Institute for Systems Research

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Author: search.filters.author.Kedem, BenjaminSubject: search.filters.subject.signal processing

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Now showing 1 - 6 of 6
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    Partial Likelihood Analysis of Categorical Time Series Models
    (1995) Fakianos, Konstantinos; Kedem, Benjamin; Short, David A.; ISR
    Partial likelihood analysis of two generalized logistic regression models for nominal and ordinal categorical time series is presented, taking into account stochastic time-dependent covariates. Under some conditions on the covariates, the resulting estimators are consistent and asymptotically normal. The analysis is applied to rainfall data where the goodness of fit is judged by a certain chi square statistic.
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    Power Considerations in Acoustic Emission
    (1995) Barnett, John T.; Clough, Roger B.; Kedem, Benjamin; ISR
    In stochastic acoustic emission, both theory and experiments suggest that the power of the acoustic emission signal is proportional to the source energy. Hence, inference about the power is equivalent to inference about the source energy except for a constant multiple. In this regard, the connection between peaks exceeding a fixed level and the power in random acoustic emission waves is explored when the source energy is an impulse of short duration. Under certain conditions, the peak distribution is sensitive to power changes, determines it and is determined by it. The maximum likelihood estimator of the power from a random sample of peaks- the peak estimator - is more efficient than the maximum likelihood estimator - average sum of squares - from a random sample of the same size of signal values. When evaluated from nonrandom samples, indications are that the peak estimator may still have a relatively small mean square error. A real data example indicates that the left-truncated Rayleigh probability distribution may serve as an adequate model for high peaks.
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    Estimation of Multiple Sinusoids by Parametric Filtering
    (1992) Li, Ta-Hsin; Kedem, Benjamin; ISR
    The problem of estimating the frequencies of multiple sinusoids from noisy observations is addressed in this paper. A parametric filtering approach, called the PF method, is proposed that leads to a consistent estimator of the AR representation of the sinusoidal signal, given the number of sinusoids. It is accomplished by using an iterative procedure to a fixed-point of the parametrized least squares estimator (from the filtered data) that comprises a contraction mapping in the vicinity of the true AR parameter. Employing appropriate filters, this method is able to achieve the accuracy of the nonlinear least squares estimator, with much less computational complexity and initialization requirement. It can also be implemented adaptively (recursively) in order to track time-varying frequencies. In this way, the PF method provides a flexible and efficient procedure of frequency estimation. An example of the AR filter is investigated in detail to illustrate the performance of the PF method.
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    On the Contraction Mapping Method for Frequency Detection
    (1992) Kedem, Benjamin; Yakowitz, S.; ISR
    The contraction mapping method for frequency estimation in the presence of noise, identifies the cosine of the frequency to be detected as a fixed point of a certain correlation mapping. At its hear, the method provides a plan for automatic self tuning of parametric filters. A variant of the method, called the HK algorithm, produces recursive zero-crossing rates (normalized HOC sequences) that converge to the frequency of interest. A statistical explanation for the contraction mapping method as epitomized by the HK algorithm is provided when the HOC sequences are produced by bandpass filters. The outright consistency of the zero-crossing rate is not required. Examples show that the method performs quite remarkably.
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    Asymptotic Normality of the Contraction Mapping Estimator for Frequency Estimation
    (1992) Li, Ta-Hsin; Kedem, Benjamin; Yakowitz, S.; ISR
    This paper investigates the asymptotic distribution of the recently-proposed contraction mapping (CM) method for frequency estimation. Given a finite sample composed of a sinusoidal signal in additive noise, the CM method applies to the data a parametric filter that matches its parameter with the first-order autocorrelation of the filtered noise. The CM estimator is defined as the fixed-point of the parametrized first-order sample autocorrelation of the filtered data. In this paper, it is proved that under appropriate conditions, the CM estimator is asymptotically normal with a variance inversely related to the signal-to-noise ratio. A useful example of the AR(2) filter is discussed in detail to illustrate the performance of the CM method.
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    Strong Consistency of the Contraction Mapping Method for Frequency Estimation
    (1992) Li, Ta-Hsin; Kedem, Benjamin; ISR
    Consider the super position of a sinusoid plus noise. By the application of certain parametric filter, the first-order autocorrelation becomes a contraction mapping. The sample estimator of the first-order autocorrelation is also a contraction whose fixed point converges almost surely to the cosine of the frequency to be detected. The theory is illustrated by two specific examples corresponding to two different parametric filters,