ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

On the relationship between the Method of Least Squares and Gram-Schmidt orthogonalization

The method of Least Squares is due to Carl Friedrich Gauss. The Gram-Schmidt orthogonalization method is of much younger date. A method for solving Least Squares Problems is developed which automatically results in the appearance of the Gram-Schmidt orthogonalizers. Given these orthogonalizers an induction-proof is available for solving Least Squares Problems.

Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squars

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services

Administrative authorization for the demolition of urban rental units pursuant to the law of 1964: a spanish example of problematic over-activity in the application of transitional laws

Transitional provisions are defined as the set of regulations that rule juridical relationships on the occasion of a legislative change. Out of this context of law succession, their indiscriminate application can lead to serious inconsistencies. The analysis of a Spanish private law example is offered to illustrate this fact. It concerns the administrative authorization for the demolition of rented buildings in the cities. A regulation repealed more than fifteen years ago and however widespread utilised on ancient constructions that, after recent urban development, have acquired great economic value; something that in the end explains the current importance of such provisions. What is happening in Spain: denaturalization of the original  figure due to a mixture of formalist interpretations and speculative market interests, is presented here to call the attention on the necessary limitation of transitional provisions’ effects.

The Influence of Body Condition on the Stopover Ecology of Least Sandpipers in the Lower Mississippi Alluvial Valley during Fall Migration

Many shorebirds are long-distance migrants and depend on the energy gained at stopover sites to complete migration. Competing hypotheses have described strategies used by migrating birds; the energy-selection hypothesis predicts that shorebirds attempt to maximize energy gained at stopover sites, whereas the time-selection hypothesis predicts that shorebirds attempt to minimize time spent at stopover sites. The energy- and time-selection hypotheses both predict that birds in better condition will depart sites sooner. However, numerous studies of stopover duration have found little support for this prediction, leading to the suggestion that migrating birds operate under energy and time constraints for only a small portion of the migratory season. During fall migration 2002, we tested the prediction that birds in better condition depart stopover sites sooner by examining the relationship between stopover duration and body condition for migrating Least Sandpipers (Calidris minutilla) at three stopover sites in the Lower Mississippi Alluvial Valley. We also tested the assumption made by the Lower Mississippi Alluvial Valley Migratory Bird Science Team that shorebirds stay in the Mississippi Valley for 10 d. The assumption of 10 d was used to estimate the amount of habitat required by shorebirds in the Mississippi Valley during fall migration; a period longer than 10 d would increase the estimate of the amount habitat required. We used multiple-day constancy models of apparent survival and program MARK to estimate stopover duration for 293 individually color-marked and resighted Least Sandpipers. We found that a four-day constancy interval and a site x quadratic time trend interaction term best modeled apparent survival. We found only weak support for body condition as a factor explaining length of stopover duration, which is consistent with findings from similar work. Stopover duration estimates were 4.1 d (95% CI = 2.8–6.1) for adult Least Sandpipers at Bald Knob National Wildlife Refuge, Arkansas, 6.5 d (95% CI = 4.9–8.7) for adult and 6.1 d (95% CI =4.2–9.1) for juvenile Least Sandpipers at Yazoo National Wildlife Refuge, Mississippi, and 6.9 d (95% CI = 5.5–8.7) for juvenile Least Sandpipers at Morgan Brake National Wildlife Refuge, Mississippi. Based on our estimates of stopover duration and the assumption made by the Lower Mississippi Alluvial Valley Migratory Bird Science Team, there is sufficient habitat in the lower Mississippi Valley to support shorebirds during fall migration.

Approximate Gauss-Newton methods for nonlinear least squares problems

The Gauss–Newton algorithm is an iterative method regularly used for solving nonlinear least squares problems. It is particularly well suited to the treatment of very large scale variational data assimilation problems that arise in atmosphere and ocean forecasting. The procedure consists of a sequence of linear least squares approximations to the nonlinear problem, each of which is solved by an “inner” direct or iterative process. In comparison with Newton’s method and its variants, the algorithm is attractive because it does not require the evaluation of second-order derivatives in the Hessian of the objective function. In practice the exact Gauss–Newton method is too expensive to apply operationally in meteorological forecasting, and various approximations are made in order to reduce computational costs and to solve the problems in real time. Here we investigate the effects on the convergence of the Gauss–Newton method of two types of approximation used commonly in data assimilation. First, we examine “truncated” Gauss–Newton methods where the inner linear least squares problem is not solved exactly, and second, we examine “perturbed” Gauss–Newton methods where the true linearized inner problem is approximated by a simplified, or perturbed, linear least squares problem. We give conditions ensuring that the truncated and perturbed Gauss–Newton methods converge and also derive rates of convergence for the iterations. The results are illustrated by a simple numerical example. A practical application to the problem of data assimilation in a typical meteorological system is presented.

On integral equation and least squares methods for scattering by diffraction gratings

In this paper we consider the scattering of a plane acoustic or electromagnetic wave by a one-dimensional, periodic rough surface. We restrict the discussion to the case when the boundary is sound soft in the acoustic case, perfectly reflecting with TE polarization in the EM case, so that the total field vanishes on the boundary. We propose a uniquely solvable first kind integral equation formulation of the problem, which amounts to a requirement that the normal derivative of the Green's representation formula for the total field vanish on a horizontal line below the scattering surface. We then discuss the numerical solution by Galerkin's method of this (ill-posed) integral equation. We point out that, with two particular choices of the trial and test spaces, we recover the so-called SC (spectral-coordinate) and SS (spectral-spectral) numerical schemes of DeSanto et al., Waves Random Media, 8, 315-414 1998. We next propose a new Galerkin scheme, a modification of the SS method that we term the SS* method, which is an instance of the well-known dual least squares Galerkin method. We show that the SS* method is always well-defined and is optimally convergent as the size of the approximation space increases. Moreover, we make a connection with the classical least squares method, in which the coefficients in the Rayleigh expansion of the solution are determined by enforcing the boundary condition in a least squares sense, pointing out that the linear system to be solved in the SS* method is identical to that in the least squares method. Using this connection we show that (reflecting the ill-posed nature of the integral equation solved) the condition number of the linear system in the SS* and least squares methods approaches infinity as the approximation space increases in size. We also provide theoretical error bounds on the condition number and on the errors induced in the numerical solution computed as a result of ill-conditioning. Numerical results confirm the convergence of the SS* method and illustrate the ill-conditioning that arises.