Introduction 2 Signal arithmetic 3 Signals and noise 6 Smoothing 11 Differentiation 17 Peak Sharpening 26 Harmonic analysis 28 Convolution 31 Deconvolution 32 Fourier filter 34 Integration and peak area measurement 35 Linear least-squares curve fitting 37 Multicomponent spectroscopy 49 Non-linear iterative curve fitting 54 Accuracy and precision of peak parameter measurement 58 SPECTRUM signal ...

Introduction 2 Signal arithmetic 3 Signals and noise 6 Smoothing 11 Differentiation 17 Peak Sharpening 26 Harmonic analysis 28 Convolution 31 Deconvolution 32 Fourier filter 34 Integration and peak area measurement 35 Linear least-squares curve fitting 37 Multicomponent spectroscopy 49 Non-linear iterative curve fitting 54 Accuracy and precision of peak parameter measurement 58 SPECTRUM signal ...

A novel ellipse fitting method which is selective for digital and noisy elliptic curves is proposed in this paper. The method aims at fitting an ellipse only when the data points are highly likely belong to an ellipse. This is achieved using the geometric distances of the ellipse from the data points. The proposed method models the non-linear problem of ellipse fitting as a combination of two o...

Abstract — We show in this paper that many different least-squares problems which have applications in signal processing may be seen as special cases of a more general vector space minimization problem called the Minimum Norm problem. We show that special cases of the Minimum Norm problem include: least squares fitting of a finite set of points to a linear equation and to a quadratic equation; ...

The article considers a new iterative algorithm for solving total least squares problems. A version of the implicit method simple iterations based on singular value decomposition is proposed biased normal system algebraic equations. use makes it possible to replace an ill-conditioned problem with sequence problems smaller condition number. This significantly increase computational stability and...