Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's

Abstract: The proposed filter assumes the noisy electrocardiography (ECG) to be modeled as a signal of deterministic nature, corrupted by additive muscle noise artefact. The muscle noise component is treated to be stationary with known second-order characteristics. Since noise-free ECG is shown to possess a narrow-band structure in discrete cosine transform (DCT) domain and the second-order statistical properties of the additive noise component is preserved due to the orthogonality property of DCT, noise abatement is easily accomplished via subspace decomposition in the transform domain. The subspace decomposition is performed using singular value decomposition (SVD), The order of the transform domain SVD filter required to achieve the desired degree of noise abatement is compared to that of a suboptimal Wiener filter using DCT. Since the Wiener filter assumes both the signal and noise structures to be statistical, with a priori known second-order characteristics, it yields a biased estimate of the ECG beat as compared to the SVD filter for a given value of mean-square error (mse). The filter order required for performing the subspace smoothing is shown to exceed a certain minimal value for which the mse profile of the SVD filter follows the minimum-mean-square error (mmse) performance warranted by the suboptimal Wiener filter. The effective filter order required for reproducing clinically significant features in the noisy ECG is then set by an upper bound derived by means of a finite precision linear perturbation model. A significant advantage resulting from the application of the proposed SVD filter lies in its ability to perform noise suppression independently on a single lead ECG record with only a limited number of data samples.

Prototype Filter Optimization to Minimize Stopband Energy With NPR Constraint for Filter Bank Multicarrier Modulation Systems

Abstract: Recently, filter bank multicarrier (FBMC) modulations have attracted increasing attention. The filter banks of FBMC are derived from a prototype filter that determines the system performance, such as stopband attenuation, intersymbol interference (ISI) and interchannel interference (ICI). In this paper, we formulate a problem of direct optimization of the filter impulse-response coefficients for the FBMC systems to minimize the stopband energy and constrain the ISI/ICI. Unfortunately, this filter optimization problem is nonconvex and highly nonlinear. Nevertheless, observing that all the functions in the optimization problem are twice-differentiable, we propose using the $\alpha$ -based Branch and Bound ( $\alpha$ BB) algorithm to obtain the optimal solution. However, the convergence time of the algorithm is unacceptable because the number of unknowns (i.e., the filter coefficients) in the optimization problem is too large. The main contribution of this paper is that we propose a method to dramatically reduce the number of unknowns of the optimization problem through approximation of the constraints, so that the optimal solution of the approximated optimization problem can be obtained with acceptable computational complexity. Numerical results show that, the proposed approximation is reasonable, and the optimized filters obtained with the proposed method achieve significantly lower stopband energy than those with the frequency sampling and windowing based techniques.

Mesh Smoothing by Adaptive and Anisotropic Gaussian Filter

Abstract: In this paper, we develop a fully automatic mesh filtering method that adaptively smoothes a noisy mesh and preserves sharp features and features consisting of only few triangle strips. In addition, it outperforms other conventional smoothing methods in terms of accuracy.

Digital filter adaptively learning filter coefficient

Abstract: A linear filter feeds back an error signal generated using the output of an adaptive filter to a learning circuit. The learning circuit learns the coefficients of the adaptive filter using the error signal. The coefficients of the linear filter are determined depending on the generation method of a target signal for error minimization. In a signal interpolation type timing recovery digital filter, the coefficients of an inverse interpolation filter are determined in such a way as to minimize an error of a signal after interpolation.

Channel estimation by adaptive interpolation

Abstract: Methods and apparatus that achieve good channel estimation without using unnecessarily complex interpolation filters are described. Adaptive interpolation filtering of a signal in a receiver includes determining at least one correlation function parameter of the channel and determining a filter configuration based on the correlation function parameter. The interpolation may be performed in time, where a Doppler frequency shift can serve as the correlation function parameter, or in frequency, where a root mean square or maximum delay spread can serve as the correlation function parameter, or both. A worst case signal-to-noise ratio may be used in determining the filter configuration, or, optionally, the signal-to-noise ratio can be determined in real time. The filter configuration can be determined in real time or selected from one of a plurality of predetermined configurations having different complexities.