Adaptive Filtering Technique Research Articles

Numerical simulations and experimental validation on passive acoustic emissions during bubble formation

In this paper, the volume of fluid (VOF) model in conjunction with continuum surface force (CSF) model is applied to numerically investigate the single bubble formation process in the bubble columns. Meanwhile, the hybrid method (large-eddy simulation (LES) and acoustic analogy method) is used for bubble acoustic prediction. Experimental results provide a reliable validation of the numerical results. Adaptive filtering technique and time–frequency analyses are adapted for the numerical acoustic data at the gas velocity of 0.1m/s. The spectrum and spectrogram approaches indicate that the numerical acoustic data covers two principal frequency components, in which, one is the bubble generation rate and the other is the natural frequency of volumetric bubble oscillation. By combining the studies of flow field and acoustic signals, volumetric bubble oscillation is found to be excited by an axial water jet up into the bubble, immediately following the bubble detachment. The results of this study lead to the proposition that numerical simulation method could be applied to well understanding of the physics behind the passive acoustic emission.

Li-ion Battery Pack State-of-Charge Estimation Disturbed by Colored Noises

In electric vehicles, onboard lithium-ion batteries need an accurate state of charge (SOC) to enhance safety, improve efficiency, and extend lifetime. However, there are noises to disturb electric quantities of batteries, especially the battery current for SOC calculation. Based on an equivalent circuit model, the battery SOC is well estimated by a residua-sequence-based adaptive extended Kalman filter (AEKF) when the battery current is polluted by colored noises. This adaptive filtering technique was implemented on the experiment data of a real lithium-ion battery pack, the current values of which were contaminated by the non-zero mean Gaussian noise. Results showed that the SOC estimation produced by the proposed AEKF was much more accurate and reliable than that caused by the conventional extended Kalman filter (EKF) in the colored noise environment.

Enhanced LPI Waveform Representation by Ambiguity-Domain Elliptical Gaussian Filtering

ESM systems need to use sophisticated signal processing, such as time-frequency representation techniques, for the interception of nonstationary LPI radar signals. In this study, an adaptive filtering technique using an ambiguity-domain elliptical Gaussian kernel is proposed to increase the readability of pseudo-Wigner–Ville distribution based representations for LPI waveform parameter extraction purposes. The complexity and information content of the outputs obtained by the proposed method are evaluated by objective criteria, such as ratio of norms, Renyi entropy, and Jubisa measure. The results quantify efficient filtering performance under severe SNR conditions with lower computational complexity.

Design Considerations for Robust Noise Rejection in Otoacoustic Emissions Measured In-Field Using Adaptive Filtering

Otoacoustic emission (OAE) measurement is a sensitive and effective technique to monitor changes in the inner ear potentially induced by noise exposure. However, outside a controlled testing environment, measurements are challenging since the level of ambient noise might be higher than the low-level OAE response. Therefore, an OAE system was designed, suitable to measure OAEs repeatedly on an individual worker in noisy test conditions. This system features a left and right earpiece, each equipped with a pair of miniature loudspeakers, an external and an internal microphone. In addition to the passive attenuation of the earpiece, adaptive filtering on the distortion product OAE (DPOAE) signals is used to further enhance the ratio between the measured OAE signal and interfering noise. The adaptive filtering technique uses the sound captured by the ipsilateral external and internal microphones as well as from the contralateral internal and external microphones. In this paper, the accuracy of DPOAE signals are studied using different combinations of the four microphones in single and dual adaptive filter topologies, as well as the benefits of adding a fixed transfer function in the adaptive filtering algorithm topology to estimate the acoustic path. Side-by-side comparison shows that a dual-stage adaptive filtering algorithm, using a combination of the contralateral internal microphone with the ipsilateral external microphone, is the most promising approach to denoise the DPOAE signal.

Gradient-Based Parameter Estimation in Pairwise Linear Gaussian System

This technical note addresses the gradient-based parameter estimation problem for pairwise linear Gaussian systems. The new adaptive filtering scheme is based on gradient-based optimization methods for estimating the uncertain system parameters and a robust square-root variant of the pairwise Kalman filter used for estimating the unknown states of the pairwise linear Gaussian systems. Hence, in the adaptive filtering techniques, the dynamic state and system parameters are estimated simultaneously. The method of the filter sensitivities computation required in gradient evaluation is derived in terms of square-root factors of covariance matrices.

A Fully Digital Background Calibration Technique for M-Channel Time-Interleaved ADCs

In this paper, we propose a pure digital blind calibration method to estimate and calibrate offset, gain and timing mismatches. Gain errors are calibrated based on first channel correction using an overall reference, whereas for the rest of the $$(M-1)$$ channels, the corrected first channel becomes the reference channel. Time skew calibration is performed using a derivative filter followed by a fractional delay filter and a scaling factor. The proposed technique significantly reduces the required hardware resources, specifically for the derivative and fractional delay filters for which no look-up table is required. In addition, the proposed method requires only two finite impulse response filters with fixed coefficients, thus reducing complexity and hardware resources, as compared to adaptive filter techniques. For a sampling frequency of 3.072 GHz, the maximum achievable signal-to-noise and distortion ratio is 67 dB, resulting in effective number of bits of 10.83 for a 12-bit resolution analog-to-digital converter.

Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Adaptive filter techniques and the filtered-x least mean square (FxLMS) algorithm have been used in Active Noise Control (ANC) systems. However, their effectiveness may degrade due to the nonlinearities and modeling errors in the system. In this paper, a new feedback ANC system with an adaptive neural controller and variable step-size learning parameters (VSSP) is proposed to improve the performance. A nonlinear adaptive controller with the FxLMS algorithm is first designed to replace the traditional adaptive FIR filter; then, a variable step-size learning method is developed for online updating the controller parameters. The proposed control is implemented without any offline learning phase, while faster convergence and better noise elimination can be achieved. The main contribution is that we show how to analyze the stability of the proposed closed-loop ANC systems, and prove the convergence of the presented adaptations. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed methods for attenuating different noise sources transferred via nonlinear paths, and show the improved performance over classical methods.

SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series

We propose a method for adaptive nonlinear sequential modeling of vector-time series data. Data is modeled as a nonlinear function of past values corrupted by noise, and the underlying non-linear function is assumed to be approximately expandable in a spline basis. We cast the modeling of data as finding a good fit representation in the linear span of multi-dimensional spline basis, and use a variant of l1-penalty regularization in order to reduce the dimensionality of representation. Using adaptive filtering techniques, we design our online algorithm to automatically tune the underlying parameters based on the minimization of the regularized sequential prediction error. We demonstrate the generality and flexibility of the proposed approach on both synthetic and real-world datasets. Moreover, we analytically investigate the performance of our algorithm by obtaining both bounds of the prediction errors, and consistency results for variable selection.

Laboratory tests on local damage detection for jacket-type offshore structures using optical FBG sensors based on statistical approaches

In this study, a local damage detection based on statistical approach for jacket-type offshore structures by principal component analysis (PCA) and linear adaptive filter (LAF) techniques using strain response data measured by FBG sensors was proposed while dynamic responses are being popularly utilized for damage detection of civil infrastructures including jacket-type offshore structures. In addition, environmental effects due to variations in temperature and external loading were intensively investigated and an efficient remedy was proposed using the nonparametric PCA and LAF models. Unlike many existing statistical damage detection methods, the mean of residual values eliminating the environmental effects was utilized as damage index for rational for enhancing the normality based on the central limit theorem and the normality was first checked before damage estimation using the mean of residual values. Laboratory tests for a scaled tidal current power plant structure, one of many jacket-type offshore structures, were conducted to investigate the technical feasibility of the proposed method for damage detection and localization. It was found that the PCA technique could more efficiently eliminate undesired environmental effects contained in the measurement data from FBG sensors without any additional information on the environmental changes, resulting in more damage-sensitive features under various environmental changes.

Characterization of Upper-Limb Pathological Tremors: Application to Design of an Augmented Haptic Rehabilitation System

In this paper, an adaptive filtering technique is proposed to estimate and characterize pathological tremors caused by Parkinson’s Disease (PD) and Essential Tremor (ET). The technique is based on the formulation of band-limited multiple Fourier Linear Combiners (BMFLC) and is called Enhanced-BMFLC (E-BMFLC). The effectiveness of the designed filter is statistically evaluated through a clinical study involving 14 PD and 13 ET patients. The hand tremors of the participants are studied in three Degrees Of Freedom (DOF). Using statistical analysis, it is shown that the new design of the filter significantly enhances the accuracy in comparison with the performance of conventional BMFLC filtering. In addition, E-BMFLC significantly reduces the sensitivity to parameter tuning and intrapatient variabilities. The observed improvements are achieved by modulating the memory of the proposed filter, and by enriching the utilized harmonic model. The proposed filter is then used to develop a safe haptics-enabled robotic rehabilitation architecture, designed for patients having hand tremors. The architecture is entitled Augmented Haptic Rehabilitation (AHR), which enables adaptive management of the involuntary components of the hand motion while delivering assist-as-needed haptic therapy (for the voluntary component) and avoiding unsafe amplification of hand tremors. Experimental evaluations are provided to evaluate the efficacy of the proposed AHR system.

Local Contrast Based Adaptive SAR Speckle Filter

In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.

Coding Assisted Adaptive Noise Cancellation in Stochastic MIMO Wireless Channels

Fading is a random behavior and generally modeled with statistical distributions. Fading caused by multipath propagation can degrade the bit error rate (BER) performance of a digital communication system. To mitigate fading and to have reliable communication in wireless channel error control coding along with adaptive filtering techniques are employed. In this paper, certain methods of noise cancellation in stochastic multiple-input-multiple output wireless channel using error correction coding as well as adaptive filter trained with Least Mean Square (LMS), Normalised Least Mean Square (NLMS) and Recursive Least Square (RLS) algorithm are explored. The experiments performed show satisfactory results in severely faded Nakagami-m channels for SIMO, MISO and MIMO set ups. The work intends to formulate a framework for developing certain insight into the use of error control coding and adaptive filtering to fight fading in stochastic wireless channel. General Terms Algorithms

Adaptive filtering parameter estimation algorithms for Hammerstein nonlinear systems

This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by filtering the input-output data. A linear filter based multi-innovation stochastic gradient (LF-MISG) algorithm is proposed by the innovation expansion, in order to improve the computational efficiency of the LF-RLS algorithm. Furthermore, a time-varying factor is introduced in the linear filter to improve the convergence speed of the LF-MISG algorithm. The efficiency of the proposed algorithms are shown in comparison with the conventional identification algorithms.

Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices

Mobile and long-term recording of biomedical signals, such as electrocardiogram (ECG), electromyogram (EMG), and EEG, can improve diagnosis and monitor the evolution of several widespread diseases. However, it requires specific solutions, such as wearable devices, that should be particularly comfortable for patients, while at the same time ensuring medical-grade signal acquisition quality, including power line interference (PLI) removal. This paper focuses on the on-board real-time PLI filtering on a low-power biopotential acquisition wearable system. This paper analyzes in depth basic and advanced PLI filtering techniques and evaluates them in a wearable real-time processing scenario, assessing performance on EMG and ECG signals. Our experiments prove that most PLI removal algorithms are not usable in this challenging context, because they lack robustness or they require offline processing and large amounts of available data. On the other hand, adaptive filtering techniques are robust and well suited for lightweight online processing. We substantiate this finding with offline analysis and comparison, as well as with a complete embedded implementation on our low-power low-cost wearable device.

Using the Matérn covariance function to enhance detection and estimation of transient infrasound

Adaptive filtering techniques are well known to enhance detection of anomolous events in the presence of slowly changing (quasi-stationary), autocorrelated noise backgrounds such as those occurring in measurements of infrasound signals in the atmosphere. The noise in this case is dominated by the effect of the turbulent wind blowing over the sensing element. While mechanical windscreens can provide significant signal-to-noise ratio gains, additional benefits can be obtained by employing adaptive filters. In this presentation, a kernel-based adaptive filter based on the Matérn Covariance Function is demonstrated to improve the probability of detection of transient infrasound signals as well as improve signal estimation errors without increasing false alarm rates in the presence of wind noise. The choice of the Matérn Covaraiance Function to represent the wind noise process is motivated by its roots in fractional-order stochastic differential equations. Because the wind noise at infrasound frequencies can be influenced by shear-turbulence interaction and turbulence-turbulence interaction more than one kernel is required to obtain optimal performance for the filter. Results are presented under a range wind noise conditions.

Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign

Air quality forecast systems need reliable and accurate representations of the planetary boundary layer (PBL) to perform well. An important question is how accurately numerical weather prediction models can reproduce conditions in diverse synoptic flow types. Here, observations from the summer 2014 HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) experimental campaign are used to validate simulations from the Weather Research and Forecasting (WRF) model over the complex, urban terrain of the Greater Athens Area. Three typical atmospheric flow types were identified during the 39-day campaign based on 2-day backward trajectories: Continental, Etesians, and Saharan. It is shown that the numerical model simulations differ dramatically depending on the PBL scheme, atmospheric dynamics, and meteorological parameter (e.g., 2-m air temperature). Eight PBL schemes from WRF version 3.4 are tested with daily simulations on an inner domain at 1-km grid spacing. Near-surface observations of 2-m air temperature and relative humidity and 10-m wind speed are collected from multiple meteorological stations. Estimates of the PBL height come from measurements using a multiwavelength Raman lidar, with an adaptive extended Kalman filter technique. Vertical profiles of atmospheric variables are obtained from radiosonde launches, along with PBL heights calculated using bulk Richardson number. Daytime maximum PBL heights ranged from 2.57km during Etesian flows, to as low as 0.37km during Saharan flows. The largest differences between model and observations are found with simulated PBL height during Saharan synoptic flows. During the daytime, campaign-averaged near-surface variables show WRF tended to have a cool, moist bias with higher simulated wind speeds than the observations, especially near the coast. It is determined that non-local PBL schemes give the most agreeable solutions when compared with observations.

Groundwater Contaminant Transport Modeling Using Multiple Adaptive Data Assimilation Techniques

In modeling the behavior of contaminants in a subsurface environment using data assimilation schemes, accurate assignment of model and observation errors are significant for the successful application of the techniques. In this study, a three‐dimensional transport model was used to simulate the advection and dispersiontransport of contaminant in the subsurface. Stochastic data assimilation schemes were coupled with the subsurface contaminant transport model to predict the state of the contaminant. Three data assimilation techniques namely the conventional Ensemble KalmanFilter, the Adaptive Ensemble Kalman Filter and the Hybrid Adaptive Ensemble Kalman Filter were adopted to improve the prediction of the contaminant fate and transport in the groundwater. The Ensemble Kalman Filter applies a Monte Carlo approach to the filtering problem. The adaptive filtering technique employs the diagnostic approach to fine tune the model and observation covariance matrix. The hybrid technique uses combination of the forecast covariance matrix and the invariant background covariance matrix to explore the Ensemble Kalman filter. The impact of the filters on the numerical model is examined by using the Normalized Root Mean Square Error (NRMSE), Average Absolute Bias (AAB) metric, and Maximum Absolute Deviation (MAD) techniques. The AAB evaluation of Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter shows error reduction of 93% and 90%, respectively, while the MAD assessment recorded 94% and 89% improvement respectively, in the numerical model. The results of simulations show that the prediction accuracy of the filters is better than numerical model. The proposed Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter, takes advantage of the adaptive factor and the weighting factor, respectively to improve the prediction efficiency of the Ensemble Kalman filter. Sensitivity analysis performed on Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter using NRMSE indicates that the adaptive factor does not affect the prediction accuracy of the former whereas the weighting factor has influence on the later.

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in $$2\times 1$$2?1 space---time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.

Проблемы синтеза адаптивных фильтров методами генетических алгоритмов с программной реализацией на языке С++ в ОС МСВС

Проблемы синтеза адаптивных фильтров методами генетических алгоритмов с программной реализацией на языке С++ в ОС МСВС

Automated Assessment of Right Ventricular Volumes and Function Using Three-Dimensional Transesophageal Echocardiography

Assessment of right ventricular (RV) function is known to be of diagnostic value in patients with RV dysfunction. Because of its complex anatomic shape, automated determination of the RV volume is difficult and strong reliance on geometric assumptions is not desired. A method for automated RV assessment was developed using three-dimensional (3-D) echocardiography without relying on a priori knowledge of the cardiac anatomy. A 3-D adaptive filtering technique that optimizes the discrimination between blood and myocardium was applied to facilitate endocardial border detection. Filtered image data were incorporated in a segmentation model to automatically detect the endocardial RV border. End-systolic and end-diastolic RV volumes, as well as ejection fraction, were computed from the automatically segmented endocardial surfaces and compared against reference volumes manually delineated by two expert cardiologists. The results reported good performance in terms of correlation and agreement with the results from the reference volumes.