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Showing papers on "Adaptive algorithm published in 1994"


Journal ArticleDOI
TL;DR: This article is to show how several different variants of the recursive least-squares algorithm can be directly related to the widely studied Kalman filtering problem of estimation and control.
Abstract: Adaptive filtering algorithms fall into four main groups: recursive least squares (RLS) algorithms and the corresponding fast versions; QR- and inverse QR-least squares algorithms; least squares lattice (LSL) and QR decomposition-based least squares lattice (QRD-LSL) algorithms; and gradient-based algorithms such as the least-mean square (LMS) algorithm. Our purpose in this article is to present yet another approach, for the sake of achieving two important goals. The first one is to show how several different variants of the recursive least-squares algorithm can be directly related to the widely studied Kalman filtering problem of estimation and control. Our second important goal is to present all the different versions of the RLS algorithm in computationally convenient square-root forms: a prearray of numbers has to be triangularized by a rotation, or a sequence of elementary rotations, in order to yield a postarray of numbers. The quantities needed to form the next prearray can then be read off from the entries of the postarray, and the procedure can be repeated; the explicit forms of the rotation matrices are not needed in most cases. >

470 citations


Journal ArticleDOI
TL;DR: This work presents a new adaptive algorithm, called the Joint-Domain Localized Generalized Likelihood Ratio detection (JDL-GLR), which is data efficient i.e., with fast convergence to the joint-domain optimum, as well as computationally efficient, together with such desirable features as the embedded constant false-alarm rate (CFAR) and robustness in non-Gaussian interference.
Abstract: Implementing the optimum spatial-temporal (angle-Doppler) processor involves two crucial issues: the selection of processing configurations, and the development of adaptive algorithms which can efficiently approach the performance potential of the selected configuration. Among the three available configurations, the joint-domain, the cascade space-time, and the cascade time-space, this work shows that, in contrast to a popular belief, the detection performance potentials of both cascade configurations can fall far below that of the joint-domain optimum. In addition, this work presents a new adaptive algorithm, called the Joint-Domain Localized Generalized Likelihood Ratio detection (JDL-GLR), which is data efficient i.e., with fast convergence to the joint-domain optimum, as well as computationally efficient, together with such desirable features as the embedded constant false-alarm rate (CFAR) and robustness in non-Gaussian interference. >

460 citations


01 Jan 1994
TL;DR: In this paper, an adaptive linear and decision feedback receiver structure for coherent demodulation in asynchronous CDMA systems is proposed. But the adaptive receiver has no knowledge of the signature waveforms and timing of other users.
Abstract: Adaptive linear and decision feedback receiver structures for coherent demodulation in asynchronous code division multiple access (CDMA) systems are considered. It is assumed that the adaptive receiver has no knowledge of the signature waveforms and timing of other users. The receiver is trained by a known training sequence prior to data transmission and continuously adjusted by an adaptive algorithm during data transmission. The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy

411 citations


Journal ArticleDOI
TL;DR: The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy.
Abstract: Adaptive linear and decision feedback receiver structures for coherent demodulation in asynchronous code division multiple access (CDMA) systems are considered. It is assumed that the adaptive receiver has no knowledge of the signature waveforms and timing of other users. The receiver is trained by a known training sequence prior to data transmission and continuously adjusted by an adaptive algorithm during data transmission. The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy. An adaptive centralized decision feedback receiver has the same advantages of the linear receiver but, in addition, achieves a further improvement in multiple access interference cancellation at the expense of higher complexity. The proposed receiver structures are tested by simulation over a channel with multipath propagation, multiple access interference, narrowband interference, and additive white Gaussian noise. >

411 citations


Book ChapterDOI
Debasis Mitra1
01 Jan 1994
TL;DR: An asynchronous adaptive algorithm for power control in cellular radio systems, which relaxes the demands of coordination and synchrony between the various mobiles and base stations and allows different links to update their power at different rates; unpredictable, bounded propagation delays are taken into account.
Abstract: We give an asynchronous adaptive algorithm for power control in cellular radio systems, which relaxes the demands of coordination and synchrony between the various mobiles and base stations. It relaxes the need for strict clock synchronization and also allows different links to update their power at different rates; unpredictable, bounded propagation delays are taken into account. The algorithm uses only local measurements and incorporates receiver noise. The overall objective is to minimize transmitters’ powers in a Pareto sense while giving each link a Carrier-to-Interference ratio which is not below a prefixed target. The condition for the existence and uniqueness of such a power distribution is obtained. Conditions are obtained for the asynchronous adaptation to converge to the optimal solution at a geometric rate. These conditions are surprisingly not burdensome.

320 citations


Journal ArticleDOI
TL;DR: A new adaptive algorithm is introduced which ensures that the plant can be locally stabilized and an upper bound on the plant control parameter is required to be known.
Abstract: This paper deals with the problem of adaptively controlling a linear time-invariant plant in the presence of constraints on the input amplitude. We introduce a new adaptive algorithm which ensures that the plant can be locally stabilized. In addition to the standard assumptions which are required for adaptive control in the ideal case, an upper bound on the plant control parameter is required to be known. The results are evaluated by simulation studies. >

266 citations


Journal ArticleDOI
TL;DR: A novel adaptive algorithm is presented that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND) and offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images.
Abstract: Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE. >

256 citations


Journal ArticleDOI
TL;DR: A novel, efficient, self-normalising, unsupervised adaptive learning algorithm for the on-line (real-time) separation of statistically independent unknown source signals from a linear mixture of them.
Abstract: The authors present a novel, efficient, self-normalising, unsupervised adaptive learning algorithm for the on-line (real-time) separation of statistically independent unknown source signals from a linear mixture of them. In contrast to the known algorithms the new algorithm allows the separation (or extraction) of extremely badly scaled signals (i.e. some or even all of the source and/or sensor signals can be very weak). Moreover, the mixing matrix can be very ill-conditioned. >

205 citations


Journal ArticleDOI
TL;DR: This paper presents a preconditioned, Krylov-subspace iterative algorithm, where a modified multipole algorithm with a novel adaptation scheme is used to compute the iterates for solving dense matrix problems generated by Galerkin or collocation schemes applied to three-dimensional, first-kind, integral equations that arise in potential theory.
Abstract: This paper presents a preconditioned, Krylov-subspace iterative algorithm, where a modified multipole algorithm with a novel adaptation scheme is used to compute the iterates for solving dense matrix problems generated by Galerkin or collocation schemes applied to three-dimensional, first-kind, integral equations that arise in potential theory. A proof is given that this adaptive algorithm reduces both matrix-vector product computation time and storage to order N, and experimental evidence is given to demonstrate that the combined preconditioned, adaptive, multipole-accelerated (PAMA) method is nearly order N in practice. Examples from engineering applications are given to demonstrate that the accelerated method is substantially faster than standard algorithms on practical problems.

188 citations


Proceedings ArticleDOI
14 Dec 1994
TL;DR: It is shown that simple algorithms can be obtained if full state feedback is assumed and the objective is to design automatically a flight control law in the presence of actuator failures or surface damage.
Abstract: The application of multivariable adaptive control techniques to flight control reconfiguration is considered. The paper first discusses three adaptation mechanisms for model reference control. It is shown that simple algorithms can be obtained if full state feedback is assumed. The respective advantages and disadvantages of the three algorithms are discussed in general terms, considering their complexity and the assumptions that they require. Next, the application of the adaptive algorithms to reconfigurable flight control is investigated. The objective is to design automatically a flight control law in the presence of actuator failures or surface damage. Design considerations for the adaptive algorithms are discussed in this context. Simulations obtained using a full nonlinear simulation of a twin-engine jet aircraft are included to illustrate the results. >

182 citations


Journal ArticleDOI
TL;DR: An adaptive algorithm for estimating from noisy observations, periodic signals of known period subject to transient disturbances and an application of the Fourier estimator to estimation of brain evoked responses is included.
Abstract: Presents an adaptive algorithm for estimating from noisy observations, periodic signals of known period subject to transient disturbances. The estimator is based on the LMS algorithm and works by tracking the Fourier coefficients of the data. The estimator is analyzed for convergence, noise misadjustment and lag misadjustment for signals with both time invariant and time variant parameters. The analysis is greatly facilitated by a change of variable that results in a time invariant difference equation. At sufficiently small values of the LMS step size, the system is shown to exhibit decoupling with each Fourier component converging independently and uniformly. Detection of rapid transients in data with low signal to noise ratio can be improved by using larger step sizes for more prominent components of the estimated signal. An application of the Fourier estimator to estimation of brain evoked responses is included. >

Journal ArticleDOI
TL;DR: An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated and some application examples are presented, finding that when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred.
Abstract: An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred. >

Journal ArticleDOI
TL;DR: The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.
Abstract: A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated. >

Journal ArticleDOI
TL;DR: In this paper, a nonlinear generalization of principal components analysis (PCA) is developed for curve and surface reconstruction and to data summarization, and a principal surface of the data is constructed adaptively, using some ideas from the MARS procedure of Friedman.
Abstract: We develop a nonlinear generalization of principal components analysis. A principal surface of the data is constructed adaptively, using some ideas from the MARS procedure of Friedman. We explore applications to curve and surface reconstruction and to data summarization.

Journal ArticleDOI
TL;DR: The problem of arbitrary trial-and-error selection of the learning and momentum ratios encountered in the momentum backpropagation algorithm is circumvented and the step length in the inexact line search is adapted during the learning process through a mathematical approach.

Journal ArticleDOI
TL;DR: An accelerated learning algorithm (ABP-adaptive back propagation) is proposed for the supervised training of multilayer perceptron networks with superior convergence speed for analog problems only, as compared to other competing methods, as well as reduced sensitivity to algorithm step size parameter variations.
Abstract: An accelerated learning algorithm (ABP-adaptive back propagation) is proposed for the supervised training of multilayer perceptron networks. The learning algorithm is inspired from the principle of "forced dynamics" for the total error functional. The algorithm updates the weights in the direction of steepest descent, but with a learning rate a specific function of the error and of the error gradient norm. This specific form of this function is chosen such as to accelerate convergence. Furthermore, ABP introduces no additional "tuning" parameters found in variants of the backpropagation algorithm. Simulation results indicate a superior convergence speed for analog problems only, as compared to other competing methods, as well as reduced sensitivity to algorithm step size parameter variations. >

Journal ArticleDOI
TL;DR: Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations, and the increase in complexity incurred in order to make these gains is minimal.
Abstract: While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. The authors present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a noncausal prediction model for an image. The authors present a backward adaptive technique and a forward adaptive technique. They then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal. >

Journal ArticleDOI
TL;DR: The adaptive rational function filter is proposed, a new nonlinear adaptive filter structure based on rational functions that is suitable for real-time adaptive signal processing and has a best approximation for a specified function.
Abstract: Proposes a new nonlinear adaptive filter structure based on rational functions. There are several advantages to the use of this filter. First, it is a universal approximator and a good extrapolator. Second, it ran be trained by a linear adaptive algorithm, which makes it suitable for real-time adaptive signal processing. Third, it has a best approximation for a specified function. To demonstrate its utility as a tool for solving adaptive signal processing problems, the authors apply the adaptive rational function filter to the problem of estimation and detection. The estimation problem pertains to the direction of arrival (DOA) estimation problem in array signal processing. For the detection problem, the authors consider the detection of a weak radar target (a small piece of ice) in an ocean environment. >

Journal ArticleDOI
TL;DR: A new adaptive algorithm based on wavelet‐encoded MRI is presented for application in dynamic imaging because the strategy for updating image data in the dynamic series of images is determined by the processing of the most recently acquired data.
Abstract: A new adaptive algorithm based on wavelet-encoded MRI is presented for application in dynamic imaging. This algorithm is adaptive because the strategy for updating image data in the dynamic series of images is determined by the processing of the most recently acquired data. The spatially selective multi-resolution properties of the wavelet transform are exploited to selectively update only those regions of the field of view where change is actually occurring. A theoretical imaging model is presented to motivate use of the adaptive algorithm, and simulation results using both artificial and experimental wavelet-encoded data are presented.

Journal ArticleDOI
TL;DR: This work proposes blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics and proposes a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing.
Abstract: Sensor response, location uncertainty, and use of sample statistics can severely degrade the performance of optimum beamformers. We propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival (DOA) information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the signal-to-interference-plus-noise ratio (SINR). Finally, we propose an adaptive version of our algorithm simulations demonstrate the excellent performance of our approach in a wide variety of situations. >

Proceedings ArticleDOI
19 Apr 1994
TL;DR: A novel algorithm as proposed in order to adaptively maximise Comon's contrast is proposed and a new criterion is defined that is free of the prewhitening step, which has the additional advantage not to require identical signs for the fourth-order cumulants of the sources.
Abstract: In order to perform separation of a mixture of sources, an interesting approach is to maximise a contrast function: e.g. the contrast of Comon. This paper brings two novel contributions (i) a novel algorithm as proposed in order to adaptively maximise Comon's contrast. However it requires a preprocessing whitening operation which is awkward when the mixture is ill-conditioned. (ii) A new criterion is defined that is free of the prewhitening step. In the case of two sources it can be proved that this criterion is a contrast. This contrast can also be adaptively maximized and has the additional advantage not to require identical signs for the fourth-order cumulants of the sources. Achievement of these two adaptive algorithms is demonstrated using a new performance index. >

Proceedings ArticleDOI
26 Jun 1994
TL;DR: By applying the AFSMC to control a nonlinear unstable inverted pendulum system, the simulation results showed the expected approximation sliding property, and the dynamic behavior of control system can be determined by the sliding surface.
Abstract: An adaptive fuzzy sliding mode controller (AFSMC) is proposed. The parameters of the membership functions in the fuzzy rule base are changed according to some adaptive algorithm for the purpose of controlling the system state to hit a user-defined sliding surface and then slide along it. The initial IF-THEN rules in the AFSMC can be randomly selected or roughly given by human experts, and then automatically tuned by a direct adaptive law. Therefore, the reduction of the expertise dependency in the design procedure of fuzzy logic control is called the rule tolerance property. By applying the AFSMC to control a nonlinear unstable inverted pendulum system, the simulation results showed the expected approximation sliding property, and the dynamic behavior of control system can be determined by the sliding surface. >

Journal ArticleDOI
TL;DR: An indirect adaptive controller with parameter projection as the only modification on the basis of conventional adaptive control algorithms can globally stabilize systems having fast parasitics, bounded external disturbances, and time-varying parameters without any restriction on signals in the closed-loop system such as persistence of excitation.
Abstract: The goal of this paper is to show that an indirect adaptive controller with parameter projection as the only modification on the basis of conventional adaptive control algorithms can globally stabilize systems having fast parasitics, bounded external disturbances, and time-varying parameters without any restriction on signals in the closed-loop system such as persistence of excitation. Further, the controller can still retain the properties of earlier unmodified conventional adaptive controllers when the controlled plant satisfies so-called "ideal assumptions" or the rates at which the plant parameters change belong to the l/sub 1/ (or l/sub 2/) space. >

Journal ArticleDOI
TL;DR: An adaptive design algorithm that minimizes the mean absolute error criterion is described as well as a more flexible adaptive algorithm that attains the optimal permutation filter under a deterministic least normed error criterion.
Abstract: Introduces and analyzes a new class of nonlinear filters that have their roots in permutation theory. The authors show that a large body of nonlinear filters proposed to date constitute a proper subset of permutation filters (/spl Pscr/ filters). In particular, rank-order filters, weighted rank-order filters, and stack filters embody limited permutation transformations of a set. Indeed, by using the full potential of a permutation group transformation, one can design very efficient estimation algorithms. Permutation groups inherently utilize both rank-order and temporal-order information; thus, the estimation of nonstationary processes in Gaussian/nonGaussian environments with frequency selection can be effectively addressed. An adaptive design algorithm that minimizes the mean absolute error criterion is described as well as a more flexible adaptive algorithm that attains the optimal permutation filter under a deterministic least normed error criterion. Simulation results are presented to illustrate the performance of permutation filters in comparison with other widely used filters. >

Journal ArticleDOI
TL;DR: In this article, a number of iterative algorithms for calculating kinoforms are discussed, including a multiplicative adaptive algorithm allowing the rate of the iterative process to be increased, fast algorithms for interpolating and extrapolating the kinoform phase pixels, and an algorithm for calculating formators of Gauss-Hermite modes in required diffraction orders.
Abstract: A number of iterative algorithms for calculating kinoforms are discussed: a multiplicative adaptive algorithm allowing the rate of iterative process to be increased, fast algorithms for interpolating and extrapolating the kinoform phase pixels, an algorithm for calculating kinoforms forming radially symmetrical images and axial light segments, an algorithm for calculating formators of Gauss-Hermite modes in required diffraction orders, and an algorithm for calculating formators of reference wavefronts. The results of computer simulation are given.

Journal ArticleDOI
TL;DR: Algorithms for mutual exclusion that adapt to the current degree of contention are developed and achieve system response times that are independent of the total number of processes and governed instead by the current level of contention.
Abstract: Algorithms for mutual exclusion that adapt to the current degree of contention are developed. A filter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time.

Journal ArticleDOI
TL;DR: The transposed VR (TQR) iteration is a square root version of the symmetric QR iteration that formulates a TQR-iteration based adaptive SVD algorithm, develops a real time systolic architecture, and analyzes performance.
Abstract: The transposed VR (TQR) iteration is a square root version of the symmetric QR iteration. The TQR algorithm converges directly to the singular value decomposition (SVD) of a matrix and was originally derived to provide a means to identify and reduce the effects of outliers for robust SVD computation. The paper extends the TQR algorithm to incorporate complex data and weighted norms, formulates a TQR-iteration based adaptive SVD algorithm, develops a real time systolic architecture, and analyzes performance. The applications of high resolution angle and frequency tracking are developed and the updating scheme is so tailored. A deflation mechanism reduces both the computational complexity of the algorithm and the hardware complexity of the systolic architecture, making the method ideal for real time applications. Simulation results demonstrate the performance of the method and compare it to existing SVD tracking schemes. The results show that the method is exceptional in terms of performance to cost ratio and systolic implementation. >

Journal ArticleDOI
TL;DR: The purpose of this paper is to derive a closed-form solution in the sense of blind equalization and it will be shown that the equalizer coefficients can be uniquely derived from the eigenvectors of a specific 4th-order cumulant matrix of the received signal.

Proceedings ArticleDOI
29 Jun 1994
TL;DR: In this paper, an adaptive feed forward cancellation (AFC) algorithm with sinusoidal regressors for repetitive control is proposed. But the adaptive algorithm is not suitable for the case of single frequency periodic disturbances.
Abstract: The paper investigates the design of adaptive feedforward cancellation (AFC) algorithms with sinusoidal regressors for repetitive control. Such adaptive algorithms are equivalent to a linear controllers based on the internal model principle. Using this equivalence and root locus rules, the phase advance of the regressor of the adaptive algorithm can be chosen to maximize the phase margin at low gains. It is shown that selecting the optimal phase advance is equivalent to placing a zero in the open right half-plane in certain cases. Complete design and analysis for the compensation of a single frequency periodic disturbances is done. A new variation of the AFC algorithm is also developed in which the adaptive portion acts in parallel with a feedthrough term. The IMP equivalent of this algorithm has two zeros instead of one. Analysis and simulation shows this method to have superior convergence and robustness properties when compared with the method having no feedthrough term.

Journal ArticleDOI
TL;DR: Fast adaptive algorithms are developed for training weighted order statistic filters and FIR-WOS hybrid (FWH) filters under the mean absolute error (MAE) criterion and two new FWH filter design strategies are found for removal of impulsive noise and for restoration of a square wave.
Abstract: Fast adaptive algorithms are developed for training weighted order statistic (WOS) filters and FIR-WOS hybrid (FWH) filters under the mean absolute error (MAE) criterion. These algorithms are based on the threshold decomposition of real-valued signals introduced in this paper. With this method an N-length WOS filter can be implemented by thresholding the input signals at most N times independent of the accuracy used. Beside saving in computations, the proposed algorithms can be applied to process arbitrary real-valued signals directly. Performance characteristics of FWH filters in 1-D and 2-D signal restoration are investigated through computer simulations. We show that both in restoration of signals containing edges and in the case of heavy tailed nonGaussian noise, considerable improvement in performance can be achieved with FWH filters over WOS filters, Ll filters, and adaptive linear filters. Two new FWH filter design strategies are found for removal of impulsive noise and for restoration of a square wave, respectively. >