The Experts below are selected from a list of 159015 Experts worldwide ranked by ideXlab platform
Roman Yuzefovych - One of the best experts on this subject based on the ideXlab platform.
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the coherent and component estimation of covariance invariants for vectorial periodically correlated Random Processes and its application
Workshop on Cyclostationary Systems and Their Applications, 2017Co-Authors: Ihor Javorskyj, Roman Yuzefovych, I Matsko, G Trokhym, P SemenovAbstract:The coherent and the component methods for the estimation of the linear covariance invariants of vectorial periodically correlated Random Processes (PCRP) are considered. The coherent estimators are calculated by averaging of the samples taken through the non-stationary period. The component estimators are built in the form of trigonometric polynomials, Fourier coefficients of which are calculated by weighted averaging of PCRP realization. The properties of the continuous and the discrete estimators are investigated, the asymptotical unbiasedness and mean square consistency are proved. The formulae for their biases and variances described dependency of these quantities on realization length, time sampling and PCRP covariance components are obtained. The conditions of the absence of the aliasing effects of the first and the second kinds are given. The comparison of the coherent and component estimators is carried out for the case of the amplitude modulated signals. It is shown that the advantage of the component method over coherent grows as a rate of PCRP correlations clumping increases. The example of the using of vectorial statistical processing for diagnosis of rolling bearing are given. The investigation results show that using of the covariance function invariants allow to improve the efficiency of the fault detection and to establish of the defect spatial properties.
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periodically correlated Random Processes application in early diagnostics of mechanical systems
Mechanical Systems and Signal Processing, 2017Co-Authors: Ihor Javorskyj, I B Kravets, Ivan Matsko, Roman YuzefovychAbstract:Abstract The covariance and spectral characteristics of periodically correlated Random Processes (PCRP) are used to describe the state of rotary mechanical systems and in their fault detection. The methods for estimation of mean function, covariance function, instantaneous spectral density and their Fourier coefficients for a given class of non-stationary Random Processes on the basis of experimental data, namely: the synchronous averaging, component, least squares method and linear filtration methods are considered. The first and second order periodicity detection methods are used for vibration signals analysis. A method for mechanical system fault identification and classification based on a harmonic series representation is developed. Examples of fault detection in rolling/sliding bearings and gearboxes are given.
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component covariance analysis for periodically correlated Random Processes
Signal Processing, 2010Co-Authors: Ihor Javorskyj, I Isayev, Jacek Majewski, Roman YuzefovychAbstract:The paper is dedicated to the component method for estimating the periodically correlated Random Processes (PCRP) mean and covariance functions, when number of harmonics is finite. This method is based on the decomposition of these time periodic functions into trigonometric polynomials and the estimation of their Fourier coefficients. Then the component estimates of the PCRP mean and covariance functions are constructed on the basis of the coefficient estimates. The properties of the PCRP mean and covariance functions component estimates are investigated, asymptotical unbiasedness and mean square consistency for these estimates, and the corresponding formulae for their biases and variances, which depend on the record length and number of Fourier coefficients, are expressed. Comparison for the component and coherent method estimates is carried out for the case of amplitude and phase modulated signals.
Mohamed-slim Alouini - One of the best experts on this subject based on the ideXlab platform.
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level crossing rate over multiple independent Random Processes an extension of the applicability of the rice formula
IEEE Transactions on Wireless Communications, 2007Co-Authors: Liuqing Yang, Mohamed-slim AlouiniAbstract:This paper presents general expressions for the calculation of the level crossing rate of various combinations of multiple independent Random Processes. These new formulas are then applied to obtain the outage rate and average outage duration of several communication systems and networks of current interest. Applications include for example multi-hop communication systems with and without selection combining as well as communication networks with routing diversity.
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level crossing rate over multiple independent Random Processes an extension of the applicability of the rice formula
Global Communications Conference, 2003Co-Authors: Liuqing Yang, Mohamed-slim AlouiniAbstract:This paper presents general expressions for the calculation of the level crossing rate of various combinations of multiple independent Random Processes. These new formulas are then applied to obtain the outage rate of several communication systems and networks of current interest. Applications include for example multihop communication systems with and without selection combining as well as communication networks with routing diversity.
Arthur Gretton - One of the best experts on this subject based on the ideXlab platform.
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a kernel test for three variable interactions with Random Processes
Uncertainty in Artificial Intelligence, 2016Co-Authors: Paul K Rubenstein, Kacper Chwialkowski, Arthur GrettonAbstract:We apply a wild bootstrap method to the Lancaster three-variable interaction measure in order to detect factorisation of the joint distribution on three variables forming a stationary Random process, for which the existing permutation bootstrap method fails. As in the i.i.d. case, the Lancaster test is found to outperform existing tests in cases for which two independent variables individually have a weak influence on a third, but that when considered jointly the influence is strong. The main contributions of this paper are twofold: first, we prove that the Lancaster statistic satisfies the conditions required to estimate the quantiles of the null distribution using the wild bootstrap; second, the manner in which this is proved is novel, simpler than existing methods, and can further be applied to other statistics.
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a kernel independence test for Random Processes
International Conference on Machine Learning, 2014Co-Authors: Kacper Chwialkowski, Arthur GrettonAbstract:A non-parametric approach to the problem of testing the independence of two Random Processes is developed. The test statistic is the Hilbert-Schmidt Independence Criterion (HSIC), which was used previously in testing independence for i.i.d. pairs of variables. The asymptotic behaviour of HSIC is established when computed from samples drawn from Random Processes. It is shown that earlier bootstrap procedures which worked in the i.i.d. case will fail for Random Processes, and an alternative consistent estimate of the p-values is proposed. Tests on artificial data and real-world forex data indicate that the new test procedure discovers dependence which is missed by linear approaches, while the earlier bootstrap procedure returns an elevated number of false positives.
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a kernel independence test for Random Processes
arXiv: Machine Learning, 2014Co-Authors: Kacper Chwialkowski, Arthur GrettonAbstract:A new non parametric approach to the problem of testing the independence of two Random process is developed. The test statistic is the Hilbert Schmidt Independence Criterion (HSIC), which was used previously in testing independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is established when computed from samples drawn from Random Processes. It is shown that earlier bootstrap procedures which worked in the i.i.d. case will fail for Random Processes, and an alternative consistent estimate of the p-values is proposed. Tests on artificial data and real-world Forex data indicate that the new test procedure discovers dependence which is missed by linear approaches, while the earlier bootstrap procedure returns an elevated number of false positives. The code is available online: this https URL .
Ihor Javorskyj - One of the best experts on this subject based on the ideXlab platform.
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the coherent and component estimation of covariance invariants for vectorial periodically correlated Random Processes and its application
Workshop on Cyclostationary Systems and Their Applications, 2017Co-Authors: Ihor Javorskyj, Roman Yuzefovych, I Matsko, G Trokhym, P SemenovAbstract:The coherent and the component methods for the estimation of the linear covariance invariants of vectorial periodically correlated Random Processes (PCRP) are considered. The coherent estimators are calculated by averaging of the samples taken through the non-stationary period. The component estimators are built in the form of trigonometric polynomials, Fourier coefficients of which are calculated by weighted averaging of PCRP realization. The properties of the continuous and the discrete estimators are investigated, the asymptotical unbiasedness and mean square consistency are proved. The formulae for their biases and variances described dependency of these quantities on realization length, time sampling and PCRP covariance components are obtained. The conditions of the absence of the aliasing effects of the first and the second kinds are given. The comparison of the coherent and component estimators is carried out for the case of the amplitude modulated signals. It is shown that the advantage of the component method over coherent grows as a rate of PCRP correlations clumping increases. The example of the using of vectorial statistical processing for diagnosis of rolling bearing are given. The investigation results show that using of the covariance function invariants allow to improve the efficiency of the fault detection and to establish of the defect spatial properties.
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periodically correlated Random Processes application in early diagnostics of mechanical systems
Mechanical Systems and Signal Processing, 2017Co-Authors: Ihor Javorskyj, I B Kravets, Ivan Matsko, Roman YuzefovychAbstract:Abstract The covariance and spectral characteristics of periodically correlated Random Processes (PCRP) are used to describe the state of rotary mechanical systems and in their fault detection. The methods for estimation of mean function, covariance function, instantaneous spectral density and their Fourier coefficients for a given class of non-stationary Random Processes on the basis of experimental data, namely: the synchronous averaging, component, least squares method and linear filtration methods are considered. The first and second order periodicity detection methods are used for vibration signals analysis. A method for mechanical system fault identification and classification based on a harmonic series representation is developed. Examples of fault detection in rolling/sliding bearings and gearboxes are given.
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component covariance analysis for periodically correlated Random Processes
Signal Processing, 2010Co-Authors: Ihor Javorskyj, I Isayev, Jacek Majewski, Roman YuzefovychAbstract:The paper is dedicated to the component method for estimating the periodically correlated Random Processes (PCRP) mean and covariance functions, when number of harmonics is finite. This method is based on the decomposition of these time periodic functions into trigonometric polynomials and the estimation of their Fourier coefficients. Then the component estimates of the PCRP mean and covariance functions are constructed on the basis of the coefficient estimates. The properties of the PCRP mean and covariance functions component estimates are investigated, asymptotical unbiasedness and mean square consistency for these estimates, and the corresponding formulae for their biases and variances, which depend on the record length and number of Fourier coefficients, are expressed. Comparison for the component and coherent method estimates is carried out for the case of amplitude and phase modulated signals.
Liuqing Yang - One of the best experts on this subject based on the ideXlab platform.
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level crossing rate over multiple independent Random Processes an extension of the applicability of the rice formula
IEEE Transactions on Wireless Communications, 2007Co-Authors: Liuqing Yang, Mohamed-slim AlouiniAbstract:This paper presents general expressions for the calculation of the level crossing rate of various combinations of multiple independent Random Processes. These new formulas are then applied to obtain the outage rate and average outage duration of several communication systems and networks of current interest. Applications include for example multi-hop communication systems with and without selection combining as well as communication networks with routing diversity.
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level crossing rate over multiple independent Random Processes an extension of the applicability of the rice formula
Global Communications Conference, 2003Co-Authors: Liuqing Yang, Mohamed-slim AlouiniAbstract:This paper presents general expressions for the calculation of the level crossing rate of various combinations of multiple independent Random Processes. These new formulas are then applied to obtain the outage rate of several communication systems and networks of current interest. Applications include for example multihop communication systems with and without selection combining as well as communication networks with routing diversity.
