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Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

ABSTRACT This study delves into the advancements, challenges, and opportunities in the solar grid technology, emphasizing its integration into the existing power infrastructure. The proposed ML‐FOGI (multilayer fourth‐order generalized integrator) algorithm presents a promising solution for modern grid synchronization requirements, addressing challenges posed by the integration of renewable energy into the distributed generation. This technology enhances system robustness and dynamic response, particularly in weak distribution grids characterized by voltage distortions and imbalances. This innovative approach enhances system stability and performance, particularly in polluted grid conditions. Through MATLAB/Simulink modelling and experimental validation, the effectiveness of the proposed control strategy is investigated across diverse testing conditions. Emphasizing the significant role of the control strategy in enhancing power quality and grid stability in the solar photovoltaic systems, this research underscores the importance of robust and adaptive control mechanisms for optimizing performance and ensuring grid reliability in modern microgrid. This study delves into this complex issue of working of the grid connected photovoltaic (GCPV) system, emphasizing the importance of balanced unit templates and a stable DC link voltage with unpredictable grid harmonics. This research aims to enhance the resilience of solar PV systems against grid disturbances by drawing inspiration from the adaptable nature of ADALINE filters and adhering to the IEEE519 requirements. This study employs OPAL‐RT's resilient experimental configuration. [ABSTRACT FROM AUTHOR]

Nonlinear ocean waves have a significant impact on the functioning of several offshore activities. Predicting the internal ocean waves plays a crucial role on submarine and ship operations. Data assimilation is a mechanism in which data observed is interpreted, processed and adapted. The existing works for estimating the future atmospheric condition are highly dependent on the exact initial state, which mostly differ from the observation. This paper proposes modelling of internal ocean waves using automatic internal wave detection and data assimilation. Ensemble Kalman filtering method is used to model ocean waves. The proposed system is focused on satellite images. The images are pre-processed for speckle noise using adaptive filters. Enhanced residual network is used for edge detection. Unlike the existing edge detection methods that have high complexity, this enhanced residual network works with low complexity and makes a direct mapping between the input wave image and wave edge. Finally, the potential edges of the internal wave are detected and adapted using ensemble Kalman filter. Adaptive thresholding technique is used to determine the appropriate threshold to segregate objects from background. The proposed enhanced edge detection model is compared w.r.t to the parameters weighted cross-entropy loss function, accuracy and root mean squared error with canny edge detection and proved to be better. The detection of internal wave is demonstrated, and the accuracy of the approach is 91% with low RMSE when compared to existing works. [ABSTRACT FROM AUTHOR]

The study of signal processing has recently devoted significantly more attention to adaptive filtering techniques. By addressing the shortcoming of the conventional geometric algebra-based fixed step-size least mean square algorithm that is unable to satisfy in terms of both reducing steady-state error and a faster convergence speed, simultaneously, this study presents an improved logarithmic function-based variable step-size least mean square geometric algebra adaptive filtering algorithm by establishing the step-size factor μ and error signal e(n) nonlinear function relationship. The instantaneous values of a current error estimate e(n) and the previous error estimate e (n - 1) are used to determine the step size of the defined algorithm. Besides, an extensive discussion is given on the performance of algorithm under influence of parameters γ and T as well as comparative analysis with other existing geometric algebra-based adaptive filters. Computer simulation reveals that the proposed approach not only has a low steady-state error, robustness against impulsive noise, and fast convergence speed, but it also overcomes some existing algorithm's instability under steady-state phase. [ABSTRACT FROM AUTHOR]

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful. [ABSTRACT FROM AUTHOR]

For the identification of long impulse response systems in impulsive noise environments, existing algorithms have disadvantages such as slow convergence speed, large steady-state error, and poor tracking performance. In this brief, we propose the nearest Kronecker product decomposition based robust hyperbolic tangent Geman-McClure adaptive filter (NKP-HTGM) and analyze its performance. This algorithm uses the Geman-McClure function under hyperbolic tangent framework to remove the characteristic of the abnormal amplitude in the dataset, significantly improving the robustness against impulsive noise. Moreover, a novel variable step-size method (VSS) is introduced to further enhance the performance of NKP-HTGM (VSS-NKP-HTGM). Finally, the simulation results validate the effectiveness of the NKP-HTGM algorithm in system identification and the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods. [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]

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.

Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters. [ABSTRACT FROM AUTHOR]

To protect civilian Global Positioning System (GPS) users from spoofing, the Air Force Research Lab has developed the chips-message robust authentication (Chimera) signal enhancement for the GPS L1C signal. With Chimera, standalone receivers that only have access to the GPS signal will be able to authenticate their received measurements once every 3 min, whereas users with access to an out-of-band source will be able to perform authentication once every 1.5 or 6 s. However, moving receivers typically rely on much faster real-time GPS update rates of 1-20 Hz. In this work, we design a spoofing-resilient filter framework that provides continuous and secure state estimation between Chimera authentication times. By leveraging self-contained sensors on-board the vehicle, such as an inertial measurement unit or wheel encoder, as well as the periodic Chimera authentication, our proposed filter determines how much to rely on the received unauthenticated GPS measurements for state estimation. In this respect, our filter relies more extensively on GPS measurements in order to improve real-time navigation performance and reduce localization errors when GPS signals are authentic, while successfully mitigating spoofing-induced errors during an experienced attack. We experimentally validate our proposed spoofing-resilient filter in a simulated test environment for a ground vehicle model with access to the 3-min Chimera channel, under various simulated spoofing attack scenarios. To the best of the authors' knowledge, this is the first adaptive filter proposed for Chimera that continuously leverages real-time GPS measurements in a spoofing-resilient manner. [ABSTRACT FROM AUTHOR]

The power quality of the grid-coupled photovoltaic system (GCPVS) is enhanced using a control scheme developed by employing a robust generalized soft-root-sign (GSRS) adaptive filter. Power quality (PQ) deterioration issues are common in the distribution grid due to the mix of irregular loads. The GSRS control scheme mitigates harmonics, compensates load unbalances, achieves unity power factor at the grid, and compensates load reactive power, thus leading to balanced sinusoidal currents at the grid side. The GSRS algorithm utilizes a soft-root-sign cost function to achieve less steady-state misalignment while estimating magnitudes of fundamental frequency load current components for PQ improvement. The designed single-stage GCPVS performs smooth power flow between local loads and the grid without necessitating an energy storage system. The control scheme initially meets load demand and then sends any surplus power to the grid. However, if the generated PV power falls below the load demand due to environmental conditions, the additional power for the load is drawn from the grid. Moreover, voltage source converter used in the GCPVS also provides the functionality of DSTATCOM, thus avoiding the usage of additional equipment. The proposed GSRS control scheme and the GCPVS are modeled in MATLAB/Simulink. A laboratory-designed experimental setup is used to carry out the experimental validation of the GSRS control scheme and found to be performing satisfactorily. The behavior of GCPVS is verified during steady-state and dynamic conditions. The GCPVS performance is verified with the standard IEEE-519-2022, and a comparison of the GSRS algorithm with other filtering algorithms is provided. [ABSTRACT FROM AUTHOR]

Esophageal pressure (Peso) is one of the most common and minimally invasive methods used to assess the respiratory and lung mechanics in patients receiving mechanical ventilation. However, the Peso measurement is contaminated by cardiogenic oscillations (CGOs), which cannot be easily eliminated in real-time. The field of study dealing with the elimination of CGO from Peso signals is still in the early stages of its development. In this study, we present an adaptive filtering-based method by constructing a reference signal based on the heart rate and sine function to remove CGOs in real-time. The proposed technique is tested using clinical data acquired from 20 patients admitted to the intensive care unit. Lung compliance (QUOTE) and esophageal pressure swings (△Pes) are used to evaluate the performance and efficiency of the proposed technique. The CGO can be efficiently suppressed when the constructional reference signal contains the fundamental, and second and third harmonic frequencies of the heart rate signal. The analysis of the data of 8 patients with controlled mechanical ventilation reveals that the standard deviation/mean of the QUOTE is reduced by 28.4–79.2% without changing the QUOTE and the △Pes measurement is more accurate, with the use of our proposed technique. The proposed technique can effectively eliminate the CGOs from the measured Peso signals in real-time without requiring additional equipment to collect the reference signal. [ABSTRACT FROM AUTHOR]

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom. [ABSTRACT FROM AUTHOR]

The discomfort caused by whole-body vibration (WBV) has long been assessed using subjective surveys or objective measurements of body acceleration. However, surveys have the disadvantage that some of participants often express their feelings in a capricious manner, and acceleration data cannot take into account individual preferences and experiences of their emotions. In this study, we investigated vibration-induced mental stress using the electroencephalogram (EEG) of 22 seated occupants excited by random vibrations. Between the acceleration and the EEG signal, which contains electrical noise due to the head shaking caused by random vibrations, we found that there was a strong correlation, which acts as an artifact in the EEG, and therefore we removed it using an adaptive filter. After removing the artifact, we analyzed the characteristics of the brainwaves using topographic maps and observed that the activities detected in the frontal electrodes showed significant differences between the static and vibration conditions. Further, frontal alpha asymmetry (FAA) and relative band power indices in the frontal electrodes were analyzed statistically to assess mental stress under WBV. As the vibration level increased, EEG analysis in the frontal electrodes showed a decrease in FAA and alpha power but an increase in gamma power. These results are in good agreement with the literature in the sense that FAA and alpha band power decreases with increasing stress, thus demonstrating that WBV causes mental stress and that the stress increases with the vibration level. EEG assessment of stress during WBV is expected to be used in the evaluation of ride comfort alongside existing self-report and acceleration methods. [ABSTRACT FROM AUTHOR]

Active noise reduction in the existential of impulsive noise poses a serious challenge in a distributed scenario. The conventional centralized multichannel active noise control (MANC) and diffusion filtered-x least mean square (F-xLMS) based distributed active noise control (DANC) systems exhibit deteriorated performance in the existence of impulse noise and outliers. In this regard, a diffusion least logarithmic hyperbolic cosine function (D-Llncosh) based algorithm for DANC system is developed with the goal of mitigating noise in such an environment. It utilizes natural logarithmic of hyperbolic cosine function integrating the mean square error (MSE) and mean absolute error (MAE) as the cost function depending upon the parameter value. The weight update rule of the introduced D-Llncosh-based DANC system is hereby derived. The convergence analysis of the suggested method is also carried out. The proposed system is validated for various types of impulsive noise under different scenarios. The extensive simulation results assert the efficacy of the posited DANC system in comparison with the existing techniques. The suggested method achieves quicker convergence and approximately a 1–5 dB improvement is the noise reduction for different noise scenarios as evident from the obtained results. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) can serve as a supplement to the radio frequency to address its inherent constraints. VLC can be easily implemented in vehicles using their existing LED components. Sunlight interference can cause significant variation in the background noise powers, leading to signal-to-noise ratio (SNR) degradation in vehicular VLC (VVLC) communication. Hence, it is necessary to improve the performance besides interference. Allan variance is a temporal analysis approach that can extract the noise from the received data. In this article, an Allan variance-based adaptive noise canceller for a VVLC system is proposed and evaluated. Based on the Allan variance obtained, the step size and the number of taps of the adaptive filter are adjusted for superior performance. This proposed filter effectively mitigates the pronounced autocorrelation induced by the noise interference. It yields about an 85% bit error rate (BER) reduction compared to the conventional filter. This improved convergence underscores the potential for enhanced communication performance and reliability. The balanced distribution of the Manchester encoded signal outperforms NRZ encoding. Thus, this work contributes a novel adaptive noise canceller design for a VVLC system and also provides valuable insights to the significance of Allan variance in the VLC system performance. [ABSTRACT FROM AUTHOR]

The most common non-invasive diagnostic model is the Electrocardiogram (ECG), which records the heart's electrical activity over time and is used to diagnose various heart conditions. Due to the requirements of a typical eHealth system, it is necessary to compress ECG signals for long-term data recording and remote transmission. Moreover, cardiovascular diseases (CVDs) have been considered the most long-lasting disorders in recent years. The transmission of information from the patient to the distant hospital is necessary because rapid analysis and treatment are essential for the condition to be cured. Also, the data must be in the form of lossless and high-predictability data. So, the goal of this study was to create a two-stage lossless Integer Adaptive Predictor (IAP) compressor that could be implemented on a Field Programmable Gate Array (FPGA) without introducing any data loss during the compression process. Before compression, the ECG signals are denoised using a Fast Normalized Least Mean Square (FNLMS) algorithm-based adaptive filter, which removes the undesirable noise presented in the signal. Here, the adaptive filter is designed based on the hybrid systolic folding structure and compressor-based multiplier architecture to minimize the power, delay and area consumption of the filter while performing the signal-denoising process. Xilinx and MATLAB are used to run simulations using the MIT-BIH Arrhythmia and PTB diagnostic databases. Several performance parameters are used to assess the proposed design's efficacy, and the results are compared to those of similar current designs. Consequently, the proposed compressor achieves a 45.23% compression ratio (CR) on MIT-BIH and a 10.87% average CR on the PTB diagnostic database, which demonstrates that the compression proficiency of the proposed design is high. [ABSTRACT FROM AUTHOR]

Complex shear modulus imaging (CSMI) is a technique used to determine the elasticity and viscosity of soft tissues; it aids in investigating tissue structure and detecting tumors. CSMI methods can be categorized into quasi-static and dynamic approaches. The dynamic method utilizes force excitation and particle velocity measurements to estimate the Complex Shear Modulus (CSM). However, noise poses a challenge to shear wave estimation, affecting accuracy. Researchers are actively exploring adaptive filtering techniques and signal processing algorithms to address the noise issue and obtain reliable shear wave estimations. By integrating the Algebraic Helmholtz Inversion (AHI) method and the Least Mean Square (LMS) filter, imaging accuracy can be enhanced by mitigating the impact of noise. This study introduces a pioneering advancement in Complex Shear Modulus Imaging (CSMI). A new approach is proposed, using a dual-frequency excitation technique, strategically employing 100 Hz and 150 Hz frequencies. The proposed method is meticulously designed to enhance the overall efficiency of CSMI. Furthermore, a highly effective signal sampling and spectrum estimation procedure is introduced, aimed at elevating the accuracy of Algebraic Helmholtz Inversion (AHI) and significantly mitigating computational complexity in contrast to prior investigations. [ABSTRACT FROM AUTHOR]

To improve the convergence speed and noise cancellation performance of the Delayed Filtered-x Least Mean Square (DFxLMS) algorithm in the hardware implementation of compressor noise cancellation system, an improved Delayed Filtered-x Least Mean Square algorithm based on field-programmable gate array (FPGA) is proposed. The algorithm uses a parallel finite impulse response (FIR) filter structure and DSP48E1 module to implement the parallel multiplication and addition operation, which decreases hardware resource consumption and increases the system's processing speed. The cut set retiming technique reallocates the location of delay units to streamline the algorithm structure, reduce the critical path delay and increase the system data throughput. Moreover, through the adaptive filtering of input and feedback signals of the filter module, the noise suppression effect of the algorithm is improved. The results show that the maximum noise cancellation of the algorithm is 10 dB in the compressor noise cancellation system, which is 3 dB higher than the DFxLMS algorithm, and has 1.5 times higher data throughput. In addition, the Lookup Table (LUT), Flip-Flop (FF), and power consumption decreased by 18.5%, 33.6%, and 26.6%, respectively, and converged 1.8 times as fast. [ABSTRACT FROM AUTHOR]

Active Magnetic Bearing (AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs, an adaptive filter based on Least Mean Square (LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm (ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table (LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Recently, maximum Versoria criterion-based adaptive algorithms have been introduced as a new solution for robust adaptive filtering. This paper studies the steady-state tracking analysis of an adaptive filter with maximum Versoria criterion (MVC) in a non-stationary (Markov time-varying) system. Our analysis relies on the energy conservation method. Both Gaussian and general non-Gaussian noise are considered, and for both cases, the closed-form expression for steady-state excess mean square error (EMSE) is derived. Regardless of noise type, unlike the stationary environment, the EMSE curves are not increasing functions of step-size parameter. The validity of the theoretical results is justified via simulation.

The family of least mean square (LMS) based adaptive filtering algorithms suffers from convergence performance limitation due to the sensitivity of such algorithms to the eigenvalue spread of the input correlation matrix. The quasi-Newton family of adaptive filtering algorithms addresses this limitation, but its performance is restricted by the estimation accuracy of the correlation matrix inverse, especially for highly correlated input signals. Furthermore, the convergence rate and the steady-state performance of both LMS and quasi-Newton families are thoroughly depending on their step-sizes. In this paper, a variable step-size regularized quasi-Newton adaptive algorithm is proposed in the context of system identification. In this algorithm, inspired by the matrix inversion lemma, a modified regularized matrix inverse with a time-varying regularization is computed such that during the convergence, the contribution of matrix inverse in the weight update is reduced, resulting in a more noise-robust algorithm. The paper further provides a convergence analysis of the proposed quasi-Newton algorithm, wherein a variable step-size is proposed to achieve a high initial convergence rate and a low steady-state error in the context of system identification applications.

Within vertebrates, central pattern generators drive rhythmical behaviours, such as locomotion and ventilation. Their pattern generation is also influenced by sensory input and various forms of neuromodulation. These capabilities arose early in vertebrate evolution, preceding the evolution of the cerebellum in jawed vertebrates. This later evolution of the cerebellum is suggestive of subsumption architecture that adds functionality to a pre-existing network. From a central-pattern-generator perspective, what additional functionality might the cerebellum provide? The suggestion is that the adaptive filter capabilities of the cerebellum may be able to use error learning to appropriately repurpose pattern output. Examples may include head and eye stabilization during locomotion, song learning, and context-dependent alternation between learnt motor-control sequences. [ABSTRACT FROM AUTHOR]

Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a fractional delay (FD) Newton structure for Lagrangian interpolation in single phase grid integrated photovoltaic (PV) system. The structure has been designed for achieving shunt compensation and as a phase locked loop circuit for synchronization. The designed controller performs multiple tasks of feeding the active power to meet the load demand and simultaneously providing reactive power compensation, power factor improvement, and compensating other power quality voltage and load current harmonics. Further, synchronization poses a severe challenge in integrating the PV power electronic converters to the grid. The FD filter is used to discretize the continuous time signal at any time instant using interpolation algorithm employing the non‐integral value of the sampling rate delays. The FD delay filter is made adaptive and self‐learning by using least mean square algorithm. The designed synchronization technique meets the objective of faithfully tracking the phase of the grid voltage under all grid disturbances such as under voltage, overvoltage, frequency variations, and phase angle disturbances in a precise manner. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for "low, slow and tiny" moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method. [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]

Descriptors serve as algorithms responsible for the representation and processing of multimedia files. One of the main challenges in these algorithms involves establishing a tradeoff among conflicting performance requirements such runtime, on the one hand, and standard metrics such as accuracy, precision, recall and F-score, on the other hand. To address this challenge, a novel descriptor named Histogram of Enhanced Gradients (HEG) is introduced for noisy face recognition. The methodology behind HEG involves enhancing local gradients prior to feature extraction, corroborated by the Histogram of Oriented Gradients (HOG) descriptor and adaptive filtering. Initially, facial images are divided into blocks, and features are extracted from each block using magnitude and orientation maps to discriminate between edges, details, and flat regions. Then, these features undergo denoising with an adaptive anisotropic diffusion filter, individually customized for each of these three types. Subsequently, the enhanced histograms from the blocks are concatenated to create a comprehensive feature vector representing the original noisy face image. Finally, the HEG descriptor is integrated within a supervised machine learning scheme with a Support Vector Machine as the classifier. The proposed descriptor is evaluated not only in terms of runtime and the standard metrics cited above, but also on the basis of six other similarity metrics, across three online datasets. Experimental results, conducted under different noise levels, clearly demonstrate that the HEG descriptor outperforms nine stateof- the-art descriptors on all three datasets yielding significant enhancements in runtime efficiency, with speed improvements ranging from 1.64 to 29.56 times, and notable refinements in F-score, ranging from 1.03 to 2.39 times. These results highlight the effectiveness of the HEG descriptor in capturing facial features from multimedia noisy files. [ABSTRACT FROM AUTHOR]

One of the important issues encountered in acoustic feedback canceler (AFC) systems is the bias occurring in the estimation of the acoustic feedback (AF) path. In the literature, a common solution is to inject the probe noise into the loudspeaker to reduce the correlation between the input signal and the loudspeaker signal, that is, to remove the bias occurring in the estimation of the AF path. This approach significantly improves the performance of the AFC systems. However, when considering high filter orders and large-scale input signals, the AFC systems suffer from the data processing cost, and thus their real-time implementations on a chip generally become challenging. To this end, in this paper, we propose an AFC system using jointly probe noise and informative data, named the probe noise-online censoring-least mean square (PN-OC-LMS) based AFC. Thanks to both the OC mechanism and the probe noise injection approach, the proposed PN-OC-LMS-based AFC system not only significantly reduces the data processing cost but also effectively removes the bias effect in the AF path. The mentioned effectiveness of the proposed AFC system is supported by three comprehensive experiments on a real-world AF path measured from a behind-the-ear hearing aid. As a result, in the proposed AFC system, the use of both informative data and the injection of probe noise will enable the design of a long-term working wearable hearing aid that provides low data processing cost and correctly estimates the AF path. [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]

In this paper, Recursive Least Square (RLS) and Affine Projection (AP) adaptive filters are designed using Xilinx System Generator and implemented on the Spartan6 xc6slx16- 2csg324 FPGA platform. FPGA platform utilizes the non-restoring division algorithm and the COordinate Rotation DIgital Computer (CORDIC) division algorithm to perform the division task of the RLS and AP adaptive filters. The Non-restoring division algorithm demonstrates efficient performance in terms of convergence speed and signal-to-noise ratio. In contrast, the CORDIC division algorithm requires 31 cycles for division initialization, whereas the non-restoring algorithm initializes division in just one cycle. To validate the effectiveness of the proposed filters, a set of ten ECG records from the BIT-MIT database is used to test their ability to remove Power Line Interference (PLI) noise from the ECG signal. The proposed adaptive filters are compared with various adaptive algorithms in terms of Signal-to-Noise Ratio (SNR), convergence speed, residual noise, steady-state Mean Square Error (MSE), and complexity.

An adaptive filter is the utmost essential filter castoff in statistical signal dealing. The fine-tuning of the filter factor in relation to the response signal is the adaptive filter's key feature due to fewer calculations, Least Mean Square (LMS) adaptive filters are widely used to remove noise from Electrocardiograms (ECG). The adaptive filters are realized as signal processing algorithms in Digital Signal Processors (DSPs) or in VLSI Signal Processors (VSPs). The technique provides a way to create a folded adaptive lattice LMS filter, which requires less hardware than an adaptive lattice filter. Folding is an algorithm that uses a time scheduling technique that combines arithmetic operations into one operation which reduces Register and silicon chip areas. The design and implementation of a folded lattice adaptive filter remove Power Line Interference (PLI) noise from ECG signals. The MATLAB Xilinx System Generator tool is used to design the Adaptive Lattice LMS Filter and Folded Adaptive Lattice LMS Filter with Folding Order K = 2 and K = 4 and realized in the Virtex 5 FPGA KIT. The results of the folded architecture show that the area is reduced for K = 2 and K = 4 by 82.60% and 91.05%, respectively compared with a normal adaptive lattice filter.

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter’s reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful.

Summary: This paper presents an innovative concept of a high‐speed and higher order adaptive filter. A fully analog artificial neural network handles the adaption by using a filter bank for filtering and learning through analog feedback inspired by backpropagation. There is no clock control used in this concept, which makes real‐time adaption possible even for high frequencies. Its functionality is shown by several electrical circuit simulations involving component inaccuracies as well as a commonly used filter example. [ABSTRACT FROM AUTHOR]

An adaptive filter is the utmost essential filter castoff in statistical signal dealing. The fine-tuning of the filter factor in relation to the response signal is the adaptive filter's key feature due to fewer calculations, Least Mean Square (LMS) adaptive filters are widely used to remove noise from Electrocardiograms (ECG). The adaptive filters are realized as signal processing algorithms in Digital Signal Processors (DSPs) or in VLSI Signal Processors (VSPs). The technique provides a way to create a folded adaptive lattice LMS filter, which requires less hardware than an adaptive lattice filter. Folding is an algorithm that uses a time scheduling technique that combines arithmetic operations into one operation which reduces Register and silicon chip areas. The design and implementation of a folded lattice adaptive filter remove Power Line Interference (PLI) noise from ECG signals. The MATLAB Xilinx System Generator tool is used to design the Adaptive Lattice LMS Filter and Folded Adaptive Lattice LMS Filter with Folding Order K = 2 and K = 4 and realized in the Virtex 5 FPGA KIT. The results of the folded architecture show that the area is reduced for K = 2 and K = 4 by 82.60% and 91.05%, respectively compared with a normal adaptive lattice filter. [ABSTRACT FROM AUTHOR]

A new method using three dimensions of cloud continuity, including range dimension, Doppler dimension, and time dimension, is proposed to discriminate cloud from noise and detect more weak cloud signals in vertically pointing millimeter-wave cloud radar observations by fully utilizing the spatiotemporal continuum of clouds. A modified noise level estimation method based on the Hildebrand and Sekhon algorithm is used for more accurate noise level estimation, which is critical for weak signals. The detection method consists of three steps. The first two steps are performed at the Doppler power spectrum stage, while the third step is performed at the base data stage. In the first step, a new adaptive spatial filter combined with the Kuwaraha filter and the Gaussian filter, using the ratio of mean to standard deviation as the adaptive parameter, is applied to initially mask the potential cloud signals to improve the detection performance at the boundary of cloud and noise. Simulations of boundary cases were performed to compare our adaptive filter and normal Gaussian filters. Box filters are used in steps two and three to remove the remaining noise. We applied our method to cloud radar observations with TJ-II cloud radar at the Nanjing University of Information Science & Technology. The results showed that our method can detect more weak cloud signals than the usual methods, which are performed only at the Doppler power spectrum stage or the base data stage. [ABSTRACT FROM AUTHOR]

For next generation wireless communication systems, high throughput, low latency, and large user accommodation are popular and important required characteristics. To achieve these requirements for next generation wireless communication systems, an in-band full-duplex (IBFD) communication system is one of the possible candidate technologies. However, to realize IBFD systems, there is an essential problem that there exists a large self-interference (SI) due to the simultaneous signal transmission and reception in the IBFD systems. Therefore, to implement the IBFD system, it is necessary to realize a series of effective SI cancellation processes. In this study, we implemented a prototype of SI cancellation processes with our designed antenna, analog circuit, and digital cancellation function using an adaptive filter. For system implementation, we introduce software-defined radio (SDR) devices in this study. By using SDR devices, which can be customized by users, the evaluations of complicated wireless access systems like IBFD can be realized easily. Besides the validation stage of system practicality, the system development can be more effective by using SDR devices. Therefore, we utilize SDR devices to implement the proposed IBFD system and conduct experiments to evaluate its performance. The results show that the SI cancellation effect can reach nearly 100 dB with  10 − 3  order bit error rate (BER) after signal demodulation. From the experiment results, it can be seen obviously that the implemented prototype can effectively cancel the large amount of SI and obtain satisfied digital demodulation results, which validates the effectiveness of the developed system. [ABSTRACT FROM AUTHOR]

The tobacco drying process in the cigarette production has an important effect on the final product quality. Therefore, the intelligent control methods have been widely investigated to ensure the stability of tobacco's outlet moisture content. The existing work mostly uses a relatively fixed set point of the outlet moisture content for different tobacco batches, which can lead to unforeseen product quality after several processes following the drying process and inaccessible amount of dehydration for the rotary dryer. Some tobacco moisture prediction methods have been studied recently while the relationship with the intelligent control methods remain largely unexplored. To deal with these issues, a novel method is proposed in this paper to identify the optimal set point of the drying outlet moisture content for each tobacco batch. An encoder-decoder model is first developed to forecast the post-drying moisture trajectory. Then, an adaptive filter with specially designed mechanisms and a confidence interval of the dehydration level are constructed to obtain the design constraints. Based on all above, a constrained optimization problem is formulated and solved by the genetic algorithm. Extensive experiments on 895 tobacco batches from a large cigarette factory are carried out, which involves both algorithmic evaluation and field test. It turns out that the proposed method achieves the superior performance and leads to an improvement of the product quality in real production. [ABSTRACT FROM AUTHOR]

The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters. [ABSTRACT FROM AUTHOR]

Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters.

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.

The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.

Power quality has become an important issue due to increasing applications of modern industrial and commercial sensitive equipment's. Due to high penetration of modern power electronics-based devices leads to voltage disturbances. To address the voltage disturbances the commonly employed series device called Dynamic Voltage Restorer (DVR) is used which requires an effective control technique. A control scheme namely Volterra adaptive expanded filter is developed which incorporate the higher ordered cost function. The conventional filters have like least mean square (LMS) algorithm is restricted to power error of second order. This restricts the computation of fundamental quantity from the disrupted signals and results in degradation under dynamical scenarios. The recommended approach surmount this aforesaid issue with a solution of a mixed learning of step size. This is derived from the error of second and fourth-order. The Volterra based LMS/Fourth (LMS/F) control has the merit of estimating the fundamental quantity under any power quality (PQ) scenario with less delay and low computational burden. The conventional controller like Proportional Integral (PI) has limited gain coefficients to achieve the optimized the voltage regulation. Furthermore, this is overcome by a Fractional Order PID (FOPID) with an additional coefficient of λ and γ obtain more stabilized DC and AC bus voltage by integrating with Kepler optimizer. For achieving optimal gain, an online tuning of FOPID is done with Kepler Optimizer. The Kepler is inspired by planet motion to achieve best gain values. The evaluation of FOPID is made by considering as a Fitness Function (FF) an Integral Time Square Error (ITSE). Owing to the superior performance of Kepler optimizer based FOPID under transitory state and an enhanced DVR performance is observed. The optimized FOPID has improved response in terms of less overshoot (2.9 %), undershoot (5.6 %), and fast settle time (0.104 s). To authenticate the simulating and experimental results dSPACE board is employed to validate the efficacy and accuracy of the proposed Volterra LMS/F technique.