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.1997.613509
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.1997.613504
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.1997.613518
A. Yamani, K. F. U. P. M. Box, Dhahran, S. Arabia, M. Bettayeb, L. Ghouti
Pulse-echo reflection techniques are used for ultrasonic flaw detection in most commercial instruments. As the measured pulse echo signal is assumed to be the result of linearly convolving the defect impulse response (IR) with the measurement system response the objective is thus, to remove the effect of the measurement system through a deconvolution operation and extract the defect impulse response. The major drawback of conventional second-order statistics (SOS)-based deconvolution techniques are their inability to identify non-minimum phase systems, and their sensitivity to additive Gaussian noise. Our contribution is to show that higher-order statistics (HOS)-based deconvolution techniques are more suitable to unravel the effects of the measurement systems and the additive Gaussian noise. Synthetic as well as real ultrasonic signals are used to support this claim.
{"title":"Higher-order statistics-based deconvolution of ultrasonic nondestructive testing signals","authors":"A. Yamani, K. F. U. P. M. Box, Dhahran, S. Arabia, M. Bettayeb, L. Ghouti","doi":"10.1109/HOST.1997.613518","DOIUrl":"https://doi.org/10.1109/HOST.1997.613518","url":null,"abstract":"Pulse-echo reflection techniques are used for ultrasonic flaw detection in most commercial instruments. As the measured pulse echo signal is assumed to be the result of linearly convolving the defect impulse response (IR) with the measurement system response the objective is thus, to remove the effect of the measurement system through a deconvolution operation and extract the defect impulse response. The major drawback of conventional second-order statistics (SOS)-based deconvolution techniques are their inability to identify non-minimum phase systems, and their sensitivity to additive Gaussian noise. Our contribution is to show that higher-order statistics (HOS)-based deconvolution techniques are more suitable to unravel the effects of the measurement systems and the additive Gaussian noise. Synthetic as well as real ultrasonic signals are used to support this claim.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"25 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":"115317599","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.1997.613532
A. Gershman, J. Bohme
A recently reported estimator bank approach (see IEEE SP Lett., vol.4, p.54, 1997) is extended below to the fourth-order direction finding algorithms. The essence of our approach is to exploit "parallel" underlying eigenstructure based estimators for removing the outliers and improving the direction finding performance in the threshold domain. The pseudorandomly generated weighted fourth-order MUSIC estimators are exploited as underlying techniques for estimator bank. Motivated by the superior performance and reduced computational complexity of beamspace and root modifications of the second-order eigenstructure techniques, beamspace root implementations of fourth-order MUSIC and fourth-order estimator bank are developed. Simulations show dramatical improvements of the threshold performance.
{"title":"Improving the threshold performance of higher-order direction finding methods via pseudorandomly generated estimator banks","authors":"A. Gershman, J. Bohme","doi":"10.1109/HOST.1997.613532","DOIUrl":"https://doi.org/10.1109/HOST.1997.613532","url":null,"abstract":"A recently reported estimator bank approach (see IEEE SP Lett., vol.4, p.54, 1997) is extended below to the fourth-order direction finding algorithms. The essence of our approach is to exploit \"parallel\" underlying eigenstructure based estimators for removing the outliers and improving the direction finding performance in the threshold domain. The pseudorandomly generated weighted fourth-order MUSIC estimators are exploited as underlying techniques for estimator bank. Motivated by the superior performance and reduced computational complexity of beamspace and root modifications of the second-order eigenstructure techniques, beamspace root implementations of fourth-order MUSIC and fourth-order estimator bank are developed. Simulations show dramatical improvements of the threshold performance.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"33 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":"124010020","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.1997.613522
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.1997.613481
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：