The Experts below are selected from a list of 48783 Experts worldwide ranked by ideXlab platform

P J W Rayner - One of the best experts on this subject based on the ideXlab platform.

  • single channel nonstationary stochastic signal separation using linear time varying filters
    IEEE Transactions on Signal Processing, 2003
    Co-Authors: James R Hopgood, P J W Rayner
    Abstract:

    Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the Signals can be separated. The paper shows that when Signals are separated using the generalized Wiener filter, the degree of separability can be deduced from the signal structure. To identify this structure, the processes are represented on an general spectral domain, and a sufficient solution to the Wiener filter is obtained. The filter is composed of a term independent of the signal values, corresponding to regions in the spectral domain where the desired signal components are not distorted by interfering noise components, and a term dependent on the signal correlations, corresponding to the region where components overlap. An example of determining perfect separability of modulated Random Signals is given with application in radar and speech processing.

  • single channel separation using linear time varying filters separability of non stationary stochastic Signals
    International Conference on Acoustics Speech and Signal Processing, 1999
    Co-Authors: James R Hopgood, P J W Rayner
    Abstract:

    Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the Signals can be separated. The paper shows that when Signals are separated using the generalised Wiener filter, the degree of separability can be deduced from the filter structure. To identify this structure, the processes are represented on an arbitrary spectral domain, and a sufficient solution to the Wiener filter is obtained. The filter is composed of a term independent of the signal values, corresponding to regions in the spectral domain where the desired signal components are not distorted by interfering noise components, and a term dependent on the signal correlations, corresponding to the region where components overlap. An example of determining perfect separability of modulated Random Signals is given.

James R Hopgood - One of the best experts on this subject based on the ideXlab platform.

  • single channel nonstationary stochastic signal separation using linear time varying filters
    IEEE Transactions on Signal Processing, 2003
    Co-Authors: James R Hopgood, P J W Rayner
    Abstract:

    Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the Signals can be separated. The paper shows that when Signals are separated using the generalized Wiener filter, the degree of separability can be deduced from the signal structure. To identify this structure, the processes are represented on an general spectral domain, and a sufficient solution to the Wiener filter is obtained. The filter is composed of a term independent of the signal values, corresponding to regions in the spectral domain where the desired signal components are not distorted by interfering noise components, and a term dependent on the signal correlations, corresponding to the region where components overlap. An example of determining perfect separability of modulated Random Signals is given with application in radar and speech processing.

  • single channel separation using linear time varying filters separability of non stationary stochastic Signals
    International Conference on Acoustics Speech and Signal Processing, 1999
    Co-Authors: James R Hopgood, P J W Rayner
    Abstract:

    Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the Signals can be separated. The paper shows that when Signals are separated using the generalised Wiener filter, the degree of separability can be deduced from the filter structure. To identify this structure, the processes are represented on an arbitrary spectral domain, and a sufficient solution to the Wiener filter is obtained. The filter is composed of a term independent of the signal values, corresponding to regions in the spectral domain where the desired signal components are not distorted by interfering noise components, and a term dependent on the signal correlations, corresponding to the region where components overlap. An example of determining perfect separability of modulated Random Signals is given.

Joanna Bruzda - One of the best experts on this subject based on the ideXlab platform.

  • on simple wavelet estimators of Random Signals and their small sample properties
    Journal of Statistical Computation and Simulation, 2015
    Co-Authors: Joanna Bruzda
    Abstract:

    In the paper we suggest certain nonparametric estimators of Random Signals based on the wavelet transform. We consider stochastic Signals embedded in white noise and extractions with wavelet denoizing algorithms utilizing the non-decimated discrete wavelet transform and the idea of wavelet scaling. We evaluate properties of these estimators via extensive computer simulations and partially also analytically. Our wavelet estimators of Random Signals have clear advantages over parametric maximum likelihood methods as far as computational issues are concerned, while at the same time they can compete with these methods in terms of precision of estimation in small samples. An illustrative example concerning smoothing of survey data is also provided.

  • on simple wavelet estimators of Random Signals and their small sample properties
    Social Science Research Network, 2014
    Co-Authors: Joanna Bruzda
    Abstract:

    In the paper we introduce certain nonparametric estimators of Random Signals based on the wavelet transform. We consider stochastic Signals lying in white noise and extractions with wavelet denoising algorithms utilising the non-decimated discrete wavelet transform and the idea of wavelet scaling. We evaluate properties of these estimators via expansive computer simulations and partially also analytically. Our wavelet estimators of Random Signals have clear advantages over parametric maximum likelihood methods as far as computational issues are concerned, while at the same time they can compete with these methods in terms of precision of estimation in small samples. An illustrative example concerning smoothing survey data is also provided.

Edmanuel Torres - One of the best experts on this subject based on the ideXlab platform.

  • Fractional Sampling Theorem for $\alpha$-Bandlimited Random Signals and Its Relation to the von Neumann Ergodic Theorem
    IEEE Transactions on Signal Processing, 2014
    Co-Authors: Rafael Torres, Zandra Lizarazo, Edmanuel Torres
    Abstract:

    Considering that fractional correlation function and the fractional power spectral density, for α-stationary Random Signals, form a fractional Fourier transform pair. We present an interpolation formula to estimate a Random signal from a temporal Random series, based on the fractional sampling theorem for α-bandlimited Random Signals. Furthermore, by establishing the relationship between the sampling theorem and the von Neumann ergodic theorem, the estimation of the power spectral density of a Random signal from one sample signal becomes a suitable approach. Thus, the validity of the sampling theorem for Random Signals is closely linked to an ergodic hypothesis in the mean sense.

  • fractional fourier analysis of Random Signals and the notion of spl alpha stationarity of the wigner ville distribution
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Rafael Torres, Edmanuel Torres
    Abstract:

    In this paper, a generalized notion of wide-sense α-stationarity for Random Signals is presented. The notion of stationarity is fundamental in the Fourier analysis of Random Signals. For this purpose, a definition of the fractional correlation between two Random variables is introduced. It is shown that for wide-sense α -stationary Random Signals, the fractional correlation and the fractional power spectral density functions form a fractional Fourier transform pair. Thus, the concept of α -stationarity plays an important role in the analysis of Random Signals through the fractional Fourier transform for Signals nonstationary in the standard formulation, but α -stationary. Furthermore, we define the α-Wigner-Ville distribution in terms of the fractional correlation function, in which the standard Fourier analysis is the particular case for α = π/2 , and it leads to the Wiener-Khinchin theorem.

H A Barker - One of the best experts on this subject based on the ideXlab platform.

  • design of ternary Signals for mimo identification in the presence of noise and nonlinear distortion
    IEEE Transactions on Control Systems and Technology, 2009
    Co-Authors: Ai Hui Tan, K R Godfrey, H A Barker
    Abstract:

    A new approach to designing sets of ternary periodic Signals with different periods for multi-input multi-output system identification is described. The Signals are pseudo-Random Signals with uniform nonzero harmonics, generated from Galois field GF(q), where q is a prime or a power of a prime. The Signals are designed to be uncorrelated, so that effects of different inputs can be easily decoupled. However, correlated harmonics can be included if necessary, for applications in the identification of ill-conditioned processes. A design table is given for q les 31. An example is presented for the design of five uncorrelated Signals with a common period N = 168 . Three of these Signals are applied to identify the transfer function matrix as well as the singular values of a simulated distillation column. Results obtained are compared with those achieved using two alternative methods.

  • design of pseudo Random perturbation Signals for frequency domain identification of non linear systems
    IFAC Proceedings Volumes, 1997
    Co-Authors: H A Barker, M Zhuang
    Abstract:

    Abstract The design of pseudo-Random Signals for frequency-domain system identification is described. The Signals are derived from pseudo-Random sequences mapped from maximum-length sequences in Galois fields. The design procedure involves choosing both the field and the mapping to ensure that harmonic multiples of specified integers are suppressed in the sequences and Signals. From among the many different mappings that may be used, those giving uniform power spectra and least peak factors are preferred. Examples are used to illustrate the underlying theory. Mappings for the suppression of harmonic multiples of 2, 3 and 5, which are particularly useful for nonlinear system identification, are given for fields of up to 31 elements.