"abstract":"Reinforcement learning (RL) is now part of the state of the art in the domain of spoken dialogue systems (SDS) optimisation. Most performant RL methods, such as those based on Gaussian Processes, require to test small changes in the policy to assess them as improvements or degradations. This process is called on policy learning. Nevertheless, it can result in system behaviours that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. Such methods usually fail to scale up and are thus not suited for real-world systems. In this contribution, a sample-efficient, online and off-policy RL algorithm is proposed to learn an optimal policy. This algorithm is combined to a compact non-linear value function representation (namely a multi-layers perceptron) enabling to handle large scale systems.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"109 1","pages":"4989-4992"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74747166","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288755
N. Kalantarova, Mehmet A. Donmez, S. Kozat
We study robust least squares problem with bounded data uncertainties in a competitive algorithm framework. We propose a competitive least squares (LS) approach that minimizes the worst case “regret” which is the difference between the squared data error and the smallest attainable squared data error of an LS estimator. We illustrate that the robust least squares problem can be put in an SDP form for both structured and unstructured data matrices and uncertainties. Through numerical examples we demonstrate the potential merit of the proposed approaches.
{"title":"Competitive least squares problem with bounded data uncertainties","authors":"N. Kalantarova, Mehmet A. Donmez, S. Kozat","doi":"10.1109/ICASSP.2012.6288755","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288755","url":null,"abstract":"We study robust least squares problem with bounded data uncertainties in a competitive algorithm framework. We propose a competitive least squares (LS) approach that minimizes the worst case “regret” which is the difference between the squared data error and the smallest attainable squared data error of an LS estimator. We illustrate that the robust least squares problem can be put in an SDP form for both structured and unstructured data matrices and uncertainties. Through numerical examples we demonstrate the potential merit of the proposed approaches.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"3841-3844"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75304374","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288493
Carlos Medina, I. Alvarez, J. C. Segura, Á. D. L. Torre, M. C. Benítez
This paper proposes TELIAMADE (an indoor location system based on ultrasonic and radiofrequency signals) to be used as a teaching tool in the context of Telecommunication Engineering. Due to its simple design, the versatility of its configuration and the characteristics of the involved signals, TELIAMADE is an appropriate tool for teaching basic aspects in location systems, digital communication systems, encoded signalling, microcontroller programming, radio protocols or advanced signal processing techniques. The TELIAMADE design allows students to sample, store and analyze signals at different points of the circuits by using conventional oscilloscopes. Furthermore, some parameters can be configured, allowing students to assess the advantages and inconveniences of each specific configuration with respect to features such as bit-rate, range, robustness against noise or updating period. Our system presents advantages in the field of teaching for understanding commercial systems for location (like GPS) or communication (like wireless digital communication systems).
{"title":"TELIAMADE ultrasonic indoor location system: Application as a teaching tool","authors":"Carlos Medina, I. Alvarez, J. C. Segura, Á. D. L. Torre, M. C. Benítez","doi":"10.1109/ICASSP.2012.6288493","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288493","url":null,"abstract":"This paper proposes TELIAMADE (an indoor location system based on ultrasonic and radiofrequency signals) to be used as a teaching tool in the context of Telecommunication Engineering. Due to its simple design, the versatility of its configuration and the characteristics of the involved signals, TELIAMADE is an appropriate tool for teaching basic aspects in location systems, digital communication systems, encoded signalling, microcontroller programming, radio protocols or advanced signal processing techniques. The TELIAMADE design allows students to sample, store and analyze signals at different points of the circuits by using conventional oscilloscopes. Furthermore, some parameters can be configured, allowing students to assess the advantages and inconveniences of each specific configuration with respect to features such as bit-rate, range, robustness against noise or updating period. Our system presents advantages in the field of teaching for understanding commercial systems for location (like GPS) or communication (like wireless digital communication systems).","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"2777-2780"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75321016","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288000
S. Chérigui, C. Guillemot, D. Thoreau, P. Guillotel, P. Pérez
Template matching has been shown to outperform the H.264 prediction modes for Intra video coding thanks to better spatial prediction and no additional ancillary data to transmit. The method indeed works well when the template and the block to be predicted are highly correlated, e.g., in homogenous image areas, however, it obviously fails in areas with non homogeneous textures. This paper explores the idea of using a block-matching intra prediction algorithm which, thanks to a Rate-Distorsion (RD) based decision mechanism, will naturally be used in image areas when template matching (TM) fails. This new method offers a significant coding gain compared to H.264 Intra prediction modes and the template matching based prediction. Indeed, the TM-based algorithm and the proposed hybrid algorithm lead, with the Bjontergaard measure, to rate gains of up to respectively 38.02% and 48.38% at low bitrates when compared with H.264 Intra only.
{"title":"Hybrid template and block matching algorithm for image intra prediction","authors":"S. Chérigui, C. Guillemot, D. Thoreau, P. Guillotel, P. Pérez","doi":"10.1109/ICASSP.2012.6288000","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288000","url":null,"abstract":"Template matching has been shown to outperform the H.264 prediction modes for Intra video coding thanks to better spatial prediction and no additional ancillary data to transmit. The method indeed works well when the template and the block to be predicted are highly correlated, e.g., in homogenous image areas, however, it obviously fails in areas with non homogeneous textures. This paper explores the idea of using a block-matching intra prediction algorithm which, thanks to a Rate-Distorsion (RD) based decision mechanism, will naturally be used in image areas when template matching (TM) fails. This new method offers a significant coding gain compared to H.264 Intra prediction modes and the template matching based prediction. Indeed, the TM-based algorithm and the proposed hybrid algorithm lead, with the Bjontergaard measure, to rate gains of up to respectively 38.02% and 48.38% at low bitrates when compared with H.264 Intra only.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"17 1","pages":"781-784"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75468680","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288712
G. Pope, Christoph Studer, M. Baes
This paper deals with the recovery of signals that admit an approximately sparse representation in some known dictionary (possibly over-complete) and are corrupted by additive noise. In particular, we consider additive measurement noise with bounded ℓp-norm for p ≥ 2, and we minimize the ℓq quasi-norm (with q ∈ (0, 1]) of the signal vector. We develop coherence-based recovery guarantees for which stable recovery via generalized basis-pursuit de-quantizing (BPDQp,q) is possible. We finally show that depending on the measurement-noise model and the choice of the ℓp-norm used in the constraint, (BPDQp,q) significantly outperforms classical basis pursuit de-noising (BPDN).
{"title":"Coherence-based recovery guarantees for generalized basis-pursuit de-quantizing","authors":"G. Pope, Christoph Studer, M. Baes","doi":"10.1109/ICASSP.2012.6288712","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288712","url":null,"abstract":"This paper deals with the recovery of signals that admit an approximately sparse representation in some known dictionary (possibly over-complete) and are corrupted by additive noise. In particular, we consider additive measurement noise with bounded ℓ<sub>p</sub>-norm for p ≥ 2, and we minimize the ℓ<sub>q</sub> quasi-norm (with q ∈ (0, 1]) of the signal vector. We develop coherence-based recovery guarantees for which stable recovery via generalized basis-pursuit de-quantizing (BPDQ<sub>p,q</sub>) is possible. We finally show that depending on the measurement-noise model and the choice of the ℓ<sub>p</sub>-norm used in the constraint, (BPDQ<sub>p,q</sub>) significantly outperforms classical basis pursuit de-noising (BPDN).","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"183 1","pages":"3669-3672"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73688973","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 : 2012-03-25DOI: 10.1109/ICASSP.2012.6288680
Zhenhua Zhou, H. So, Frankie K. W. Chan
In this paper, the problem of fundamental frequency estimation for real harmonic sinusoids is addressed. By making use of the subspace technique and Markov-based eigenanalysis, an optimally weighted harmonic multiple signal classification (OW-HMUSIC) estimator is devised. The fundamental frequency estimates are computed in an iterative manner. The performance of the proposed method is derived. Computer simulations are performed to compare the proposed approach with nonlinear least squares and HMUSIC methods as well as Cramér-Rao lower bound.
{"title":"Optimally weighted music algorithm for frequency estimation of real harmonic sinusoids","authors":"Zhenhua Zhou, H. So, Frankie K. W. Chan","doi":"10.1109/ICASSP.2012.6288680","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288680","url":null,"abstract":"In this paper, the problem of fundamental frequency estimation for real harmonic sinusoids is addressed. By making use of the subspace technique and Markov-based eigenanalysis, an optimally weighted harmonic multiple signal classification (OW-HMUSIC) estimator is devised. The fundamental frequency estimates are computed in an iterative manner. The performance of the proposed method is derived. Computer simulations are performed to compare the proposed approach with nonlinear least squares and HMUSIC methods as well as Cramér-Rao lower bound.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"11 1","pages":"3537-3540"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78405217","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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