Pub Date : 1997-07-21DOI: 10.1109/HOST.1997.613519
A. Nandi, C. Schmitdt
Mobile communication links require adaptive equalization with a fast rate of convergence while keeping computational effort at reasonable levels. In this paper we propose to combine known algorithms for blind equalization in order to exploit their desirable properties to reach this goal. A switching criterion is proposed which is based on the change in the equalizer impulse response between iterations of the adaption algorithm and may be used to detect changes of the channel impulse response. Algorithms under consideration include Godard's algorithm, stop-and-go algorithm, and tricepstrum equalization algorithm (TEA).
{"title":"Combination of HOS based blind equalization algorithms for use in mobile communications","authors":"A. Nandi, C. Schmitdt","doi":"10.1109/HOST.1997.613519","DOIUrl":"https://doi.org/10.1109/HOST.1997.613519","url":null,"abstract":"Mobile communication links require adaptive equalization with a fast rate of convergence while keeping computational effort at reasonable levels. In this paper we propose to combine known algorithms for blind equalization in order to exploit their desirable properties to reach this goal. A switching criterion is proposed which is based on the change in the equalizer impulse response between iterations of the adaption algorithm and may be used to detect changes of the channel impulse response. Algorithms under consideration include Godard's algorithm, stop-and-go algorithm, and tricepstrum equalization algorithm (TEA).","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"91 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":"123137821","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.1997.613494
F. Gani, G. Giannakis
Parameter estimation for a combination of a polynomial phase signal (PPS) and a frequency modulated (FM) signal is addressed. A novel approach is proposed that allows one to decouple estimation of the FM parameters from that of the PPS parameters, exploiting the properties of the multi-lag high-order ambiguity function (ml-HAF). Performance analysis is carried out and Cramer-Rao bounds are compared with simulation results.
{"title":"Hybrid FM-polynomial phase signal modeling: parameter estimation and performance analysis","authors":"F. Gani, G. Giannakis","doi":"10.1109/HOST.1997.613494","DOIUrl":"https://doi.org/10.1109/HOST.1997.613494","url":null,"abstract":"Parameter estimation for a combination of a polynomial phase signal (PPS) and a frequency modulated (FM) signal is addressed. A novel approach is proposed that allows one to decouple estimation of the FM parameters from that of the PPS parameters, exploiting the properties of the multi-lag high-order ambiguity function (ml-HAF). Performance analysis is carried out and Cramer-Rao bounds are compared with simulation results.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"13 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":"126035502","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.1997.613480
M. Shen, Fenglin Shen
Time-varying third-order cumulant spectra for analyzing phonocardiographic signals has been proposed as an effective tool to detect and quantity the temporal quadratic nonlinear interactions. The cumulant-based Wigner bispectra (CWB) are applied to investigate the nonstationarity and non-Gaussianity of both actual normal and clinical phonocardiograms. Significant time-varying bispectral structure is found and discussed. It is expected to use the Wigner bispectra in the understanding of the heart sound mechanism and the improvement of the assistant diagnosis of some heart diseases.
{"title":"Time-varying third-order cumulant spectra and its application to the analysis and diagnosis of phonocardiogram","authors":"M. Shen, Fenglin Shen","doi":"10.1109/HOST.1997.613480","DOIUrl":"https://doi.org/10.1109/HOST.1997.613480","url":null,"abstract":"Time-varying third-order cumulant spectra for analyzing phonocardiographic signals has been proposed as an effective tool to detect and quantity the temporal quadratic nonlinear interactions. The cumulant-based Wigner bispectra (CWB) are applied to investigate the nonstationarity and non-Gaussianity of both actual normal and clinical phonocardiograms. Significant time-varying bispectral structure is found and discussed. It is expected to use the Wigner bispectra in the understanding of the heart sound mechanism and the improvement of the assistant diagnosis of some heart diseases.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"1 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":"128772636","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.1997.613507
Hyungsuk Yoo, E. Powers
The effects of conventional data windows on Volterra transfer function estimation are investigated. The input/output data for two known second-order systems are utilized to estimate the transfer functions, and the results are compared with true values. In addition, the use of window correction factors to offset the bias introduced into the higher-order moment spectra, by the fact that the data is attenuated at the beginning and end of a record, is investigated. In all cases, it is found that the rectangular window results in the smallest NMSE (normalized mean square error) for the estimated quadratic transfer functions.
{"title":"Windows and Volterra transfer function estimation","authors":"Hyungsuk Yoo, E. Powers","doi":"10.1109/HOST.1997.613507","DOIUrl":"https://doi.org/10.1109/HOST.1997.613507","url":null,"abstract":"The effects of conventional data windows on Volterra transfer function estimation are investigated. The input/output data for two known second-order systems are utilized to estimate the transfer functions, and the results are compared with true values. In addition, the use of window correction factors to offset the bias introduced into the higher-order moment spectra, by the fact that the data is attenuated at the beginning and end of a record, is investigated. In all cases, it is found that the rectangular window results in the smallest NMSE (normalized mean square error) for the estimated quadratic transfer functions.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"1 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":"128986062","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.1997.613503
Min-Cheol Hong, T. Stathaki, A. Katsaggelos
In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.
{"title":"An iterative mixed norm image restoration algorithm","authors":"Min-Cheol Hong, T. Stathaki, A. Katsaggelos","doi":"10.1109/HOST.1997.613503","DOIUrl":"https://doi.org/10.1109/HOST.1997.613503","url":null,"abstract":"In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"2 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":"114326689","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.1997.613492
G. D'Urso, P. Prieur, C. Vincent
In this article, the authors present results obtained on industrial data with source separation techniques in an instantaneous mix. They introduce three applications developed to perform the monitoring of Electricite de France civil works and power plants. The first application concerns the monitoring of nuclear power plants. Each internal component generates specific vibration modes and "neutron noise" which is a combination of all modes generated. The aim of this study is to separate such independent vibration modes. The second application concerns dams supervision: it consists in separating the various types of motion of a dam according to their physical origin. The third application concerns nondestructive testing on steam generators in nuclear power plants. The aim is to reduce the flattening noise. The classical methods operate only when a noise reference is available. They propose to use a multi-sensor approach with the blind separation methods (the noise reference is not necessary). Considering the specifications of the signals, they obtain better performance using a two-order statistical algorithm than a higher-order statistical algorithm.
{"title":"Blind identification methods applied to Electricite de France's civil works and power plants monitoring","authors":"G. D'Urso, P. Prieur, C. Vincent","doi":"10.1109/HOST.1997.613492","DOIUrl":"https://doi.org/10.1109/HOST.1997.613492","url":null,"abstract":"In this article, the authors present results obtained on industrial data with source separation techniques in an instantaneous mix. They introduce three applications developed to perform the monitoring of Electricite de France civil works and power plants. The first application concerns the monitoring of nuclear power plants. Each internal component generates specific vibration modes and \"neutron noise\" which is a combination of all modes generated. The aim of this study is to separate such independent vibration modes. The second application concerns dams supervision: it consists in separating the various types of motion of a dam according to their physical origin. The third application concerns nondestructive testing on steam generators in nuclear power plants. The aim is to reduce the flattening noise. The classical methods operate only when a noise reference is available. They propose to use a multi-sensor approach with the blind separation methods (the noise reference is not necessary). Considering the specifications of the signals, they obtain better performance using a two-order statistical algorithm than a higher-order statistical algorithm.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"17 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":"116490300","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.1997.613550
J. Galy, C. Adnet, É. Chaumette
Blind source separation is now a well known problem. Various methods have been proposed for instantaneous and convolutive mixtures of sources. Conventional antenna array processing techniques are based on the use of second order statistics but rest on restrictive assumptions. Thus, when a priori informations about the propagation or the geometry of the array are not available, the model can be generalized to a blind sources separation model. It supposes the statistical independence of the sources and their non-gaussianity. In this paper, we focus on the narrow band source separation problem embedded in wide band jammers. We show that the JADE algorithm made for instantaneous mixture is still valid in a wide band context where only the signals of interest are narrow-band. We also prove that a wide band signal tends to occupy all the degrees of freedom of the covariance matrix and modifies the signal subspace dimension.
{"title":"Narrow band source separation in wide band context applications to array signal processing","authors":"J. Galy, C. Adnet, É. Chaumette","doi":"10.1109/HOST.1997.613550","DOIUrl":"https://doi.org/10.1109/HOST.1997.613550","url":null,"abstract":"Blind source separation is now a well known problem. Various methods have been proposed for instantaneous and convolutive mixtures of sources. Conventional antenna array processing techniques are based on the use of second order statistics but rest on restrictive assumptions. Thus, when a priori informations about the propagation or the geometry of the array are not available, the model can be generalized to a blind sources separation model. It supposes the statistical independence of the sources and their non-gaussianity. In this paper, we focus on the narrow band source separation problem embedded in wide band jammers. We show that the JADE algorithm made for instantaneous mixture is still valid in a wide band context where only the signals of interest are narrow-band. We also prove that a wide band signal tends to occupy all the degrees of freedom of the covariance matrix and modifies the signal subspace dimension.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"568 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113996522","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.1997.613495
E. Powers, In-Seung Park, S. Im, S. Mehta, E. Yi
A sparse second-order time-domain Volterra model is used to decompose a random (sea) wave train into its first- and second-order components. Extreme waves are shown to result from short-term phase locking of the first- and second-order components. The feasibility of using a wavelet-based bicoherence "spectrum" to detect the strong, but short lived, phase coupling is investigated. The results are encouraging and suggest the wavelet-based bicoherence is a topic worth considering further.
{"title":"Higher-order statistics and extreme waves","authors":"E. Powers, In-Seung Park, S. Im, S. Mehta, E. Yi","doi":"10.1109/HOST.1997.613495","DOIUrl":"https://doi.org/10.1109/HOST.1997.613495","url":null,"abstract":"A sparse second-order time-domain Volterra model is used to decompose a random (sea) wave train into its first- and second-order components. Extreme waves are shown to result from short-term phase locking of the first- and second-order components. The feasibility of using a wavelet-based bicoherence \"spectrum\" to detect the strong, but short lived, phase coupling is investigated. The results are encouraging and suggest the wavelet-based bicoherence is a topic worth considering further.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"101 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":"124144024","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.1997.613476
L. Hadjileontiadis, S. Panas
The use of higher-order statistics in an autoregressive modeling of lung sounds is presented resulting in a characterization of their source and transmission. The lung sound source in the airway is estimated using the prediction error of an all-pole filter based on higher-order statistics (AR-HOS), while the acoustic transmission through the lung parenchyma and chest wall is modeled by the transfer function of the same AR-HOS filter. The parametric bispectrum, using the estimated a/sub i/ coefficients of the AR-HOS model, is also calculated to elucidate the frequency characteristics of the modeled system. The implementation of this approach on pre-classified lung sound segments in known disease conditions, selected from teaching tapes, was examined. Experiments have shown that a reliable and consistent with current knowledge estimation of lung sound characteristics can be achieved using this method, even in the presence of additive Gaussian noise.
{"title":"Autoregressive modeling of lung sounds using higher-order statistics: estimation of source and transmission","authors":"L. Hadjileontiadis, S. Panas","doi":"10.1109/HOST.1997.613476","DOIUrl":"https://doi.org/10.1109/HOST.1997.613476","url":null,"abstract":"The use of higher-order statistics in an autoregressive modeling of lung sounds is presented resulting in a characterization of their source and transmission. The lung sound source in the airway is estimated using the prediction error of an all-pole filter based on higher-order statistics (AR-HOS), while the acoustic transmission through the lung parenchyma and chest wall is modeled by the transfer function of the same AR-HOS filter. The parametric bispectrum, using the estimated a/sub i/ coefficients of the AR-HOS model, is also calculated to elucidate the frequency characteristics of the modeled system. The implementation of this approach on pre-classified lung sound segments in known disease conditions, selected from teaching tapes, was examined. Experiments have shown that a reliable and consistent with current knowledge estimation of lung sound characteristics can be achieved using this method, even in the presence of additive Gaussian noise.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"32 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":"127697415","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.1997.613510
C. Coroyer, D. Declercq, P. Duvaut
This study presents a new method for textures classification based on higher order statistics (HOS). We propose the use of third order correlation tools for texture analysis. We compare the performance of three different tools: the bicorrelation in the spatial domain, the bispectrum in the frequency domain and the bicorspectrum which is a spatial/frequency representation in that case. We test classification on representative textures of Brodatz album.
{"title":"Texture classification using third order correlation tools","authors":"C. Coroyer, D. Declercq, P. Duvaut","doi":"10.1109/HOST.1997.613510","DOIUrl":"https://doi.org/10.1109/HOST.1997.613510","url":null,"abstract":"This study presents a new method for textures classification based on higher order statistics (HOS). We propose the use of third order correlation tools for texture analysis. We compare the performance of three different tools: the bicorrelation in the spatial domain, the bispectrum in the frequency domain and the bicorspectrum which is a spatial/frequency representation in that case. We test classification on representative textures of Brodatz album.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"1 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":"125787465","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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