An effective technique for extracting an audio input from a composite signal that contains a nonstationary noise-corrupted periodic disturbance is presented. The proposed technique cancels the periodic disturbance using a synthesized signal whose parameters are adjusted adaptively. A combination of a generalized phase-locked loop (PLL) and an adaptive least mean square (LMS) method is used. The...

selecting a IUDFWLRQDO H[SRQHQWLDO )(;3 model for a Gaussian long-memory time series. A regression approach is used to obtain the least squares estimator of d of the memory parameter, and mean squared error is minimized. The paper overlooks the fact that in the case of regression models, there is a relationship between $NDLNH¶V LQIRUPDWLRQ FULWHULRQ $,& and 0DOORZV¶ / & , which can also be used...

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for recursive least square (RLS) algorithms in interference suppression applications. The proposed VFF mechanism employs an updated component related to the time average of the error correlation to automatically adjust the forgetting factor in order to ensure fast convergence and good tracking of the interferen...

Different predictors and their approximators in nonlinear prediction regression models are studied. The minimal value of the mean squared error (MSE) is derived. Some approximate formulae for the MSE of ordinary and weighted least squares predictors are given.

Under minimal assumptions, it is established that the sample second moments of the errors of out-of-sample (real time) forecasts of possibly incorrect regARIMA models have asymptotic limits with useful frequency domain formulas. Both OLS and GLS estimates of the mean function are considered. With misspecified regressors, under additional assumptions that do not appear to exclude any regressors ...

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incre-mental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance th...

Tufts-Kumaresan (TK) method, which is based on linear prediction approach, is a standard algorithm for estimating the frequencies of sinusoids in noise. In this Letter, the TK algorithm is improved by attenuating the noise in the observation vector with the use of the reduced rank data matrix. It is shown that the proposed modification can provide smaller mean square frequency errors with lower...

A method of obtaining approximate redshifts and spectral types of galaxies using a photometric system of six broad-bandpass filters is developed. The technique utilizes a smallest maximum difference approach rather than a least-squares approach, and does not consider a galaxy’s apparent magnitude in the determination of its redshift. In an evalution of its accuracy using two distinct galaxy sam...

In this paper, the performance of the singleestimation (SE) and multiple-estimation (ME) is investigated in multiple-input multiple-output (MIMO) Rician flat fading channels using the traditional least squares (LS) estimator and the Bayesian minimum mean square error (MMSE) estimator. The closed form equations are obtained for mean square error (MSE) of the estimators in SE and ME cases under o...

This paper deals with the application of the weighted mixed regression estimation of the coe cients in a linear model when some values of some of the regressors are missing Taking the weight factor as an arbitrary scalar the performance of weighted mixed regression estimator in relation to the conventional least squares and mixed regression estimators is analyzed and the choice of scalar is dis...