Add to ORKGThe problem of the extraction of the useful signal from a noisy background is one of the most important areas of signal processing. Order Statistic (OS) smoothers, based on amplitude ordering of signal samples, have been shown to offer an effective alternative to linear smoothers. It is the case particularly when there is uncertainty concerning noise statistics, or when the useful signal possesses local features such as sharp edges. In this paper we consider some linear and nonlinear (OS) smoothers, and propose a new smoothing algorithm. Simulation results are presented to illustrate the performance of the proposed smoother
In the literature on unobservable component models , three main statistical instruments have been us...
L—smoothers and M—smoothers are introduced as generalizations of the median filter for nonlinear smo...
The paper presents a method for smoothing signals represented by a single realization of a finite...
The problem of the extraction of the useful signal from a noisy background is one of ...
Time series data can be decomposed as signal plus noise. A good smoother should be able to recover a...
In applications such as signal/image restoration and detection, linear techniques may be unable to d...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
A comparison paper is presented to evaluate the result from five smoothing filters. The filters are ...
Much attention has been devoted in the recent past to signal processing schemes based on order stati...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
We discuss robust filtering procedures for signal extraction from noisy time series. Particular atte...
This book describes the classical smoothing, filtering and prediction techniques together with some ...
This chapter introduces two new empirical methods for obtaining optimal smoothing of noise‐ridden st...
Compound smoother is a non-linear smoothing technique that has the ability to reduce heavy noise fro...
Abstract Penalized least squares (PLS) is a popular data smoothing technique. However, existing PLS ...
In the literature on unobservable component models , three main statistical instruments have been us...
L—smoothers and M—smoothers are introduced as generalizations of the median filter for nonlinear smo...
The paper presents a method for smoothing signals represented by a single realization of a finite...
The problem of the extraction of the useful signal from a noisy background is one of ...
Time series data can be decomposed as signal plus noise. A good smoother should be able to recover a...
In applications such as signal/image restoration and detection, linear techniques may be unable to d...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
A comparison paper is presented to evaluate the result from five smoothing filters. The filters are ...
Much attention has been devoted in the recent past to signal processing schemes based on order stati...
In this paper, we present a new and efficient method to implement robust smoothing of low-level sign...
We discuss robust filtering procedures for signal extraction from noisy time series. Particular atte...
This book describes the classical smoothing, filtering and prediction techniques together with some ...
This chapter introduces two new empirical methods for obtaining optimal smoothing of noise‐ridden st...
Compound smoother is a non-linear smoothing technique that has the ability to reduce heavy noise fro...
Abstract Penalized least squares (PLS) is a popular data smoothing technique. However, existing PLS ...
In the literature on unobservable component models , three main statistical instruments have been us...
L—smoothers and M—smoothers are introduced as generalizations of the median filter for nonlinear smo...
The paper presents a method for smoothing signals represented by a single realization of a finite...