LMS Adaptive Filter Research Articles

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.

A linearly constrained framework for the analysis of the deficient length least-mean square algorithm

Most adaptive system identification analyses assume the length of the adaptive filter to match the length of the unknown system response. This assumption tends to be unrealistic, and its conclusions may not apply to some practical applications. The behavior of deficient length adaptive filters has been studied using different approaches. This work formulates the deficient length adaptive system problem using a Linearly Constrained Minimum Mean Squared Error (LCMMSE) framework. This new formulation leads to a very interesting interpretation that allows the utilization of results from the study of constrained adaptive filters to understand the behavior of the deficient length LMS adaptive filter. The reduced number of coefficients is formulated as a linear optimization constraint that defines a projection onto the feasible space for the adaptive weight vector. We derive analytical models for the mean and mean-square behavior of the adaptive weights. In addition, we derive an analytical model for the variance of the steady-state squared estimation error which provides important design information. Simulation results show excellent matching between theory and actual algorithm behavior.

Power Optimized VLSI Architecture of Distributed Arithmetic Based Block LMS Adaptive Filter

In this paper, we are presenting a power-efficient Distributed Arithmetic (DA) based Block Least Mean Square (BLMS) Adaptive Digital Filter (ADF). The proposed DA BLMS architecture proposes a shared area-efficient Multiplier Accumulate Block that calculates both the partial filter products and the weight increment terms in the same module. It also uses Multiplexers (MUX) and Demultiplexers (DEMUX) which passes only L out of N inputs, where N and L are the filter length and chosen block size respectively, into the MAC thus helping in achieving the DA functionality along with reduced power consumption. Also, efficient truncation of the obtained error and weight update terms is performed by being able to select the non-zero-bit part of the signal to be fed back. The entire architecture is driven by a single slow clock which reduces the power consumption of the device further. On comparing with the best existing DA BLMS Structures, the proposed architecture uses 15% lesser power, 14% lesser EPS according to ASIC Synthesis, and for a filter length of N=16 and a block size of L=4 respectively.

Analysis of the Notch Filter Insertion Position for Natural Frequency Vibration Suppression in a Magnetic Suspended Flywheel Energy Storage System

The composite material flywheel rotor of a flywheel energy storage system (FESS) has a low natural frequency. When the system suffers from noise interference, the magnetic bearing generates a force with the same frequency as the natural frequency and causes vibration to occur. Thus, it is necessary to suppress the natural vibration of the magnetic suspended (MS) FESS. The LMS adaptive notch filter is generally adopted for vibration suppression. The vibration suppression performance of the system is different when the insertion position of the notch filter is different. This paper analyzes the influence of the notch filter in different insertion positions of the control system. Through the transfer function from noise to magnetic bearing force, theoretical analysis of the influence of different positions of the notch filter is performed. Corresponding experiments are performed in a 500 kW MS FESS prototype. The theoretical analysis is verified experimentally.

Pedestrian mobility management for heterogeneous networks

<p>Pending the arrival of the next generation of 5G which is not yet deployed in<br />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<br />process is performed by the VSS-NLMS algorithm, an adaptive filter<br />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.</p>

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.

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal.

Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

Separating FECG from MECG and Analyzing Fetal Heart Rate

Figuring out fetal coronary heart can be essential for monitoring the situation of a fetus. While an ECG signal is recorded by way of putting electrodes on the maternal stomach, the fetal ECG is regularly masked by using the maternal ECG sign and noise. Via using an LMS adaptive filter to extract the fetal ECG signal, after which the use of the Pan & Tompkins algorithm to hit upon the QRS complexes, the fetal heart may be determined with very low errors.

VLSI Implementation of DENLMS Adaptive Filter for Biomedical Applications

Very Large Scale Integration (VLSI) implementation of the Delayed Error Normalized LMS (DENLMS) adaptive filter using a pipelined architecture is proposed and it can be used for biomedical applications. The proposed pipelined VLSI architecture increases the performance of adaptive filters by lowering the amount of time spent on critical path calculation. When compared to the current Error Normalized Least Mean Square (ENLMS) method for pipelining purposes, the DENLMS technique requires an additional delay element, and because of the delay element, the algorithm functions faster and more effective in low-power applications. The proposed pipelined architecture of the DENLMS filter, on the other hand, improves the efficiency while consuming less power overall. The cell leakage power reduction and total dynamic power reduction obtained by using the DENLMS filter are 31.8% and 33.5%, respectively, although the overall area of the filter has increased by 20.4%.

Variable Step Normalized LMS Adaptive Filter for Leak Localization in Water-Filled Plastic Pipes

Acoustic waves induced by leakage attenuate as they propagate in the piping system. Difficulties are generally encountered in leak localization in plastic pipes since these waves are heavily attenuated than in metallic pipes. This study presents a leak localization method based on the variable step normalized least mean square (VS-LMS) adaptive filter. In the method, the optimal weight vector of the filter is determined for time delay estimation (TDE) without requirement of the prior knowledge of statistical characteristics of leak and noise signals. It has a desirable feature of sharpening the main peak of TDE curve through adaptive iterative learning process, thus improving the accuracy of TDE for leak localization. By adjusting the update mode of iteration step, the convergence speed of the VS-LMS algorithm can be greatly improved. A simulation model of leak wave propagation is established and is subsequently analyzed for leak localization with respects to the TDE accuracy. Experimental work is further carried out on a plastic pipe to verify the effectiveness of the proposed method. The simulation sand experimental results demonstrate that the proposed method outperforms the original LMS and basic cross-correlation methods for the leak localization in a water-filled plastic pipe.

Adaptive intensity noise suppression of fiber optic gyroscopes based on period LMS algorithm

Reducing the noise level of fiber optic gyroscope (FOG) has been paid much attention. The relative intensity noise (RIN) of the light source is a dominant noise source and different methods have been used to reduce the RIN. However, the performance of the RIN suppression methods suffering from parameter variation is rarely discussed. In this paper, we introduced a method that uses the period LMS (PLMS) adaptive filter in the FOG system with noise subtraction implementation to ensure the sensor’s performance in varying environment. By analyzing the sampled detector signals in the system, the parameters in the system are determined based on the convergence requirement and noise performance of PLMS. Simulations as well as experiments of a FOG system with the PLMS algorithm implemented in the signal processing board are used to verify the feasibility of the method. The performance comparison between the PLMS filter and the predetermined Wiener filter in different temperature is tested and analyzed, which reveals the variability of the system parameters and verifies the necessity and ability of the introduced method to adapt to the varying environment.

Early arrhythmia prediction based on Hurst index and ECG prediction using robust LMS adaptive filter

This paper aims to early arrhythmia prediction and investigate the use of robust adaptive filters to forecast the ECG signal. Different robust adaptive filters are examined for ECG prediction. Features in time and time-frequency domains have been extracted, and the Hurst index has been calculated in two domains. The performance of the SVM, KNN, and the ensemble of LogitBoost trees for model construction has been examined for detecting the occurrence of an arrhythmia in the predicted ECGs in an inter-patient scenario. Results show that pseudo-Huber adaptive filter is the best choice for ECG prediction. Also, classification performance measures besides the McNemar test show that the predicted signal is suitable to use for early arrhythmia detection with accuracy, precision, sensitivity, and specificity of at least 98 $$\%$$ .

Implementation of different techniques for removing artifacts in diaphragmatic SEMG, and evaluation of these in automatic and online applications

Respiratory muscles superficial electromyography (SEMG) is an important source of information in the monitoring of ventilated patients. One of the main problems in the acquisition of SEMG signals is the different sources of interference. The most common artifacts are the baseline wander (BW) normally generated by motion, and power line interference (PLI). In this paper, different methods were selected and evaluated for the removal of these artifacts in a simulated SEMG signal of the right diaphragm muscle. The best performance technique for the removal of each artifact was determined using frequency analysis and estimation of criteria such as the signal to noise ratio, relative error, cross-correlation, and coherence of the power spectrum density. The computational cost of each of the techniques was estimated to also assess how appropriate it is to implement in online applications and limited hardware. The study demonstrates that the spectral interpolation technique has a good performance in removing PLI from the SEMG signal but has a high computational cost, unlike the adaptive LMS filter. On the other hand, the SSA-based technique proved to be the best performing for BW removal and its computational cost is adequate in a more limited hardware system.

Application of a New CO₂ Prediction Method Within Family House Occupancy Monitoring

The article describes the application of Python for verification of a newly designed method of CO2 prediction from measurements of indoor parameters of temperature and relative humidity within occupancy monitoring in real conditions of a family home. The article describes the implementation of non-electric quantities (indoor CO2 concentration, indoor temperature, indoor relative humidity) measurement in five rooms of a family home (living room, kitchen, children’s room, bathroom, bedroom) using Loxone technology sensors. The IBM IoT (Internet Of Things) was used for storing and subsequent processing of the measured values within the time interval of December 22, 2018, to December 31, 2018. The devised method used radial basis function (artificial neural networks (ANN)) mathematical method (implementation in Python environment) to perform accurate predictions. For further increase of the accuracy and reduction of prediction noise from the obtained course of the predicted signal, multiple variations of the LMS adaptive filter algorithm (Sign, Sign-Sign, Sign-Regressor) were used (implemented in the MATLAB SW tool). The accuracy of the newly proposed CO2 concentration prediction method exceeds 95% in the selected experiments.

Power line interference removal from electrocardiogram signal using multi-order adaptive LMS filtering

Electrocardiogram (ECG) signals are susceptible to noise and interference from the external world. This paper presents the reduction of unwanted 50 Hz power line interference in ECG signal using multi-order adaptive LMS filtering. The novelty of the present method is the actual hardware implementation for power line interference removal. The design of adaptive filter is carried out by the simulink-based model and hardware-based design using FPGA. The performance measures used are signal to noise ratio (SNR), PSNR, MSE and RMSE. The novelty of the proposed method is to achieve better SNR by careful selection of the filter order using hardware.

ASIC Implementation of Linear Equalizer Using Adaptive FIR Filter

Power consumption plays a crucial role in the design of portable wireless communication devices, as it has a direct influence on the battery weight and volume required for operation. This article presents a novel design for a linear LMS equalizer for the optimization of filter order. The article describes the use of a variable length algorithm for dynamically updating the tap-length of the LMS adaptive filter to optimize the performance and for reducing the power in the adaptive filter core. An algorithm is applied to reduce and adjust the order of the filter in linear equalizer according to the channel conditions. The proposed design is implemented in the synopsis TSMC 65nm technology. The results from using the algorithm uses 28% less power when compared with the conventional 64-tap fixed length adaptive filter design. It has also been shown that the low-complexity of the additional circuitry needed for the variable length adaptive filter presents minimal overhead for this architecture.

A kernel least mean square algorithm for fuzzy differential equations and its application in earth’s energy balance model and climate

This paper concentrates on solving fuzzy dynamical differential equations (FDDEs) by use of unsupervised kernel least mean square (UKLMS). UKLMS is a nonlinear adaptive filter which works by applying kernel trick to LMS adaptive filter. UKLMS estimates multivariate function which is embedded to estimate the solution of FDDE. Adaptation mechanism of UKLMS helps for finding solution of FDDE in a recursive scenario. Without any desired response, UKLMS finds nonlinear functions. For this purpose, an approximate solution of FDDE is constructed based on adaptable parameters of UKLMS. An optimization algorithm, optimizes the values of adaptable parameters of UKLMS. The proposed algorithm is applied for solving Earth energy balance model (EBM) which is considered as a fuzzy differential equation for the first time. The method in comparison with the other existing approaches (such as numerical methods) has some advantages such as more accurate solution and also that the obtained solution has a functional form, thus the solution can be obtained at each time in training interval. Low error and applicability of developed algorithm are examined by applying it for solving several problems. After comparing the numerical results, with relative previous works, the superiority of the proposed method will be illustrated.

Bird Species Recognizer using LMS Algorithm

Birds play a vital role in many ecosystems, acting as both predators and preys for other living organisms. Therefore its important to monitor the population of various bird species in the environment in order to maintain balance in the ecosystem. This process will become tedious if it is done manually as it involves handling large sets of data at the same instant. We can do this by developing an automatic bird species recognizer which identifies the bird species based on bird songs and voice signals. In this research, we have used a tenth-order LMS adaptive filter to remove noise from bird voice signals which are recorded in different environmental conditions where different noise frequencies are present. The design of a tenth-order LMS adaptive filter using MATLAB has been implemented. The performance and characteristics of the filter for five different methods of LMS has been shown. After removal of noise from the noisy bird voice signal using LMS algorithm, we have made use of cross correlation to identify the bird species that it corresponds to. Signal to Noise Ratio (SNR) and Mean Square Error (MSE) of the filtered bird signals obtained using the variants of LMS like Normalized LMS, Sign-Data LMS, Sign-Error LMS and Sign-Sign LMS have been estimated and compared. We have made use of signal processing tool kits and various noise parameter schemes have been computed to show the effectiveness of the designed filter in the field of bird recognition.

Comparative study for Active Noise Cancellation using Adaptive filtering and Standing wave pattern

Noise pollution is one of the most fundamental challenges facing our environment, causes health problem, communication inefficiency and degrade the performance of works due to lack of concentration, thus, mitigating this impact becomes an unavoidable requirement of time to protect people's health and the environment. This noise may originate from several sources including industrial machinery, system parts wear out, and adjacent environmental acoustics. To mitigate this noise effect, an Active Noise Cancellation (ANC) headphone is achieved by two effective techniques; Adaptive filtering and Standing wave phenomenon. In this work, an ANC system is designed using both adaptive filtering and standing wave techniques, the former one basically utilizes single-channel feedforward whereas the latter one utilizes both single-channel feedforward and feedback control. LMS adaptive filter algorithm is the basic component of the designed ANC headphone. For simulation, a noise-free signal will be used as the desired audio signal and a gaussian distributed noise as the unwanted noise signal, these are combined to form noise corrupted speech signal. Propose algorithms performance were evaluated based on the ability to mitigate effects of different frequency broad-band noise signals and of different Noise to Signal ratio. Evaluation measures used are; convergence rate and noise reduction in dB. Result reveals ANC headphone using standing wave technique has better performance at mitigating noise frequency below 800Hz, with low SNR than Adaptive filtering. However, at higher frequencies above 1000Hz, ANC headphone using Adaptive filtering has good performance of masking high frequencies up to 22dB.