Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572525
S. Kwong, K.F. Man
This paper presents a new transform coder called tlie Lapped Transform Coder (LTC) for high fidelity coding of music signal. The word "Extended" contained in tlie name of this coder is simply because we adopt the Lapped Transform in tlie coder. It is also found that ELT with larger block size and statistics block number provides a better Signal-to-Noise (SNR) ratio in our studies. Thus, we used the ELT with overlapping factor of four in the LTC with the block size 64 and the statistics block number 64. The performance of the LTC is good and it has many favourable results for practical implementation.
{"title":"A Audio Codec Based on Adaptive Transform Coding with Extended Lapped Transform","authors":"S. Kwong, K.F. Man","doi":"10.1109/SSAP.1994.572525","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572525","url":null,"abstract":"This paper presents a new transform coder called tlie Lapped Transform Coder (LTC) for high fidelity coding of music signal. The word \"Extended\" contained in tlie name of this coder is simply because we adopt the Lapped Transform in tlie coder. It is also found that ELT with larger block size and statistics block number provides a better Signal-to-Noise (SNR) ratio in our studies. Thus, we used the ELT with overlapping factor of four in the LTC with the block size 64 and the statistics block number 64. The performance of the LTC is good and it has many favourable results for practical implementation.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027317","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572513
I. Fijalkow, P. Loubaton
Subspace methods for blind multichannel identification can not be extended to the case of a non white noise. For an unknown temporally white but spatially correlated perturbation, we pr+ pose a method based on a stochastic realization approach. It relies on the fact that the observed signal spectral density matrix is the s u m of a rational rank 1 spectral density and of a constant positive definite matrix (the noise Covariance matrix). The generic unicity of this decomposition is shown. An identification method based on the parametrization of the (external) stochastic realizations of the observed signal whose innovation sequence has a prescribed dimension is developped. It results in a state-space realization of the multichannel transfer function and in the noise covariance matrix.
{"title":"Multichannel Blind Identification from Noisy Sensor Array Observations: A Stochastic Realization Approach","authors":"I. Fijalkow, P. Loubaton","doi":"10.1109/SSAP.1994.572513","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572513","url":null,"abstract":"Subspace methods for blind multichannel identification can not be extended to the case of a non white noise. For an unknown temporally white but spatially correlated perturbation, we pr+ pose a method based on a stochastic realization approach. It relies on the fact that the observed signal spectral density matrix is the s u m of a rational rank 1 spectral density and of a constant positive definite matrix (the noise Covariance matrix). The generic unicity of this decomposition is shown. An identification method based on the parametrization of the (external) stochastic realizations of the observed signal whose innovation sequence has a prescribed dimension is developped. It results in a state-space realization of the multichannel transfer function and in the noise covariance matrix.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127413739","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572537
G. Edelson, I. Kirsteins
We propose a maximum likelihood type approach for estimating the arrival times of signals which have propagated via a continuum of paths, i.e. temporally spread channels. The channel spreading is included in the model by using a discrete prolate spheroidal sequence (DPSS) to represent the channel impulse response of given duration, but unknown shape. The unknown parameters are estimated using an iterative methodology which decomposes the original data into its constituent components and then estimates the parameters of the individual components through a sequence of one dimensional searches. Computer simulation examples indicate that the method performs well.
{"title":"Modeling and Suppression of Reverberation Components","authors":"G. Edelson, I. Kirsteins","doi":"10.1109/SSAP.1994.572537","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572537","url":null,"abstract":"We propose a maximum likelihood type approach for estimating the arrival times of signals which have propagated via a continuum of paths, i.e. temporally spread channels. The channel spreading is included in the model by using a discrete prolate spheroidal sequence (DPSS) to represent the channel impulse response of given duration, but unknown shape. The unknown parameters are estimated using an iterative methodology which decomposes the original data into its constituent components and then estimates the parameters of the individual components through a sequence of one dimensional searches. Computer simulation examples indicate that the method performs well.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972939","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572536
W. Du, H. Leong, A. Gevins
The problem of real-time ocular or eye artifact correction is addressed in this paper based on the framework of the general adaptive interference canceler, wherein the EOG signals are used as the reference signal. Adaptive algorithms such as LMS, recursive LS, or exponentially weighted LS can be used to update the coefficients of the adaptive filter. The major problem associated with an adaptive eye artifact canceler is found to be the unwanted correlations between the desired and reference signals. This is especially problematic when slow cognitive potentials or slow head or body movement artifacts coexist with eye artifacts in the recorded EEG. Undesired correlations can result in over-correction of ocular artifacts if a standard adaptive filter is used. We tackle this problem by taking into account a priori information regarding the ocular artifacts, that is, the spatietemporal statistics of the transmission coefficients. This strategy yields an adaptive artifact canceler combined with leakage and signal subspace enhancement.
{"title":"Ocular Artifact Minimization by Adaptive Filtering","authors":"W. Du, H. Leong, A. Gevins","doi":"10.1109/SSAP.1994.572536","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572536","url":null,"abstract":"The problem of real-time ocular or eye artifact correction is addressed in this paper based on the framework of the general adaptive interference canceler, wherein the EOG signals are used as the reference signal. Adaptive algorithms such as LMS, recursive LS, or exponentially weighted LS can be used to update the coefficients of the adaptive filter. The major problem associated with an adaptive eye artifact canceler is found to be the unwanted correlations between the desired and reference signals. This is especially problematic when slow cognitive potentials or slow head or body movement artifacts coexist with eye artifacts in the recorded EEG. Undesired correlations can result in over-correction of ocular artifacts if a standard adaptive filter is used. We tackle this problem by taking into account a priori information regarding the ocular artifacts, that is, the spatietemporal statistics of the transmission coefficients. This strategy yields an adaptive artifact canceler combined with leakage and signal subspace enhancement.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086890","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572496
J. Xin, Y. Murakami, A. Sano
In this paper, on the motivation of arbitrariness of frequency resolution at all frequencies and property of orthogonalization of wavelet packets, we investigate new adaptive algorithms based on wavelet packets. Moreover, the active noise cancellation with stabilization is investigated by using the presented adaptive algorithm. The effectiveness is demonstrated through numerical simulation.
{"title":"Adaptive Filter Algorithm Based on Wavelet Packets and Application to Adaptive Active Noise Cancellation","authors":"J. Xin, Y. Murakami, A. Sano","doi":"10.1109/SSAP.1994.572496","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572496","url":null,"abstract":"In this paper, on the motivation of arbitrariness of frequency resolution at all frequencies and property of orthogonalization of wavelet packets, we investigate new adaptive algorithms based on wavelet packets. Moreover, the active noise cancellation with stabilization is investigated by using the presented adaptive algorithm. The effectiveness is demonstrated through numerical simulation.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133177258","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572476
M. Boumahdi, J. Lacoume
In this paper we present a method to estimate nonminimum phase finite impulse response (FIR) system, using Moving-Average (MA) model. It is based on maximum kurtosis properties. The spectrally equivalent minimum phase (SEMP) filter is estimated from second order statistics of the output system. The kurtosis allow us first to localise the zeros of the associated transfer_ function from the zeros of its SEMP filter, then to estimate the true order of the MA model. On simulated seismic data we compare the proposed method to Gianakis and Mendel's algorithm and Tugnait's algorithm. The results obtained confm the robustness of the method to hard conditions of process. INTRODUCTION The classical approach to solve the problem of blind identification of linear time invariant system only uses second order statistics (autocomelation or spectrum). This approach does not provide a complete statistical description. It only allows to identify minimum phase, maximum phase or zero phase system. Recently Higher order statistics (HOS) than two (multicorrelation or polyspectrum) ($1) have received the attention of the statistics signal processing, and theory literature, for processing non-gaussian linear or non-linear processes. For gaussian processes all their HOS are identically zero. Furthermore, all odd order statistics are identically zero for processes with symmetric Probability Density Function (PDF), that is why we choose to use fourthorder statistics. The use of HOS in time domain using parametric approach based on AR, MA, or ARMA model, has provided different solutions to non-minimum phase blind identification problem (92). To identify finite impulse response (FIR) system, our purpose is to estimate using second order statistics, the spectrally equivalent minimum phase (SEMP) systcm, and using the maximum kurtosis properly to recover the true system (03). For given order of the MA, we compare the method lo Gianakis-Mendel's algorithm and Tugnait's algorithm. This comparison is made on simulated seismic dah, with hard condition : short data Icngth and high order of die MA (54.1). Using the same data we show the capacity of the method to estimate the true order (94.2). 1) HIGH ORDER STATISTICS The description of HOS for random variables is essentially made using the cumulants. Let us take ( Xl , . . , X,, ) n-real valued random variable, their crosscumulants of order "m" can be defined from the Taylor series expansion of their second characteristic function by:
{"title":"Blind Identification of Non-minimum Phase FIR Systems Using the Kurtosis","authors":"M. Boumahdi, J. Lacoume","doi":"10.1109/SSAP.1994.572476","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572476","url":null,"abstract":"In this paper we present a method to estimate nonminimum phase finite impulse response (FIR) system, using Moving-Average (MA) model. It is based on maximum kurtosis properties. The spectrally equivalent minimum phase (SEMP) filter is estimated from second order statistics of the output system. The kurtosis allow us first to localise the zeros of the associated transfer_ function from the zeros of its SEMP filter, then to estimate the true order of the MA model. On simulated seismic data we compare the proposed method to Gianakis and Mendel's algorithm and Tugnait's algorithm. The results obtained confm the robustness of the method to hard conditions of process. INTRODUCTION The classical approach to solve the problem of blind identification of linear time invariant system only uses second order statistics (autocomelation or spectrum). This approach does not provide a complete statistical description. It only allows to identify minimum phase, maximum phase or zero phase system. Recently Higher order statistics (HOS) than two (multicorrelation or polyspectrum) ($1) have received the attention of the statistics signal processing, and theory literature, for processing non-gaussian linear or non-linear processes. For gaussian processes all their HOS are identically zero. Furthermore, all odd order statistics are identically zero for processes with symmetric Probability Density Function (PDF), that is why we choose to use fourthorder statistics. The use of HOS in time domain using parametric approach based on AR, MA, or ARMA model, has provided different solutions to non-minimum phase blind identification problem (92). To identify finite impulse response (FIR) system, our purpose is to estimate using second order statistics, the spectrally equivalent minimum phase (SEMP) systcm, and using the maximum kurtosis properly to recover the true system (03). For given order of the MA, we compare the method lo Gianakis-Mendel's algorithm and Tugnait's algorithm. This comparison is made on simulated seismic dah, with hard condition : short data Icngth and high order of die MA (54.1). Using the same data we show the capacity of the method to estimate the true order (94.2). 1) HIGH ORDER STATISTICS The description of HOS for random variables is essentially made using the cumulants. Let us take ( Xl , . . , X,, ) n-real valued random variable, their crosscumulants of order \"m\" can be defined from the Taylor series expansion of their second characteristic function by:","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124028104","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572533
J. Byrne, D. Cyganski, R. Vaz, C. R. Wright
of propagation. The signal phase difference between any two adjacent sensors in radians Dimensional Direction of Arrival (N-D DOA) estimated by measuring the phase difference technique is based between the signal values at each sensor, or mulation was motivated by previous work in tained in a “snapshot” of data from all the sensors. which the Cram& Rao Bound (CRB) for coherent wave N-D DOA was developed. Means formance for low SNR are also presented. rithm which is Our target application [3] generates a set of values corresponding to samples from an N-dimensional lattice of senIntroduction sors, the plane wave frequency components of The DOA problem involves estimation of which are the parameters revealing the object plane wave frequency components from data identity and pose. This motivates an extencollected by a uniformly spaced grid of sension of the DOA algorithm to N-D. sors. One and two-dimensional versions of There are a variety of techniques for perthe DOA problem arise in sonar and radar forming 1D DOA estimation, c.f. [4, 5 , 61; direction finding and target tracking applicaone such method, the state space technique, tions [l, 21; the need for an N-D DOA techwas chosen for this extension to N-D. The nique arises in a recently developed object state space DOA method involves determirecognition algorithm [3]. Figure 1 shows a nation of a system, the impulse response of plane wave impinging at an angle t9 on a 1which would produce the sensor data. Once D array of linearly spaced sensors. The dissuch a system is found, we may perform an tance between each sensor is I , The waveeigenvalue decomposition of the system malength of the plane wave is X = c/fo, where trix in order to determine the modes of the c is the speed of propagation of the wave system. These modes are the estimated freand fo is its spatial frequency. The plane quency components of the plane wave along wave is constant along a front perpendicuthe direction of the array of sensors. Given lar to the vectors that indicate the direction the distance 1 between each sensor, we can In this paper, we describe a to the Nis (2T1 sin e)/X. Thus the parameter 8 can be The N-D On a state ‘pace and its forequivalently by estimating the frequency confor improving the N-D DOA estimation perThe model based object recognition alga-
{"title":"An N-D Technique for Coherent Wave Doa Estimation","authors":"J. Byrne, D. Cyganski, R. Vaz, C. R. Wright","doi":"10.1109/SSAP.1994.572533","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572533","url":null,"abstract":"of propagation. The signal phase difference between any two adjacent sensors in radians Dimensional Direction of Arrival (N-D DOA) estimated by measuring the phase difference technique is based between the signal values at each sensor, or mulation was motivated by previous work in tained in a “snapshot” of data from all the sensors. which the Cram& Rao Bound (CRB) for coherent wave N-D DOA was developed. Means formance for low SNR are also presented. rithm which is Our target application [3] generates a set of values corresponding to samples from an N-dimensional lattice of senIntroduction sors, the plane wave frequency components of The DOA problem involves estimation of which are the parameters revealing the object plane wave frequency components from data identity and pose. This motivates an extencollected by a uniformly spaced grid of sension of the DOA algorithm to N-D. sors. One and two-dimensional versions of There are a variety of techniques for perthe DOA problem arise in sonar and radar forming 1D DOA estimation, c.f. [4, 5 , 61; direction finding and target tracking applicaone such method, the state space technique, tions [l, 21; the need for an N-D DOA techwas chosen for this extension to N-D. The nique arises in a recently developed object state space DOA method involves determirecognition algorithm [3]. Figure 1 shows a nation of a system, the impulse response of plane wave impinging at an angle t9 on a 1which would produce the sensor data. Once D array of linearly spaced sensors. The dissuch a system is found, we may perform an tance between each sensor is I , The waveeigenvalue decomposition of the system malength of the plane wave is X = c/fo, where trix in order to determine the modes of the c is the speed of propagation of the wave system. These modes are the estimated freand fo is its spatial frequency. The plane quency components of the plane wave along wave is constant along a front perpendicuthe direction of the array of sensors. Given lar to the vectors that indicate the direction the distance 1 between each sensor, we can In this paper, we describe a to the Nis (2T1 sin e)/X. Thus the parameter 8 can be The N-D On a state ‘pace and its forequivalently by estimating the frequency confor improving the N-D DOA estimation perThe model based object recognition alga-","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529336","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572419
P. Djurić
The two most popular model selection rules in the signal processing literature are the Akaike’s criterion AIC and the Rissanen’s principle of minimum description length (MDL). These rules are similar in form in that they both consist of data and penalty terms. Their data terms are identical, while the penalties are different, the MDL being more stringent towards overparameterization. The two rules, however, penalize for each additional model parameter with an equal incremental amount of penalty, regardless of the parame ter’s role in the model. In this paper we attempt to show that this should not be the case. We derive an asymptotical maximum a posteriori (MAP) rule with more accurate penalties and provide simulation results that show improved performance of the so derived rule over the AIC and MDL.
{"title":"Model Selection Based On Asymptotic Bayes Theory","authors":"P. Djurić","doi":"10.1109/SSAP.1994.572419","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572419","url":null,"abstract":"The two most popular model selection rules in the signal processing literature are the Akaike’s criterion AIC and the Rissanen’s principle of minimum description length (MDL). These rules are similar in form in that they both consist of data and penalty terms. Their data terms are identical, while the penalties are different, the MDL being more stringent towards overparameterization. The two rules, however, penalize for each additional model parameter with an equal incremental amount of penalty, regardless of the parame ter’s role in the model. In this paper we attempt to show that this should not be the case. We derive an asymptotical maximum a posteriori (MAP) rule with more accurate penalties and provide simulation results that show improved performance of the so derived rule over the AIC and MDL.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116077445","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 : 1994-06-26DOI: 10.1109/SSAP.1994.572488
C. Detka, A. El-Jaroudi
The concept underlying the evolutionary spectrum is generalized to processes composed of chirp components. This generalization leads the the definition of the frequency chirp evolutionary spectrum. Then, the duality of time and frequency is applied to further expand the application of evolutionary spectral theory resulting in the time chirp evolutionary spectrum. Finally, an example is presented that demonstrates the value of these spectra.
{"title":"The Chirped Evolutionary Spectrum","authors":"C. Detka, A. El-Jaroudi","doi":"10.1109/SSAP.1994.572488","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572488","url":null,"abstract":"The concept underlying the evolutionary spectrum is generalized to processes composed of chirp components. This generalization leads the the definition of the frequency chirp evolutionary spectrum. Then, the duality of time and frequency is applied to further expand the application of evolutionary spectral theory resulting in the time chirp evolutionary spectrum. Finally, an example is presented that demonstrates the value of these spectra.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000945","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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