Adaptive LMS Research Articles

Enhancing power quality of PV-DSTATCOM integrated grid with modified adaptive LMS control

Enhancing power quality of PV-DSTATCOM integrated grid with modified adaptive LMS control

Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization

The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, improving the accuracy of signal detection and reducing the probability of false alarms. The results of the study confirm the perspective of using adaptive methods to increase the reliability and efficiency of spectral monitoring in real operating conditions.

Secure multiple adaptive kernel diffusion LMS algorithm for distributed estimation over sensor networks

Secure multiple adaptive kernel diffusion LMS algorithm for distributed estimation over sensor networks

Power quality improvement of grid-tied PV systems with continuous-time adaptive LMS and FOPI control-based DSTATCOM

A control technique for the single-stage solar photovoltaic-distribution static compensator (PV-DSTATCOM) integrated to a three-phase grid is presented in this article. The continuous-time adaptive Least Mean Square (CLMS) algorithm with Fractional Order Proportional Integral (FOPI) controller has been proposed to control the DSTATCOM. Perturb and Observe-based Maximum Power Point Tracking yields peak power production from the solar photovoltaic array. The precise control of PV-DSTATCOM by the proposed CLMS with FOPI controller has improved the power quality of the grid. This is achieved by utilizing the proposed algorithm's capability to effectively track the time-varying nature of irradiance and load changes, adjust weighting factors, and minimize errors. Simulations in MATLAB/Simulink demonstrate its superior performance in optimizing system behavior under intermittent sunlight, nonlinearity, and load imbalances. The grid current has been sinusoidally balanced with reduced total harmonic distortion and meets IEEE-519 standards in both steady-state and dynamic situations. The proposed CLMS with FOPI controller performs better than other methods in the literature.

DSBuilder-CJS: An Adaptive LMS Fuzzy Inference Powered, Based on Cognitive and Job Skills for Improving Job Effectiveness in a Mexican Local Government

DSBuilder-CJS: An Adaptive LMS Fuzzy Inference Powered, Based on Cognitive and Job Skills for Improving Job Effectiveness in a Mexican Local Government

Dense-BiGRU: Densely Connected Bi-directional Gated Recurrent Unit based Heart Failure Detection using ECG Signal

Heart failure, a state marked by the heart's inefficiency in pumping blood adequately., can lead to serious health complications and reduced quality of life. Detecting heart failure early is crucial as it allows for timely intervention and management strategies to prevent progression and improve patient outcomes. The effectiveness of integrating ECG and AI for heart failure detection stems from AI's capacity to meticulously analyze extensive ECG datasets, facilitating the early identification of nuanced cardiac irregularities and enhancing diagnostic precision. While the current research lacks sufficient accuracy and is burdened by complexity issues. To overcome this issue, we proposed a novel Densely Connected Bi-directional Gated Recurrent Unit (Dense-BiGRU) model for accurate heart failure detection. In this work, we enhanced collected ECG signal in terms of performing multiple data pre-treatment including as denoising, powerline interference and normalization utilizing Collaborative Empirical Mode Decomposition (CEMD) algorithm, Adaptive Least Mean Square (Adaptive LMS) and min-max normalization method, respectively. Here, we utilized the LiteStream_Net layer for extracting appropriate feature from pre-processed signal. Finally, based on extracted features heart failure detection is implemented through introducing Dense-BiGRU algorithm. The proposed research is implemented using MATLAB simulation tools, and its validation is conducted through various simulation metrics including accuracy, recall, precision, F1-score, and AUC. The results of the implementation demonstrate that the proposed research surpasses existing state-of-the-art methodologies.

A Prony Method Variant Which Surpasses the Adaptive LMS Filter in the Precision of the Output Signal's Representation of the Input

A Prony Method Variant Which Surpasses the Adaptive LMS Filter in the Precision of the Output Signal's Representation of the Input

A Discrete Multitone Wireline Transceiver Datapath With On-Chip Sign-Sign LMS Adaptation And Loading Profile Optimization On RFSoC

A Discrete Multitone Wireline Transceiver Datapath With On-Chip Sign-Sign LMS Adaptation And Loading Profile Optimization On RFSoC

Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications

Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications

Efficient utilization of adaptive LMS channel estimation with activity detection guidance and tap decoupling in indoor optical wireless communication systems

Efficient utilization of adaptive LMS channel estimation with activity detection guidance and tap decoupling in indoor optical wireless communication systems

ILA Optimisation Based Control for Enhancing DC Link Voltage with Seamless and Adaptive VSC Control in a PV-BES Based AC Microgrid

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline.

Comparative Performance Study of DVR Using Adaptive LMS Filtering-Based Algorithms

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes.

Signal enhancement in wireless sensor networks based on adaptive filters

Wireless sensor networks are widely used in communication, medical treatment, radar and detection. With the vigorous development of computer science and intelligent technology, wireless sensor networks are also constantly improving in the development. Sensor networks are prone to noise interference when input signals, which will affect the estimation accuracy of the network. In order to enhance the signal of sensor network and improve its accuracy, a distributed filtering algorithm based on fusion adaptive weighting is proposed. Before building the model, the experiment first studied the three traditional adaptive filtering algorithms, LMS, RLS and AP, as the basis for building the experimental model. Then, combined with the distributed characteristics of the sensor network, the attributes of the nodes and their influence in the network were considered in the experiment, and the importance and support of the nodes were linearly weighted to obtain the estimated certainty of each sensor node to the target. Finally, a fusion adaptive weighted distributed filtering algorithm is constructed in the experiment. The simulation experiment verifies that the constructed model can reduce the noise interference to a certain extent, which is conducive to the enhancement of its network signal, and its error estimation accuracy is also improved.

Transmission loss measurement of recycled granular material using wave decomposition by impulse response extraction based on deconvolution

A simplified method for measuring transmission loss with a single fixed microphone inside an impedance tube is proposed. The separation of the incident wave from the reflected wave is achieved by deconvolving the loudspeaker driver voltage signal with the microphone signal to extract the impulse response. Random noise and a sinusoidal sweep signal were used as excitation signals for extraction of the impulse response, and two deconvolution methods were employed: linear deconvolution based on the adaptive LMS algorithm and simple spectral division. To avoid the overlap of incident and reflected pulses, a long impedance tube was used. Using a long impedance tube eliminates the need for an anechoic tube termination and the need to calibrate microphones. It is found that a reflective tube termination provides more accurate results compared to an approximately anechoic termination. The approximately anechoic termination shortens the available time window of the reflected pulse because it reflects some of the low-frequency sound. The standard deviation of the results and the dynamic range of the proposed method were determined experimentally. The applicability of the new method was tested on samples of unconsolidated granular material recycled from construction sites. The measured transmission losses of the different granular fractions were compared with a theoretical model for transmission losses at low frequencies. Samples consisting of smaller granule fractions were found to have surprisingly high transmission loss values. The design of the extended vertical impedance tube in combination with the proposed method is cost effective, speeds up the measurement procedure and provides reliable results.

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

L2 Norm Enabled Adaptive LMS Control for Grid-Connected Photovoltaic Converters

This article presents an L2 norm-enabled variable step-size adaptive least mean square controller for grid-connected photovoltaic converter. The variable step size is adopted to damp out the weight oscillations and to enable faster convergence under exposure to disturbances in the load and solar insolation. More precisely, to achieve oscillations free response, a nonlinear relationship is developed between step size and estimated error. This way, it significantly improves the convergence speed and stability. The iteration function of the presented variable step-size feature includes the input signal to study the impact of harmonically polluted load current on the weight extraction performance. Besides, the designing procedure of step size considers both current and previous error states, thereby ensuring noise immunity. With the inclusion of these features, the devised controller is applied to accurately extracting the fundamental weight component of the load current and in turn enabling various ancillary services. The proposed control algorithm is designed in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matlab</small> /Simulink software and its effectiveness is examined under both steady-state and transient conditions during grid disturbances. Furthermore, the practicability of the proposed controller is verified and compared with conventional controls using ARM cortex M4 microcontroller-based laboratory test bed. In a nutshell, the results confirm the oscillations free weight component with higher convergence and better stability under various operating conditions.

Exploration of Generator Noise Cancelling Using Least Mean Square Algorithm

Generator noise can be categorized as monotonous noise, which is very annoying and needs to be eliminated. However, noise-cancelling is not easy to do because the algorithm used is not necessarily suitable for each noise. In this study, generator noise was obtained by recording near the generator (outdoor signal) and from the room (indoor signal). Noise generator exploration is carried out to determine whether the noise signal can be removed using the Adaptive LMS method. Exploration was carried out by analyzing statistical signals, spectrum with Fast Fourier Transform (FFT) and Inverse FFT (IFFT), and analyzing the frequency distribution of the remaining noise. The results showed that the correlation coefficients were close to each other. Outdoor and indoor signals are at low frequency. The behavior of FFT and IFFT if described in two dimensions, namely real and imaginary axes, formed a circle with a zero center and has parts that come out of the circle. It confirms that noise-cancelling with adaptive LMS can be realized well even though some noise is still left. The residual noise has formed an impulse that showed normally distributed with mean=-0.0000735 and standard deviation =0.000735. This indicates that the residual noise was no longer disturbing.

Noise reduction in speech signal of Parkinson’s Disease (PD) patients using optimal variable stage cascaded adaptive filter configuration

In hands-free communications, including mobile phones, teleconferencing, and video conferencing, the ultimate aim is to obtain a noise-free signal. The adaptive filters that utilize the LMS adaptation algorithm are the best suited for speech signal de-noising among the existing speech signal enhancement techniques. LMS adaptive filter is preferred due to its low computational complexity and robustness, but it fails to simultaneously achieve a faster convergence speed and minimum steady-state MSE. This work introduces a novel cascaded adaptive filter structure for speech signal de-noising wherein the objective is to estimate the noise-free signal with high accuracy. The proposed optimal variable stage cascaded adaptive filter model utilizes a variable stage cascaded adaptive filter structure to estimate the noise-free signal, which is then employed to recover the clean signal with high accuracy. The proposed filter model is tested for reducing the noise from a normal speech signal taken from the NOIZEUS database and an impaired speech signal of Parkinson’s Disease affected patients taken from the MDVR-KCL dataset. The signals are corrupted by various stationary and non-stationary noise signals of different input SNR levels. The noisy signal is used to test the performance of the proposed optimal filter model. The signal de-noising performance of the proposed filter is evaluated in MSE, SNR, and ANR. The results have shown that the proposed filter model provides remarkable performance and an output SNR of 10–15 dB higher than the existing cascaded filter structures. Further, the proposed filter model employs LMS adaptive algorithm, offering a cost-effective and straightforward hardware implementation of ANC with improved accuracy.

Improved Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters

Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy.