Pub Date : 1997-07-21DOI: 10.1109/HOST.1997.613522
J.-L. Vauttoux, E. Le Carpentier
The problem of estimating the parameters of a noncausal ARMA system, driven by an unobservable input noise is addressed. We propose a method based on a generalized version of the prediction error minimum variance approach and on the maximum kurtosis properties. Firstly, a spectrally equivalent (SE) model is identified with the generalized minimum variance approach. Secondly, the kurtosis allows us to identify the phase of the true model by localizing its zeros and poles from the SE model. Finally, we propose a new method which is a closed-loop form of the preceding method allowing to improve the accuracy of the parameter estimation and to obtain a better reconstruction of the estimated model phase. Simulation results seem to confirm the good behavior of the proposed methods compared to methods using higher order statistics.
{"title":"Non-causal ARMA model identification by maximizing the kurtosis","authors":"J.-L. Vauttoux, E. Le Carpentier","doi":"10.1109/HOST.1997.613522","DOIUrl":"https://doi.org/10.1109/HOST.1997.613522","url":null,"abstract":"The problem of estimating the parameters of a noncausal ARMA system, driven by an unobservable input noise is addressed. We propose a method based on a generalized version of the prediction error minimum variance approach and on the maximum kurtosis properties. Firstly, a spectrally equivalent (SE) model is identified with the generalized minimum variance approach. Secondly, the kurtosis allows us to identify the phase of the true model by localizing its zeros and poles from the SE model. Finally, we propose a new method which is a closed-loop form of the preceding method allowing to improve the accuracy of the parameter estimation and to obtain a better reconstruction of the estimated model phase. Simulation results seem to confirm the good behavior of the proposed methods compared to methods using higher order statistics.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127307619","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 : 1997-07-21DOI: 10.1109/HOST.1997.613525
Hyoungill Kim, Bum-Ki Jeon, Taewon Yang, K. Sung
A recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. From the 3rd and 4th order cumulants relationship, we construct a certain matrix form whose entry consists of the system output sequence. Using this matrix form, the proposed recursive algorithm is developed by the overdetermined recursive instrumental variable (ORIV) method. The proposed algorithm provides improved estimation accuracy when additive Gaussian noise is present and can be applied to a time varying system as well. Simulation results are presented to compare the performance with other HOS-based algorithms.
{"title":"Recursive estimation algorithm for FIR systems using the 3rd and 4th order cumulants","authors":"Hyoungill Kim, Bum-Ki Jeon, Taewon Yang, K. Sung","doi":"10.1109/HOST.1997.613525","DOIUrl":"https://doi.org/10.1109/HOST.1997.613525","url":null,"abstract":"A recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. From the 3rd and 4th order cumulants relationship, we construct a certain matrix form whose entry consists of the system output sequence. Using this matrix form, the proposed recursive algorithm is developed by the overdetermined recursive instrumental variable (ORIV) method. The proposed algorithm provides improved estimation accuracy when additive Gaussian noise is present and can be applied to a time varying system as well. Simulation results are presented to compare the performance with other HOS-based algorithms.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124258044","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 : 1997-07-21DOI: 10.1109/HOST.1997.613504
Ching-Hsiang Tseng
A practical method for identifying cubically nonlinear systems is presented in this paper. This method identifies the system by using the higher-order spectra of the system input and output. Compared to the conventional method, which requires the system output to be sampled at six times the bandwidth of the input, the proposed method only requires the system output to be sampled at twice the bandwidth of the system input. This greatly reduces the required computation and processing speed of the circuits. The advantage of the proposed method over the conventional one is demonstrated via computer simulation.
{"title":"On identifying Volterra transfer functions of cubically nonlinear systems using minimally sampled data","authors":"Ching-Hsiang Tseng","doi":"10.1109/HOST.1997.613504","DOIUrl":"https://doi.org/10.1109/HOST.1997.613504","url":null,"abstract":"A practical method for identifying cubically nonlinear systems is presented in this paper. This method identifies the system by using the higher-order spectra of the system input and output. Compared to the conventional method, which requires the system output to be sampled at six times the bandwidth of the input, the proposed method only requires the system output to be sampled at twice the bandwidth of the system input. This greatly reduces the required computation and processing speed of the circuits. The advantage of the proposed method over the conventional one is demonstrated via computer simulation.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108495","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 : 1997-07-21DOI: 10.1109/HOST.1997.613527
A. Tesei, R. Bozzano, C. Regazzoni
This paper focuses on the problem of multilevel digital signal estimation in the presence of generic noise in a communication system. Noise is assumed unimodal, independent identically distributed, generically non Gaussian, that is eventually asymmetric, impulsive or not. The proposed solution is based on a previously developed estimator which requires the analytical probability density function model of the noise. The selected estimator was originally applied under the assumption of S/spl alpha/S noise distribution. In this paper the asymmetric generalized Gaussian (agg) model is selected as a suitable model to describe the noise processes: hence, it is discussed and compared with the S/spl alpha/S distributions in terms of decoding performances. Tests were performed on simulated binary sequences corrupted by interference generated as S/spl alpha/S processes. Test results outlines comparable performances of the two families of parametric noise models.
{"title":"Comparison between asymmetric generalized Gaussian (AGG) and symmetric-/spl alpha/-stable (S/spl alpha/S) noise models for signal estimation in non Gaussian environments","authors":"A. Tesei, R. Bozzano, C. Regazzoni","doi":"10.1109/HOST.1997.613527","DOIUrl":"https://doi.org/10.1109/HOST.1997.613527","url":null,"abstract":"This paper focuses on the problem of multilevel digital signal estimation in the presence of generic noise in a communication system. Noise is assumed unimodal, independent identically distributed, generically non Gaussian, that is eventually asymmetric, impulsive or not. The proposed solution is based on a previously developed estimator which requires the analytical probability density function model of the noise. The selected estimator was originally applied under the assumption of S/spl alpha/S noise distribution. In this paper the asymmetric generalized Gaussian (agg) model is selected as a suitable model to describe the noise processes: hence, it is discussed and compared with the S/spl alpha/S distributions in terms of decoding performances. Tests were performed on simulated binary sequences corrupted by interference generated as S/spl alpha/S processes. Test results outlines comparable performances of the two families of parametric noise models.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114371791","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 : 1997-07-21DOI: 10.1109/HOST.1997.613509
S. Zozor, P. Amblard
We present in this paper the stochastic resonance phenomenon in a discrete time context. Indeed, stochastic resonance has been commonly investigated in continuous-time. Analytical results given by a simple bistable nonlinear SETAR (1,2,0,0) are studied. Then, the ability of such a system to be used in signal processing is discussed.
{"title":"Stochastic resonance in a discrete time nonlinear SETAR (1,2,0,0) model","authors":"S. Zozor, P. Amblard","doi":"10.1109/HOST.1997.613509","DOIUrl":"https://doi.org/10.1109/HOST.1997.613509","url":null,"abstract":"We present in this paper the stochastic resonance phenomenon in a discrete time context. Indeed, stochastic resonance has been commonly investigated in continuous-time. Analytical results given by a simple bistable nonlinear SETAR (1,2,0,0) are studied. Then, the ability of such a system to be used in signal processing is discussed.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987789","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 : 1997-07-21DOI: 10.1109/HOST.1997.613565
J. Tourneret, A. Ferrari, B. Lacaze
The spectrum of a signal subjected, to sampling jitter can be significantly different from the spectrum of the same signal sampled without jitter. The first part of the paper shows that the spectrum of a continuous Gaussian signal can be reconstructed from a combined use of the sampled (with jitter) signal second and fourth-order statistics. This spectral reconstruction is then used to detect the presence or absence of jitter in a sampled signal. A likelihood ratio detector based on the spectral corrective term is studied. It gives a reference to which suboptimal detectors can be compared.
{"title":"Sampling jitter detection using higher-order statistics","authors":"J. Tourneret, A. Ferrari, B. Lacaze","doi":"10.1109/HOST.1997.613565","DOIUrl":"https://doi.org/10.1109/HOST.1997.613565","url":null,"abstract":"The spectrum of a signal subjected, to sampling jitter can be significantly different from the spectrum of the same signal sampled without jitter. The first part of the paper shows that the spectrum of a continuous Gaussian signal can be reconstructed from a combined use of the sampled (with jitter) signal second and fourth-order statistics. This spectral reconstruction is then used to detect the presence or absence of jitter in a sampled signal. A likelihood ratio detector based on the spectral corrective term is studied. It gives a reference to which suboptimal detectors can be compared.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130774310","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 : 1997-07-21DOI: 10.1109/HOST.1997.613481
M. Shen, Lisha Sun
This paper investigates the application of a non-Gaussian AR model and parametric bispectral estimation in analyzing normal and pathological heart sound signals. The non-Gaussian AR model of PCG signals (phonocardiogram) is used to detect quadratic nonlinear interactions and to classify the two patterns of phonocardiograms in terms of the parametric bispectral estimate. The bispectral cross-correlation is proposed for the order determination of the model. Real PCG data are implemented to show that the quadratic nonlinearity exists in both normal and clinical heart sounds. It was found that parametric bispectral techniques are effective and useful tools in analyzing PCG and other biomedical signals, such as EMG, ECG and EEG.
{"title":"The analysis and classification of phonocardiogram based on higher-order spectra","authors":"M. Shen, Lisha Sun","doi":"10.1109/HOST.1997.613481","DOIUrl":"https://doi.org/10.1109/HOST.1997.613481","url":null,"abstract":"This paper investigates the application of a non-Gaussian AR model and parametric bispectral estimation in analyzing normal and pathological heart sound signals. The non-Gaussian AR model of PCG signals (phonocardiogram) is used to detect quadratic nonlinear interactions and to classify the two patterns of phonocardiograms in terms of the parametric bispectral estimate. The bispectral cross-correlation is proposed for the order determination of the model. Real PCG data are implemented to show that the quadratic nonlinearity exists in both normal and clinical heart sounds. It was found that parametric bispectral techniques are effective and useful tools in analyzing PCG and other biomedical signals, such as EMG, ECG and EEG.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133078912","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 : 1997-07-21DOI: 10.1109/HOST.1997.613506
S. Im
The objective of the paper is to introduce a new adaptive filtering algorithm for estimating frequency-domain second-order Volterra filter coefficients. The approach rests upon the normalized LMS (NLMS) algorithm and the frequency-domain block LMS algorithm. The utilization of the normalized LMS algorithm facilitates choice of a proper step size, with which the adaptive frequency domain Volterra filter is guaranteed to be convergent in the mean-squared sense, and improves convergence rate. The frequency-domain block LMS algorithm estimates frequency-domain second-order Volterra filter coefficients which correspond to the DFT of the time-domain Volterra filter coefficients.
{"title":"A normalized block LMS algorithm for frequency-domain Volterra filters","authors":"S. Im","doi":"10.1109/HOST.1997.613506","DOIUrl":"https://doi.org/10.1109/HOST.1997.613506","url":null,"abstract":"The objective of the paper is to introduce a new adaptive filtering algorithm for estimating frequency-domain second-order Volterra filter coefficients. The approach rests upon the normalized LMS (NLMS) algorithm and the frequency-domain block LMS algorithm. The utilization of the normalized LMS algorithm facilitates choice of a proper step size, with which the adaptive frequency domain Volterra filter is guaranteed to be convergent in the mean-squared sense, and improves convergence rate. The frequency-domain block LMS algorithm estimates frequency-domain second-order Volterra filter coefficients which correspond to the DFT of the time-domain Volterra filter coefficients.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132758986","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 : 1997-07-21DOI: 10.1109/HOST.1997.613490
U. Abeyratne, A. Petropulu
We model tissue as a collection of point scatterers embedded in a uniform media, and show that the higher order statistics (HOS) of the scatterer spacing distribution can be estimated from digitized RF scan line segments and be used in obtaining tissue signatures. Based on our model for tissue microstructure, we estimate resolvable periodicity and correlations among non-periodic scatterers. Using higher-order statistics of the scattered signal, we define as tissue "color" a quantity that describes the scatterer spatial correlations, show how to estimate it from the higher-order correlations of the digitized RF scan line segments, and investigate its potential as a tissue signature.
{"title":"Higher-order statistics for tissue characterization from ultrasound images","authors":"U. Abeyratne, A. Petropulu","doi":"10.1109/HOST.1997.613490","DOIUrl":"https://doi.org/10.1109/HOST.1997.613490","url":null,"abstract":"We model tissue as a collection of point scatterers embedded in a uniform media, and show that the higher order statistics (HOS) of the scatterer spacing distribution can be estimated from digitized RF scan line segments and be used in obtaining tissue signatures. Based on our model for tissue microstructure, we estimate resolvable periodicity and correlations among non-periodic scatterers. Using higher-order statistics of the scattered signal, we define as tissue \"color\" a quantity that describes the scatterer spatial correlations, show how to estimate it from the higher-order correlations of the digitized RF scan line segments, and investigate its potential as a tissue signature.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114172940","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 : 1997-07-21DOI: 10.1109/HOST.1997.613539
C. Servière
A new method of source separation with noisy observations is proposed in the case of two sensors. Each observation contains a mixture of two signals with noise. The objective is to estimate the frequency spectra of the linear filters that combine the two signals in the data stream. The main characteristic of the method is to take into account additive noises. No hypotheses on their probability densities are made. We derive for that an original objective function, based on nonlinear functions of the observations. Specific properties of these functions, chosen as exponential functions, and the hypothesis of independent sources lead to a direct solution for the estimation of the filters. An analytic solution may be computed from it, using only the data. The convergence speed of the method and its robustness against non gaussian noise are illustrated in the paper with simulation results.
{"title":"Blind source separation with noisy sources","authors":"C. Servière","doi":"10.1109/HOST.1997.613539","DOIUrl":"https://doi.org/10.1109/HOST.1997.613539","url":null,"abstract":"A new method of source separation with noisy observations is proposed in the case of two sensors. Each observation contains a mixture of two signals with noise. The objective is to estimate the frequency spectra of the linear filters that combine the two signals in the data stream. The main characteristic of the method is to take into account additive noises. No hypotheses on their probability densities are made. We derive for that an original objective function, based on nonlinear functions of the observations. Specific properties of these functions, chosen as exponential functions, and the hypothesis of independent sources lead to a direct solution for the estimation of the filters. An analytic solution may be computed from it, using only the data. The convergence speed of the method and its robustness against non gaussian noise are illustrated in the paper with simulation results.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120950762","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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