Linear Finite Impulse Response Filters Research Articles

MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer.

Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface’s feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.

Design of Linear Phase High Pass FIR Filter using Weight Improved Particle Swarm Optimization

The design of Finite Impulse Response (FIR) digital filter involves multi-parameter optimization, while the traditional gradient-based methods are not effective enough for precise design. The aim of this paper is to present a method of designing 24th order high pass FIR filter using an evolutionary heuristic search technique called Weight Improved Particle Swarm Optimization (WIPSO). A new function of the weight parameters is constructed for obtaining a better optimal solution with faster computation. The performance of the proposed algorithm is compared with two other search optimization algorithms namely standard Genetic Algorithm (GA) and conventional Particle Swarm Optimization (PSO). The simulation results show that the proposed WIPSO algorithm is better than GA and PSO in terms of the magnitude response accuracy and the convergence speed for the design of 24th order high pass FIR filter.

Validation of a novel nonlinear black box Wiener System model for arterial pulse transmission

Numerous linear dynamic models exist for describing the arterial pulse transmission phenomenon. We introduce a novel Wiener system based model in which a linear filter representing large arteries is coupled with a hysteresis-free nonlinear function representing complex wave transmission of branching arteries and the periphery. Experimental datasets (n = 7) are used to first estimate the Wiener model with linear, quadratic and cubic function for the aorta to radial artery pulse transmission and aorta to femoral artery pulse transmission. To model the nonlinear memoryless monotonic function in the Wiener System model, a correlation study is performed for linear finite impulse response (FIR) filter simulated peripheral pressure vs. measured peripheral pressure waveform. Each of this correlation curves were fitted to linear, quadratic and cubic polynomial equation. Wiener model is then simulated for aortic-to-radial artery as well as aortic-to-femoral artery to reconstruct radial and femoral pressure waveform. It was found that Wiener model with 3rd order polynomial function yielded better modeling accuracy (with average RMSE = 2.187 mmHg for radial and 4.281 mmHg for femoral pressure) than that from 2nd order polynomial function (with average RMSE = 2.242 mmHg for radial and 4.355 mmHg for femoral pressure) which in turn was better than mere linear FIR filter (with average RMSE = 2.604 mmHg for radial and 4.810 mmHg for femoral pressure).

Aortic Pressure Estimation Using Blind Identification Approach on Single Input Multiple Output Nonlinear Wiener Systems

Aortic pressure () is important for diagnosis of cardiovascular diseases, but it cannot be directly measured by noninvasive means. We present a method for its estimation by modeling arterial system as multichannel Weiner system with linear finite impulse response filter accounting for larger arteries transmission channel and nonlinear memoryless function block accounting for all nonlinearities due to narrowing of arteries, branching and visco-elastic forces. With this structure when pressure waveforms are measured from two distinct peripheral locations, multichannel blind system identification (MBSI) technique can be used to estimate common input pressure signal or . Nonlinear MBSI method was employed on previously acquired human hemodynamic measurements (seven datasets); results show can be accurately derived. This method by nature is self-calibrating to account for any interpersonal, along with intrapersonal, vascular dynamics inconstancy. Besides Pa estimation, the proposed MBSI method also allows extraction of system dynamics for vascular channels.

Optimal States Estimation of an LTI System using the Unbiased FIR Filter

The unbiased linear finite impulse response (FIR) filter and the two-state Kalman filter are investigated in the optimal estimation of the two state (position and velocity) in a linear time invariant (LTI) systems in presence of additive white Gaussian noise (AWGN). In opposite to the Kalman filter, the unbiased linear FIR filter dońt need previous knowledge about noise process, this algorithm only needs two specific parameters: optimal time step and optimal number of the points in the average. We show that both algorithms produce a similar lower mean square error (MSE).

Advanced signal separation and recovery algorithms for digital x-ray spectroscopy

X-ray spectroscopy is widely used for in-situ applications for samples analysis. Therefore, spectrum drawing and assessment of x-ray spectroscopy with high accuracy is the main scope of this paper. A Silicon Lithium Si(Li) detector that cooled with a nitrogen is used for signal extraction. The resolution of the ADC is 12 bits. Also, the sampling rate of ADC is 5MHz. Hence, different algorithms are implemented. These algorithms were run on a personal computer with Intel core TM i5-3470 CPU and 3.20GHz. These algorithms are signal preprocessing, signal separation and recovery algorithms, and spectrum drawing algorithm. Moreover, statistical measurements are used for evaluation of these algorithms. Signal preprocessing based on DC-offset correction and signal de-noising is performed. DC-offset correction was done by using minimum value of radiation signal. However, signal de-noising was implemented using fourth order finite impulse response (FIR) filter, linear phase least-square FIR filter, complex wavelet transforms (CWT) and Kalman filter methods. We noticed that Kalman filter achieves large peak signal to noise ratio (PSNR) and lower error than other methods. However, CWT takes much longer execution time. Moreover, three different algorithms that allow correction of x-ray signal overlapping are presented. These algorithms are 1D non-derivative peak search algorithm, second derivative peak search algorithm and extrema algorithm. Additionally, the effect of signal separation and recovery algorithms on spectrum drawing is measured. Comparison between these algorithms is introduced. The obtained results confirm that second derivative peak search algorithm as well as extrema algorithm have very small error in comparison with 1D non-derivative peak search algorithm. However, the second derivative peak search algorithm takes much longer execution time. Therefore, extrema algorithm introduces better results over other algorithms. It has the advantage of recovering and measuring over 21,941 peaks. Although, 1D non-derivative peak search algorithm has better extraction of peak height than other algorithms.

Embedded-Optimization-Based Loudspeaker Precompensation Using a Hammerstein Loudspeaker Model

This paper presents an embedded-optimization-based loudspeaker precompensation algorithm using a Hammerstein loudspeaker model, i.e. a cascade of a memoryless nonlinearity and a linear finite impulse response filter. The loudspeaker precompensation consists in a per-frame signal optimization. In order to minimize the perceptible distortion incurred in the loudspeaker, a psychoacoustically motivated optimization criterion is proposed. The resulting per-frame signal optimization problems are solved efficiently using first-order optimization methods. Depending on the invertibility and the smoothness of the memoryless nonlinearity, different first-order optimization methods are proposed and their convergence properties are analyzed. Objective evaluation experiments using synthetic loudspeaker models and real loudspeakers show that the proposed loudspeaker precompensation algorithm provides a significant audio quality improvement, especially so at high playback levels.

Adaptive Polynomial Filtering using Generalized Variable Step-Size Least Mean pth Power (LMP) Algorithm

This correspondence presents the adaptive polynomial filtering using the generalized variable step-size least mean $$p$$ p th power (GVSS-LMP) algorithm for the nonlinear Volterra system identification, under the $$\alpha $$ ? -stable impulsive noise environment. Due to the lack of finite second-order statistics of the impulse noise, we espouse the minimum error dispersion criterion as an appropriate metric for the estimation error, instead of the conventional minimum mean square error criterion. For the convergence of LMP algorithm, the adaptive weights are updated by adjusting $$p\ge 1$$ p ? 1 in the presence of impulsive noise characterized by $$1<\alpha <2$$ 1 < ? < 2 . In many practical applications, the autocorrelation matrix of input signal has the larger eigenvalue spread in the case of nonlinear Volterra filter than in the case of linear finite impulse response filter. In such cases, the time-varying step-size is an appropriate option to mitigate the adverse effects of eigenvalue spread on the convergence of LMP adaptive algorithm. In this paper, the GVSS updating criterion is proposed in combination with the LMP algorithm, to identify the slowly time-varying Volterra kernels, under the non-Gaussian $$\alpha $$ ? -stable impulsive noise scenario. The simulation results are presented to demonstrate that the proposed GVSS-LMP algorithm is more robust to the impulsive noise in comparison to the conventional techniques, when the input signal is correlated or uncorrelated Gaussian sequence, while keeping $$1<p<\alpha <2$$ 1 < p < ? < 2 . It also exhibits flexible design to tackle the slowly time-varying nonlinear system identification problem.

DAPSO and PSO-VAF in Linear Phase Digital Low Pass FIR Filter Design

Digital filters play a key role in the field of digital signal processing. This paper presents a linear phase digital low pass finite impulse response (FIR) filter design using particle swarm optimization and its two new variants, dynamic and adjustable particle swarm optimization (DAPSO) and particle swarm optimization with variable acceleration factor (PSO-VAF) and illustrates the superiority of the PSO-VAF method over PSO based methods. Two fitness functions are considered. The fitness1 is used to find the possible minimum ripples in pass band and stop band in case of PSO, DAPSO and PSO-VAF. Fitness2 is able to control the ripples in both bands separately. A comparison of simulation results demonstrates the performance of PSO and its methods in designing digital low pass FIR filters.

1D and 2D economical FIR filters generated by Chebyshev polynomials of the first kind

Christoffel–Darboux formula for Chebyshev continual orthogonal polynomials of the first kind is proposed to find a mathematical solution of approximation problem of a one-dimensional (1D) filter function in the z domain. Such an approach allows for the generation of a linear phase selective 1D low-pass digital finite impulse response (FIR) filter function in compact explicit form by using an analytical method. A new difference equation and structure of corresponding linear phase 1D low-pass digital FIR filter are given here. As an example, one extremely economic 1D FIR filter (with four adders and without multipliers) is designed by the proposed technique and its characteristics are presented. Global Christoffel–Darboux formula for orthonormal Chebyshev polynomials of the first kind and for two independent variables for generating linear phase symmetric two-dimensional (2D) FIR digital filter functions in a compact explicit representative form, by using an analytical method, is proposed in this paper. The formula can be most directly applied for mathematically solving the approximation problem of a filter function of even and odd order. Examples of a new class of extremely economic linear phase symmetric selective 2D FIR digital filters obtained by the proposed approximation technique are presented.

Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization

This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.

Christoffel-Darboux Formula Most Directly Applied in Generating Economical Linear Phase Low-Pass Digital FIR Filter Functions

The approximation problem of a filter function of even and odd order is solved mathematically in this paper most directly applying the proposed Christoffel-Darboux formula for two continual orthogonal polynomials on the equal finite segment. As a result, a linear phase digital finite impulse response (FIR) filter function is generated in compact explicit form. In addition, a new difference equation and structure of linear phase digital FIR filter are proposed. Two examples of the extremely economic FIR filter (with four adders and without multipliers) designed by the proposed technique are presented. The proposed solutions are extremely efficiency in regard to energy consumption. DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2639

Nonlinear Equalization of the Super-Resolution Near-Field Structure Read-Out Signal Using the Adaptive Amplitude Nonlinear Gradient Descent Algorithm with the Sigmoid Function

In this study, to mitigate nonlinearity in super-resolution near-field structure (super-RENS) read-out signals, we investigate nonlinear equalization based on the adaptive amplitude nonlinear gradient descent (AANGD) algorithm, which is suitable for processing nonlinear and nonstationary input signals with a large dynamic range. Note that the sigmoid function is employed as the nonlinear activation function since it is commensurate with the experimental data and entails simple implementation. The experiment results regarding bit error rate (BER), convergence speed, lookup table (LUT) size, and computational complexity show that the proposed equalizer outperforms the Volterra filter and linear finite impulse response filter with the normalized least-mean square (NLMS) algorithm.

Linear Phase Two-Dimensional FIR Digital Filter Functions Generated by applying Christoffel-Darboux Formula for Orthonormal Polynomials

Global Christoffel-Darboux formula for four orthonormal polynomials on two equal finite segments for generating linear phase two-dimensional finite impulse response (FIR) digital filter functions in a compact explicit representative form is proposed in this paper. The formula can be most directly applied for solving mathematically the approximation problem of a filter function of even and odd order. An example of a new class extremely economic linear phase two-dimensional FIR digital filter without multipliers obtained by the proposed approximation technique is presented. The generated linear phase two-dimensional FIR filter functions have two symmetries, that is, the following relations are valid: H(z1,z2)=H(z2,z1) and H(z1,z2)=H(-z1,-z2) and . Ill. 3, bibl. 15 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.120.4.1449

Digital filtering of neurophysiological data can introduce artifacts

Event Abstract Back to Event Digital filtering of neurophysiological data can introduce artifacts Benedek Tihanyi1*, Balázs Hangya1 and Viktor Varga1 1 Hungarian Academy of Sciences, Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Hungary Filtering is a widespread method to remove a selected frequency range from a physiological signal. However, frequency filtering can also have undesired effects like distorting the signal waveform or introducing phase shifts of periodic signals. Here we analyzed systematic artifacts caused by commonly used digital filters on real and simulated data. We recorded local field potentials (LFP) from the pyramidal layer of the CA1 region from urethane anesthetized rats. We also generated artificial data by adding Gaussian noise to a sine wave or to the sum of two sine waves of different frequencies. The use of artificial data allowed us to fully control the frequency components present in the signal and to exactly determine the local minima and maxima. We examined two widely used digital filters, the Butterworth filter and the linear finite impulse response filter. To determine the distortions introduced by filtering, we calculated the mean square difference of the local extrema of the original and the filtered data and quantified the effects of filtering on the phase histograms of oscillatory signals. We also visualized filter artifacts caused by abrupt changes in the frequency spectrum of the signal. Increasing the filter order resulted in sharper cut-off but also in a substantial distortion of local extrema. Unidirectional filtering caused a significant shift in the phase of periodic signals, while filtering the data in both directions cancelled this effect. Both filters introduced artificial waves at abrupt onsets of large amplitude oscillations. Thus, reducing potential filter artifacts by choosing an appropriate way of filtering is essential to the analysis of any physiological data. We suggest i) adjusting the filter order to the sampling rate and the amplitude of the data; ii) using bidirectional filtering to prevent phase distortions and iii) comparing the filtered signal to the original data to confirm the presence of the filtered components in the signal. Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Abstracts Citation: Tihanyi B, Hangya B and Varga V (2010). Digital filtering of neurophysiological data can introduce artifacts. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00263 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 May 2010; Published Online: 05 May 2010. * Correspondence: Benedek Tihanyi, Hungarian Academy of Sciences, Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary, tihanyibene@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Benedek Tihanyi Balázs Hangya Viktor Varga Google Benedek Tihanyi Balázs Hangya Viktor Varga Google Scholar Benedek Tihanyi Balázs Hangya Viktor Varga PubMed Benedek Tihanyi Balázs Hangya Viktor Varga Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Mapping Fractures and High-Permeability Channels in Waterfloods by Use of Injection and Production Rates

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 121353, ’A Novel Method for Mapping Fractures and High-Permeability Channels in Waterfloods Using Injection and Production Rates,’by Kun-Han Lee, SPE, Antonio Ortega, Amir Mohammad Nejad, SPE, Nelia Jafroodi, SPE, and Iraj Ershaghi, SPE, University of Southern California, prepared for the 2009 SPE Western Regional Meeting, San Jose, California, 24-26 March. The paper has not been peer reviewed. This paper extends previous work for managing waterfloods by estimating flow characteristics from only injection and production rates. The method estimates the finite-impulse-response (FIR) curve corresponding to the fluid flow between all injector/producer pairs. Reservoir parameters, such as connectivity between wells, can be estimated from this curve, which can be used to characterize variations of relative flow as a function of storage capacity, thus making it possible to quantify the het-erogeneity of flow paths between wells. Introduction In waterflooding, injection rates often can be correlated with gross rates of the surrounding producers by monitoring pressure data. To estimate this correlation, the whole region of injectors/producers is viewed as a system, with the injectors as the system input and the producers as the system output. Methods to estimate the directional transmissivity of flow on the basis of injection- and production-rate data are desirable because injection- and production-rate data are routinely available with high temporal resolution for many reservoirs. The problem of estimating reservoir properties from injection/production rates is considered a system-identification problem, where injection rates are input and production rates are output. This method could be considered a standard multiple-input/-output (MIMO) system. It is reasonable to assume that the effect on production of each injector can be modeled as a linear FIR filter applied to the injection rates. This FIR curve can be analogous to the response obtained from pulse testing. Modeling the reservoir as a linear FIR-MIMO system is proposed, for which system identification can be solved by use of the least-squares method with truncated order. The main contribution is providing a dynamic approach to estimate the flow characteristics between wells by use of only injection and production data. Injector/Producer Relationships In a waterflood, production rates are influenced by reservoir transmissibility, bottomhole flowing pressures, and the pressure changes caused by fluid injections. To capture the injector/producer relationships, various models describe the production rates resulting from injection rates and other factors. Injector/Producer Model. A general linear-FIR model was used in which production rates were determined partly by a linear combination of the surrounding FIR-filtered version of injection rates. This FIR-MIMO model is very general and can be used to approximate many different models developed by previous researchers, such as a streamline model that describes the relationship between injectors and producers by many imaginary streamlines and capacitance models that use only two parameters to describe the relationship between each injector/producer well pair.

Linear parameter varying, finite impulse response filter design with application to reducing rise-time for rapid signal changes

Classical linear finite impulse response filters (FIR) with constant coefficients have solid and comprehensive theoretical foundations and design methods. The main advantage of using FIR filters is good linearity of phase diagram. Although filters with constant coefficients guarantee particular properties in frequency domain, i.e. noise damping, they also increase rise time for rapid signal changes. In order to avoid such blurring effects a low-pass parameter varying FIR filter is proposed. To synthesize the filter two cut-off frequencies are needed accompanied with given filter order, shape tuning function and threshold detection condition for sequential operation. Properties of the filter with time-varying coefficients are analyzed using proposed previously by the author simplified frequency approach for time-varying systems. The frequency approach for filter analysis takes advantage of SVD-DFT transformation of linear time-varying discrete-time system, employing singular value decomposition, discrete Fourier transformation and power spectral density properties. Considerations are illustrated by numerical example.

Spectral design of weighted median filters admitting negative weights

A closed-form spectral optimization method for the design of weighted median (WM) filters admitting negative weights is presented. The algorithm is a generalization of Mallows' (1980) theory for nonlinear smoothers that consists of first finding a set of positive weights for a WM filter whose sample selection probabilities are as close as possible to the coefficients of a corresponding finite impulse response (FIR) filter with the desired spectral response. The signs of the weights associated with general WM filtering structures are then coupled with the input samples prior to replication by the weight magnitudes. The spectral characteristics of these WM filters, designed under the proposed method, are shown to be very similar to those of the equivalent linear FIR filters and arbitrary spectral behavior can be achieved. Unlike their FIR filter counterparts, WM filters are robust to impulsive noise, as demonstrated by simulations.

Best Linear Unbiased Fir Filters For Continuous‐Time State Space Models

ABSTRACTThis paper proposes a new linear finite impulse response (FIR) filter called the best linear unbiased FIR (BLUF) filter for the state estimation in continuous‐time state space models. The proposed BLUF filter for continuous‐time state space models is obtained by a formal limiting procedure of discretized systems. The BLUF filter is represented in an iterative form and then in a standard FIR form. It is shown that the proposed BLUF filter has deadbeat and unbiasedness properties. It is also shown that the BLUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical while the latter is heuristic due to handling of infinite covariance of the initial state.

A general weighted median filter structure admitting negative weights

Weighted median smoothers, which were introduced by Edgemore in the context of least absolute regression over 100 years ago, have received considerable attention in signal processing during the past two decades. Although weighted median smoothers offer advantages over traditional linear finite impulse response (FIR) filters, it is shown in this paper that they lack the flexibility to adequately address a number of signal processing problems. In fact, weighted median smoothers are analogous to normalized FIR linear filters constrained to have only positive weights. It is also shown that much like the mean is generalized to the rich class of linear FIR filters, the median can be generalized to a richer class of filters admitting positive and negative weights. The generalization follows naturally and is surprisingly simple. In order to analyze and design this class of filters, a new threshold decomposition theory admitting real-valued input signals is developed. The new threshold decomposition framework is then used to develop fast adaptive algorithms to optimally design the real-valued filter coefficients. The new weighted median filter formulation leads to significantly more powerful estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior weighted median smoother structures.