Application Of Adaptive Filter Research Articles

Application of adaptive lattice filters to internal model control

AbstractAn adaptive internal model control (IMC) framework is proposed in this article for infinite impulse response systems. The innovation in this study stems from the relaxed assumption that the controller needs to know a priori the system order. To bypass this restriction, a lattice filter identifies the system's order as well as its reflection coefficients. Within the IMC structure, a lattice‐based controller is utilized in the forward path in cascade with a low‐pass detuning filter. The controller self‐configures its structure according to the estimated system order, while the filter's bandwidth increases as the identifier estimates more accurately the system dynamics. Copyright © 2007 John Wiley & Sons, Ltd.

Fast Affine Projection Adaptation Algorithms With Stable and Robust Symmetric Linear System Slovers

This paper proposes two noniterative approaches to solve a symmetric linear system associated with the fast affine projection adaptation algorithm. The first approach, using matrix LDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> factorization, can provide an exact solution at a moderate complexity, in contrast with the fact that existing stable approaches are all approximate. Based on a reciprocating recursion scheme, the second approach has a very low complexity and gives a good approximate solution. Steady-state and transient properties of the proposed and certain previous FAP algorithms are studied in detail. Being stable and optimal under all step size conditions, fast affine projection algorithms incorporating the proposed approaches are promising in telecom and other applications of adaptive filtering

An Adaptive Differentiation Filter for Tracking Instantaneous Frequency in Power Systems

This paper presents an application of adaptive differentiation filter in tracking instantaneous frequency in electrical power systems. For each new sample, the filter automatically selects an optimal window length that maximizes measurement accuracy. Large window length is selected if the frequency is slow varying or steady state, to increase efficiency in filtering noise and harmonics. For fast-varying frequency, the window length is automatically reduced in order to make frequency tracking more accurate, sacrificing filtering efficiency. Automatic selection of the optimal window length that balances between tracking and filtering performance is the unique feature of this technique. This paper concludes with the presentation of the representative results obtained in the simulation study as well as in some practical applications. The results show the adaptive differentiation filter gives accurate frequency measurement under both steady-state and dynamic conditions

Detection of Weak Seismo-Electric Signals Upon the Recordings of the Electrotelluric Field by Means of Neuro-Fuzzy Technology

This letter presents the development and application of prediction-based adaptive filters incorporated with neuro-fuzzy technology for the emergence and detection of weak electrotelluric potential anomalies appearing upon recordings of the Earth's electric field. Electric earthquake precursors (EEPs) are considered to be related with forthcoming seismic events and, in many cases, are hidden in the electrotelluric field background. Their detection can be further complicated by the probable appearance of severe transient fluctuations induced mainly by magnetic storms and/or other physical and anthropogenic types of noise. This application presents two neuro-fuzzy models trained to predict the recorded magnetic and electric fields' variations, respectively. The overall method aims to reveal and detect EEP signals upon the error signal, defined as the difference between the recorded electric field signal and the electric field signal predicted by the secondary neuro-fuzzy model, while the primary neuro-fuzzy model minimizes the effect of noise of magnetotelluric origin upon the recorded electric field

Cetacean vocalization tracking using an adaptive Poisson rate estimator

This paper presents algorithms for tracking the formant structure of cetacean vocalizations using auditory modeling and adaptive filtering to estimate the Poisson intensity of nerve firing rates. These rates are in turn related to the instantaneous frequency of the formant vocalisation. The auditory model considered in this paper produces a process described as a spike train representing the auditory nerve firing, the Poisson intensity of which is determined by an underlying modulation process. Previous work [Kenny et al., Adaptive Filter for Speech Enhancement Using Poisson Rates from an Auditory Model, paper presented at the WESPAC VIII, Melbourne, Australia (2003); C. McPherson, Development of a Toolkit for Analysing and Localising Underwater Acoustic Signals (James Cook University, Townsville, 2005)] has shown that this modulation process can be evaluated from the conditional expectation of the Poisson rate given interspike arrival time observations. The paper also addresses the application of adaptive filters to update the estimator parameters for different acoustic environments, which is unknown. These algorithms have been applied to real field recordings of interacting false killer whales (Pseudorca crassidens) and results from experiment of shown individualisation and extraction of individual narrow-band vocalizations.

Comparison of convergence characteristics of adaptive IIR and FIR filters for active noise control in a duct

There are perceived drawbacks to using adaptive IIR filters, as opposed to adaptive FIR filters, for active noise control (ANC). These include stability issues, the possible convergence of estimated parameters to biased and/or local minimum solutions and relatively slow rate of convergence. Stability issues can generally be resolved easily using well-established methods. In this Technical Note convergence rates are compared with particular reference to the active control of noise in a duct, for which the dynamics of the cancellation path are important. The characteristics of this application of ANC set it apart from usual signal processing applications of adaptive IIR filters and this has implications for the convergence properties. Various control approaches are considered: IIR least mean squares (IIR-LMS), IIR recursive least squares (IIR-RLS) with FASPIS (Fast Algorithm Secondary Path Integration Scheme) and FIR-LMS. Numerical examples are presented. It is seen that the cancellation path dynamics generally have the effect of changing the performance surface of the estimated IIR filter from bimodal to unimodal, which has consequences for improving the convergence rate of adaptive IIR filters. It is also seen that IIR-RLS has a comparable rate of convergence to FIR-LMS, with the steady-state performance being as good or better.

A Design Method of Parallel Fast RLS Second-Order Adaptive Volterra Filter

The adaptive Volterra filter (AVF) is attractive in adaptive filtering applications because its expansion is a linear combination of the input and output signals. However, the formidable computational work of AVF is prohibitive for practical applications. In this letter, we present a parallel fast recursive least squares (RLS) second-order adaptive Volterra filter (PAVF) to reduce computational load. Our discussion is based on the approach of the fast RLS AVF [3], by which the computational complexity has been reduced to O(N3) multiplications per time instant, where O(·) denotes order of, and N is the filter length. Proposed PAVF consists of several subfilters partitioned from the conventional AVF, with parallel implementation, the computational work can be reduced effectively. Several simulation results are presented to validate the proposed method.

A Proposal of an Adaptive Line Enhancer by Using Wavelet Transform

Adaptive line enhancer which is one of the application of adaptive digital filter can extract only periodic signals buried in a broad-band noise, under the condition of the frequencies of the periodic signals are unknown. However it is known that adaptive line enhancer gets impossible to remove noise for a certain period after a strong pulse noise is put to the adaptive line enhancer with input signal. In this paper, an adaptive line enhancer by using wavelet transform is proposed to get over the problem. Here, discrete wavelet transform is used for the proposed method, however, the discrete wavelet transform based on multiresolution analysis has a demerit of the lack of shift invariance inevitably. Hence considering the demerit of discrete wavelet transform, in this paper, redundant discrete wavelet transform is used which realizes the shift invariance. The effectiveness of the proposed method is shown through simulations. Moreover voice signals that are sampled from human voice are used in simulation to remove noise to show the effectiveness of the proposed method even for voice signals.

Application of adaptive filters to track finding

We study the application of adaptive methods such as the Gaussian-sum filter and the Deterministic Annealing Filter to track finding in various scenarios with large amounts of noise. It is shown in simulation studies that such adaptive methods are competitive alternatives to a combinatorial Kalman filter, and that in some cases there are appreciable gains in speed of the track finding procedure with respect to the Kalman filter.

Clinical application of noise adaptive non-linear Gaussian filter in FDG-PET study

Clinical application of noise adaptive non-linear Gaussian filter in FDG-PET study

Application of adaptive filtering by selecting the parameter weight factor in precise kinematic GPS positioning

ABSTRACT A suitable correlation can be made to represent the simulated distillation of heavy oils starting from thennoaravimetric measurements. This method is applicable to hydrocarbons having an initial boiling point equal or greater than 200°C. The simulated thermogravimetric distillation fit was obtained from experiments with the standard compounds obtained from ASTM D-2887-83 (Boiling range distribution of petroleum fractions by gas chromatography). This method is simple, fast and without problems when applied to heavy feedstocks The data were used in the determination of average boiling temperatures of products from thermal cracking and thermal hydrocracking. It was also possible to quantify coke yields Average relative molecular masses of products from the above processes correlated well with the average boiling point temperatures. It indicates that, with respect to the hydrocarbon types, thermal cracking is not selective in comparison with thermal hydrocracking The equation applied to find the average boiling temperature is following: T = (1/al) (Ttg-a2). T is the boiling temperature, al and a2 are the correction factors, Ttg is the thermogravimetric temperature.

Application of adaptive filters to visual testing and treatment in acquired pendular nystagmus

Acquired pendular nystagmus (APN) complicates multiple sclerosis and other neurological disorders, causes visual impairment, and frequently resists treatment. Vision could be improved by a visual aid that gates or shifts the seen world in lockstep with the APN. Since the pathological oscillations are embedded in normal eye movements, such a device must track the nystagmus selectively. We evaluated the ability of an adaptive filter to perform this tracking and improve acuity when coupled to either of two devices--a shutter that permitted brief glimpses of the world synchronized with the nystagmus, or simulated image-shifting optics. In 10 normal subjects whose decimal acuity averaged 1.46 +/- 0.20, acuity fell to 0.36 +/- 0.08 under viewing conditions simulating APN. The synchronized shutter restored acuity to 0.60 +/- 0.12, while image-stabilization raised it to 1.17 +/- 0.13. Adaptive filters provide a practical means by which to track nystagmus. The most effective visual aid would couple such filters to image-stabilizing optics.

Nonlinear Dynamical Systems and Adaptive Filters in Biomedicine

In this paper we present the application of a method of adaptive estimation using an algebra–geometric approach, to the study of dynamic processes in the brain. It is assumed that the brain dynamic processes can be described by nonlinear or bilinear lattice models. Our research focuses on the development of an estimation algorithm for a signal process in the lattice models with background additive white noise, and with different assumptions regarding the characteristics of the signal process. We analyze the estimation algorithm and implement it as a stochastic differential equation under the assumption that the Lie algebra, associated with the signal process, can be reduced to a finite dimensional nilpotent algebra. A generalization is given for the case of lattice models, which belong to a class of causal lattices with certain restrictions on input and output signals. The application of adaptive filters for state estimation of the CA3 region of the hippocampus (a common location of the epileptic focus) is discussed. Our areas of application involve two problems: (1) an adaptive estimation of state variables of the hippocampal network, and (2) space identification of the coupled ordinary equation lattice model for the CA3 region.

A sparse block exact affine projection algorithm

The so-called affine projection algorithm (APA) has become a popular method in adaptive filtering applications and fast versions of it have been developed previously, such as fast affine projection (FAP) and the frequency domain block exact fast affine projection (BEFAP). While it is known that regularization is an absolute necessity in affine projection based adaptive filtering algorithms, FAP and BEFAP rely on an implicit regularization parameter assumption. In this paper, a block exact affine projection algorithm (BE-APA) is derived that does not rely on the assumption of a small regularization parameter. It is an exact frequency domain translation of the original AP algorithm, and still has about the same complexity as, block exact FAR Unlike BEFAP, it can be used with strong regularization. It is then extended to incorporate an alternative to explicit regularization that is based on so-called sparse equations.

A Nonlinear Adaptive Filter for Online Signal Analysis in Power Systems: Applications

This paper presents various applications of a nonlinear adaptive notch filter that operates based on the concept of an enhanced phase-locked loop (PLL). Applications of the filter for online signal analysis for power systems protection, control, and power quality enhancement are presented. The proposed scheme can be applied for signal analysis both under stationary and nonstationary conditions. Based on digital time-domain simulations, applications of the filter for (a) sinusoidal waveform peak detection, (b) harmonic identification/detection, (c) detection/extraction of individual components of a signal, (d) instantaneous reactive current extraction, (e) disturbance detection, (f) noise reduction in zero-crossings detection, and (g) amplitude (phase) demodulation for flicker estimation are presented.

A Morphological Optical Survey of Nearby AGN

AbstractWe present preliminary results of an optical survey of a spectroscopically selected sample of nearby AGNs (z &lt; 0.1). The aim of the program that we are currently carrying out is to demonstrate that the activity phenomenon is strongly related to galaxy interactions. In detail, we want to study the morphology of our sample of galaxies in order to reveal the presence of features like bars, rings, double nuclei, isophotal distortions and tidal tails, most of which are not detectable on the POSS plates. The work is based on the analysis of broad band B, V and R images, the study of their photometric parameters and the results of the application of a Laplacian adaptive filter, able to enhance hidden and faint structures.

Blind adaptive detection using differential CMA for CDMA systems

AbstractIn this paper, we consider the application of adaptive linear filters adjusted by stochastic gradient algorithms to the suppression of multiple‐access interference in DS/CDMA communications. It is pointed out that the linearly constrained constant modulus algorithm (LCCMA) cannot converge to the optimum point if the desired user magnitude is less than a critical value. Next, we propose to use the linearly constrained differential constant modulus algorithm (LCDCMA), and show that the LCDCMA always converges to the optimum point regardless of the desired user magnitude. Several simulation results show the following three points. First, the LCDCMA provides better performance than existing algorithms because the variance of the weight vector adjusted by the LCDCMA is less than that by existing algorithms. Second, the LCDCMA is relatively insensitive to the estimation error of the desired signal vector. Third, the LCDCMA combined with a blind channel estimation algorithm is useful. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 85(1): 1–13, 2002

Adaptive filtering in exercise high resolution ECG as applied to the hypertrophic cardiomyopathy.

The application of adaptive filtering to ECG signals has been investigated for many years. This study shows that the exercise high resolution ECG (HRECG) can also be processed successfully in a similar way. Two groups were included consisting of 20 healthy individuals and 24 patients with hypertrophic cardiomyopathy (HCM). The HRECG parameters for both groups were similar (QRSdur: 107 +/- 7 vs 114 +/- 18 ms NS, LAS: 25 +/- 8 vs 22 +/- 6 ms NS). In the first step, the HRECG signal was acquired at rest to obtain the averaged reference pattern. The next step was associated with peak exercise in which one could calculate short duration averaging (approximately 30 beats) or apply adaptive filtering in which the exercise component (EC) was extracted. Exercise was performed in the supine position on a bicycle ergometer. The load of 50 W was incremented by 50-W steps in 3-minute intervals and the test was ended by fatigue. Signals were recorded in X, Y, and Z bipolar leads with a 20-Hz high pass filter. The short time average QRS duration mostly was abbreviated in normal individuals in contrast to HCM patients in which ventricular activity prolonged with sensitivity, specificity, and negative and positive predictive values: 79%, 65%, 73%, and 72%, respectively. The adaptive recurrent filtration (ARF) after cutoff of the EC at the level of 70 ms (this level is the EC mean value of both groups) showed the following statistics: 63%, 90%, 88%, and 90%. The Student's t-test as applied to the duration of EC allowed a statistically significant difference between normals and HCM patients (66 +/- 4 vs 71 +/- 6 ms, P < 0.0052) and between HCM patients with and without ventricular tachyarrhythmia and DS (74 +/- 6 vs 69 +/- 6 ms, P < 0.046).

Adaptive 1-D and 2-D Filtering with Internally Passive Structures

Summary Internally passive filters such as wave digital filters (WDF) and orthogonal filters, which have desirable properties, are developed as filter structures for adaptive filters. The equations for updating the multiplier coefficients as well as for computing the gradients are derived for these filters. The stability monitoring during the adaptation process for these filters is simple to implement. Simulation results are included to demonstrate the capability of these filters in system identification and noise cancellation. Moreover, motivated by applications of multidimensional adaptive filters in video compression and image enhancement, the proposed adaptive wave digital filters have been extended to two dimensions and applied as adaptive fan filters. Computer simulations are also conducted to show their potential applications to two-dimensional system identification and image enhancement.

Application of linear and nonlinear adaptive filters for the compensation of disturbances in the laminar boundary layer

AbstractIn order to delay the laminar‐turbulent transition in the boundary layer of airfoils the compensation of instability waves by artificially excited disturbances is studied. It turns out that also nonlinear processes occur in the spatial development of disturbances. This can be modeled by different types of nonlinear filter structures.