Pub Date : 2005-12-13DOI: 10.1109/CAMAP.2005.1574225
D. Gutiérrez, A. Nehorai
We present analytical forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG) assuming a single-shell ellipsoidal geometry that approximates the anatomy of the head and a dipole current models the source. The structure of our solution facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Furthermore, the use of an ellipsoidal geometry is useful for cases when incorporating the anisotropy of the head is important but a better model cannot be defined
{"title":"Array response kernels for EEG/MEG in single-shell ellipsoidal geometry","authors":"D. Gutiérrez, A. Nehorai","doi":"10.1109/CAMAP.2005.1574225","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574225","url":null,"abstract":"We present analytical forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG) assuming a single-shell ellipsoidal geometry that approximates the anatomy of the head and a dipole current models the source. The structure of our solution facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Furthermore, the use of an ellipsoidal geometry is useful for cases when incorporating the anisotropy of the head is important but a better model cannot be defined","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134248","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574174
F. Ahmad, M. Amin, S. Kassam
A data-adaptive stepped-frequency synthetic aperture radar system based on quadratically constrained Capon beamforming is presented for through-the-wall wideband microwave imaging applications. Various effects of the presence of the wall, such as refraction, change in speed, and attenuation, are incorporated into the beamformer design. Proof of concept is provided using real data collected in a laboratory environment. The results show that the proposed Capon beamformer outperforms the non-adaptive through-wall delay-and-sum beamformer.
{"title":"A beamforming approach to stepped-frequency synthetic aperture through-the-wall radar imaging","authors":"F. Ahmad, M. Amin, S. Kassam","doi":"10.1109/CAMAP.2005.1574174","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574174","url":null,"abstract":"A data-adaptive stepped-frequency synthetic aperture radar system based on quadratically constrained Capon beamforming is presented for through-the-wall wideband microwave imaging applications. Various effects of the presence of the wall, such as refraction, change in speed, and attenuation, are incorporated into the beamformer design. Proof of concept is provided using real data collected in a laboratory environment. The results show that the proposed Capon beamformer outperforms the non-adaptive through-wall delay-and-sum beamformer.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076249","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574202
M. Rajih, P. Comon
When the number of inputs (sources) is larger than the number of outputs (observations), linear mixtures are referred to as Under-Determined (UDM). The algorithms proposed here aim at identifying UDM using the second characteristic function (c.f.) of observations, without any need of sparsity assumption on sources, but assuming their statistical independence. The first algorithm, already proposed by the authors in P. Comon and M. Rajih (2005), assumes that the source c.f.'s are unknown. In this paper, a variant of the algorithm is described, which allows to take into account the knowledge of source c.f.'s. Performances of both algorithms are compared based on computer simulations
{"title":"Blind identification of under-determined mixtures based on the characteristic function: influence of the knowledge of source PDF's","authors":"M. Rajih, P. Comon","doi":"10.1109/CAMAP.2005.1574202","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574202","url":null,"abstract":"When the number of inputs (sources) is larger than the number of outputs (observations), linear mixtures are referred to as Under-Determined (UDM). The algorithms proposed here aim at identifying UDM using the second characteristic function (c.f.) of observations, without any need of sparsity assumption on sources, but assuming their statistical independence. The first algorithm, already proposed by the authors in P. Comon and M. Rajih (2005), assumes that the source c.f.'s are unknown. In this paper, a variant of the algorithm is described, which allows to take into account the knowledge of source c.f.'s. Performances of both algorithms are compared based on computer simulations","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114486348","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574176
R. Rao, B. Himed
High resolution imaging of objects in the Terahertz region of the electromagnetic spectrum requires operating in the near field region. Correlative interferometric approaches based on mathematical expressions for near field measurements are developed for reconstructing images of 1-D extended objects. The approach relies on providing constrained least squares fit between computed autocorrelation from sensor measurements and the expression for the near field autocorrelation.
{"title":"Correlative interferometric imaging of extended objects for near field arrays","authors":"R. Rao, B. Himed","doi":"10.1109/CAMAP.2005.1574176","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574176","url":null,"abstract":"High resolution imaging of objects in the Terahertz region of the electromagnetic spectrum requires operating in the near field region. Correlative interferometric approaches based on mathematical expressions for near field measurements are developed for reconstructing images of 1-D extended objects. The approach relies on providing constrained least squares fit between computed autocorrelation from sensor measurements and the expression for the near field autocorrelation.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121520873","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574217
Yan Gao, M. Schubert
We address the problem of multi-stream multicasting, where independent data streams are transmitted from a multi-antenna transmitter to several groups of independent single-antenna receivers. Multi-antenna multicasting allows forming spatial beams. By properly shaping the beam patterns, intergroup interference can be avoided and the SINR at all receivers can be jointly controlled. In addition, we assume that dirty paper preceding (DPC) is employed at the transmitter to further reduce the interference. We first consider the problem of minimizing the total power subject to individual SINR constraints. We propose a near-optimal algorithm based on semidefinite relaxation, combined with optimal power allocation. We also derive a low-complexity algorithm, which shows good performance in the low-SNR regime. Then, we consider the problem of maximizing the worst-case SINR subject to a total power constraint. We propose a near-optimal solution based on a bisection strategy
{"title":"Group-oriented beamforming for multi-stream multicasting based on quality-of-service requirements","authors":"Yan Gao, M. Schubert","doi":"10.1109/CAMAP.2005.1574217","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574217","url":null,"abstract":"We address the problem of multi-stream multicasting, where independent data streams are transmitted from a multi-antenna transmitter to several groups of independent single-antenna receivers. Multi-antenna multicasting allows forming spatial beams. By properly shaping the beam patterns, intergroup interference can be avoided and the SINR at all receivers can be jointly controlled. In addition, we assume that dirty paper preceding (DPC) is employed at the transmitter to further reduce the interference. We first consider the problem of minimizing the total power subject to individual SINR constraints. We propose a near-optimal algorithm based on semidefinite relaxation, combined with optimal power allocation. We also derive a low-complexity algorithm, which shows good performance in the low-SNR regime. Then, we consider the problem of maximizing the worst-case SINR subject to a total power constraint. We propose a near-optimal solution based on a bisection strategy","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124140757","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574178
J. Singh
Though there are a no. of methods for target tracking described in literature like Kalman filtering, extended Kalman filtering, Bayesian approach, IMM-PDA, ML-PDA, particle filters, random set theory, covariance intersection, neuro-fuzzy methods, tracking through genetic algorithms and so on, the goal has always been to bring adaptivity to tackle the changing situations. Since, no one sensor can perform well in all the conditions, Multi-sensor adaptive processing has been the inherent focus. This paper presents a brief account of the target tracking algorithms developed till date and to be developed in future and brings out the main development trends. As a novel way of presentation, a Boston Consulting Group (BCG) matrix analysis has been performed and the algorithms have been classified in four classes i.e. Question marks, stars, cash cows and dogs. It has been applied to the radar target tracking algorithms. The evolution and further discussion about future trends clearly show a shift towards knowledge based adaptivity and sensor fusion. Though a number of papers have come out bringing complete account of target tracking algorithms but their presentation format does not provide a way of their practical utilization in the system development. The mathematical formulations are complex and mixing is too much for a non-expert or even a system manager to take decisions. Thus a need was felt to provide a suitable format to the decision makers and provide the non-expert a balanced simple account of the algorithms. Further, a knowledge based perspective has been brought out well in this paper. Knowledge based theme though shown in target tracking here is not limited but applies to other areas of radar, ATR, air traffic control & collision avoidance, network centric warfare etc. also. Latest knowledge based research has been incorporated in a broader sense to cover ANNs, CI, fuzzy etc. also.
{"title":"Evolution of the radar target tracking algorithms: a move towards knowledge based multi-sensor adaptive processing","authors":"J. Singh","doi":"10.1109/CAMAP.2005.1574178","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574178","url":null,"abstract":"Though there are a no. of methods for target tracking described in literature like Kalman filtering, extended Kalman filtering, Bayesian approach, IMM-PDA, ML-PDA, particle filters, random set theory, covariance intersection, neuro-fuzzy methods, tracking through genetic algorithms and so on, the goal has always been to bring adaptivity to tackle the changing situations. Since, no one sensor can perform well in all the conditions, Multi-sensor adaptive processing has been the inherent focus. This paper presents a brief account of the target tracking algorithms developed till date and to be developed in future and brings out the main development trends. As a novel way of presentation, a Boston Consulting Group (BCG) matrix analysis has been performed and the algorithms have been classified in four classes i.e. Question marks, stars, cash cows and dogs. It has been applied to the radar target tracking algorithms. The evolution and further discussion about future trends clearly show a shift towards knowledge based adaptivity and sensor fusion. Though a number of papers have come out bringing complete account of target tracking algorithms but their presentation format does not provide a way of their practical utilization in the system development. The mathematical formulations are complex and mixing is too much for a non-expert or even a system manager to take decisions. Thus a need was felt to provide a suitable format to the decision makers and provide the non-expert a balanced simple account of the algorithms. Further, a knowledge based perspective has been brought out well in this paper. Knowledge based theme though shown in target tracking here is not limited but applies to other areas of radar, ATR, air traffic control & collision avoidance, network centric warfare etc. also. Latest knowledge based research has been incorporated in a broader sense to cover ANNs, CI, fuzzy etc. also.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120826006","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574207
H. T. Nguyen, G. Leus, N. Khaled
In wireless communications, mobility can make the available channel information out of date. A timely update of the channel state information is an obvious solution to improve the system performance in a time-varying channel. However, this comes at the cost of a decrease in the system throughput since many pilots have to be inserted. Thus, predicting the future channel conditions can improve not only the performance but also the throughput of many types of wireless systems. This is especially true when multiple antennas are applied at both link ends. In this paper, we propose and evaluate the performance of a prediction scheme for multiple input multiple output (MIMO) systems that apply spatial multiplexing. We aim at predicting the future precoder/decoder directly without going through the prediction of the channel matrix. The results show that in a slowly time-varying channel an increase in the system performance by a factor of two is possible
{"title":"Precoder and decoder prediction in time-varying MIMO channels","authors":"H. T. Nguyen, G. Leus, N. Khaled","doi":"10.1109/CAMAP.2005.1574207","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574207","url":null,"abstract":"In wireless communications, mobility can make the available channel information out of date. A timely update of the channel state information is an obvious solution to improve the system performance in a time-varying channel. However, this comes at the cost of a decrease in the system throughput since many pilots have to be inserted. Thus, predicting the future channel conditions can improve not only the performance but also the throughput of many types of wireless systems. This is especially true when multiple antennas are applied at both link ends. In this paper, we propose and evaluate the performance of a prediction scheme for multiple input multiple output (MIMO) systems that apply spatial multiplexing. We aim at predicting the future precoder/decoder directly without going through the prediction of the channel matrix. The results show that in a slowly time-varying channel an increase in the system performance by a factor of two is possible","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130726677","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574193
P. Djuric, M. Bugallo, Jae-Chan Lim
In this paper we address the problem of positioning of multiple targets based on measurements obtained by sensors comprising a sensor network. The measurements represent a superposition of signals that carry information about the positions of the various targets. The sensors send the sensed information to a fusion center that combines the received data from all the sensors and carries out necessary computations. The number of targets may vary with time in an unknown way. We propose a particle filtering-based method for detecting the number of active targets and for estimating their positions. The particle filtering was carried out on data that represent measurements of acoustic signals, but it can also be applied to other types of signals. We provide simulations that show the performance of the particle filtering method in scenarios with one and two targets.
{"title":"Positioning a time-varying number of targets by a wireless sensor network","authors":"P. Djuric, M. Bugallo, Jae-Chan Lim","doi":"10.1109/CAMAP.2005.1574193","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574193","url":null,"abstract":"In this paper we address the problem of positioning of multiple targets based on measurements obtained by sensors comprising a sensor network. The measurements represent a superposition of signals that carry information about the positions of the various targets. The sensors send the sensed information to a fusion center that combines the received data from all the sensors and carries out necessary computations. The number of targets may vary with time in an unknown way. We propose a particle filtering-based method for detecting the number of active targets and for estimating their positions. The particle filtering was carried out on data that represent measurements of acoustic signals, but it can also be applied to other types of signals. We provide simulations that show the performance of the particle filtering method in scenarios with one and two targets.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126691478","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574188
R. Mitric, P. Schiavone
A modified boundary integral equation method is used to solve a specific type of mixed boundary value problem in an enhanced theory of bending of elastic plates in which the effects of transverse shear deformation and transverse normal strain are taken into account. The boundary conditions consist of a combination of transverse displacement and bending and twisting moments. The discussion covers both interior and exterior problems, for which existence and uniqueness results are derived. This type of problem has direct application in the calibration and performance testing of mechanical sensors for large industrial structures.
{"title":"Integral methods for mechanical sensor design and performance testing in plates with transverse shear deformation and transverse normal strain","authors":"R. Mitric, P. Schiavone","doi":"10.1109/CAMAP.2005.1574188","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574188","url":null,"abstract":"A modified boundary integral equation method is used to solve a specific type of mixed boundary value problem in an enhanced theory of bending of elastic plates in which the effects of transverse shear deformation and transverse normal strain are taken into account. The boundary conditions consist of a combination of transverse displacement and bending and twisting moments. The discussion covers both interior and exterior problems, for which existence and uniqueness results are derived. This type of problem has direct application in the calibration and performance testing of mechanical sensors for large industrial structures.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"18 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126248473","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 : 2005-12-13DOI: 10.1109/CAMAP.2005.1574224
David Gutiérrez, Fabián Garcı́a-Nocetti, Julio Solano-González
We propose the use of length and energy transforms in the classification of multichannel EEG data to identify different cognitive activity using a reduced set of recording electrodes. The length transform (ET) represents a temporarily smoothed time course of the data, while the energy transform (ET) can be interpreted as a short-term energy estimate. The transformation of the data in the length/energy domain allows to effectively preserving important data features when autoregressive (AR) models are used to reduce the dimension of the classification problem. We evaluate the performance of the ET and ET on the classification of real cognitive EEG data for the case when the optimal AR model is selected under the Schwarz's Bayesian criterion (SBC) and a Mahalanobis distance-based classifier is used. Our results show that accurate classification is achieved when the data is transformed through the ET or ET even for low-order AR models, having the ET slightly better performance
{"title":"Classification of multichannel EEG data using length/energy transforms","authors":"David Gutiérrez, Fabián Garcı́a-Nocetti, Julio Solano-González","doi":"10.1109/CAMAP.2005.1574224","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574224","url":null,"abstract":"We propose the use of length and energy transforms in the classification of multichannel EEG data to identify different cognitive activity using a reduced set of recording electrodes. The length transform (ET) represents a temporarily smoothed time course of the data, while the energy transform (ET) can be interpreted as a short-term energy estimate. The transformation of the data in the length/energy domain allows to effectively preserving important data features when autoregressive (AR) models are used to reduce the dimension of the classification problem. We evaluate the performance of the ET and ET on the classification of real cognitive EEG data for the case when the optimal AR model is selected under the Schwarz's Bayesian criterion (SBC) and a Mahalanobis distance-based classifier is used. Our results show that accurate classification is achieved when the data is transformed through the ET or ET even for low-order AR models, having the ET slightly better performance","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123789693","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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