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This paper proposes a digital morphological filtering method based on a novel structuring element (SE) formulated using fractional Fourier transform (FrFT) and cross-convolution of window functions. The highlighting feature of this newly formulated filter is its flexibility in adding an adaptive nature to classical morphological filtering. Until now, every method listed in the literature makes a convenient assumption that noises corrupting ECG signal are Gaussian and model the filter around this assumption. Addressing this shortcoming, the first-of-its-kind filter adopts the α -stable distribution model (of which Gaussian distribution is a special case) of noise to better replicate the real-time noises interfering with ECG signals. The designed filter can suppress the noise and adapt to the changes in ECG signal morphology for better reconstruction. The proposed filter is tested on MIT-BIH Arrhythmia Database. The simulation results show improved performance in several quantitative metrics, demonstrating the superiority of our suggested method over the currently used state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In the past decade, the augmented complex-valued least mean square adaptive filtering algorithm based on the minimum mean square error criterion and widely-linear model has attracted much attentions due to its simplicity and applicability to non-circular signals. However, the system noise with impulse characteristics will dramatically affect the convergence of the augmented complex-valued least mean square algorithm. Recently, a robust widely-linear complex-valued least mean M-estimate algorithm has been proposed, which significantly improves the robustness of the augmented complex-valued least mean square algorithm to impulsive noise. But it should be noted that the widely-linear complex-valued least mean M-estimate algorithm with fixed step size faces the trade-off between convergence speed and steady-state accuracy. To overcome this drawback, a variable step size widely-linear complex-valued least mean M-estimate algorithm is presented in this paper, in which the optimal step size at each iteration is obtained by maximizing the difference between the mean square deviations of the WL-CLMM algorithm at adjacent moments. In order to reveal the statistical convergence behavior of the proposed variable-step-size widely-linear complex-valued least mean M-estimate algorithm to better guide the parameter selection in practical applications. Theoretical transient and steady-state mean-square deviation convergence behaviors of the variable-step-size widely-linear complex-valued least mean M-estimate algorithm are analyzed and the corresponding mean-square deviation expressions are also derived. Computer simulations on system identification and stereophonic acoustic echo cancellation in impulsive noise environments confirm the validity of the analysis results and the performance improvement from the variable step size strategy. [ABSTRACT FROM AUTHOR]

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture search algorithms or just manually, which are short of efficiency. In this paper, we propose a novel interpretable task-inspired adaptive filter pruning method for neural networks to solve the above problems. First, we treat filters as semantic detectors and develop the task-inspired importance criteria by evaluating correlations between input tasks and feature maps, and observing the information flow through filters between adjacent layers. Second, we refer to the human neurobiological mechanism for the better interpretability, where the retained first layer filters act as individual information receivers. Third, inspired by the phenomenon that each filter has a deterministic impact on FLOPs and network parameters, we provide an efficient adaptive compression ratio allocation strategy based on differentiable pruning approximation under multiple budget constraints, as well as considering the performance objective. The proposed method is validated with extensive experiments on the state-of-the-art neural networks, which significantly outperforms all the existing filter pruning methods and achieves the best trade-off between neural network compression and task performance. With ResNet-50 on ImageNet, our approach reduces 75.49% parameters and 70.90% FLOPs, only suffering from 2.31% performance degradation. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is presented for the nonparametric variable step-size normalized least-mean-square (NP-VSS-NLMS) algorithm. This algorithm has demonstrated potential in practical applications and hence a deeper understanding of its behavior becomes crucial. In this context, model expressions are obtained for characterizing the algorithm behavior in the transient phase as well as in the steady state, considering a system identification problem and Gaussian input data. Such expressions reveal interesting algorithm characteristics that are useful for establishing design guidelines and for the advancement of more refined algorithms. Simulation results for various operating scenarios ratified both the model's accuracy and the algorithm's superior performance relative to other recent and relevant algorithms from the literature. [ABSTRACT FROM AUTHOR]

A novel multi-combined-step-size normalized subband adaptive filtering algorithm is proposed. Different from the traditional combined-step-size method, by employing a variable mixing parameter on each subband, our designed algorithm is able to combine a large step size with a small one more effectively for each subband. The subband mixing parameters are derived from the variance of the noise-free a priori subband error signals. In this design, a noniterative shrinkage strategy is also utilized to estimate the noise-free a priori subband error signals. Moreover, the mean-square and steady-state performances of the proposed algorithm are studied. Simulation results illustrate that the proposed algorithm outperforms other algorithms mentioned in this paper in terms of tracking capability, convergence speed and steady-state error. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an improved high-speed adaptive filter is proposed and implemented using a field-programmable gate array platform. Specifically, a new filter structure combining systolic and convex architectures has been analyzed and compared with conventional filter architectures. The new filter structure efficiently removes power line interference noise from electrocardiogram signals at high convergence speeds. The systolic architecture is used to improve the convergence speed of the filter, and the convex architecture is used in combination to improve the signal-to-noise ratio of the filter. One fast filter was designed using the retimed delay recursive least square algorithm (recursive least square filter design with systolic architecture), and it was combined with one slow filter designed using the least mean square algorithm based on convex combination architecture. The proposed filter architectures are assessed for electrocardiogram noise cancellation, obtained from the MIT-BIH database, and the performance is compared with various filter structures in terms of the signal-to-noise ratio, convergence speed, learning behaviors, and complexity. The results show an improvement in signal-to-noise ratio of 24.2% and an increase in convergence speed of 50% when compared with conventional filter structures. [ABSTRACT FROM AUTHOR]

The standard proportionate normalized least mean square (PNLMS) adaptive algorithm suffers from convergence performance limitation due to a constant step-size during the convergence period. In this paper, a switching-based variable step-size PNLMS is proposed to improve the convergence performance in sparse system identification. To adjust the step-size, the convergence performance of PNLMS is first analysed in the statistical sense and by exploiting the analysis, a switching-based method is then proposed, which brings about a fast convergence towards the desired steady-state mean-square weight deviation. The step-size reduces during the convergence period in a few steps, while in the case of abrupt change in the system impulse response, the step-size increases to its initial value. A sub-band version of the proposed adaptive algorithm is further proposed for highly correlated input signals. Simulation results confirm the superiority of the proposed full-band and sub-band algorithms in terms of convergence performance compared to some competing adaptive algorithms. [ABSTRACT FROM AUTHOR]

A method of determining the optimal kernel size for filtering noise in vortex dominated flow-fields, as found in the cardiac chambers is presented in this paper. Using synthetic flow fields generated using harmonic functions and perturbed using Gaussian noises of different amplitudes and spreads, the effect of kernel size on noise removal using the Median filter is tested systematically. It is shown that there exists an optimal kernel size at which the Median filter works best. The size of the optimal kernel is shown to be related to the vortex size. When applied to MRI generated cardiac flow-fields, the approach is seen to reveal underlying vortex patterns thereby aiding as an effective tool in the diagnosis and prognosis of cardiac diseases based on vortices as clinical biomarkers. The behavior of the restored cardiac flow fields which are filtered with the optimal kernel size and also with some values preceding and succeeding it are similar to that observed in studies with synthetic flow fields. This confirms that the optimal size of the kernel is related to the cardiac vortex size as is observed in the case of synthetic flow fields. [ABSTRACT FROM AUTHOR]

Frequency domain filtered-x least mean square algorithms can reduce the computational complexity of the time domain counterpart with long filters; however, they suffer from large block delay, additional quantization error due to large size transformations and implementation difficulties in existing DSP hardware. In this paper, a time–frequency domain flexible structure is proposed using the partitioned block frequency domain adaptive filtering technique, which has no signal path delay and is well suited for low-cost DSP implementation. The proposed structure divides the long filters into many equal partitions and carries out the control filter update in frequency domain while generating the control signal in both time and frequency domains, thereby eliminating the forward path delay completely while maintaining low computational complexity. The proposed structure has a potential benefit for controlling broadband noise, where the causality constraint is more important. The simulation results using the measured acoustic paths demonstrate that the proposed structure maintains similar control performance as that of the time domain algorithm but with much less computational complexity. Furthermore, the tracking performance of the proposed structure under different levels of measurement noise is investigated. [ABSTRACT FROM AUTHOR]

Under α stable distribution impulse noise environment, the least mean p-power algorithm (LMP) cannot handle the instability of the algorithm well, due to the large amplitude of the input signal and the large number of useless small weight coefficients in the sparse channel, which delay the convergence speed of the algorithm. In this paper, we develop a new adaptive filtering algorithm, named the proportional normalization least mean p-power (PNLMP) adaptive filtering algorithm. We first introduce a step size control matrix to improve the overall convergence speed and convergence accuracy of the algorithm. Next, to reduce the influence of a large impulse response on the LMP algorithm, a high-order tongue-line function about error is introduced in the normalization processing. Finally, we extend the tongue-line function to ensure that the cost function can also switch freely between V-shaped and M-shaped. This new algorithm overcomes the problem that the traditional LMP algorithm is only applicable to a specific environment and solves the problem of slow convergence speed and low convergence accuracy. Under α stable distribution impulse noise environment, the simulations show that the PNLMP algorithm can improve the convergence speed and stronger system tracking capability compared with existing algorithms, overcoming the problems of slow overall convergence and low convergence accuracy in the traditional algorithm. [ABSTRACT FROM AUTHOR]

Recording the speech signals of Parkinson's Disease (PD)-affected patients is challenging due to the surrounding noise. Therefore there is a need to denoise the signals. This paper proposes an Adaptive Noise Canceller-based model for signal denoising. This paper introduces an optimal adaptive filter structure using a signed LMS algorithm to compute the best estimate of a clean signal. A noise-corrupted signal is sent across multiple adaptive filters connected in series. Multiple stages are added automatically, and the filtering algorithm for each stage is also adjusted automatically. The proposed multi-stage switched adaptive filter model is tested for reducing the noise from a speech signal recorded from Parkinson's Disease-affected patients and corrupted by Gaussian signals of different input SNR levels. The simulation results prove that the proposed filter model performs remarkably well and provides 20–30 dB higher SNR values than the existing cascaded LMS filter models. The MSE value is improved by 85–97%, and the PSNR values are increased by 7 dB. Using the Sign LMS algorithm in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost. [ABSTRACT FROM AUTHOR]

This paper presents a robust distributed adaptive algorithm called diffusion recursive least M-estimate (DRLM), which enhances the robustness of the diffusion recursive least square (DRLS) algorithm against impulsive noise by incorporating the strong resistance to impulsive noise of the modified Huber function (MHF). However, the tracking speed of the proposed DRLM algorithm is not favorable in scenario where the network parameter vector of interest changes abruptly. To overcome this drawback, a new variable forgetting factor (VFF) strategy is devised and the VFF-DRLM algorithm is obtained. Then, the asymptotic unbiasedness and steady-state network mean square deviation (NMSD) performance of the DRLM algorithm are analyzed. Finally, the accuracy of the steady-state analysis result of the DRLM algorithm and the superiority of the proposed algorithms compared to other competing algorithms are demonstrated by extensive numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the significance of restoring harmonics of the fundamental frequency (pitch) in the deep neural network (DNN)-based speech enhancement. The parameters of the DNN can be estimated by minimizing the mask loss, but it does not restore the pitch harmonics, especially at higher frequencies. In this paper, we propose to restore the pitch harmonics in the spectral domain by minimizing cepstral loss around the pitch peak. Restoring the cepstral pitch peak, in turn, helps in restoring the pitch harmonics in the enhanced spectrum. The proposed cepstral pitch-peak loss acts as an adaptive comb filter on voiced segments and emphasizes the pitch harmonics in the speech spectrum. The network parameters are estimated using a combination of mask loss and cepstral pitch-peak loss. We show that this combination offers the complementary advantages of enhancing both the voiced and unvoiced regions. The DNN-based methods primarily rely on the network architecture, and hence, the prediction accuracy improves with the increasing complexity of the architecture. The lower complex models are essential for real-time processing systems. In this work, we propose a compact model using a sliding-window attention network (SWAN). The SWAN is trained to regress the spectral magnitude mask (SMM) from the noisy speech signal. Our experimental results demonstrate that the proposed approach achieves comparable performance with the state-of-the-art noncausal and causal speech enhancement methods with much lesser computational complexity. Our three-layered noncausal SWAN achieves 2.99 PESQ on the Valentini database with only 10 9 floating-point operations (FLOPs). [ABSTRACT FROM AUTHOR]

Electromyogram (EMG) signal is the electrical form of muscular activity that could be used to diagnose myopathy and neuropathy disorders. Several artefacts are getting imposed on the EMG during the recording process, affecting its performance. This paper proposes a novel noise clampdown method for surface electromyogram signals to employ powerful evolutionary algorithms such as cat swarm optimisation (CSO), binary gravitational search algorithm (BGSA) and spotted hyena optimisation (SHO) for the optimisation purpose of the adaptive filter. The proposed technique has been appraised on records from the standard database, corrupted by baseline wander, electrode motion, power-line noise and different additive white Gaussian noise (AWGN) levels. The potency of the proposed method is studied in terms of standard metrics, namely signal-to-noise ratio (SNR), normalised root mean square error, mean squared error (MSE), peak reconstruction error, mean difference and maximum error. Results exemplify that the proposed scheme outpaces the benchmark algorithm-based techniques with an average SNR of 87.618 dB and MSE of 3.91E−10, across different datasets, in contrast to the recently employed noise reduction algorithms at 10 dB AWGN. [ABSTRACT FROM AUTHOR]

Noise and interference are the two most common and basic problems in wireless communication systems. The noise in wireless communication channels has the characteristics of randomness and impulsivity, so the performance of adaptive filtering algorithms based on geometric algebra (GA) and second-order statistics is greatly reduced in the wireless communication systems. In order to improve the performance of adaptive filtering algorithms in wireless communication systems, this paper proposes two novel GA-based adaptive filtering algorithms, which are deduced from the robust algorithms based on the minimum error entropy (MEE) criterion and the joint criterion (MSEMEE) of the MEE and the mean square error (MSE) with the help of GA theory. The noise interference in wireless communication is modeled by α -stable distribution which is in good agreement with the actual data in this paper. Simulation results show that for the mean square deviation (MSD) learning curve, the GA-based MEE (GA-MEE) algorithm has faster convergence rate and better steady-state accuracy compared to the GA-based maximum correntropy criterion algorithm (GA-MCC) under the same generalized signal-to-noise ratio (GSNR). The GA-MEE algorithm reduces the convergence rate, but improves the steady-state accuracy by 10–15 dB compared to the adaptive filtering algorithms based on GA and second-order statistics. For GA-based MSEMEE (GA-MSEMEE) algorithm, when GA-MSEMEE and the adaptive filtering algorithms based on GA and second-order statistics keep the same convergence rate, its steady-state accuracy is improved by 10–15 dB, and when GA-MSEMEE and GA-MEE maintain approximately steady-state accuracy, its convergence rate is improved by nearly 100 iterations. In addition, when the algorithms are applied to noise cancellation, the average recovery error of the two proposed algorithms is 7 points lower than that of other GA-based adaptive filtering algorithms. The results validate the effectiveness and superiority of the GA-MEE and GA-MSEMEE algorithms in the α -stable noise environment, providing new methods to deal with multi-channel interference in wireless networks. [ABSTRACT FROM AUTHOR]

To improve the performance of identifying the block-sparse systems, firstly, a new sparsity aware block-sparse proportionate affine projection algorithm (BS-PAPA) with a fixed zero attractor is proposed in this paper. The proposed algorithm is obtained by integrating a weighted l 0 -norm penalty into the cost function of the BS-PAPA. Adding the penalty results in zero attraction terms in the weight update recursive equation of the BS-PAPA, which helps in the shrinkage of inactive coefficients. The proposed algorithm is named l 0 -block-sparse proportionate affine projection algorithm ( l 0 -BS-PAPA). The convergence analysis in the mean is derived for the l 0 -BS-PAPA. Secondly, for applications having time varying measurement noise, an adaptive zero attractor l 0 -BS-PAPA is also developed by adaptive optimisation of the zero attractor. This one is more robust to the varying degree of measurement noise than the former. A variable step-size l 0 -BS-PAPA is also introduced to further enhance the performance of the l 0 -BS-PAPA. Computer simulation experiments reveal that the l 0 -BS-PAPA outperforms the existing algorithms in block-sparse systems in terms of convergence rate, normalised misalignment, and tracking ability. [ABSTRACT FROM AUTHOR]

In adaptive filters, variable step-size-based algorithms have demonstrated better performance than conventional least mean square (LMS) and normalized LMS (NLMS) algorithms in terms of convergence speed, mean square error (MSE), and signal-to-noise ratio (SNR) improvement. Recently, adaptive filters have been recommended to be implemented on field programmable gate array (FPGA) devices due to their flexibility and high speed. Thus, recent research has focused on not only the performance measures of the algorithm but also on the required area, operating frequency, and power consumed to evaluate the proposed design after implementation. This paper first demonstrates the superiority of the regularized square root absolute error LMS (R-SRAE-LMS) for acoustic noise cancellation compared to other variable step size algorithms through a comparative study. Furthermore, transient and steady state analyses are discussed for the R-SRAE-LMS algorithm. Then, a detailed design of the R-SRAE-LMS adaptive filter is proposed in this paper. The design is divided into a forward path and two feedback paths. The device utilization, operating frequency and power consumption are also presented after a complete FPGA implementation process. The results show that R-SRAE-LMS has a high and stable SNR improvement curve compared to that of other acoustic noise cancellation algorithms. Moreover, the proposed design has remarkable implementation results compared to those of other variable step size adaptive filter designs. The output signal of the implemented proposed filter design attains performance measures very close to those of the fixed-point case. [ABSTRACT FROM AUTHOR]

Kernel adaptive filtering algorithms have been successfully applied in many areas, among which the fraction lower power order statistics error criterion (FLP) is a better choice due to its prominent performance in robust design. However, the growth of network size in kernel adaptive filtering affects the convergence rate and testing accuracy. This paper proposes a sparse Gaussian kernel adaptive filtering algorithm based on the Softplus function framework to address the network size. The framework is constituted by the fraction lower power order statistics error criterion and an improved novelty criterion. The exponential loss function is employed to describe a new novel criterion, which can accurately achieve fast classification and limit the data selection of the dictionary size. This new algorithm is called the kernel fraction lower power order statistics error criterion based on the Softplus function with a modified novelty criterion (SKFLP-MNC) algorithm. In particular, the proposed method is employed for the Mackey–Glass chaotic time series prediction and noise cancellation under the cases of Gaussian and non-Gaussian noise. Simulation results show that the filtering accuracy of the SKFLP-MNC algorithm, the dictionary size, and steady-state mean-squared errors rival some of the sparse kernel least mean square algorithms. [ABSTRACT FROM AUTHOR]

To make the zero attractor sign subband adaptive filter (ZA-SSAF) algorithm more suitable for sparse systems, where the impulse response is sparse and disturbed with impulse interference, this paper proposes an improved sign subband adaptive filtering algorithm that takes advantage of the splendid robustness of the arctangent function against impulse interference. Based on the ZA-SSAF algorithm, this algorithm introduces a proportionate coefficient matrix composed of a nonlinear function (the arctangent function) to assign different step sizes for the tap coefficients that need to be updated. The step size of the algorithm is updated in proportion to the magnitude of the weight coefficient in the adaptive process according to the relationship of the arctangent function, which greatly shortens the calculation convergence time and improves the overall convergence performance. The simulation results show that the proposed algorithm takes into account the trade-off between a faster convergence rate and a lower steady-state error and is superior to the traditional sign subband algorithm and zero attractor sign subband adaptive filtering algorithm in terms of the convergence rate and robustness against impulse noise. [ABSTRACT FROM AUTHOR]

In this paper, two fractional embedded cubature Kalman filters are proposed. Based on Masreliez–Martin (M–M) method, the first filter named M–M fractional embedded cubature Kalman filter (MMFECKF) increases the robustness of estimation under the situations where the measurement noise is non-Gaussian. To deal with state estimation of fractional nonlinear discrete stochastic models with unknown measurement noise covariance, the second filter named adaptive M–M fractional embedded cubature Kalman filter (AMMFECKF) is put forward by introducing the direct covariance matching approach to the first filter. The simulations on re-entry ballistic target tracking system have demonstrated the effectiveness and accuracy of the two proposed filters. Moreover, the influences of initial measurement noise covariance and contaminated measurement noise on AMMFECKF are analyzed, with the conclusion that AMMFECKF can achieve more accurate and robust state estimation than MMFECKF. [ABSTRACT FROM AUTHOR]

Compared with a single-channel echo cancellation, the spatial information of dual-channel echo cancellation enables the listener to obtain more potential information. This paper presents a dual-channel nonlinear acoustic echo cancellation method in α-stable distributed noise environment using a collaborative adaptive filter. Based on the minimum deviation criterion, the normalized minimum lp norm of error is used to update the weights of linear and nonlinear filters extended by nonlinear functions. The output signals are cooperatively combined to eliminate the linear and nonlinear echo. The weight error vector norm and echo return loss enhancement are both used to evaluate the effect of echo cancellation. Simulation results show that the presented echo cancellation method has a better performance than other dual-channel echo cancellation methods in the presence of α-stable distributed noise and nonlinear echo environment. [ABSTRACT FROM AUTHOR]

Stereophonic acoustic echo cancellation (SAEC) applications of adaptive filters are complex compared to monophonic AEC due to the presence of an additional acoustic channel. In long room impulse response, SAEC is carried out with the help of more than one adaptive filter, each having hundreds to thousands of filter coefficients. A large number of filter weights result in degraded convergence and enhances filter design complexity. Multiple sub-filters (MSF) and variable tap-length (VT) algorithms are independently proposed for SAEC scenarios to address these issues. The MSF-based design improves the convergence; on the other hand, the VT algorithm optimizes the weight requirement for adaptive filters. Pseudo-optimum filter order is a common phenomenon in the VT algorithm, which results in undermodelling in SAEC applications. This paper analyses the convergence, mean-square convergence, and stability of an undermodelled final error VT-MSF-SAEC (FE-MSF-SAEC) and VT-MSF-SAEC. The mathematical analysis and the supported simulation with three variants of inputs represent the effect of pseudo-fractional undermodelling for a VT-MSF-SAEC. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing academic interest in the utilization of correntropy to improve the robustness of adaptive filtering, which generally employs Gaussian function as the kernel. However, it is an inappropriate choice for asymmetrically distributed noise. The asymmetric correntropy proposed recently adopts an asymmetric Gaussian function as the kernel, and the maximum asymmetric correntropy criterion (MACC) shows great superiority in presence of asymmetric noise. Inspired by it, we aim at proposing a more suitable cost function and its corresponding algorithm for more effective adaptive filtering in the case where the complex-valued system is disturbed by some noises with asymmetric distributions. In this paper, combined with complex correntropy, we define a new variant, called asymmetric complex correntropy, which employs an asymmetric complex Gaussian function as the kernel. Then, we propose a novel optimization criterion, called maximum asymmetric complex correntropy criterion (MACCC). Besides that, we further develop a stochastic gradient-based MACCC algorithm for complex-domain filtering. The steady-state performance analysis derives the bound of step size and the theoretical results of excess mean square error. Simulations are provided to verify the correctness of theoretical value and the superiority of MACCC algorithm. [ABSTRACT FROM AUTHOR]

Powerline interference (PLI) more frequently affects the electrocardiogram (ECG) signal during the recording and acquisition. This paper proposes a unique method for suppressing the PLI in the ECG signal using an adaptive filter (AF) with a modified lightning search algorithm (MLSA). The MLSA approach automatically updates the filter coefficient weights to minimize the error between the noisy ECG signal and the filter output. The input signal of a filter is also optimized based on the error signal using MLSA. The proposed methodology's effectiveness is verified by adding PLI noise to the ECG records from the MIT-BIH arrhythmia database at different signal-to-noise ratios (SNRs). The potency of the proposed methodology is assessed by evaluation metrics, namely SNR improvement, mean square error (MSE), mean absolute error (MAE), percentage of root-mean-square difference (PRD), Huber loss (HL), and the correlation coefficient (CC). The results of the proposed methods outperform the existing techniques of empirical mode decomposition, variational mode decomposition with a non-local mean approach, empirical wavelet transform, variational mode decomposition with a variable notch filter, and an AF with the least mean square (LMS) approach in terms of evaluation metrics and visual observation. [ABSTRACT FROM AUTHOR]

This paper proposed a new polynomial eigenvalue decomposition (PEVD)-based adaptive independent component analysis (ICA) for acoustic echo cancellation (AEC) during double-talk situation. The cancellation of acoustic echo using an adaptive filter fails during the double-talk situation. In this proposed method, the acoustic echo signal is removed from the near-end speech signal during the double-talk case without an explicit double-talk detector by using adaptive ICA techniques. The PEVD is a pre-processing phase before the implementation of adaptive ICA techniques. The PEVD is used to decorrelate the echo signal from the near-end signal, and it is also more effective with convolutive mixtures. The proposed method incorporates the PEVD pre-processing phase for AEC which aims at increasing the performance and it also compares the performance of echo cancellation with other singular value decomposition- and eigenvalue decomposition-based ICA techniques. Furthermore, the suggested technique is similarly compared to the traditional time-domain, frequency-domain adaptive filters, frequency-domain Kalman filter, and PEVD-based adaptive Kalman filter. Our simulation result shows that when compared with other pre-processing methods, the proposed PEVD-based ICA yields higher echo cancellation and less computation time. [ABSTRACT FROM AUTHOR]

In this article, a widely linear complex-valued affine projection sign algorithm is proposed for handling nonstationary noise in the complex domain. Apart from using the second-order statistical information in the complex domain adequately, the proposed algorithm can succeed in obtaining the robustness against the impulsive noise. With the help of the Price's theorem and some rational assumptions, the steady-state mean-square analysis of the proposed algorithm has been shown in this paper. In addition, we achieve a low computational complexity combining the l1-norm operation of the error signal. Moreover, we still analyze the stability of our finding, providing the range of the step size of its widely linear model. In the end, the results of experiments with the impulsive noise make clear that the proposed algorithm obtains the robustness for both complex circular and noncircular signals. However, simulation results without the impulsive noise indicate that the proposed algorithm gets a worse behavior in terms of normalized mean-square deviation compared with various existing algorithms. In the stereophonic acoustic echo cancellation, our finding still achieves a good performance. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a critical type of biological signal that brings significant data about the patients. The morphological structure of ECG signals is usually distorted by the recording and transmission processes. As a result of this distortion, the proper diagnosis of diseases related to the cardiac system is getting affected. In this paper, a new FPGA design of variable step-size variable tap length delayed error normalized least mean square (VSS-VT-DENLMS) noise removal algorithm is proposed that modifies the weight update equation of DENLMS algorithm by varying the step sizes and the tap lengths simultaneously to find better trade-off among the fast convergence and error tracking. Also, the adaptive filter structure of the proposed VSS-DENLMS is developed by considering both systolic and folding structure with compressor-based booth multiplier for improving the performance in terms of speed and area. The proposed filter design is validated by considering different ECG signals from MIT-BIH Arrhythmia database, and the filtered outputs are investigated using certain performance measures including signal-to-noise ratio (SNR), mean square error (MSE), root-mean-square error (RMSE) and hardware complexity in terms of area and delay. The simulation results illustrate that the proposed filter overtakes existing filters and minimizes hardware complexities, which proves the suitability of this approach on real-time applications of ECG signals. [ABSTRACT FROM AUTHOR]

The recursive-least-squares (RLS) algorithm is one of the most representative adaptive filtering algorithms. ℓ 1 -norm full-recursive RLS has also been successfully applied to various sparsity-related areas. However, computing the autocorrelation matrix inverse in the ℓ 1 -norm full-recursive RLS generates numerical instability that results in divergence. In addition, the regularization coefficient calculation for ℓ 1 -norm often requires actual channel information or relies on empirical methods. The iterative Wiener filter (IWF) has a similar performance to the RLS algorithm and does not require the inverse of the autocorrelation matrix. Therefore, IWF can be used as a numerically stable RLS. This paper proposes ℓ 1 -norm IWF for sparse channel estimation using the IWF and ℓ 1 -norm. The algorithm proposed in this paper includes a realistic regularization coefficient calculation that does not require actual channel information. The simulation shows that the sparse channel estimation performance of the proposed algorithm is similar to the conventional ℓ 1 -norm full-recursive RLS using real channel information as well as being superior in terms of numerical stability. [ABSTRACT FROM AUTHOR]

In this paper, a generalized maximum correntropy normalized subband adaptive filter (GMCNSAF) algorithm is proposed for enhancing the convergence behavior of generalized maximum correntropy criterion algorithm under correlated signals. To promote the convergence behavior of GMCNSAF when identifying the sparse system, the polynomial zero attraction constraint is incorporated into it to yield the PZAGMCNSAF algorithm. Furthermore, to alleviate the conflicting requirements with regard to convergence rate and steady-state misalignment, a variable step-size scheme is applied into the PZAGMCNSAF, obtaining the VSSPZAGMCNSAF algorithm. Also, we analyze the convergence condition of GMCNSAF and give the step-size bound. Simulations prove that the proposed algorithms obtain superior performance for sparse system identification and echo cancelation scenario under impulsive interference as compared to related algorithms. [ABSTRACT FROM AUTHOR]

Detection and recovery of noise corrupted signals using adaptive filters are becoming popular due to their application in several fields, including communication and biomedical. Adaptive filters broadly utilize the LMS algorithm since it has low computational complexity and is robust in behavior. However, it fails to achieve a faster speed of convergence and minimum steady-state MSE simultaneously. Therefore, the objective is to estimate the noise-free signal faster with improved accuracy and obtain a lower steady-state MSE with a higher convergence speed. This paper introduces a multi-stage adaptive filtering model wherein the noisy signal is analyzed through a cascade of stages. The proposed signal de-noising scheme utilizes the automatic selection of stages to be cascaded such that the steady-state MSE is minimum. Further, to enhance convergence speed, the LMS adaptive filter's step-size is automatically adjusted at each stage. Hence, by controlling the number of stages to be cascaded automatically and utilizing a different step-size for each stage, we obtain a high convergence speed and minimum steady-state MSE. The proposed filter structure is tested for signal de-noising application to assess its performance concerning MSE, Signal-to-Noise Ratio (SNR), Average Noise Reduction (ANR), and convergence speed. The results attained have shown that the proposed filter structure provides remarkable performance improvement at different input noise levels. Further, the proposed Improved Variable-Stage (IVS) cascaded adaptive filter model employs LMS adaptive algorithm, hence offering a cost-effective and straightforward hardware implementation of ANC. [ABSTRACT FROM AUTHOR]

Linear active noise control (ANC) systems have been used in the past to effectively suppress Gaussian noise. A practical ANC system must consider nonlinearities in the secondary path with a non-minimum phase. For an ANC system to effectively operate in a modern-day acoustic environment with multiple electrical/electronic systems operating in the vicinity, the reference noise source is taken as a non-Gaussian stochastic process. Linear systems have shown unacceptable performance in countering the disturbances that are impulsive in nature. Considering these issues, we propose a family of adaptive algorithms for ANC systems that employ a second-order Volterra filter for accurate modeling of the impulsive disturbances. We utilize the maximum correntropy criterion as the cost function to improve the adaptive filtering process, which generates the most appropriate output signal in the ANC system's successive iterative stages. The proposed algorithms feature dynamic learning-rate parameters, which improve the tracking performance of the algorithms. Also, a careful selection of the Volterra filter's kernel size in the proposed algorithms ensures a balance between system stability and convergence rate. These parameters are made automatically adjustable, based on the residual error and the reference input signals, to optimize performance in non-Gaussian environments. A comparison of the proposed algorithms with its counterparts is presented through computer simulations in terms of average noise reduction for different levels of impulsive noise. The proposed algorithms are also compared with each other to find the best performing algorithm in a non-Gaussian environment. Achieved results exhibit the effectiveness of the proposed algorithms in their ability to attenuate impulsive noise in comparison with the existing adaptive algorithms for ANC systems. Later, the proposed algorithm has been implemented for speech enhancement, where the noise from the speech sample is identified and removed using an adaptive filter algorithm. Simulations reveal that the devised algorithm can effectively denoise speech signals. Further, in the last part of the paper, the VF-MCCRMC is modified in accordance with the energy of the error signal, and the simulation results demonstrated the improvement in stability and error performance. [ABSTRACT FROM AUTHOR]

The complex kernel adaptive filter (CKAF) has been widely applied to the complex-valued nonlinear problem in signal processing and machine learning. However, most of the CKAF applications involve the complex kernel least mean square (CKLMS) algorithms, which work in a pure complex or complexified reproducing kernel Hilbert space (RKHS). In this paper, we propose the generalized complex kernel affine projection (GCKAP) algorithms in the widely linear complex-valued RKHS (WL-RKHS). The proposed algorithms have two main notable features. One is that they provide a complete solution for both circular and non-circular complex nonlinear problems and show many performance improvements over the CKAP algorithms. The other is that the GCKAP algorithms inherit the simplicity of the CKLMS algorithm while reducing its gradient noise and boosting its convergence. The second-order statistical characteristics of WL-RKHS have also been developed. An augmented Gram matrix consists of a standard Gram matrix and a pseudo-Gram matrix. This decomposition provides more underlying information when the real and imaginary parts of the signal are correlated and learning is independent. In addition, some online sparsification criteria are compared comprehensively in the GCKAP algorithms, including the novelty criterion, the coherence criterion, and the angle criterion. Finally, two nonlinear channel equalization experiments with non-circular complex inputs are presented to illustrate the performance improvements of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In reality, the range of sensor response is limited in many sensor systems due to the saturation characteristics of the sensor. That is, the value exceeding the sensor response range is not observed. Using traditional adaptive algorithms to identify the system of this type may lead to the performance degradation. To address this problem, the censored regression algorithms have been proposed. However, when the mixed sub-Gaussian and super-Gaussian/impulsive noises occur, these algorithms may fail to work. To overcome these drawbacks, a family of robust M-shaped (FRMS) functions for censored regression (CR-FRMS) is proposed in this paper. When the system to be identified exhibits a certain degree of sparsity, the CR-FRMS algorithm cannot fully utilize the characteristics of the sparse system. Therefore, in this paper, proportionate FRMS (PFRMS) algorithm based on l 0 -norm constraint for censored regression ( l 0 -CRPFRMS) is also proposed accordingly. The simulations using Gaussian white noise as the input signal and the non-Gaussian mixed noise as the background noise show that the proposed algorithm performs better than other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a generalized maximum Versoria criterion algorithm (GMVC) based on wiener spline adaptive filter, called SAF–GMVC, is proposed. The proposed algorithm is used for nonlinear system identification under non-Gaussian environment. To improve the convergence performance of the SAF–GMVC, the momentum stochastic gradient descent (MSGD) is introduced. In order to further reduce the steady-state error, the variable step-size algorithm is introduced, called as SAF–GMVC–VMSGD. Simulation results demonstrate that SAF–GMVC–VMSGD achieves better filtering effective against non-Gaussian noise. [ABSTRACT FROM AUTHOR]

Estimation in a cooperative and distributed manner in wireless sensor networks (WSNs) has considered much attention in recent years. When this distributed estimation is performed adaptively, the concept of adaptive networks will develop. In such networks, proper selection of the unknown parameter length is an issue in itself. A deficient filter length results in an additional steady-state error while selecting a large length will impose a more computational load on the nodes, which is critical in sensor networks due to the lack of energy resources. This motivates the use of variable tap-length adaptive filters in the context of the adaptive networks. This has been achieved in adaptive networks using the distributed fractional tap-length (FT) algorithm. This algorithm requires proper selection of the length adaptation parameters, such as error width and length adaptation step-size. This paper proposes an automatic method for selecting these parameters. In the proposed method, these parameters are adapted based on the estimated gradient vector. The proposed method is fully distributed and presented in a diffusion strategy. Simulation results show that the proposed algorithm has both the advantage of fast length convergence and an unbiased steady-state tap-length. [ABSTRACT FROM AUTHOR]

A filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter. However, the FDSAF algorithm is unable to suppress non-Gaussian impulsive noises. To suppression non-Gaussian impulsive noises along with having comparable operation time, a maximum correntropy criterion (MCC) based frequency domain spline adaptive filter called frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper. Further, the bound on learning rate for convergence of the proposed algorithm is also studied. And through experimental simulations verify the effectiveness of the proposed algorithm in suppressing non-Gaussian impulsive noises. Compared with the existing frequency domain spline adaptive filter, the proposed algorithm has better performance. [ABSTRACT FROM AUTHOR]

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion μ -law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, speech enhancement is carried out using a modified nonlocal means adaptive filter, and an FPGA prototype is proposed for its implementation. Nonlocal means (NLM) adaptive filtering has been a widely used approach for denoising image and electrocardiogram signals. The temporal and spectral characteristics of the speech signal vary with time. Consequently, NLM filtering is ineffective in enhancing the noisy speech signal. In this study, the weight values estimated during NLM filtering are nonuniformly smoothed through energy-dependent scaling to address this issue. The proposed modification in NLM adaptive filtering significantly improved speech enhancement performance compared to other classic NLM filtering and enhancement methods. The proposed modified NLM technique provides better efficacy for objective quality measures by observing the simulation results presented on the TIMIT database. This study is performed on signals corrupted with white, babble, and factory noises at different SNR levels taken from the Noisex-92 database. The hardware architecture of the proposed method is designed and verified by implementing it on a Zynq-7000 (xc7z045ffg900-2) FPGA using the Xilinx system generator. [ABSTRACT FROM AUTHOR]

In this paper, a new variable step size least mean square adaptive filter-based control algorithm is proposed to generate reference current for voltage source converter of a grid-integrated PV system. The proposed control generates reference current by extracting the fundamental active and reactive component from load current with fast convergence. The proposed control offers better initial transients and dynamic performance than conventional LMS and synchronous reference frame-based control. Grid-integrated PV system is modelled in MATLAB/Simulink and is tested on different loading condition viz. nonlinear load, unbalanced load and variable load. Further, to validate the efficacy of the proposed control, it is also tested in real time on prototype hardware in the laboratory. It has been observed that proposed control provides compensation of harmonic and reactive power of the local load. Distortion at grid side of the system is within the limits as per IEEE-519 standard. [ABSTRACT FROM AUTHOR]

The constrained least mean square (CLMS) algorithm is one of the most popular online linear-equality-constrained beamforming algorithms. This paper demonstrates for the first time that it solves a deterministic minimum-disturbance optimization problem in an exact manner. Such a framework is employed to insert the coefficient reusing technique into the algorithm, engendering a new low-complexity constrained adaptive filter, designated as RC-CLMS, that trades convergence rate for asymptotic performance. A stochastic model that predicts the average evolution of adaptive weights is derived. Through simulations, the advanced reusing coefficient extension of the constrained least mean-square algorithm enhanced the asymptotic signal-to-interference-plus-noise ratio and decreased the steady-state mean output energy. Furthermore, the resulting beam pattern is analyzed with an antenna analysis tool, confirming the efficacy of the advanced algorithm in a realistic setting, when the electromagnetic coupling between the antennas is taken into account. [ABSTRACT FROM AUTHOR]

To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments. [ABSTRACT FROM AUTHOR]

The widely linear model has attracted much attention due to its good features for non-circular adaptive signal processing in recent years. In this paper, a sparsity-induced augmented complex-valued NLMS algorithm is proposed to promote the performance of the adaptive filter for estimating sparse systems, which is established by incorporating the l 0 -norm regularization into the squared error normalized by the input vector. To address the problem of trade-off between fast convergence rate and low steady-state misalignment, we minimize the variance of the a posteriori error to derive an optimal step-size and then some practical problems are considered. Simulation results are provided to verify the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to its low computational burden, the affine-projection-like (APL) adaptive filtering algorithm has been extensively studied for colored signal input. Recently, a robust APL algorithm was designed by adopting the M-estimate cost function in impulsive noise environment; however, its convergence rate is very slow for sparse system identification. This paper proposed a proportionate APL M-estimate (PAPLM) algorithm, which is derived by using the proportionate matrix to heighten the convergence rate. To maintain good steady-state performance of the PAPLM algorithm, a time-varying parameter PAPLM (TV-PAPLM) algorithm is proposed, which uses a modified exponential function to adjust the time-varying parameter according to the ratio of the mean square score function to the system noise variance. Moreover, the steady-state excess mean-square error performance of PAPLM algorithm is analyzed and obtained in detail. Simulation results reveal that the proposed PAPLM and TV-PAPLM algorithms achieve fast convergence rate and good steady-state performance in sparse system identification. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) is a biomedical technique for capturing the human brain's electrical information to process its activities and actions. EEG is one of the main methods commonly used in neuroscience, from clinical analysis to the design of brain–computer interfaces. The first one of the many challenges in an EEG system design regards the ultra-low amplitude of signals (i.e., of the order of 20 μ V ) and their susceptibility to several kinds of interferences. Electromagnetic interference (EMI) omnidirectionally irradiated by the power line supplies disturbs the EEG instrumentation system with a noise power mostly concentrated from 50 to 60 Hz . Our paper investigates the energy efficiency of adaptive filtering (AF) techniques based on the least mean square (LMS), normalized LMS, and set-membership (SM) families for meeting the protection against EMI in EEG systems. The results demonstrate that the LMS algorithm presents an unstable behavior with unsatisfactory results against the normalized LMS filters when subject to noisy scenarios. Therefore, we herein explore dedicated VLSI hardware architectures for the following filters: (a) normalized LMS (NLMS), (b) SM-NLMS, (c) partial update NLMS (PU-NLMS), and (d) SM bi-normalized LMS (SM-BNLMS). PU-NLMS architecture offers the best trade-off between the capability of eliminating the EMI versus its power dissipation, circuit area, and maximum clock frequency. PU-NLMS provides an artifact reduction level of up to 8.8 dB , with low energy consumption of 0.62 nJ/operation and just 1.41 % larger circuit area than the NLMS architecture. [ABSTRACT FROM AUTHOR]

The stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations. [ABSTRACT FROM AUTHOR]

This paper proposes a normalized subband spline adaptive filter (SAF-NSAF) algorithm to solve the problem that linear subband adaptive filtering cannot identify nonlinear systems. The weight update of the proposed algorithm is conducted using the principle of minimum disturbance. Since a delayless structure is used in the proposed algorithm, a delay is not introduced into the update process. The effectiveness of the proposed algorithm is verified by simulations. Also, the mean and mean square stability of the proposed algorithm are evaluated using the principle of conservation of energy. The simulation results demonstrate that the performance of the proposed algorithm outperforms other cited nonlinear algorithms. [ABSTRACT FROM AUTHOR]