IIR Filter Research Articles

A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard LMS algorithm is hybridized with GA (Genetic Algorithm) to obtain a new integrated learning algorithm, namely, LMS-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard LMS algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed LMS-GA technique, first, it works as the standard LMS algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard LMS algorithm gets stuck in local minimum, the LMS-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed LMS-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input’s correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed LMS-GA under input signals with different characteristics.

Ensemble learning algorithm based on multi-parameters for sleep staging.

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Graphical abstract.

Анализ двуступенчатой структуры узкополосных БИХ-фильтров.

Анализ двуступенчатой структуры узкополосных БИХ-фильтров.

Optimizing the spectral characteristics of the finite-difference schemes for the unsteady Schrödinger equation

The spectral consistency of the finite-difference theta-method for the unsteady Schrödinger equation is investigated. Optimal sampling parameters providing a minimum error for a given spectral range are obtained. It is shown that the op ti mized scheme provides a reduction (by a factor of 5–6) in the error of the approximate solution in comparison with the 4th order accuracy scheme. It is shown that the 4th order scheme provides the best spectral consistency only in the case if the spectral range length tends to zero. The conditions for equivalence between the finite-difference scheme and the scheme in the form of two first-order conjugated IIR filters are found. The obtained scheme is the best scheme in the class of conservative finite difference schemes for solving the Schrödinger equation. Practical issues arising in the process of implementing a numerical solution are considered. The obtained results can be efficiently used for solving linear and non-linear Schrödinger equations.

Analytic design of directional and square-shaped 2D IIR filters based on digital prototypes

This paper proposes an analytic design method for a class of 2D recursive filters, namely with directional and square-shaped frequency response. The design technique is based on a specified 1D digital prototype filter and a complex frequency transformation which is determined using various rational approximations. Several design examples are presented for given specifications. The resulted filters are efficient and adjustable, being at the same time of low complexity and relatively high selectivity. These filters may have useful applications in image processing, like detecting lines with a given orientation from an image, as shown through simulation results on test images. The novelty of the proposed method consists in deriving the transfer function of the desired 2D filter only by applying successive frequency transformations to a prototype filter. Its advantages over most state-of-the-art design methods, which generally use global optimization numerical algorithms, is the filter tunability, and also its versatility; since the filter parameters, namely orientation and selectivity appear explicitly in the 2D filter matrices, they result directly for any specified parameters, therefore the design procedure does not need to be resumed every time from the start.

Abstract No. 494 Advanced non-contrast power Doppler ultrasound imaging: feasibility for immediate evaluation of transarterial chemoembolization

Conventional non-contrast Doppler ultrasound (US) techniques are limited to relatively high velocities in large, resolvable vessels. Therefore, US without contrast has historically been ineffective for evaluating transarterial chemoembolization (TACE) treatment of hepatic malignancies, which involves acute reduction in tumor perfusion. Recent advancements in power Doppler (PD) US beamforming and tissue filtering have substantially improved sensitivity to slow blood flow in small-diameter vessels, potentially making non-contrast perfusion US imaging possible. Among these are angled plane wave beamforming, adaptive tissue clutter demodulation (AD), and singular value decomposition (SVD) filtering. Here we aim to demonstrate feasibility of these advancements for immediate evaluation of TACE. This study was IRB approved. Nine patients undergoing 10 TACE procedures were included. A Verasonics C5-2 probe with a 4.2MHz frequency was used to acquire raw angled plane wave channel data immediately before and after treatment. Images were post-processed as follows: channel data were beamformed and filtered using conventional (IIR filtering and a 16-sample ensemble) and advanced (AD+SVD filtering and a 300-sample ensemble) processing methods. Tumor-to-background contrast in PD images was computed before and after TACE and averaged across acquisitions for each processing technique. Minimal change was detected in tumor blood flow between before and after TACE images when using conventional methods. With advanced processing, visible reduction in flow within tumors was clearly identifiable. On average, conventional and advanced processing methods resulted in 0.8dB (± 1.2dB) and 8.0dB (± 1.1dB) decreases in tumor-to-background contrast, respectively. Advanced non-contrast power Doppler US to detect treatment-induced changes in blood flow is feasible immediately following TACE. As post-processing becomes more efficient, on table assessment of TACE completion may be quantifiably obtained in real time. We plan on correlating these findings with gold-standard follow-up imaging as a next step.

Development of an Adaptive IIR Filter Based on Modified Robust Mixed-Norm Algorithm for Adaptive Noise Cancellation

Noise cancellation is one of the most important applications of adaptive filters. The employment of adaptive filtering in most digital signal processing tasks is currently an area of growing interest as adaptive filters, due to their dynamic nature, perform better than the traditional filters in compensating for random noise in their environment. However, the compensation for impulsive interference or noise is desired since most adaptive algorithms earlier proposed modelled noise as a random process of the White Gaussian distribution. A modified robust mixed-norm (MRMN) algorithm recently proposed to compensate for impulsive interference has been found to be hardware efficient, however the MRMN algorithm has only been tested on adaptive FIR system identification task. In this paper, an adaptive IIR filter based on MRMN adaptive algorithm is proposed and tested for noise cancellation task. The developed filter structure was modelled and simulated in MATLAB environment. The results obtained showed that the MRMN algorithm does in fact compensate for the presence of impulsive interference, however, at a higher computational complexity relative to the LMS algorithm. Keywords: Noise cancellation, adaptive filtering, impulsive noise, adaptive algorithm, system identification, random noise DOI : 10.7176/CEIS/10-2-01 Publication date :March 31 st 2019

Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor

Non-invasive electrohysterogram (EHG) could be a promising technique for the preterm birth prediction, which could enable us to diagnose the preterm birth before the labor and reduces the infant mortality and morbidity. Previous studies on the preterm birth prediction with EHG have conducted comprehensive researches on various signal features and classification algorithms, but most of them adopted prefilters based on the linear transforms using fixed basis function, although they are suboptimal for the nonlinearity and nonstationarity of the EHG signal. In this paper, multivariate empirical mode decomposition (MEMD) is applied to decompose the electrical activity signal measured on the uterus. After the decomposition, features are calculated for the corresponding oscillations to the uterine contraction. To investigate the performance of the features, three-channel EHG signals of 254 patients (224 term, 30 preterm) are chosen among 300 patients from Physionet term-preterm electrohysterogram (TPEHG) database to extract features from the EHG signals and classify the features using machine learning algorithms. Classification results shows that the proposed method with MEMD achieved 94.66% correctly classified rate (CCR) and 0.987 area under the curve (AUC), which outperformed those with IIR filter implying MEMD provides a new prospect to improve the current preterm birth prediction approach.

Search of Optimal s-to-z Mapping Function for IIR Filter Designing without Frequency Pre-warping

ABSTRACT Bilinear transformation is one of the most popular s-to-z mapping functions and widely used to design digital filters, disregarding its susceptibility to “frequency warping”. Although “frequency pre-warping” is used to counter the warping effect, it cannot be applied uniformly in all the cases. Moreover, the pre-warping technique is unable to remove the warping-effect completely. It is a general convention that the higher-order s-to-z transform gives more linear mapping than the lower order counterparts. Surprisingly, the 1st-order bilinear transformation is still preferred over higher-order s-to-z mapping functions. An investigation has been performed to develop a generalized criterion for selecting optimal s-to-z mapping function so that IIR filters can be designed without the need of “frequency pre-warping”. A couple of IIR filters (integer and fractional order) have been designed using several s-to-z mapping schemes, without applying the frequency pre-warping technique. MATLAB and Simulink responses have been given to support and validate the developed generalized criterion, for selecting optimal s-to-z mapping function.

An Adaptive Decomposition Scheme for Wideband Signals of Power Systems Based on the Modified Robust Regression Smoothing and Chebyshev-II IIR Filter Bank

Distributed energy resources and new types of electrical loads are morphologically changing the signals of power grids. Grasping the wideband information of field data will play an important role in helping smart grids to operate in a safe manner. A wideband signal model composed of deterministic and stochastic components is proposed to enhance the wideband measurement. Based on the model, an adaptive decomposition scheme is designed for the offline analysis of wideband signals. In the scheme, a modified robust local regression smoothing method is presented to denoise the wideband signal. A totally data-dependent threshold is established to extract adaptively interharmonic components. Further, the Taylor-Fourier transform algorithm is used to estimate the parameters of deterministic components based on the subsignals extracted by the Chebyshev-II infinite impulse response filter bank. Finally, the stochastic components are extracted using the adaptive threshold based on the signal that the deterministic components are removed. The performances of the decomposition scheme under different conditions have been validated. In the end, the proposed decomposition scheme is carried out and verified with the field data of the 24-h current signals at one residence community.

An FPGA-Oriented Algorithm for Real-Time Filtering of Poisson Noise in Video Streams, with Application to X-Ray Fluoroscopy

In this paper we propose a new algorithm for real-time filtering of video sequences corrupted by Poisson noise. The algorithm provides effective denoising (in some cases overcoming the filtering performances of state-of-the-art techniques), is ideally suited for hardware implementation, and can be implemented on a small field-programmable gate array using limited hardware resources. The paper describes the proposed algorithm, using X-ray fluoroscopy as a case study. We use IIR filters for time filtering, which largely simplifies hardware cost with respect to previous FIR filter-based implementations. A conditional reset is implemented in the IIR filter, to minimize motion blur, with the help of an adaptive thresholding approach. Spatial filtering performs a conditional mean to further reduce noise and to remove isolated noisy pixels. IIR filter hardware implementation is optimized by using a novel technique, based on Steiglitz–McBride iterative method, to calculate fixed-point filter coefficients with minimal number of nonzero elements. Implementation results using the smallest StratixIV FPGA show that the system uses only, at most, the 22% of the resources of the device, while performing real-time filtering of 1024 × 1024@49fps video stream. For comparison, a previous FIR filter-based implementation, on the same FPGA, in the same conditions and constraints (1024 × 1024@49fps), requires the 80% of the logic resources of the FPGA.

一种级联两个IIR滤波器的微波光子滤波器的特性分析

一种级联两个IIR滤波器的微波光子滤波器的特性分析

Study of structural complexity of optimal order digital filters for de-noising ECG signal

Selection and implementation of optimal order digital filter for denoising ECG signal on FPGA, based on Signal to Noise Ratio (SNR), error and accuracy using wavelet toolbox is a tedious task. To overcome this problem, an attempt has been made in MATLAB to obtain a noise-free ECG signal based on SNR and mean square error to select optimal order digital filter. An ECG sample from MIT-BIH arrhythmia database is artificially corrupted by adding noise and is filtered through different low-pass IIR and FIR digital filters. The hardware complexity of optimal order digital filters structure is checked in terms of multipliers, adders and delays in MATLAB and their performance is compared based on the number of basic elements and visual inspections. It is found that the elliptic IIR filter and Kaiser window FIR filter require low-filter order for a given specification and hence, requires less number of basic elements.

Filtering Techniques for Chaotic Signal Processing

The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications. Traditional signal processing techniques provide only limited possibilities for efficient filtering broadband chaotic signals. In this paper, we provide a comparative study of several denoising and filtering approaches: a recursive IIR filter, a median filter, a wavelet-based denoising method, a method based on empirical modes decomposition, and, finally, propose the new filtering algorithm based on the cascade of driven chaotic oscillators. Experimental results show that all the considered methods make it possible to increase the permissible signal-to-noise ratio to provide the possibility of message recognition, while the new proposed method showed the best performance and reliability.

On the use of Youla–Kucera parametrisation in adaptive active noise and vibration control – a review

ABSTRACTYoula–Kucera parametrisation plays a very important role in adaptive active vibration control and adaptive active noise control. This concerns both vibration and noise attenuation by feedback as well as by feedforward compensation when a measurement of an image of the disturbance (noise or vibration) is available. The paper will review the basic algorithms and various extensions trying to emphasise the advantages of using Youla–Kucera parametrisation. Specific aspects related to the use of this approach in adaptive active vibration and noise control will be mentioned. A brief review of applications and experimental testing will be provided.Abbreviations: ANC: Active noise control system; AVC: Active vibration control system; FIRYK: Youla–Kucera parametrised FIR adaptive feedforward compensator using an FIR Youla–Kucera filter; IIR: IIR adaptive feedforward compensator; IIRYK:Youla–Kucera parametrised IIR adaptive feedforward compensator using an IIR Youla–Kucera filter; IMP: Internal model principle; PAA: Parameter adaptationalgorithm; QFIR: Youla–Kucera FIR filter; QIIR: Youla–Kucera IIR filter; SPR: Strictly positive real (transfer function); YK: Youla–Kucera

Loop-Filter Design and Analysis for Delta-Sigma Modulators and Oversampled IIR Filters

Delta-sigma modulators spectrally shape quantization noise in discrete systems and are used extensively in communications and signal-processing systems. They are implemented using a loop-filter that processes the input and feed-back signals such that the closed loop behavior has a separate signal transfer function (STF) and noise transfer function (NTF). Loop-filter design often relies on special cases for the NTF or simplified loop-filter structures. This paper presents a method for generalized loop-filter synthesis that can implement a chosen NTF and STF independent of internal loop-filter structure or filter order. The transfer functions are formulated as polynomials of integration, $(z-1)^{-n}$ , instead of delay, $z^{-n}$ , to enable practical numerical loop-filter synthesis even for high-order and highly oversampled loop-filters. An example design shows how to use this synthesis method to design a high-order modulator that directly filters and re-modulates a direct stream digital audio signal.

Multichannel Equalization and Crosstalk Cancellation Using Fixed-Pole IIR Filters

Multichannel Equalization and Crosstalk Cancellation Using Fixed-Pole IIR Filters

Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis

Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.

EEG Based Strategies for Human Gustation Classification Using Spartan—6 FPGA

Gustation is one of the five sensations and is responsible for the inhibition of electrical stimulation, thus reflecting in the overall cortical activity of the brain due to the electrical stimulation. The information processing of gustation are employed for disorder recognition such as Parkinson’s disease, etc. and is a source of reimbursement for better clinical diagnosis. EEG signal of gustatory stimulus is preprocessed using IIR band pass filter to remove the artifacts due to noise. The advantage of using IIR filter is the requirement of lesser memory space and works faster than FIR filter. Extraction of features such as Short Time Fourier Transform and Fast Fourier Transform are frequency-domain analysis in which the signal is assumed to be stationary. An algorithm for feature extraction used in the proposed work is Stationary Wavelet Transform (SWT) which provides time frequency representation. In time domain, the statistical features of the detailed coefficients are computed and a filtered EEG signal is decomposed using SWT. The statistical features computed here are the mean which is an average of the absolute value of the EEG signal, variance which uses the power of the EEG signal as a feature of the signal and power spectral density. The different gustatory stimuli of bitter, sour and sweet are classified based on the features extracted. Lastly, the feature extraction and preprocessing algorithms are implemented in Spartan—6 FPGA kit. The proposed system possesses an advantage of increased computational speed and reduced area utilization compared to existing method.

Efficient calibration and modelling of charge amplifiers for dynamic measurements

Charge amplifier calibration at low frequencies is a time-consuming service. This paper presents a method to speed up the process by measuring decades of frequencies in a single run. Based on the calibration results gained, a filter design process is introduced that facilitates a multi-rate approach to digital IIR filters in order to circumvent the complications arising from high-pass filtering with low corner frequency at high sampling rates.