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Background: Acoustic emission (AE) is a widely used non-destructive testing (NDT) technique for materials and structures, particularly for damage detection and health monitoring of structures. The interference of noise signals with the damage-related AE signals is one of the elements impacting the performance of the AE approach used for real-time monitoring of structures, leading to erroneous inspection findings. Objective: This article addresses the problem of noise reduction in AE signals by proposing a unique technique based on the bio-inspired Antlion Optimization (ALO) algorithm. Method: The ALO algorithm is based on the hunting instincts of antlions. In this research, ALO is used to reduce the adaptive filter's error fitness in an adaptive noise cancellation (ANC) setup, and the suggested method is used to filter AE signals generated during concrete compression and composite drilling tests. Results: The performance of the ALO-based ANC is assessed quantitatively using the signal to noise ratio (SNR), peak signal to noise ratio (PSNR), and mean square error (MSE), as well as qualitatively through visual inspection of spectrograms and waveforms. The results are compared to those obtained using the gradient-based recursive least squares technique (RLS). Conclusion: The ALO-based technique is of great significance to reduce the noise in AE signals by dramatically lowering the MSE and increasing the SNR when compared to the gradient-based approach RLS. [ABSTRACT FROM AUTHOR]

Recursive Least Square (RLS) is a popular algorithm for noise cancellation in non-stationary signals; however, it demands more computational resources and more difficult mathematical operations. Also, RLS has less performance stability. The present research explores a novel idea of an RLS adaptive noise cancellation through a modified method that uses an RLS adaptive filter by introducing an additional constant multiplier, along with their in-depth analysis. The proposed algorithm is analyzed using three primary performance metrics: mean square error (MSE), signal-to-noise ratio (SNR), and convergence rate. The obtained results demonstrate that the proposed algorithm has reduced the MSE by 79.65%, which leads to an improvement in SNR by 86.16% compared to the traditional RLS algorithm. The additional constant multiplier is optimized for SNR, and the optimized value is found to be equal to 0.65, which gives the best possible SNR value of 19.38 dB. Also, the proposed algorithm has been successfully applied to a real-world scenario in acoustic echo cancellation (AEC). The experimental setup for the echo canceller is simulated on MATLAB to measure the echo canceller efficiency in terms of MSE and echo return loss enhancement. Based on performance evaluation, the proposed algorithm has been found to better echo cancellation in AEC as compared to traditional RLS. [ABSTRACT FROM AUTHOR]

The article contains the operational principle of adaptive active wideband acoustic noise cancellation system. The algorithm of operation of these systems provides to work with changes of the medium, acoustic or electrical parameters of their elements, is described. A modification of this algorithm is proposed, which can increase the efficiency of the system and reduce the level of residual noise during system calibration. The results of simulation the modified algorithm of an active noise cancellation system are presented. [ABSTRACT FROM AUTHOR]

The proliferation of cardiac signals, such as high-resolution electrocardiograms (HRECGs), ultra-high-frequency ECGs (UHF–ECGs), and intracardiac electrograms (IEGMs) assist cardiologists in the prognosis of critical cardiac diseases. However, the accuracies of such diagnoses depend on the signal qualities, which are often corrupted by artifacts, such as the power line interference (PLI) and its harmonics. Therefore, state space adaptive filters are applied for the effective removal of PLI and its harmonics. Moreover, the state space adaptive filter does not require any reference signal for the extraction of desired cardiac signals from the observed noisy signal. Nevertheless, the state space adaptive filter inherits high computational complexity; therefore, filtration of the increased number of PLI harmonics bestows an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed framework for the state space least mean square with adoptive memory (PD–SSLMSWAM) is introduced, which runs the computationally expensive SSLMSWAM adaptive filter parallelly. The proposed architecture efficiently removes the PLI along with its harmonics even if the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSLMSWAM scheme provides less computational costs as compared to the sequentially operated SSLMSWAM algorithm. A comparison was drawn among the proposed PD–SSLMSWAM, sequentially operated SSLMSWAM, and state space normalized least mean square (SSNLMS) adaptive filters in terms of qualitative and quantitative performances. The simulation results show that the proposed PD–SSLMSWAM architecture provides almost the same qualitative and quantitative performances as those of the sequentially operated SSLMSWAM algorithm with less computational costs. Moreover, the proposed PD–SSLMSWAM achieves better qualitative and quantitative performances as compared to the SSNLMS adaptive filter. [ABSTRACT FROM AUTHOR]

Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article. [ABSTRACT FROM AUTHOR]

• This study introduces the effective utilization of the highly efficient RSA [41] to optimize the filter weights of the proposed cascaded ANC implemented with an infinite impulse response (IIR) digital filter structure. The goal is to eliminate various artefacts from noise-contaminated ECG signals. • Remarkably, RSA has not been previously used to optimize AFs in ECG noise elimination. Moreover, the proposed optimally designed cascaded ANC is employed for the first time to effectively suppress various artefacts, including BW, EM, MA, PLI, and AWGN. • The effectiveness of the RSA-optimized ANC is compared against competitive techniques such as artificial bee colony (ABC) [42] and flower pollination algorithm (FPA) [43] , showcasing superior performance. Notably, the proposed model outperforms all documented methods in existing ECG noise cancellation literature. • To assess the robustness of the proposed ANC, varying levels of AWGN covering signal-to-noise ratio (SNR) values from −5 to 10 dB, as well as real noises like BW, EM, and MA sourced from the MIT/BIH noise stress test database (NSTDB) [44] , along with simulated 50 Hz PLI noise, have been introduced to the raw ECG signals from the PhysioNet datasets [45]. These contaminated signals create a diverse set of noisy ECG data for evaluating the noise suppression capability of the proposed multistage ANC. • The effectiveness of the proposed ECG noise cancellation technique in preserving crucial heart-health information is further explored through the implementation of a superlet transform (SLT) [46] and a deep CNN [47] based ECG multi-class diagnosis system. This system proficiently classifies arrhythmia (ARR) and normal sinus rhythm (NSR) after noise elimination, compared to the artefact-contaminated ECG. Electrocardiogram (ECG) signals are widely used in medical diagnostics to analyze the electrical activity of the heart. However, in clinical practice, these signals are often contaminated by various types of noise and artefacts, such as powerline interference (PLI), muscle artefacts (MA), electrode motion (EM), and baseline wander (BW), which can significantly modify the morphology of ECG signal. Therefore, the development of efficient noise cancellation technique is crucial for reliable interpretation of ECG signals in order to diagnose the cardiac diseases automatically. This study proposes an efficient approach for noise cancellation in ECG signals using an optimally designed adaptive noise cancellation (ANC) technique. The optimized ANC filter dynamically adjusts its coefficients based on the input signal to effectively eliminate unwanted noise and artefact components. The proposed method utilizes the reptile search algorithm (RSA), which updates the filter coefficients iteratively to minimize the root mean square error (RMSE) between the filtered signal and the desired signal. To evaluate the performance of the proposed method, experiments were conducted using real PhysioNet ECG signals contaminated with true BW, EM, MA, PLI, and additive white Gaussian noise (AWGN). The proposed RSA-based ANC demonstrates superiority over artificial bee colony (ABC) and flower pollination algorithm (FPA)-based ANC techniques, along with state-of-the-art ECG noise removal methods. This highlights the efficacy of the proposed RSA-based ANC technique in noise reduction while preserving vital cardiac information through standard metrics. Moreover, the proposed ECG noise cancellation technique's ability to preserve crucial heart-health information is further confirmed through the implemented superlet transform (SLT) and deep CNN-based ECG multi-class diagnosis system. The proposed system effectively classifies arrhythmia (ARR) and normal sinus rhythm (NSR) after noise elimination from the contaminated ECG. [ABSTRACT FROM AUTHOR]

In view of the problem of serious distortion of gravitational acceleration information caused by bottom-hole vibration and noise during measurement while drilling, a method that combines generalized regression neural network and improved recursive least squares is proposed to remove noise. Using the principle of mutual cancellation of adaptive noise, the accelerometer measurement signal and drilling tool vibration signal are used as the main noise model, and the drilling tool vibration signal processed by the generalized regression neural network is used as the auxiliary noise model. Recursive least squares filter processing is used to improve the accuracy of drilling tool accelerometer measurement and calculate real-time drilling attitude information. The simulation results show that the proposed algorithm can effectively remove the vibration and noise of the drilling tool accelerometer and improve the attitude measurement accuracy. After denoising, the calculated deviation angle error is within 1.45°, and the tool face angle error is within 1.65°. [ABSTRACT FROM AUTHOR]

This article proposes a frequency-dependent adaptive noise cancellation-based tracking controller for the trajectories’ stabilization of a flexible rotor supported on full lubricated journal bearings. The aim is to provide a general solution to the control problem by introducing a modular and closed-form formulation. Starting from the medialization of the journal bearings nonlinear fluid film forces acting on the rotor during the motion, a manual matrix solution of equations is achieved. The formulation takes in account the journal bearing lubricating problem in terms of both cavitated and uncavitated short bearing analytical solutions. The adaptive vibration-reducing tracking controller is based on a quick online estimation of the mutual forces acting on the rotor, in the presence of the imposed frequency disturbances, under the assumption of a known exosystem dynamics. The controller acts as a force observer and modal disturbance estimator. Then, it is independent of the operating conditions of the bearings. A mathematical proof and numerical simulations are provided to validate the proposed control. The control response results very fast. [ABSTRACT FROM AUTHOR]

Cardiac signals are often corrupted by artefacts like power line interference (PLI) which may mislead the cardiologists to correctly diagnose the critical cardiac diseases. The cardiac signals like high resolution electrocardiogram (HRECG), ultra-high frequency ECG (UHF-ECG) and intracardiac electrograms are the specialized techniques in which higher frequency component of interest up to 1 KHz are observed. Therefore, a state space recursive least square (SSRLS) adaptive algorithm is applied for the removal of PLI and its harmonics. The SSRLS algorithm is an effective approach which extracts the desired cardiac signals from the observed signal without any need of reference signal. However, SSRLS is inherited computational heavy algorithm; therefore, filtration of increased number of PLI harmonics bestow an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed SSRLS (PD-SSRLS) algorithm is introduced which runs the computationally expensive SSRLS adaptive algorithm parallely. The proposed architecture efficiently removes the PLI along with its harmonics even the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSRLS scheme provides less computational cost as compared to sequentially operated SSRLS algorithm. A comparison has been drawn between the proposed PD-SSRLS algorithm and sequentially operated SSRLS algorithm in term of qualitative and quantitative performances. The simulation results show that the proposed PD-SSRLS architecture provides almost same qualitative and quantitative performances than that of sequentially operated SSRLS algorithm with less computational cost. [ABSTRACT FROM AUTHOR]

In this article, a method combining array signal processing and adaptive noise cancellation is proposed to suppress the ambient interferences of in-situ measurement. First, the directions of arrival and classes of all signals are obtained by performing blind source separation and sparse reconstruction on the received signals. Then, the received signals are reused to form two different data channels. In both channels, the interferences that are coherent with the radiated signals of the equipment under test and have larger amplitudes are eliminated based on null trap forming, whereas the coherent interferences with smaller amplitudes are ignored after being attenuated. Besides, the other interferences are suppressed by the adaptive noise cancellation based on their correlation in the two channels. For transient signals, overlapping window technique is used to avoid the losses of the spectrums. Each frame of the signals is cyclically extended to provide sufficient points for adaptive noise cancellation. The proposed method achieves the suppression of multisource ambient interferences with fewer array elements. Simulation and experiment results are given to verify the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Motion artefacts in electrocardiographic (ECG) signal are suppressed mainly by adaptive noise cancellation and wavelet denoising. While the former requires a motion sensor in addition to ECG electrodes, the latter removes some of the desired low-frequency components in the signal. In this paper spectral trimming technique is being introduced for suppressing the motion artefacts in stress electrocardiography. In this method, Fourier spectral coefficients up to 1.221 Hz of noisy signal are trimmed on the basis of template derived from resting ECG signal in the same subject. The proposed spectral trimming technique has yielded the lowest value of mean ± standard deviation for root mean square error (18.92 ± 8.71) and highest value of the signal to noise ratio (6.439 ± 4.266) as compared to other three methods, namely adaptive noise cancellation, wavelet decomposition and adaptive line enhancement with compatible value of correlation coefficient. Subsequently, spectral trimming technique has been implemented in real-time (deferred by 8.2 s) application for stress electrocardiography. Spectral trimming technique thus offers a method of choice for motion artefact suppression in offline as well as deferred online applications. This method takes care of the limitations of conventional methods such as adaptive noise cancellation or wavelet denoising for suppressing motion artefacts in stress electrocardiography. [ABSTRACT FROM AUTHOR]

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking single-layer CMAC models into multiple layers to form a DCMAC model, and derive a backpropagation training algorithm to learn the DCMAC parameters. Compared with the conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the proposed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that the DCMAC provides enhanced capability to model channel characteristics.

Algorithms for separation of heart sounds from background lung sound noises are vital for accurate diagnosis of heart diseases. In this paper, an improved adaptive noise cancellation technique based on the Least Mean Square (LMS) algorithm is used to separate heart sounds from lung sounds. The step size parameter in the LMS algorithm is optimally chosen using a hybrid Nelder-Mead (H-NM) optimization algorithm. The NM algorithm is initialized with a good initial solution by using computationally cheap random search to compute a rough estimate of the global minimum. Initialization of the NM algorithm with a good initial guess avoided convergence to shallow local minima and improved the quality of the final solution. The effects of using two state-of-the-arts biologically inspired heuristic optimization algorithms instead of the H-NM algorithm and three variants of the standard LMS algorithm are investigated. The correlation coefficient between the ideal and filtered heart sound signal and running time-complexity of different algorithms are taken as the metric for comparison of different heart sound separation approaches. Simulation results indicate that the approach presented in this paper performs significantly better than a variety of alternate approaches on heart sound separation problems.

To deal with the problem of resource occupation and computing speed of adaptive noise cancellation algorithm on FPGA, signum-LMS algorithm that can reduce the computation complexity is proposed after analyzing its advantages over the traditional LMS algorithm, and then an adaptive noise canceller based on state machine and signum-LMS algorithm is designed. First, the audio codec chip WM8731 is used to sample the speech signal with noise and noise signal, and then the canceller based on FPGA runs the signum-LMS algorithm to reduce the noise signal, finally, the audio codec output the clean speech signal. Experimental results show that the noise canceller can reduce the noise quickly and effectively. [ABSTRACT FROM AUTHOR]

Heart Sound Signal (HSS) is considered as one of the important bio-signals. It carries vital information about the heart functions. For bio-acoustic observations, the HSS is diagnosed and recorded with auscultatory procedures. During auscultation, the noisy components gets added along with the reading. The physician’s individual diagnostic experience, ecological noise and the intersection of heart and lung sound signal (LSS) are considered as the major noisy components in HSS diagnosis. Suppression of LSS from the HSS is a challenging task. Due to its quasi stationary nature, adaptive filtering techniques are used for the noise removal. In this paper, Recursive Least Square (RLS) adaptive algorithm is proposed to obtain the HSS from the noisy mixture. Faster convergence is a benefit in selecting RLS algorithm over other adaptive algorithms. The forgetting factor is one of the important parameters of RLS which defines the convergence. The RLS performance is improved by choosing an optimal forgetting factor. A Particle Swarm Optimization (PSO) based search algorithms are deployed for optimization. To enhance the implementation time, a Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNL-PSO) is analysed. In DNL-PSO, each particle studies from its knowledge in dynamically varying neighbourhood that prevents early convergence. The normal HSS with different LSS interference is taken to assess the RLS filter performance. In this paper, the RLS algorithm performance is compared with Least Mean Square (LMS) adaptive algorithms. Various metrics are used to compare the performance of both RLS and optimization algorithms. [ABSTRACT FROM AUTHOR]

Energy detector is the simplest spectrum sensing technique in cognitive radio in terms of computation complexity as it is developed assuming only channel noises for the detection of primary user signal. However in realistic sense the typical communication system comprises of various other sources of noise (like thermal noise) that significantly affect the sensing performance of the detector. The conventional energy detector is also not a coherent detector as the attributes of the primary user signal is unknown. Considering these limitations a system is proposed that can have the knowledge of the primary user signal attributes under realistic situation at the secondary user (SU) receiver thereby enhancing the accuracy of the sensing significantly. In this work a new coherent energy detector system architecture is proposed that first separates the signal components from a noisy signals (non Gaussian noise) at SU receiver, then from the separated signals it estimates the signal attributes (power of the signal, noise variance, signal-to-noise ratio) required for conventional energy detector. The work is developed as a hybrid system model utilizing blind source separation algorithm and neural network (NN) known hereafter as 'integrated blind signal separation (BSS)-NN based energy detector'. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) is a highly sensitive instrument and is frequently corrupted with eye blinks. Methods based on adaptive noise cancellation (ANC) and discrete wavelet transform (DWT) have been used as a standard technique for removal of eye blink artefacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artefactual components from the EEG signal. The proposed work describes an automated windowed method with a window size of 0.45 s that is slid forward and fed to a support vector machine (SVM) classifier for identification of artefacts, after the identification of artefacts, it is fed to an autoencoder for correction of artefacts. The proposed method is evaluated on the data collected from the project entitled 'Analysis of Brain Waves and Development of Intelligent Model for Silent Speech Recognition'. From the results it is observed that the proposed method performs better in identifying and removing artefactual components from EEG data than existing wavelet and ANC based methods. The proposed method does not require the application of independent component analysis (ICA) before processing and can be applied to multiple channels in parallel. [ABSTRACT FROM AUTHOR]

In this paper, a new optimal variable step size-adaptive regularized-affine projection algorithm (OVSS-AR-APA) is proposed and compared with existing variable step size APA family of algorithms. Instead of having a constant regularization parameter, the modified sigmoid function variation is used to choose the optimum regularization parameter in the proposed algorithm. Here, the algorithm dynamically adjusts the regularization parameter according to input noise variations. Also, the exponentially weighted value of the error with variable regularization parameter is used for making the step size parameter variable. The existing and proposed algorithms are implemented for noise cancellation from audio signals at various input SNR levels for different filter orders. It is observed from simulations that the proposed algorithm outperforms the existing algorithms in terms of SNR improvements, MSE and convergence rate at all input SNR levels for different filter orders with moderate computational complexity. The OVSS-AR-APA algorithm shows a maximum of 21.54 dB of improvement in SNR at -20dB input SNR level at filter order 5. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The elimination or reduction of audio signal noise and interference is a significant challenge in signal processing. Researchers have introduced various methods, including those based on adaptive filters, to address this issue. However, these existing methods still exhibit limitations in effectively removing specific types of noise. In this study, we aim to develop a robust enhancement of the subband version of the adaptive filter using an adaptive framework founded on the recursive least squares (RLS) filter. Our proposed method incorporates from natural logarithm and a hyperbolic cosine loss function within the cost function, resulting in superior noise reduction compared to existing techniques for speech enhancement. Furthermore, the mean and mean-square convergence of the proposed method are demonstrated theoretically. To evaluate the performance of our approach, we conducted experiments comparing it against several other methods proposed in this field. The results obtained convincingly support the efficacy of our proposed method, highlighting its potential for advanced noise cancellation in audio signal processing. • A robust enhancement of the subband version of the adaptive filter on the recursive least squares (RLS) filter is proposed. • The proposed method incorporates from natural logarithm and hyperbolic cosine loss function. • Performance analysis is discussed theoretically. • This method is proposed for advanced noise cancellation in audio signal processing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

Electrocardiogram (ECG) is a non-invasive method to monitor electric activities inside the heart. The signals observed on the surface of human body have very low amplitude, and thus, ECG is highly vulnerable to noise. One of the most devastating noise is power line interference (PLI) and its harmonics, which are interlaced with ECG signal even if the ECG equipment is operated on battery. The problem is further complicated when the frequency of PLI is not static, making the conventional notch filter completely ineffective. High-resolution electrocardiogram (HRECG) is a specialized technique in which higher frequency components present in the ECG signal are observed; here, we need to eliminate the harmonics of PLI as well. In this paper, we propose an intelligent adaptive noise rejection filter, which tracks and eliminates PLI as well as its harmonics. The proposed system can estimate the frequency of PLI and tune the adaptive filter for precise elimination of PLI as well as its harmonics without the requirement of an auxiliary reference input. The proposed system is based on recursive state space model, inherited with less computational complexity and performs well in a non-stationary environment. The proposed system responds well to the ongoing variations in amplitude and frequency of PLI present in the HRECG signal as well as intracardiac signal. The proposed system does not require any reference signal for tracking the PLI and its harmonics, and it is capable to self-adjust its tracking frequency for highly precise filtration of first, third, and fifth harmonics of PLI.

Low frequency communication, taking advantage of the features of low frequency electromagnetic signals in near field, is widely used in through-the-earth (TTE) wireless applications. However, the low frequency non-Gaussian noise severely limits the communication performance. In this paper, an adaptive noise cancellation algorithm based on time delay estimation (ANC-TDE) and maximum correntropy criterion (MCC) is proposed. The explicit time delay estimation algorithm based on MCC (MCC-ETDE) is used to estimate the time-varying delay and the noise correlationship between the primary input and reference inputs. With a reference noise selected and time delay compensated, the non-Gaussian noise is canceled by adaptive filter based on MCC. The proposed algorithm is implemented on field programmable gate array (FPGA) and the performance is evaluated by simulation and experiment. As shown in the results, the ANC-TDE algorithm can reduce the complexity of ANC filter and adaptively compensate the varied time delay between the primary input and reference input. With the time delay compensated, the ANC-TDE algorithm has better performance in non-Gaussian noise environment and is more suitable for real-time systems. [ABSTRACT FROM AUTHOR]

Signal de-noising has been sparked and given a great attention by signal processing community since its applications are found in a diverse range of digital signal processing and computer vision problems. To improve signal quality, phase, frequency and power are the important features that should be preserved during the de-noising process. Adaptive filters have been widely used for this purpose due to their ability to cancel out noise signal from the corrupted one precisely. This paper presents a robust adaptive estimator for solving the problem of signal noise cancellation, based on a new adaptive algorithm derived from a new constrained optimization. Simulation results evaluated using MATLAB show that the proposed algorithm is appropriate for several forms of signals contaminated by diverse levels of noise power. The performance of the proposed algorithm is illustrated to be preferable in terms of the power signal to noise ratio, mean square error and time of speed convergence of filter parameters. It is compared to other conventional approaches such as least mean square and normalized least mean square algorithms with various values of white noise power, variance. It exhibits lower steady-state error and faster convergent time than the other implementations. Finally, an efficient performance is achieved comparable with recursive least square and affine projection algorithms. [ABSTRACT FROM AUTHOR]