Adaptive FIR Filter Research Articles

FIR noise reduction using GWO and Fast Kurtogram for extracting fault characteristic frequencies in rolling bearings

Abstract Fast Kurtogram (FK) is a widely recognized method for detecting characteristic fault frequency bands. Nevertheless, its sensitivity to noise often causes diagnostic errors. This research seeks to enhance FK’s identification performance by implementing an adaptive FIR filter that mitigates noise. To validate this approach, single-point fault signals were obtained from the outer race, inner race, and rolling elements of bearings operating at four distinct motor speeds. These raw signals were then subjected to statistical analysis after normalization. To design the adaptive FIR filter, the study employs an enhanced Grey Wolf algorithm along with the Parks-McClellan algorithm to determine the optimal filter order and impulse response coefficients. Subsequently, band-pass filtering is applied to the fault characteristic frequency bands identified through FK. The fault characteristic frequencies are then extracted using envelope spectrum analysis. The bandwidth and fault characteristics derived from the processed signals are compared against those obtained from the unfiltered original signals. The findings indicate that optimal normalization preprocessing is crucial before filter design. The determined optimal order and impulse response coefficients enable the adaptive design of FIR filters. As a result, the FIR-filtered signal exhibits significantly fewer noise-induced anomalous peaks, thereby improving the signal-to-noise ratio and enhancing FK’s robustness against noise interference. Implementing an adaptive FIR filter for noise reduction significantly aids in more precise analysis and diagnosis of bearing conditions. Looking ahead, this study proposes integrating machine learning algorithms to further enhance the accuracy in identifying characteristic frequency bands associated with single-point faults in rolling elements.

Energy-efficient architecture for high-performance FIR adaptive filter using hybridizing CSDTCSE-CRABRA based distributed arithmetic design: Noise removal application in IoT-based WSN

An energy-efficient architecture of high-performance FIR adaptive filter design using approximate distributed arithmetic (DA), which is integrated with canonic signed digit-based triangular common sub expression elimination (CSDTCSE) and carry-resist adder based Booth recorder adder (CRABRA) is proposed for noise removal in sensor nodes. Distributed arithmetic is coupled with two signed 32-bit, 16-bit radix-8 Booth algorithms and approximate computation under 2-bit adder to design FIR adaptive filter for decreasing partial products (PP) together with accumulation circuits. The truncation of LSB in the PP is presented to approximate the PP to reduce memory complexity and hardware overhead. An approximation recoding adder decreases the energy usage, area, and critical path. Approximate Wallace trees are applied to the PP accumulation to lessen the latency. The canonic signed digit-based triangular common sub-expressions elimination framework is proposed, which significantly reduces a count of logic operators and logic depth in implementing the FIR filter. The proposed algorithm is activated in Verilog coding and synthesized using Xilinx 14.5 ISE simulation software. The proposed design successfully reduces delay, area, and power by maintaining better accuracy with performance.

A Novel Influence Function M-Estimator-Based for Active Noise Control

M-estimators are widely used in active noise control (ANC) systems in order to update the adaptive FIR filter taps. ANC systems reduce the noise level by generating anti-noise signals. Up to now, the evaluation of M-estimators capabilities has shown that there exists a need for further improvements in this area. In this paper, a new improved M-estimator is proposed. The sensitivity of the proposed algorithm to the variations of its constant parameter is checked in feedforward control. The effectiveness of the algorithm in both types is proved by comparing it with previous studies. Simulation results show the steady performance and fast initial convergence of the proposed algorithm.

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.

Energy-efficient, high-performance and memory efficient FIR adaptive filter architecture of wireless sensor networks for IoT applications

Energy-efficient, high-performance and memory efficient FIR adaptive filter architecture of wireless sensor networks for IoT applications

Low power low area VLSI implementation of adaptive FIR filter using DA for decision feed back equalizer

In this paper, a low-complexity pipelined adaptive FIR filter is designed using Distributed Arithmetic (DA) architecture for signal processing applications. Generally adaptive filters will occupy more area and power consumption because of using memories in the filters for partial product (PP) generation. To get rid of this, We use the pipeline concept to reduce the registers in the filters and also to reduce the area further compressor adders are used in the adaptive filter architectures instead of using normal adders. With these two concepts the area and power consumption of the adaptive filters will be reduced. The proposed design is coded in Verilog HDL language and synthesized in Synapsis design compiler tool with SAED 90 nm technology for finding the area, power, minimum sampling period, maximum sampling frequency, area delay product (ADP), power delay product (PDP). By using proposed adaptive filter we can design and implement higher order filters more easily and also the complexity of the proposed design is very less when compared with the existing designs. When we observe the synthesis results the proposed design will occupy 30% less area when compared with the two memories based existing architecture. Also the power consumption is 25% less when compared with the block based adaptive filters. The ADP and PDP of the proposed design is very less when compared with existing architectures. The proposed design is well suited for signal processing application designs such as adaptive decision feed back equalizers for removing the signal noises and inter symbol interference, hearing aids, ECG signal analysis and software defined radio.

Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation

In this paper, the recursive form of an optimal finite impulse response filter is proposed for discrete time-varying state-space models. The recursive form of the finite impulse response filter is derived by employing finite horizon Kalman filtering with optimally estimated initial conditions. The horizon initial state and its error covariance on the horizon are optimally estimated by using recent finite measurements, in the sense of maximum likelihood estimation, then initiating the finite horizon Kalman filter. The optimality and unbiasedness of the proposed filter are proved by comparison with the conventional optimal finite impulse response filter in batch form. Moreover, an adaptive FIR filter is also proposed by applying the adaptive estimation scheme to the proposed recursive optimal FIR filter as its application. To evaluate the performance of the proposed algorithms, a computer simulation is performed to compare the conventional Kalman filter and adaptive Kalman filters for the gas turbine aircraft engine model.

WAVE communication based on improved NLMS adaptive filtering delay compensation method*

In order to improve the signal quality of the Wireless Access Vehicle Environment (WAVE), in this paper, the dynamic step factor is introduced into the Normalized Least Mean Square (NLMS) algorithm based on multi-step step descent, so that the algorithm has a wide dynamic adaptive range. And an improved NLMS adaptive filtering method is proposed to reduce the noise of the communication signal of the WAVE system. At the same time, by introducing the unit delay operator z −Δ of the adaptive FIR filter and the transmission delay t p of the WAVE system, a delay compensation function is constructed to solve the delay problem in the signal transmission and processing process of the WAVE system and meet the real-time requirement of the system. In this paper, the above method is simulated and tested under typical working conditions of the Cooperative Adaptive Cruise Control (CACC) system. Experimental results show that the NLMS adaptive filtering method can effectively reduce the error between the acquired signal and the expected signal and make the signal waveform smoother by introducing the dynamic step factor. By introducing the filtering delay compensation function into the signal filtering results, it can effectively reduce the adverse effects of the delay problem caused by signal transmission and signal processing on the desired target signal.

An integrated FIR adaptive filter design by hybridizing canonical signed digit (CSD) and approximate booth recode (ABR) algorithm in DA architecture for the reduction of noise in the sensor nodes

An integrated FIR adaptive filter design by hybridizing canonical signed digit (CSD) and approximate booth recode (ABR) algorithm in DA architecture for the reduction of noise in the sensor nodes

A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters.

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of , that is, = 1 for reflection and = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

Design and Development of Adaptive filter for Real Time Signal Processing

In Communication the signal is distorted due to noise and the notch filters are used for eliminating unwanted frequencies For filtering the signal in a specific band Notch filter is used. Notch filter can pass the signal below and above notch frequency. In most of the signal processing applications frequency estimation of narrow band signals in noisy environment are required For frequency detection and tracking Adaptive IIR filter or Adaptive FIR filters can be used. Adaptive FIR Filter is more stable than Adaptive IIR filter. In most practical applications Adaptive IIR Notch Filter (AIIRNF)is used The advantage of Adaptive IIR notch filter over FIR Filter is less number of filter coefficients and less complexity in design In this paper Adaptive IIR Notch filter is designed for real time applications. The performance of designed adaptive IIR Notch filter is evaluated based on Magnitude and Frequency response.

WITHDRAWN: Low power implementation of adaptive block FIR filter design using offset binary coding

Abstract This paper presents a power efficient realization of block least mean square (BLMS) algorithm based adaptive finite impulse response filter (ADF) using offset binary coding (OBC). OBC method is one of the area optimization methods of distributed arithmetic (DA). In the DA scheme partial products of the filter are stored in a look-up table (LUT) followed by a shift accumulate unit (SA). The size of the LUT increases exponentially with length of the filter. Using the OBC method, the size of the LUT is reduced. Memory sharing concept is used to design the registers, which are in turn used for input sample generation. A high range of parallelism is included in the architecture that enhances its performance. The adaption delay is decreased by doing parallel LUT update, filtering and weight updation operations. The results prove that the proposed design consumes 42% less power in comparison to the best existing architectural designs.

Merenje komponenti električne snage po standardu IEEE 1459-2010

In this paper, the design and implementation of a novel recursive method for the power measurement ac-cording to the IEEE Standard 1459-2010 have been described. The most important parts are adaptive band and low-pass FIR filters that extract fundamental and dc components, respectively. In addition, by using oversampling techniques and decimation filters, coefficient sensitivity problems of the large-order FIR comb cascade structure are overridden and the parameter estimation accuracy is improved. The symmetrical components are estimated through a transformation matrix of adaptive phase shifters. The effectiveness of the proposed techniques is demonstrated by simulation results.

Adaptive virtual referencing for the extraction of extracellularly recorded action potentials in noisy environments

Objective. Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. Approach. An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. Main results. By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. Significance. AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.

Efficient Architecture for the Realization of 2-D Adaptive FIR Filter Using Distributed Arithmetic

This paper presents an efficient architecture for two-dimensional (2-D) adaptive FIR filter architecture using the distributed arithmetic (DA) algorithm. DA-based filter architectures essentially require look-up tables (LUT). In the proposed filter architecture, RAM- or ROM-based LUT is replaced by adders- and logic gates-based structure that generates the LUT value corresponding to the input. Therefore, the MAC unit requires fewer logic gates and adders in DA-based realization. In addition, the memory sharing concept in architecture reduces the delay elements. Moreover, the complexity of the LUT hardware of higher-order filters is reduced by parallelly dividing the internal MAC block for the DA decomposition which offers a higher degree of modularity and parallelism in the proposed architecture. Further, 2-D delayed LMS algorithm is used for the updation of the filter coefficient weights. Furthermore, two-stage pipelining is used to reduce the critical path of the architecture and it also makes critical path delay independent of the order of the filter. ASIC synthesis results reveal the advantages of the proposed structure by reducing the area, power, ADP and EDP by 54%, 48.19%, 55% and 49%, respectively, as compared with the existing architecture for filter size 8 × 8.

An Adaptive Feed-Forward Phase Locked Loop for Grid Synchronization of Renewable Energy Systems under Wide Frequency Deviations

Synchronization is a crucial problem in the grid-connected inverter’s control and operation. A phase-locked loop (PLL) is a typical grid synchronization strategy, which ought to have a high resistance to power system uncertainties since its sensitivity influences the generated reference signal. The traditional PLL catches the phase and frequency of the input signal via the feedback loop filter (LF). In general, to enhance the steady-state capability during distorted grid conditions generally, a filter tuned for nominal frequency is used. This PLL corrects large frequency deviations around the nominal frequency, which increases the PLL’s locking time. Therefore, this paper presents an adaptive feed-forward PLL, where the input signal frequency and phase under large frequency deviations are tracked precisely, which overcomes the above-mentioned limitations. The proposed adaptive PLL consists of a feedback loop that reduces the phase error. The feed-forward loop predicts the frequency and phase error, and the frequency adaptive FIR filter reduces the ripples in output, which is due to input distortions. The adaptive mechanism adjusts the gain of the filter in accordance with the supply frequency. This reduces the phase and frequency error and also decreases the locking time under wide frequency deviations. To verify the effectiveness of the proposed adaptive feed-forward PLL, the system was tested under different grid abnormal conditions. Further, the stability analysis has been carried out via a developed prototype test platform in the laboratory. To bring the proposed simulations into real-time implementations and for control strategies, an Altera Cyclone II field-programmable gate array (FPGA) board has been used. The obtained results of the proposed PLL via simulations and hardware are compared with conventional techniques, and it indicates the superiority of the proposed method. The proposed PLL effectively able to tackle the different grid uncertainties, which can be observed from the results presented in the result section.

Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture

In this paper, we propose a low complex architectural design for hearing aid applications. In this, we recast the hearing aid using distributed arithmetic (DA), which enables the implementation of hearing aid without multipliers. The design is based on the distributed arithmetic based formulation of it. It is further shown that high order filters, which are required to implement high-speed hearing aid can be realized using only look-up-tables and shift-accumulate operations. A novel approach was proposed to replace the decimation filter of a hearing aid using multiplier less architecture with a single DA unit. By proper initialization, it is shown that low complexity hearing aid architecture can be obtained. The proposed distributed arithmetic architecture is implemented in ASIC SAED 90 nm technology. The application of hearing aid is implemented in Matlab Simulink and Xilinx system generator tool. The obtained results show $$20\%$$ less area delay product and $$40\%$$ less power delay product when compared with the existing architecture.

Design of an Optimized Twin Mode Reconfigurable Adaptive FIR Filter Architecture for Speech Signal Processing

Reconfigurability, low complexity and low power are the key requirements of FIR filters employed in multi-standard wireless communication systems. Digital Filters are used to filter the audio data stream and increase the reliability of speech signal. Therefore, it is imperative to design an area optimized and low power based reconfigurable FIR filter architectures. The reconfigurable architecture designed in this research is capable of achieving lower adaptation-delay and area-delay-power efficient implementation of a Delayed Least Mean Square (DLMS) adaptive filter with reversible logic gates. The Optimized Adaptive Reconfigurable Adaptive Reconfigurable (OAR) FIR filter architectures are proposed. The optimized architectures are implemented across the combinational blocks by reducing the pipeline delays, sampling period, energy consumption and area, to increase the Power-Delay Product (PDP) and Energy Per Sample (EPS).The noisy speech signals are used for verifying the efficiency of the proposed architectures. The efficiency of the architecture is verified by implementing the proposed scheme in signal corrupted by various real-time noises at different Signal to Noise Ratios (SNRs).

An adaptive FIR filter for trajectory prediction and latency reduction in direct Human–Computer interactions

The problem of latency reduction in direct Human–Computer interactions is considered. The proposed method is based on a frequency-domain approximation of a non-causal ideal predictor with a finite impulse response filter. Given a sufficiently rich dataset, the parameters of the filter can be either optimized off-line or tuned on-line with the proposed adaptive algorithm. The performance of the proposed solution is evaluated in an experimental study consisting of drawings on a touchscreen.

Adaptive FIR Filtering for PAPR Reduction in OFDM Systems

Adaptive FIR Filtering for PAPR Reduction in OFDM Systems