Adaptive Noise Cancellation Filter Research Articles

Sigmoid distance metric-based spline adaptive filters for nonlinear adaptive noise cancellation

This article devises a spline adaptive filter (SAF) algorithm with robustness for nonlinear adaptive noise cancellation (NANC), called the SAF-SDM, which is built on a distance-based metric with a sigmoid kernel. The presented SAF-SDM delivers insignificant coefficient updates when the system is subjected to remarkable outliers, which leads to low steady-state errors. Additionally, we thoroughly analyze the mean behaviour and mean-square performance of the SAF-SDM at the theoretical level and validate the analysis results using nonlinear system identification (NSI) simulations. To fulfill the higher computational efficiency when a longer linear filter order is selected in the SAF, an improved SAF-SDM is developed through a frequency domain (FD) filtering program, called the FDSAF-SDM. Then, we provide an analysis of the calculation complexity of the two presented algorithms and compare them with those of several similar algorithms. Finally, simulation and experiment results demonstrate the excellence of the presented algorithms in NSI and NANC.

Modified Model of RLS Adaptive Filter for Noise Cancellation

Modified Model of RLS Adaptive Filter for Noise Cancellation

A new particle swarm optimization aided evolutionary digital filter for noise cancellation in early fault diagnosis of rotating machinery

Noise cancellation plays a crucial role in enhancing the signal-to-noise ratio (SNR) of fault characteristics, making it an essential step in fault diagnosis, particularly during the early stages of a fault. Among various adaptive noise cancellation (ANC) methods, evolutionary digital filter (EDF) stands out as an effective approach. EDF employs an adaptive algorithm based on biological evolutionary computation to control its operations. The quality of noise cancellation performance directly relies on the global optimization ability of EDF. However, conventional EDF and its improved versions mainly rely on random search strategies involving cloning and mating, which lack an explicit search direction akin to gradient-based methods. Consequently, substantial computational resources are wasted. To address this problem, an improved algorithm called particle swarm optimization-based evolutionary digital filter (PSO-EDF) is newly proposed. The central concept of this method is to integrate the PSO algorithm into the global optimization process of EDF. By introducing PSO, explicit search directions are provided to individuals adopting the cloning strategy, thus promoting cooperation and information sharing among individuals. The PSO-aided EDF significantly enhances the global optimization capability of ANC filter parameters. Through the proposed PSO-EDF method, the ability to achieve global optimization of ANC filter parameters is substantially improved. This approach mitigates the resource wastage associated with traditional EDF techniques, offering a more efficient and effective solution for noise cancellation in fault diagnosis.

Low Energy Less Area Less Delay Fixed Point LMS Method for Adaptive Noise Cancellation Filter

Present paper is about the high speed low complexity implementation derived by its architecture using least mean square (LMS) adaptive filtering. Here straight form LMS adaptive filter has almost the similar critical path as it is a reverse from of the counter path hoiver it has a fast coverage and also a loir register complication. Here critical path evaluation tells that no pipelining is necessary for implementation of straight form LMS adaptive filtering in most of the practical cases requires a realized extremely small adaptive delay and very high sampling rate. Here based on these finding LMS adaptive filtering is divided into 3 structural proposal designs. a) There is no adaption delay b) Only one adaption delay c) Only two adaption delay. Here first one includes least area and least energy per sample (EPS).

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

The proposed paper, presents the construction of adaptive noise cancellation filter based on gray wolf optimization (GWO) optimization technique.The relative investigation of different strategies uncovers that the presentation of GWO calculation is better in boisterous condition. The objective of proposed paper is structure ANC channel utilizing GWO method that improves association involving output with pure EMG signal.The results of proposed strategy are contrasted through gray wolf optimizer (GWO) and other evolutionary algorithms.The presentation of these calculations is assessed regarding signal-to-noise ratio (SSNR), mean square error (SMSE), maximum error (SME) mean, convergence rate (CR) plus correlation feature (Sr). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. The ANC filter based on GWO technique provides 28 dB improvement in output SNR, 81% reduction in MSE, and 84% lower ME as compared to reported ANC filter based on RLS algorithm. Further, ANC filter based on GWO technique provides 7 dB improvement in output SNR, 59.5% reduction in MSE, and 69.2% lower ME as compared to recently reported ANC filter based on ABC-MR algorithm.

Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network

This article aims to develop a more efficient adaptive filter for the active noise cancellation (ANC). A novel recurrent interval type-2 fuzzy brain emotional learning filter (RT2BELF) is proposed for achieving favourable filtering performance. The ANC is a method to eliminate noise by creating an anti-noise signal which has the same magnitude but opposite phase with the unwanted noise. In order to adapt to the change of the noise, the parameters for the RIT2BELF are online updated based on the adaptive laws, which are derived by the steepest descent gradient approach. The performance of the proposed ANC design method is successfully demonstrated based on numerical simulation results in the real signals. Finally, the superiority of the proposed method is confirmed by the results comparison with some noise cancellation methods.

Modified Model and Algorithm of LMS Adaptive Filter for Noise Cancellation

The present research investigates the innovative concept of LMS adaptive noise cancellation by means of a modified algorithm using an LMS adaptive filter along with their detailed analysis. Three types of equations viz. output, error, and weight update are used in the LMS algorithm. The error equation of traditional LMS algorithm is modified which gives better results as compared to traditional LMS algorithm and their types. The comparison parameters used in the present analysis are signal-to-noise ratio, mean square error, maximum absolute error, and energy ratio between signals of error and output signals. The proposed modified model was found to have a higher signal-to-noise ratio with respect to the traditional model of LMS adaptive noise cancellation. The signal-to-noise ratio of the proposed model has also been compared with some other types of LMS algorithms and was found better in most of the cases.

An Effective Method to Denoiseeeg, ECG and PPG Signals based on Meyer Wavelet Transform

In this analysis, the prevailing role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the constant and the discrete transform are considered in turn.A Wavelet denoising is functional on the original signal to eradicate high frequency noise, and then a process based on Meyer wavelet transform combined with adaptive filter is functional to eradicate the motion artifact. This approach uses Meyer Wavelet decomposition to extract the motion artifact, which is subsequently utilized as the reference input of an adaptive filter for noise cancellation. The technique diminishes the overhead of the circuit because it does not need a separate collection of reference input signal which link to noise. Testing results illustrate that this approach can efficiently remove motion artifact and make better the signal quality.

Hardware Reduction in Cascaded LMS Adaptive Filter for Noise Cancellation Using Feedback

The present work investigates the innovative concept of adaptive noise cancellation (ANC) using feedback connection of least-mean-square (LMS) adaptive filters for the sake of hardware reduction. The concept of cascading and feedback for real-time LMS-ANC are also described. The simulation model gives variation in the distinct signals of LMS-ANC like error signal, output signal and weights at various LMS filter parameters. An attempt has been made to provide solution in order to improve the performance of cascaded LMS adaptive noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm and feedback structure algorithm of LMS-ANC with different filter lengths and step sizes which provide high convergence speed of error signal. The signal-to-noise ratio for closed-loop LMS-ANC was found to be higher than single LMS-ANC system and equivalent to cascaded LMS-ANC. The novelty of the proposed model lies in reduction in hardware and low instantaneous power consumption making the model cost-effective as well as less complicated as compare to cascaded LMS-ANC.

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS) technology. Seismocardiography (SCG) has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs) can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females) using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions), three body postures (i.e., sitting, standing, and supine), and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h). The motion data (i.e., six-axis accelerometer and gyroscope) and heart rate (i.e., electrocardiogram (ECG)) were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main contributions are as follows: (1) the CNN model enhanced heart rate estimation with the benefits of automatic feature extraction from the data; (2) the proposed method was compared with the previous SCG method using signal processing; (3) the method was tested in 12 measurement conditions related to daily motion for a more practical application.

LMS Adaptive Filters for Noise Cancellation: A Review

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.

A Study of Sensitivity of a Least Mean Square Filter on its Tap Weight Vector Length and Step Size in Adaptively Cancelling Noise in an Electrocardiogram Signal

m (ECG) is a procedure that records the activity of the heart and presents it as an electrical signal. It is an immensely important diagnostic tool since any deviation from the characteristic shape of an ECG may point to a cardiac anomaly, making it important for instrument and processing to be very accurate. Since the nature of noise that could corrupt the ECG is non-stationary, Adaptive Noise Cancellation (ANC) filters are required. In this paper, the Adaptive Noise Cancellation algorithm Least Mean Squares (LMS) has been implemented to reduce the noise in a corrupted ECG signal. The parameter sensitivity of the filter on its result had been studied, how the values of tap weight vector length and step size of the filter influence the quality of the resultant signal of the filter. This has been done through simulations in MATLAB, the noise have been simulated in MATLAB and the ECG signals have been collected from the ECG-ID database at PhysioNet. For various values of tap weight vector length and step size the performance of the LMS filter is analyzed in terms of PRD and MSE and the observations are tabulated. At the end of this study parameter values are suggested that render the most optimum results.

ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

Abstract. Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

A Multitasking Control Algorithm for Grid-Connected Inverters in Distributed Generation Applications Using Adaptive Noise Cancellation Filters

This paper proposes a multitasking control algorithm for grid-connected inverters (GCIs) in distributed generation (DG) applications. A single-phase H-bridge voltage source inverter is used as a power electronic interface between the DG system and the grid. The proposed control algorithm operates the GCI in current control mode to achieve desired active power injection to the grid. In addition to active power injection, the proposed control algorithm has current harmonics mitigation and reactive power compensation capabilities for power quality enhancement. The control algorithm utilizes only the remaining capacity of the GCI for power quality improvement. The proposed control algorithm employs a current decomposition structure based on multiple adaptive noise cancellation (ANC) filters for extraction of harmonic and reactive currents of the local loads. The extracted harmonics are used in estimating compensating currents to be injected by the GCI. A single-phase phase-locked loop (PLL) using ANC filters has been developed to synchronize the GCI at fundamental frequency, which makes the proposed control algorithm adaptive to grid frequency fluctuations and immune to grid voltage distortions. The effectiveness of the proposed control is illustrated through computer simulation and experimental results. The influence of PLL dynamics on GCI performance has also been studied using results.

Design and Analysis of Cascaded LMS Adaptive Filters for Noise Cancellation

Adaptive filters have become active research area in the field of communication system. This paper investigates the innovative concept of adaptive noise cancellation (ANC) using cascaded form of least-mean-square (LMS) adaptive filters. The concept of cascading and its algorithm for real-time LMS-ANC are also described in detail. The model of the cascaded LMS-ANC is designed and simulated on MATLAB Simulink. The simulation model gives variation in the distinct signals of LMS-ANC like error, output and weights at various LMS filter parameters. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately and results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm with variable step size and different initial weight of filters which provides high convergence speed of error signal. This paper also includes the derivation for the convergence rate at different conditions and concludes that cascaded LMS-ANC results in higher convergence rate and better output signal as compared to single LMS-ANC. Higher signal-to-noise ratio for cascaded system is obtained for cascaded LMS-ANC as compared to that of single LMS-ANC system.

Performance Analysis of Adaptive Filters for Noise Cancellation in Audio Signal for Hearing Aid Application

Performance Analysis of Adaptive Filters for Noise Cancellation in Audio Signal for Hearing Aid Application

Design of ANC filter using modified cuckoo search technique for ECG signal enhancement

In this work, the design of an adaptive noise canceller (ANC) filter is presented using modified cuckoo search (MCS) optimization technique. The proposed scheme is applied for de-noising of ECG signals. Our simulation results reveal that the ANC filter based on MCS algorithm provides superior performance than other optimization techniques used to enhance the ECG signal. The performance of ANC filter is compared with other reported algorithms by evaluating the fidelity parameters such as the signal to noise ratio (SNR), maximum error (ME) and mean square error (MSE). The proposed ANC filter design with MCS scheme gives 18% improvement in output SNR, 87% decrease in ME, and 85% reduction in MSE over the recently reported Hilbert Huang Transform (HHT) technique.

Adaptive filtering using PSO, MPSO and ABC algorithms for ECG signal

In this paper, the design of Adaptive Noise Canceller (ANC) filter using evolutionary algorithms such as Particle Swarm Optimisation (PSO), Modified PSO (MPSO) and Artificial Bee Colony (ABC) algorithms is presented. The performance of proposed ANC filter is tested on a corrupted ECG signal. Based on simulation results, it is observed that the ANC filter constructed using these evolutionary algorithms achieves significant improvement in fidelity parameters such as SNR, MSE, ME and correlation factor when compared with other reported techniques in literature. ANC filter based on ABC with scaling factor provides 78% improvement in output SNR, 76% and 87% reduction in MSE and ME, respectively, as compared to ANC filter based on PSO. Further, ANC filter designed using ABC technique enhances the correlation between output and pure ECG signal.

Multiple Spectral Peak Tracking for Heart Rate Monitoring from Photoplethysmography Signal During Intensive Physical Exercise

We propose a multiple initialization based spectral peak tracking (MISPT) technique for heart rate monitoring from photoplethysmography (PPG) signal. MISPT is applied on the PPG signal after removing the motion artifact using an adaptive noise cancellation filter. MISPT yields several estimates of the heart rate trajectory from the spectrogram of the denoised PPG signal which are finally combined using a novel measure called trajectory strength. Multiple initializations help in correcting erroneous heart rate trajectories unlike the typical SPT which uses only single initialization. Experiments on the PPG data from 12 subjects recorded during intensive physical exercise show that the MISPT based heart rate monitoring indeed yields a better heart rate estimate compared to the SPT with single initialization. On the 12 datasets MISPT results in an average absolute error of 1.11 BPM which is lower than 1.28 BPM obtained by the state-of-the-art online heart rate monitoring algorithm.