This paper presents a new algorithm that is capable of extracting circles from complicated and heavily corrupted images. The algorithm uses a least-squares fitting algorithm for arc segments. The arcs are segmented by using the short straight lines which are extracted by a fast line extraction algorithm. The arc segments are used to yield accurate circle parameters. Tests performed on synthetic...

Orbital solutions for binary or multiple stellar systems that combine astrometry (e.g., position angles and angular separations) with spectroscopy (radial velocities) have important advantages over astrometric-only or spectroscopic-only solutions. In many cases they allow the determination of the absolute masses of the components, as well as the distance. Yet, these kinds of combined solutions ...

Iterative processes for the inversion of structured matrices can be further improved by using a technique for compression and refinement via the least-squares computation. We review such processes and elaborate upon incorporation of this technique into the known frameworks.

A novel parallel method for determining an approximate total least squares (TLS) solution is introduced. Based on domain distribution, the global TLS problem is partitioned into several dependent TLS subproblems. A convergent algorithm using the parallel variable distribution technique (Ferris and Mangasarian, 1994) is presented. Numerical results support the development and analysis of the alg...

Previous studies on fuzzy linear regression analysis have a common characteristic of increasing spreads for the estimated fuzzy responses as the independent variable increases its magnitude, which is not suitable for general cases. This paper proposes a two-stage approach to construct the fuzzy linear regression model. In the 2rst stage, the fuzzy observations are defuzzi2ed so that the traditi...

Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D-optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied and explicit characterisations of the Bayesian D-optimal saturated designs for the Michaelis-M...

A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals subject to white measurement noise. It is shown that the corrupting noise variance, which determines the bias in the standard least-squares (LS) parameter estimator, can be estimated by simply using the expected LS errors when the ratio between the driving noise variance and the corrupting noise...

The paper is a contribution to parameter estimation in fuzzy regression models with random fuzzy sets. Here models with crisp parameters and fuzzy observations of the variables are investigated. This type of regressionmodelsmay be understood as an extension of the ordinary single equation linear regression models by integrating additionally the physical vagueness of the involved items. So the s...

Since there are more equations than unknowns (m > n), this is an overdetermined set of equations. If the measurements of the independent variables xi are known precisely, then the only difference between the model, ŷ(xi; a), and the measured data yi, must be due to measurement error in yi and natural variability in the data that the model is unable to reflect. In such cases, the ‘best’ approxim...

Since there are more equations than unknowns (m > n), this is an overdetermined set of equations. If the measurements of the independent variables xi are known precisely, then the only difference between the model, ŷ(xi; a), and the measured data yi, must be due to measurement error in yi and natural variability in the data that the model is unable to reflect. In such cases, the ‘best’ approxim...