Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606875
A. Hjørungnes, D. Palomar
A systematic and simple method is proposed for how to find the derivative of complex-valued matrix functions which depend on matrix arguments that contain patterns. The proposed method is developed by means of the chain rule and it is able to handle both linear and nonlinear patterns. One main issue of the proposed method is to identify a matrix function which depends on independent variables and its domain must have the same dimension as the dimension of the set of patterned matrices. In addition, this function must produce all the matrices within the pattern of interest. Some illustrative examples which are relevant for problems in the area of signal processing for communications are presented.
{"title":"Patterned complex-valued matrix derivatives","authors":"A. Hjørungnes, D. Palomar","doi":"10.1109/SAM.2008.4606875","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606875","url":null,"abstract":"A systematic and simple method is proposed for how to find the derivative of complex-valued matrix functions which depend on matrix arguments that contain patterns. The proposed method is developed by means of the chain rule and it is able to handle both linear and nonlinear patterns. One main issue of the proposed method is to identify a matrix function which depends on independent variables and its domain must have the same dimension as the dimension of the set of patterned matrices. In addition, this function must produce all the matrices within the pattern of interest. Some illustrative examples which are relevant for problems in the area of signal processing for communications are presented.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124434388","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606866
D. McGarry, D. Zasada, P. Sanyal, R. Perry
The ability to detect and track moving targets in synthetic aperture radar (SAR) images has become a topic of much current research. The authors have been reporting on a multi-channel phase interferometry technique for detecting moving targets in SAR images for some time. They have also shown that the phase interferometry between two channels can be utilized to cancel ground clutter from one channel by subtracting a phase-weighted version of the image from the other channel. This is analogous to, but not the same as, the well known DPCA (displaced phase center antenna) technique for clutter cancellation. This paper reports on a clustering technique we have successfully used to detect moving targets, which in almost all cases occupy many more than one or a few pixels in the clutter-cancelled SAR image. This technique greatly reduces false alarms.
{"title":"Using a clustering technique for detection of moving targets in clutter-cancelled QuickSAR images","authors":"D. McGarry, D. Zasada, P. Sanyal, R. Perry","doi":"10.1109/SAM.2008.4606866","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606866","url":null,"abstract":"The ability to detect and track moving targets in synthetic aperture radar (SAR) images has become a topic of much current research. The authors have been reporting on a multi-channel phase interferometry technique for detecting moving targets in SAR images for some time. They have also shown that the phase interferometry between two channels can be utilized to cancel ground clutter from one channel by subtracting a phase-weighted version of the image from the other channel. This is analogous to, but not the same as, the well known DPCA (displaced phase center antenna) technique for clutter cancellation. This paper reports on a clustering technique we have successfully used to detect moving targets, which in almost all cases occupy many more than one or a few pixels in the clutter-cancelled SAR image. This technique greatly reduces false alarms.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116166850","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606921
A. Karfoul, L. Albera, L. De Lathauwer
A new family of methods, named 2q-ORBIT (q > 1), is proposed in this paper in order to blindly identify potentially underdetermined mixtures of statistically independent sources. These methods are based on the canonical decomposition of q-th order (q ges 2) cumulants. The latter decomposition is brought back to the decomposition of a third order array whose one loading matrix is unitary. Such a decomposition is then computed by alterning and repeating two schemes until convergence: the first one consists in solving a Procrustes problem while the second one needs to compute the best rank-1 approximation of several q-th order arrays. Computer results show a good efficiency of the proposed methods with respect to classical cumulant-based algorithms especially in the underdetermined case.
{"title":"Canonical decomposition of even higher order cumulant arrays for blind underdetermined mixture identification","authors":"A. Karfoul, L. Albera, L. De Lathauwer","doi":"10.1109/SAM.2008.4606921","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606921","url":null,"abstract":"A new family of methods, named 2q-ORBIT (q > 1), is proposed in this paper in order to blindly identify potentially underdetermined mixtures of statistically independent sources. These methods are based on the canonical decomposition of q-th order (q ges 2) cumulants. The latter decomposition is brought back to the decomposition of a third order array whose one loading matrix is unitary. Such a decomposition is then computed by alterning and repeating two schemes until convergence: the first one consists in solving a Procrustes problem while the second one needs to compute the best rank-1 approximation of several q-th order arrays. Computer results show a good efficiency of the proposed methods with respect to classical cumulant-based algorithms especially in the underdetermined case.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124672563","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606849
V. Cevher, Richard Baraniuk
We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an lscr1-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.
{"title":"Compressive sensing for sensor calibration","authors":"V. Cevher, Richard Baraniuk","doi":"10.1109/SAM.2008.4606849","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606849","url":null,"abstract":"We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an lscr1-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124735292","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606915
A. J. Poulsen, R. Nadakuditi, A. Baggeroer
We present analytical results which quantify the effect of system mismatch and finite sample support on acoustic vector sensor array performance. One noteworthy result is that the vector aspect of the array ldquodampensrdquo the effect of array mismatch, enabling deeper true nulls. This is accomplished because the variance of the vector sensor array spatial response (due to rotational, positional and filter gain/phase perturbations) decreases in the sidelobes, unlike arrays of omnidirectional hydrophones. When sensor orientation is measured within a reasonable tolerance, the beampattern variance dominates the average sidelobe power response. Our analysis also suggests that vector sensor array gain performance is less sensitive to rotational than to positional perturbations in the regions of interest. We analytically characterize the eigen-SNR threshold, which depends on the signal and noise covariance and the number of noise-only and signal-plus-noise snapshots, below which (asymptotically speaking) reliable detection using sample eigenvalue based techniques is not possible. Thus for a given number of snapshots, since the dimensionality of the snapshot in a vector sensor array is larger than that of a hydrophone-only array, the eigen-SNR detection threshold will be greater whenever the eigenvector information is discarded. We present processing techniques customized to the unique characteristics of vector sensors, which exploit information encoded in the sample eigenvectors and are robust to the mismatch and finite sample support issues. These methods include adaptive processing techniques with multiple white noise constraints.
{"title":"Robust adaptive vector sensor processing in the presence of mismatch and finite sample support","authors":"A. J. Poulsen, R. Nadakuditi, A. Baggeroer","doi":"10.1109/SAM.2008.4606915","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606915","url":null,"abstract":"We present analytical results which quantify the effect of system mismatch and finite sample support on acoustic vector sensor array performance. One noteworthy result is that the vector aspect of the array ldquodampensrdquo the effect of array mismatch, enabling deeper true nulls. This is accomplished because the variance of the vector sensor array spatial response (due to rotational, positional and filter gain/phase perturbations) decreases in the sidelobes, unlike arrays of omnidirectional hydrophones. When sensor orientation is measured within a reasonable tolerance, the beampattern variance dominates the average sidelobe power response. Our analysis also suggests that vector sensor array gain performance is less sensitive to rotational than to positional perturbations in the regions of interest. We analytically characterize the eigen-SNR threshold, which depends on the signal and noise covariance and the number of noise-only and signal-plus-noise snapshots, below which (asymptotically speaking) reliable detection using sample eigenvalue based techniques is not possible. Thus for a given number of snapshots, since the dimensionality of the snapshot in a vector sensor array is larger than that of a hydrophone-only array, the eigen-SNR detection threshold will be greater whenever the eigenvector information is discarded. We present processing techniques customized to the unique characteristics of vector sensors, which exploit information encoded in the sample eigenvectors and are robust to the mismatch and finite sample support issues. These methods include adaptive processing techniques with multiple white noise constraints.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292427","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606820
J. Choi, B. Shim, A. Singer, N. Cho
In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.
{"title":"A low-complexity near-ML decoding technique via reduced dimension list stack algorithm","authors":"J. Choi, B. Shim, A. Singer, N. Cho","doi":"10.1109/SAM.2008.4606820","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606820","url":null,"abstract":"In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284482","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606822
J. Lopez Vicario, A. Morell, A. Bel, G. Seco-Granados
In this paper, we study the impact of outdated channel state information (CSI) on a cooperative system based on opportunistic relay selection (ORS). The study is carried out by obtaining an analytical expression for the outage probability, defined as the probability that the instantaneous mutual information is lower than a target rate. Besides, we propose the optimal power allocation aimed at minimizing the outage probability when the available CSI is subject to impairments. As shown in the paper, the proposed strategy provides significant gains when compared with uniform power allocation.
{"title":"Optimal power allocation in opportunistic relaying with outdated CSI","authors":"J. Lopez Vicario, A. Morell, A. Bel, G. Seco-Granados","doi":"10.1109/SAM.2008.4606822","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606822","url":null,"abstract":"In this paper, we study the impact of outdated channel state information (CSI) on a cooperative system based on opportunistic relay selection (ORS). The study is carried out by obtaining an analytical expression for the outage probability, defined as the probability that the instantaneous mutual information is lower than a target rate. Besides, we propose the optimal power allocation aimed at minimizing the outage probability when the available CSI is subject to impairments. As shown in the paper, the proposed strategy provides significant gains when compared with uniform power allocation.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116687875","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606823
A. Amar, A. Weiss
Differential Doppler is a common technique for emitter localization in which the signal of a stationary emitter is intercepted by at least two moving receivers. The frequency difference between the receivers is measured at several locations along their trajectories and the emitterpsilas position is then estimated based on these measurements. This two-step approach is suboptimal since each measurement is performed independently, although all measurements correspond to the same position. Instead, a single step maximum likelihood approach is proposed here for known and unknown waveforms. The position is determined directly from all the observations by a search in the position space. Simulations show that the proposed method outperforms the differential Doppler method for weak signals while both methods converge to the Cramer Rao bound for strong signals. Also, in some cases of interest the proposed method inherently selects reliable observations while ignoring unreliable data.
{"title":"Optimal radio emitter location based on the Doppler effect","authors":"A. Amar, A. Weiss","doi":"10.1109/SAM.2008.4606823","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606823","url":null,"abstract":"Differential Doppler is a common technique for emitter localization in which the signal of a stationary emitter is intercepted by at least two moving receivers. The frequency difference between the receivers is measured at several locations along their trajectories and the emitterpsilas position is then estimated based on these measurements. This two-step approach is suboptimal since each measurement is performed independently, although all measurements correspond to the same position. Instead, a single step maximum likelihood approach is proposed here for known and unknown waveforms. The position is determined directly from all the observations by a search in the position space. Simulations show that the proposed method outperforms the differential Doppler method for weak signals while both methods converge to the Cramer Rao bound for strong signals. Also, in some cases of interest the proposed method inherently selects reliable observations while ignoring unreliable data.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128904266","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}
Conventional blind beamformers in DS-CDMA systems generally suffer from either unreliable convergence rate or signal cancellation in practical signal environment. In this paper, we propose a novel orthogonal projection scheme to extract spatial interference structure for the blind beamformer, which requires neither the direction of the desired signal nor the array geometry information. Exploiting more signal-free interference samples makes the convergence of the beamformer fast and independent of the desired signal strength. Simulation results demonstrate its better interference suppression ability than the pre- and post- correlation (PAPC) scheme and the filter pair (FP) scheme. Furthermore, the proposed recursive algorithm can null the newly entering interferers within a few symbols, making it suitable for dynamic multiple access channels.
{"title":"An orthogonal projection based blind beamformer for DS-CDMA systems","authors":"Jianshu Chen, Jian Wang, Pengyu Zhang, Jian Yuan, X. Shan","doi":"10.1109/SAM.2008.4606819","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606819","url":null,"abstract":"Conventional blind beamformers in DS-CDMA systems generally suffer from either unreliable convergence rate or signal cancellation in practical signal environment. In this paper, we propose a novel orthogonal projection scheme to extract spatial interference structure for the blind beamformer, which requires neither the direction of the desired signal nor the array geometry information. Exploiting more signal-free interference samples makes the convergence of the beamformer fast and independent of the desired signal strength. Simulation results demonstrate its better interference suppression ability than the pre- and post- correlation (PAPC) scheme and the filter pair (FP) scheme. Furthermore, the proposed recursive algorithm can null the newly entering interferers within a few symbols, making it suitable for dynamic multiple access channels.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116494819","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606836
Tingting Liu, Jian-Kang Zhang, K. M. Wong
For precoder design problems in a multi-input multi-output (MIMO) communication system, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the optimum design of a precoder for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full CSI is known at the receiver, but only the first- and second-order statistics of the channels are available at the transmitter. Our goal is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing decision feedback (ZF-DF) detection subject to a constraint on the total transmitting power. We transform this non-convex optimization problem into a convex geometrical programming problem, which can then be efficiently solved using an interior point method. For the case when the transmission channels are uncorrelated, a closed-form solution of the optimum precoder has been obtained. The superior performance of our MIMO system equipped with the optimum precoder is verified by computer simulations.
{"title":"Optimal precoder design for mimo systems using decision feedback receivers","authors":"Tingting Liu, Jian-Kang Zhang, K. M. Wong","doi":"10.1109/SAM.2008.4606836","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606836","url":null,"abstract":"For precoder design problems in a multi-input multi-output (MIMO) communication system, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the optimum design of a precoder for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full CSI is known at the receiver, but only the first- and second-order statistics of the channels are available at the transmitter. Our goal is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing decision feedback (ZF-DF) detection subject to a constraint on the total transmitting power. We transform this non-convex optimization problem into a convex geometrical programming problem, which can then be efficiently solved using an interior point method. For the case when the transmission channels are uncorrelated, a closed-form solution of the optimum precoder has been obtained. The superior performance of our MIMO system equipped with the optimum precoder is verified by computer simulations.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968495","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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