Adaptive Filtering System Research Articles

Welding Arc Maculopathy Caused by Malfunction of a High-tech Electronic Protection Shield

Welderʼs arc maculopathy is a rarely described outer retinal photic damage mainly caused by radiation in the short-wave visual light spectrum [1], [2]. Contemporary shielding devices convey effective protection by electronically controlled adaptive filter systems installed in welding helmets. In this report, the observation of welding arc retinopathy is described in a case after use of a malfunctioning high-tech welding helmet.

Comparison of affine-projection adaptive filter vs LMS speech enhancement systems

Evaluation of a two systems for speech enhancement is presented. A clasical Widrow dual channel noise canceller adaptive filter is used introducing the implemenation of Affine Projection Algorithm and comparing results versus traditional LMS implementation. Clasical Widrow noise canceller concept is used under reallistic input considerations, where signal of interest and noise are present at both inputs. Simple Root Mean Square Error (RMSE), Itakura-Saito Distance (ISD) and subjective evaluation tests are made in order to ease comparisons.

Comparison of affine-projection adaptive filter vs LMS speech enhancement systems

Comparison of affine-projection adaptive filter vs LMS speech enhancement systems

An adaptive nonlinear filter for integrated navigation systems using deep neural networks

This paper presents a novel nonlinear adaptive sensor fusion method for integrated navigation systems with varying noise parameters. The innovation is utilizing deep neural networks to mine the noise-related patterns of specific sensors and combining it with conventional nonlinear filters. This hybrid approach improves the feasibility and robustness of adaptive filtering by achieving an effective estimation of the originally weakly observable noise parameters. The specific sensors are defined as α-type sensors whose errors are entirely generated by themselves. The mathematical model for analyzing α-type sensors output sequence and the deep neural network for mining the patterns of interest are established. All adaptive filtering systems using α-type sensors can benefit from this paper. Specifically, it is applied to inertial and satellite integrated navigation system. The numerical experiments indicate that the proposed filter achieves promising accuracy and robustness improvement as compared to conventional nonlinear filters. And the comparisons between different nonlinear approximation algorithms indicate that the first-order approximation is accurate enough for our application.

Adaptive spam filtering system using complement Naïve Bayes model

Adaptive spam filtering system using complement Naïve Bayes model

Development of Multi-Staged Adaptive Filtering Algorithm for Periodic Structure-Based Active Vibration Control System

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles.

Modified LMS Strategies Using Internal Model Control for Active Noise and Vibration Control Systems

Traditional adaptive filtering algorithms are non-recursive systems that cannot use a time-variant reference input in real time and are not appropriate for control signals with uncertainties and unanticipated conditions. The main purpose of this research is to design novel adaptive digital filtering algorithms based on internal model control (IMC). The new methods consist of a process model for the target plant so as to estimate its dynamic behavior for active vibration and noise attenuation schemes in order to improve the stability, robustness, and tracking performance. On the basis of on the existing least mean squares, the methods are combined with an internal model controller, or the whole adaptive filtering system could become a feedback control system structure based on IMC. The performances were validated in numerical simulations with various conditions that could have happened in realistic applications, and the results were compared with the original algorithms. This study shows that the active noise and vibration systems that are applied to vehicles, mechanical systems, and other targets are enhanced through improving the performance of conventional adaptive filtering algorithms and by using internal model control effectively.

Noninvasive Fetal Heart Rate Monitoring: Validation of Phonocardiography-Based Fiber-Optic Sensing and Adaptive Filtering Using the NLMS Algorithm

Here we present the evaluation results of our novel noninvasive phonocardiographic-based fiber-optic sensor for fetal Heart Rate (fHR) detection using adaptive filtering and the NLMS Algorithm. The sensor uses two interferometric probes encapsulated inside a PolyDiMethylSiloxane (PDMS) polymer. Based on real data acquired from pregnant women in a suitable research laboratory environment, once they had given their written informed consents, we created a simplified dynamic signal model of the distribution of maternal and fetal heart sounds inside the maternal body. Building upon this signal model, we verified the functionality of our novel fiber-optic sensor and its associated adaptive filtering system using the NLMS Algorithm. The main reason why we chose this technology to develop our system was that it allows monitoring the fHR without exposing the fetus to any external energies or radiation (in contrast to the ultrasound-based Cardiotocography Method). We used objective criteria such as: Signal to Noise Ratios: SNR_in, SNR_out and Percentage Root-mean-square Difference (PRD) for our evaluations.

ACTIVE VIBRATIONAL CONTROL OF FLEXIBLE MANIPULATOR USING FILTERED-X LMS ALGORITHM

This paper describes the vibration control of a flexible manipulator using Filtered-x LMS algorithm. In this study adaptive notch filter is applied to the vibration control of a flexible manipulator model. The adaptive notch filter is designed to estimate multiple vibration mode frequencies of the flexible manipulator and to minimize the effect of vibration. The filtered-x LMS algorithm is applied to make the root strain error and the system input as close to minimization as possible. In the process, the adaptive notch filter learns to eliminate the resonant vibration frequencies of the system. The experimental results show that this presented adaptive notch filter system can suppress the vibration of the flexible manipulator and track the desired joint angles.

Adaptive Newsfeed Filter System for the Social Networking Site Using Artificial Bee Colony Optimization

Social networking sites have recently received a lot of attention, which is particularly popular, provides a personal newsfeed of posts that is shared with friends. To enable users to quickly browse the most important and useful newsfeed posts from friends, the sites provides a newsfeed filter to show the top news for users. However, the selected results may not conform to the individual’s viewing needs. This study proposes a newsfeed filter system on Facebook, which utilizes an artificial bee colony algorithm to provide adaptive newsfeed posts based on individual preferences. The experimental results indicate that the proposed method improves the accuracy of the newsfeed filter and outperforms other methods.

Discrete linear canonical transform computation by adaptive method

The linear canonical transform (LCT) describes the effect of quadratic phase systems on a wavefield and generalizes many optical transforms. In this paper, the computation method for the discrete LCT using the adaptive least-mean-square (LMS) algorithm is presented. The computation approaches of the block-based discrete LCT and the stream-based discrete LCT using the LMS algorithm are derived, and the implementation structures of these approaches by the adaptive filter system are considered. The proposed computation approaches have the inherent parallel structures which make them suitable for efficient VLSI implementations, and are robust to the propagation of possible errors in the computation process.

Discrete fractional Fourier transform computation by adaptive method

The continuous fractional Fourier transform (FRFT) can be interpreted as a rotation of a signal in the time-frequency plane and is a powerful tool for analyzing and processing nonstationary signals. Because of the importance of the FRFT, the discrete fractional Fourier transform (DFRFT) has recently become an important issue. We present the computation method for the DFRFT using the adaptive least-mean-square algorithm. First, the DFRFT computation scheme with single angle parameter of the signal block using the adaptive filter system is introduced. Second, considering the transform angles always change in practical applications, the DFRFT computation scheme with adjustable-angle parameter of the signal block using the adaptive filter system is presented. Then we construct two realization structures of the DFRFT computation with simultaneous multiple-angle parameters for each signal block. The proposed computation approaches have the inherent parallel structures, which make them suitable for efficient very large scale integration implementations.

ANALYSIS OF MNLMS AND KLMS ALGORITHM FOR UNDERWATER ACOUSTIC COMMUNICATIONS

The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance measure characteristics such as signal to noise ratio (SNR) and mean square error (MSE). The MNLMS is developed by calculating an optimum learning parameter that best suits for the acoustic signal used. The analysis is carried out for a range of 100 Hz to 10 KHz source signals and the algorithm proves that any ambient noise signals against the source signal in this range can be eliminated and the source signal can be reconstructed. Our simulation results show that KLMS and MNLMS algorithms achieve remarkable performance even in the very low SNR region as compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for MNLMS and KLMS.

SNR and MSE analysis of KLMS algorithm for underwater acoustic communications

Adaptive filters are used to alleviate the degradation caused by wind-driven ambient noise in shallow water. Underwater acoustic signals are greatly affected by ocean interference and ambient noise disturbances propagated through underwater channels. Therefore, an effective adaptive filtering system is necessary for denoising signals degraded by noise. In this paper, adaptive algorithms, such as Least Mean Square (LMS), Normalised LMS (NLMS) and Kalman LMS (KLMS) are analysed in terms of their performance with the aid of performance measure characteristics, such as Signal to Noise Ratio (SNR) and Mean Square Error (MSE) for various wind speeds ranging from 2m/s to 6m/s. Simulation results show that KLMS algorithms achieve remarkable performance, even in the very low SNR region, compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for KLMS and the performance of the KLMS algorithm is unswerving for different wind speeds.

Control of modulated vibration using an enhanced adaptive filtering algorithm based on model-based approach

Conventional adaptive filtering algorithms, typically limited to the control of single or multiple sinusoids, are not appropriate to control modulated vibrations, especially in the presence of rich side band structures. To overcome this deficiency, a new control algorithm is proposed that introduces a feedback loop with the model predictive sliding mode control (MPSMC) in the adaptive filtering system. Several amplitude and frequency modulation cases are first computationally studied, and conventional and proposed methods are comparatively evaluated in terms of estimation error, performance in time and frequency domains, stability, and uncertainty in the reference signal. To experimentally validate the proposed algorithm, an active strut (with longitudinal vibrations) is constructed. Overall, the proposed adaptive algorithm yields superior reductions at the main frequencies and at side bands; also, good attenuation is found on a broadband basis.

Control of incommensurate sinusoids using enhanced adaptive filtering algorithm based on sliding mode approach

This article focuses on the active control of a combination of incommensurate sinusoids and its subsets. Two sliding mode control (SMC) algorithms are proposed to enhance the conventional least mean squares (LMS) algorithm maintaining its inherent advantages. The SMC is utilized to update the filter coefficients, and a feedback loop with the SMC is introduced in the adaptive filtering system. The proposed methods are evaluated for several cases by comparing the results with the LMS algorithm. An experiment with an active strut is also constructed and longitudinal vibrations excited by an amplitude modulated type signal are measured before and after the control for each adaptive filtering algorithm. Overall, the proposed algorithms demonstrate faster convergence and reduce amplitudes at multiple peaks even when only one frequency is targeted.

CPR Artifact Removal in Ventricular Fibrillation ECG Signals Using Gabor Multipliers

We present an algorithm for discarding cardiopulmonary resuscitation (CPR) components from ventricular fibrillation ECG (VF ECG) signals and establish a method for comparing CPR attenuation on a common dataset. Removing motion artifacts in ECG allows for uninterrupted rhythm analysis and reduces "hands-off" time during resuscitation. The current approach assumes a multichannel setting where the information of the corrupted ECG is combined with an additional pressure signal in order to estimate the motion artifacts. The underlying algorithm relies on a localized time-frequency transformation, the Gabor transform, that reveals the perturbation components, which, in turn, can be attenuated. The performance of the method is evaluated on a small set of test signals in the form of error analysis and compared to two well-established CPR removal algorithms that use an adaptive filtering system and a state-space model, respectively. We primarily point out the potential of the algorithm for successful artifact removal; however, on account of the limited set of human VF and animal asystole CPR signals, we refrain from a statistical analysis of the efficiency of CPR attenuation. The results encourage further investigations in both the theoretical and the clinical setup.

A Low Processing Cost Adaptive Algorithm Identifying Nonlinear Unknown System with Piecewise Linear Curve

This paper proposes an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.

A Self-Reconfigurable Adaptive FIR Filter System on Partial Reconfiguration Platform

This paper presents a self-reconfigurable adaptive FIR filter system design using dynamic partial reconfiguration, which has flexibility, power efficiency, advantages of configuration time allowing dynamically inserting or removing adaptive FIR filter modules. This self-reconfigurable adaptive FIR filter is responsible for providing the best solution for realization and autonomous adaptation of FIR filters, and processes the optimal digital signal processing algorithms, which are the low-pass, band-pass and high-pass filter algorithms with various frequencies, for noise removal operations. The proposed stand-alone self-reconfigurable system using Xilinx Virtex4 FPGA and Compact-Flash memory shows the improvement of configuration time and flexibility by using the dynamic partial reconfiguration techniques.

A modified adaptive IIR filter design via wavelet networks based on Lyapunov stability theory

In this paper, we present a wavelet network IIR filtering system satisfying asymptotic stability in the sense of Lyapunov unlike many other gradient descent algorithms based adaptive filtering systems. The proposed system also carries the advantages of the time-frequency specific properties of wavelet networks embedded into the proposed filter dynamics. Two experiments for system identification problems corresponding to the infinite impulse response filter design are proposed. The results verified that the proposed wavelet network infinite impulse response adaptive filtering system not only performs better than gradient descent based algorithms but also performs as good as other stability theory based optimization algorithms.