Normalized Least Mean Square Research Articles

GFDM TRANSCEIVER BASED ON ANN CHANNEL ESTIMATION

Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08.

Validation of target tracking performance through signal feature extraction method based on 1D convolutional neural network

In order to perform active vibration and noise control, a variety of adaptive algorithms have been investigated and developed. Especially, neural network-based signal tracking algorithms, such as the radial basis function neural network (RBFNN), are being widely utilized. For signal tracking problems, a reference signal has a significantly important role, while it is difficult to determine the reference signal when it has relatively complex spectrum. Thus, this study focuses on the signal feature extraction for manipulating appropriate reference signals easily and validation of signal tracking performance with it. In order to carried out the signal feature extraction, 1D convolutional neural network (1D CNN) is implemented and trained based on the CWRU bearing dataset. In addition, multi normalized least mean square (NLMS), neural network-based signal tracking algorithm, and diagonal recurrent neural network (DRNN) is employed to confirm the signal tracking performance. The proposed method shows that the 1D CNN-based signal feature extraction method could find the signal feature properly and the reference signal obtained from this methodology is employed to a target tracking problem, which shows a great performance.

Modeling, analysis, and control of shaft transverse vibration from rotating systems through active bearing concept

Rotating parts are widely applied to mechanical systems such as pumped-storage hydroelectricity, nuclear power plant, machining tools and so on. While operating, they can be easily damaged or destroyed by unbalanced mass, bending, torsion and misalignment. In order to solve this problem, rotor vibration control can be conducted through active bearing concept. In this work, active bearing system which consists of piezo actuators and rubber grommets is proposed and applied to a rotating system motivated from a pumped-storage hydroelectricity, for performing active vibration control. The main point of this paper is to prevent damage or failure caused by harsh transverse vibration through active bearings. First, the rotating system is modeled by transfer matrix method (TMM) based on Euler-Bernoulli beam theory and in order to check accuracy of this model, the responses of TMM are compared with the responses from the finite element method (FEM). For implementing active control in real time, normalized least mean square (NLMS) algorithm is utilized. The results show that the proposed active bearing concept shows great performance on the attenuation of shaft transverse vibration.

High-precision Positioning Method for BD3 System Based on Adaptive Optimization

It is of great significance to locate faults quickly and accurately in power distribution system to improve system reliability and recover quickly. With high positioning accuracy, strong anti-interference ability in mountainous areas and cities, and the ability to provide all-weather and all-round services, the BeiDou-3 (BD3) navigation satellite system has broad application prospects in power systems. Using the filtering principle of normalized least mean square (NLMS) filter, a high-precision positioning method for BD3 system based on adaptive optimization is propose to eliminate the pseudo-range error caused by electromagnetic interference. Simulation results show that NLMS-based adaptive pseudo-range filtering method can availably decrease the pseudo-range error caused by high proportional electromagnetic interference in the power system, achieve adaptive optimization of power system device positioning, and improve positioning accuracy.

Active Mitigation Strategy of Structure-Borne Vibration with Complex Frequency Spectra from Asymmetric Plate-like Mounting Systems in Next Generation Mobilities

The complicated spectrum produced by electric and hybrid car engines is particularly sensitive to the mid-frequency range. Furthermore, sensor placement in future mobility is crucial because when the positions and orientations of sensors are altered by excessive vehicle vibration, it results in the malfunctioning of autonomous driving systems. Smart structure-based active mounting approaches have been developed to reduce engine-induced vibration. These are made to continually adjust the mounts’ dynamic properties and enhance their performances in terms of noise, vibration, and harshness (NVH) under diverse operating circumstances. It can take the place of the engine support system’s current mount technique. The performance of the source part for reducing vibration when the structure is triggered by a sinusoidal and multi-frequency signal is the main subject of this study. The overall structure, which has two active mounts based on the source-paths-receiver structure, was modeled using a lumped parameter model. In the source section, sinusoidal, amplitude modulation (AM), and frequency modulation (FM) signals were used in order to assess the effectiveness of vibration reduction in the mid-frequency band. The normalized least mean-square (NLMS) technique was utilized to assess the effectiveness of an active mounting system, and a tracking signal was employed as a control signal. The algorithm was further expanded to the multi-NLMS algorithm to monitor the complex spectral signal. This demonstrates how an active mounting system can successfully reduce vibrations when the structure is activated by many mid-frequency complex signals.

ReAdapt: A Reconfigurable Datapath for Runtime Energy-Quality Scalable Adaptive Filters

This paper proposes ReAdapt–a reconfigurable datapath architecture for scaling the energy-quality trade-off of adaptive filtering at runtime. The ReAdapt can dynamically select four adaptive filtering algorithms for gradating complexity levels during runtime by reconfiguring the processing flow in its datapath and by blocking the switching activity (e.g., reducing the CMOS dynamic power) of unused modules with data-gating. The ReAdapt proposal can scale the energy-quality trade-off by choosing the following four different levels of filter algorithms complexity: 1) least mean square (LMS); 2) partial update normalized LMS (PU-NLMS); 3) set-membership normalized LMS (SM-NLMS); 4) normalized LMS (NLMS). The ReAdapt architecture reuses common modules of each adaptive filter, resulting in a compact VLSI hardware implementation. The ReAdapt architecture operation is implemented in a case-study for interference mitigation for electroencephalogram (EEG) signal processing. The hardware synthesis results show an increase of 6.80 times in throughput and at least a reduction of 2.84 times in energy per operation compared with the state-of-the-art adaptive filters. This paper also investigates the benefits of dynamically reconfiguring the four ReAdapt operating modes at runtime for different levels of signal-to-noise ratio (SNR) for the processed signals. We also demonstrate that dynamically reconfiguring the ReAdapt operating modes during runtime results in an optimal energy-quality trade-off which is advantageous over the conventional single static mode.

Coefficients-Switched Normalized Least-Mean- Squares Adaption in Echo Canceler of Sparse-Echo-Path

The Normalized Least-Mean-Squares (NLMS) algorithm commonly used in echo cancelers suffers from a number of limitations due to the existing sparsity in the echo path. Although some sparsity-aware algorithms have been proposed, the high computational complexity greatly increases the their difficulty of implementation. To reduce the complexity and improve the performance in echo cancelers, we introduce a Coefficients-Switched (CS) processing into the NLMS algorithm and propose the CS-NLMS algorithm and the Mean-square-deviation (MSD) analysis to evaluate its effectiveness. Simulations of the MSD were performed with different system parameters. The performance of the proposed algorithm is verified by comparison with the simulation results of the previously proposed algorithms. The superiority of the proposed algorithm in terms of computational complexity is also discussed.

Prediction Error Method (PEM)-Based Howling Cancellation in Hearing Aids: Can We Do Better?

This work develops an effective technique acoustic feedback cancellation (AFC) in the digital hearing aid (DHAid) devices. The normalized least mean square (NLMS) algorithm-based AFC method may suffer from a biased convergence. The biased convergence problem is considerably resolved by the prediction error method (PEM)-based AFC (PEM-AFC); however, it may demonstrate a slow convergence. The proposed method’s main structure is based two adaptive filters. The main adaptive AFC filter receives its input from the DHAid receiver signal, while the auxiliary AFC filter is activated by a probe signal. The main idea is to apply a lattice filtering-based pre-processing for decorrelation in the main AFC filter’s update equation. This produces a Newton-like adaptive algorithm with fast convergence. Additionally, the lattice filtering is executed on a sample-by-sample basis, in contrast to the frame-based execution in the traditional PEM-AFC method. As the AFC system converges, the level of the probe signal is decreased to improve the output SNR; however, the low-level input signal slows down auxiliary AFC filter’s convergence. In order to improve the convergence speed, the gradient information from a maximum Versoria-criterion (MVC) is incorporated into the auxiliary AFC filter’s update algorithm. The two adaptive filters’ coefficients are exchanged, to ensure that both adaptive filters converge to a good estimate of the true acoustic feedback path. Simulations show that the proposed method works well for speech/signals and for DHAid devices with different gain settings. Additionally, the proposed method shows robust performance in the event of a sudden change in the acoustic environment.

Recursive Least Squares-Algorithm-Based Normalized Adaptive Minimum Symbol Error Rate Equalizer

The adaptive minimum symbol error rate (AMSER) equalizer is known to have better symbol error rate (SER) performance than the adaptive minimum mean square error equalizer. Furthermore, the normalized AMSER (NAMSER) equalizer outperforms the AMSER equalizer, which can be regarded as the improvement of the normalized least mean square (NLMS) equalizer by incorporating the minimum SER (MSER) criterion. Inspired by that, we propose an improved recursive least squares-based NAMSER equalizer (RLS-NAMSER) that takes the advantage of faster convergence of the RLS algorithm over the NLMS algorithm. The RLS algorithm is first reconsidered from the perspective of optimization problem and an approximate RLS (ARLS) algorithm is proposed which converges faster than the NLMS algorithm. The RLS-NAMSER equalizer is then proposed by combining the ARLS equalizer with the MSER criterion. Simulation results show that the RLS-NAMSER equalizer has better convergence performance than the NAMSER equalizer while having nearly the same steady state performance as the NAMSER equalizer.

Diffusion Bayesian Decorrelation Algorithms Over Networks

The convergence rate of the diffusion normalized least mean squares (NLMS) algorithm can be speeded up by decorrelation of signals. However, the diffusion decorrelation NLMS algorithm confronts the conflicting requirements of fast convergence rate and small steady-state error. To address the issue, this paper proposes a family of diffusion Bayesian decorrelation least mean squares (DBDLMS) algorithms based on decorrelated observation models. Firstly, the weight update equations of the proposed DBDLMS algorithms are obtained by performing Bayesian inference over the decorrelated observation models, with variable step-sizes emerging naturally to help address the conflicting requirement. Secondly, the update equations of the decorrelation coefficient vectors are inferred from the Bayesian perspective, with the variable step-sizes emerging again. Subsequently, the performance analysis of the proposed DBDLMS algorithms is carried out. Moreover, a simple and effective approach is derived to estimate the free parameters for the proposed DBDLMS algorithms. Finally, the learning performance of the proposed algorithms are verified by Monte Carlo simulations.

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

An effective communication application necessitates the cancellation of Impulsive Noise (IN) from Orthogonal Frequency Division Multiplexing (OFDM), which is widely used for wireless applications due to its higher data rate and greater spectral efficiency. The OFDM system is typically corrupted by Impulsive Noise, which is an unwanted short-duration pulse with random amplitude and duration. Impulsive noise is created by humans and has non-Gaussian characteristics, causing problems in communication systems such as high capacity loss and poor error rate performance. Several techniques have been introduced in the literature to solve this type of problem, but they still have many issues that affect the performance of the presented methods. As a result, developing a new hybridization-based method is critical for accurate method performance. In this paper, we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM. The proposed method is also compared to Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Recursive Least Square (RLS) adaptive filters. It has also been tested using the binary phase-shift keyed (BPSK), four quadrature amplitude modulation (QAM), sixteen QAM, and thirty-two QAM modulation techniques. Bit error Rate (BER) simulations are used to evaluate system performance, and improved performance is obtained. Furthermore, the proposed method is more effective than recent methods.

Speech enhancement for noisy signals using adaptive algorithms

Speech enhancement is a signal processing technique used to improve the quality and intelligibility of speech recordings that contain noise or interference. Its main goal is to eliminate unwanted background noise while preserving the clarity and naturalness of the speech signal. This paper provides a comprehensive analysis of three widely used adaptive filtering algorithms, Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Affine Projection Algorithm (APA). The limitations of LMS, such as slow convergence and sensitivity to input variations, are addressed in this study. By incorporating normalization, NLMS improves convergence speed and robustness to input power levels. The Affine Projection Algorithm (APA) is known for its exceptional performance in non-stationary environments, achieved through subspace projection to estimate optimal filter coefficients, resulting in faster convergence and improved tracking capabilities. In this paper, the algorithms are compared using Signal-to-Noise Ratio (SNR), Mean-Square Error (MSE), and Root-Mean-Square-Error (RMSE) values.

Noise Reduction Based on Adaptive Prediction Fitting Algorithm for a Heterodyne Φ-OTDR System

Aiming to reduce the noise better in distributed optical fiber vibration sensing system, an adaptive predictive filtering algorithm is proposed to improve the overall signal-to-noise ratio (SNR) of vibration detection. A phase-sensitive optical time domain reflectometric ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system based on heterodyne detection with dual AOMs is designed to reduce the requirement of DAQ high sampling rate, and an adaptive prediction noise reduction method based on normalized least mean square (NLMS) is adopted to reduce the non-stationary noise. Experimental results show that the SNR is increased from 8.14dB to 22.31dB. Compared with moving difference average method, wavelet denoising method and Prewitt edge detection method, the SNR of the adaptive prediction noise reduction algorithm is the highest, which would have potential application in disturbance detection under a strong noise environment.

Extension and Analysis of the ARG algorithm to 2D

Two-dimensional (2D) adaptive filtering is a technique that has been used for denoising in applications such as biomedical image processing in recent years. In this paper, we design the extension of the 1D-ARG adaptive filtering schemes to form new 2D-ARG adaptive filters. To compare the performance of the proposed algorithm in noise reduction in digital images, three images of different sizes from the Matlab library, Moon, Pout, and Cameraman, are taken as reference. This reduction is compared with two other gradient algorithms least mean squares (LMS) and normalized least mean squares (NLMS) for 2D adaptive filter design. Based on the simulation results and the established metrics, we demonstrate that the proposed method achieves a noise reduction eventually superior to the other 2D gradient algorithms.

BP-LMS-based BDS-3 power system positioning method

The BeiDou-3 navigation satellite system (BDS-3) provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation. However, BDS-3 power system positioning faces certain challenges, such as complex electromagnetic interference and incomplete error elimination. Herein, a back propagation neural network- improved least mean square (BP-LMS) adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning, which utilizes the loss function to update the weight of the BP hidden layer, computes the pseudo compensation range, and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning. Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination. Compared with LMS and normalized least mean square (NLMS), the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14% and 51.38%, respectively.

Pedestrian mobility management for heterogeneous networks

&lt;p&gt;Pending the arrival of the next generation of 5G which is not yet deployed in&lt;br /&gt;some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction&lt;br /&gt;process is performed by the VSS-NLMS algorithm, an adaptive filter&lt;br /&gt;known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.&lt;/p&gt;

Simultaneous micropositioning and microvibration control of a magnetostrictive Stewart platform with synthesized strategy

In this paper, we present a hybrid adaptive feedforward and feedback controller with hysteresis compensator on a magnetostrictive-actuated multi degree of freedom (DOF) positioning and vibration isolation integrated system (PVIIS). The PVIIS is custom-developed and based on “cubic” Stewart configuration. It is challenging to develop an effective controller to systematically address the issues of cross-coupling, strut hysteresis and dynamics for the PVIIS. Magnetostrictive asymmetric hysteresis leads to the loss of closed-loop output performance with conventional linear controller. To this end, a novel comprehensive controller is proposed in this paper. With the inverse kinematics analysis, the spatial motion of the mobile plate of the PVIIS is decoupled into the independent motions of six actuator struts. As for controlling the single strut of the PVIIS, the hybrid adaptive feedforward (AFF) and proportional–integral–derivative (PID) feedback controller with asymmetric hysteresis compensator (HC) is constructed. Among them, the AFF sub-controller is characterized by an adaptive finite impulse response (FIR) filter whose coefficients are updated by normalized least-mean squares (NLMS) algorithm. The PID feedback sub-controller is introduced to enhance the ability of positioning and vibration isolation. The arctangent-polynomial modified Prandtl–Ishlinskii (APMPI) model is used to build the inversion-based asymmetric hysteresis compensator. The experimental set-up is built. The positioning and vibration isolation experiments are conducted. Experimental studies show that the output performances are best with the proposed controller compared against sole AFF, hybrid AFF and PID controllers. With the proposed controller, the relative positioning error can maintain around 2.83% when the PVIIS performs 2-DOF rotation in the presence of 0.5 Hz disturbance.

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator.

The electro-hydrostatic actuator (EHA) is a promising actuating apparatus used in flight control systems for more electric aircraft (MEA) due to its high power density and low maintenance. Since the reliability of the system decreases with increasing complexity, fault detection is becoming increasingly important. In this paper, an adaptive filter was designed based on a normalized least mean square (NLMS) algorithm, which could identify the resistance of the motor windings online to detect electrical faults in the EHA. Additionally, based on the analytical relationship between rotational speed and displacement, a rotational speed estimation method was designed. By comparing the actual rotational speed with the estimated one, hydraulic faults could be detected. To verify the efficacy of the aforementioned method, software was applied for the modeling and simulations, which included fault injection and detection. On this basis, an experimental platform was built and then subjected to a series of validation experiments. The results indicate that the fault detection method has the potential to detect electrical and hydraulic faults in an EHA.

Arctangent Function-Based Normalized Least Mean Squares Algorithm for a Battery Supported Wind Energy Conversion System

Wind energy conversion systems (WECSs) based on doubly fed induction generator (DFIG) are often connected to local loads, and excess power is supplied to the grid. The nonlinear and unbalanced local loads integrated with WECS, degrade the power quality by injecting harmonics and negative sequence components in grid currents. Another major concern in grid-integrated WECS is related to sudden steep fluctuations in wind and load powers, which bring about frequency and voltage deviations that ultimately affect grid stability. Thus, the objective of this work is twofold. First, to suppress harmonics and negative sequence components in grid currents, using a normalized least mean squares (NLMS) adaptive filtering scheme based on arctangent cost function (Arc-NLMS). The Arc-NLMS adaptive algorithm is robust against model uncertainties and exhibits optimal convergence performance. Second, to guarantee grid security amidst sudden erratic variations in wind speed or load power, through implementation of a power management scheme (PMS) using a battery energy storage. The PMS allocates exponential values to weight components, to diminish the effects of power fluctuations. Based on performance evaluation of the system using a developed laboratory prototype, the PMS proves to be effective in smoothening power fluctuations while the power quality issues related with connection of local nonlinear and unbalanced loads are also alleviated.

A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter.

Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.