A new deconvolution algorithm for binary images based on the theory of discrete tomography is proposed. The proposed algorithm is inherently binary as opposed to traditional filtering techniques such as Wiener filtering which require thresholding to produce binary images. Time and space complexity of the proposed algorithm are polynomial in the image size whereas the two-dimensional Viterbi met...

Volterra and Wiener series are perhaps the best-understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estimated. We show that Volterra and Wiener series c...

A new approach to robust estimation of signals and prediction of time{series is considered. Signal and system parameter deviations are represented as random variables, with known covariances. A robust design is obtained by minimizing the squared estimation error, averaged both with respect to model errors and noise. A polynomial solution, based on averaged spectral factor-izations and averaged ...

Context. We investigate the Kolmogorov-Wiener filter weight function for prediction of continuous stationary telecommunication traffic in GFSD (Gaussian fractional sum-difference) model.
 Objective. The aim work is to obtain an approximate solution corresponding and illustrate convergence truncated polynomial expansion method used this paper.
 Method. obtaining KolmogorovWiener under ...

A new approach to robust estimation of signals and prediction of time{ series is considered. Possible modelling errors are described by sets of systems , parametrized by random variables, with known covariances. A robust design is obtained by minimizing the squared estimation error, averaged both with respect to model errors and noise. A polynomial solution, based on averaged spectral factoriza...

The purpose of this paper is to investigate neural network capability systematically. The main results are: 1) every Tauber-Wiener function is qualified as an activation function in the hidden layer of a three-layered neural network; 2) for a continuous function in S'(R(1 )) to be a Tauber-Wiener function, the necessary and sufficient condition is that it is not a polynomial; 3) the capability ...

Wiener systems consist of a linear dynamic system whose output is measured through a static non-linearity. In this paper we study the identification of single-input single-output Wiener systems with finite impulse response dynamics and polynomial output non-linearities. Using multi-index notation, we solve a least squares problem to estimate products of the coefficients of the non-linearity and...