Cancellation Filter Research Articles

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For this purpose, various methodologies were developed to ensure accurate and efficient monitoring. In this context, this paper proposes an approach for bearing fault early diagnosis based on the variational mode decomposition (VMD), used as a notch filter for dominant mode cancellation, and a machine learning approach, namely the one-dimensional convolution neural network (1D-CNN), for detection and diagnosis purposes. Specifically, the proposed approach first performs features extraction using VMD for fault detection, and then triggers to multi-scale features extraction using CNN convolution and pooling layers for classification and diagnosis. The proposed bearing fault detection and diagnosis approach is evaluated, in terms of robustness and performances, using the well-known Case Western Reserve University experimental dataset. In addition, performances are evaluated versus well-established demodulation techniques, in terms of fault detection, and machine learning strategies, in terms of fault diagnosis. The achieved results show that the proposed VMD notch filter-based 1D-CNN approach is clearly promising for bearing degradation monitoring.

A Joint Azimuth Multichannel Cancellation (JAMC) Antibarrage Jamming Scheme for Spaceborne SAR

The azimuth multichannel synthetic aperture radar (SAR) increases the swath width by reducing the pulse repetition frequency (PRF), thus providing an effective scheme for realizing high-resolution and wide-swath (HRWS) imaging simultaneously. However, the quality of multichannel SAR images will suffer from electromagnetic jamming in the electronic antagonism environment. The existing channel cancellation methods not only require high positioning accuracy of the jammer and high PRF of the SAR system, but also remain periodic dark stripes, which reduce the readability of the SAR images. To solve these problems, this article proposes a scheme for multichannel SAR to locate the barrage jammer and suppress jamming signals. Firstly, by establishing the least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bm {L}}^{1}}$</tex-math></inline-formula> -norm optimization model on the echoes after channel cancellation, the location of the jammer in both azimuth and range directions can be accurately estimated. Then, based on the estimated jammer position, a joint cancellation filter is constructed to remove the jamming signal from the contaminated multichannel SAR data. And the reconstruction filter is designed according to the characteristics of the remaining signal, so that the SAR system reduces the requirement for high PRF. Finally, for the case of dark strips, this paper calculates the image amplitude equalization coefficient by deducing the azimuth modulation function, and a uniform HRWS SAR image without jamming is acquired. Results of simulated and measured data validate that the proposed method can effectively remove jamming signals and obtain the HRWS SAR images.

Reference-free adaptive filtering of extracellular neural signals recording in ultra-high field magnetic resonance imaging scanners: Removal of periodic interferences

This paper focuses on the removal of periodic artifacts from neural signals recorded in rats in ultra-high field (UHF) MRI scanners, using a reference free adaptive feedforward method. Recording extracellular neural signals in the UHF environment is motivated by the desire to combine neural recording and UHF functional magnetic resonance imaging (fMRI) to better understand brain function. However, the neural signals are found to have extremely high noise artifacts of a periodic nature due to electromagnetic interference and due to small oscillatory motions. In particular, noise at 60 Hz and several harmonics of 60 Hz, sinusoidal noise from a pump, and low frequency breathing motion artifacts are observed. Due to significant overlap between the noise frequencies and the neural frequency region of interest, band pass filters cannot be effectively utilized in this application. Hence, this paper develops adaptive least squares feedforward cancellation filters to remove the periodic artifacts. The interference fundamental frequency is identified precisely using an implementation of k-means in an iterative approach. The paper includes significant animal data from rats recorded in an IACUC-approved procedure in 9.4 T and 16.4 T MRI machines. For breathing artifacts filtered from 4 rats, the mean signal cancellation values at the harmonic interference frequencies are 5.18, 12.97, and 20.87 dB/Hz for a sliding template subtraction, a single-stage impulse reference method, and the cascaded adaptive filtering approach respectively. For pump artifacts filtered from 2 chronically implanted rats, mean signal cancellation values are 2.85, 9.52 and 12.06 dB/Hz respectively. The experimental results show that periodic noise is very effectively removed by the developed cascaded adaptive least squares feedforward algorithm.

Adaptive filter and pulse-shaping for interference cancellation of differential quadrature phase-shift keying modulation

Differential quadrature phase-shift keying (DQPSK) modulation methods and variations are commonly used in wireless communication, including high-speed optical fiber, Bluetooth, and satellite communication. DQPSK cannot be isolated from possible interference with its present application. The interference cancellation process has been observed and analyzed using a device model built in this study. The core components of the supporting block for this device model have raised cosine filters for pulse shaping and adaptive filters. The presence of these core components has resulted in a dramatic improvement in device efficiency, according to robust Simulink data. The best bit error rate (BER) of 5.7e-03 provides evidence of this.

The Use of the Hybrid Active Power Filter in LCC-HVDC Considering the Delay-Dependent Stability

The line commutated converter (LCC) based HVDC projects generally use passive AC filters for harmonic cancellation, which has disadvantages of large footprint, poor filtering effect due to the offset of the resonance point, etc. This paper introduces a high-power LC hybrid active power filter (HAPF), which can meet the requirements of filtering and dynamic reactive power compensation. First, the passive part of the HAPF adopts two parallel single-tuned LC filters, which has low impedance at 12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 24 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> . Second, considering the effects of the time delay of the high-voltage sensors, current controller and valve controllers, the possible negative impedance of the HAPF may bring a great risk of resonance and thus endanger the stability of the system. Third, an impedance model of the HAPF is established and the impact of the time delay on system stability is analyzed. Further, an adaptive resonance damping strategy based on least mean square (LMS) algorithm is proposed. Finally, the overall performances of the HAPF are verified by simulations on PSCAD/EMTDC.

A Multiple Listener Crosstalk Cancellation System Using Loudspeaker-Dependent Regularization

Binaural audio requires the use of crosstalk cancellation if reproduced using loudspeakers. A three-listener crosstalk cancellation system has been designed and built as part of this work. Simulations for different loudspeaker distributions are presented and a large system span is shown to increase low-frequency crosstalk cancellation performance while a denser loudspeaker distribution in front of a given listener increases mid to high-frequency crosstalk cancellation. The system's performance under perturbation of the listeners and loudspeakers is investigated and at high frequencies loudspeakers further away from any given listener are shown to be affected more by perturbations than those nearer the listener. To this issue, a novel implementation of weighting loudspeaker source strengths using loudspeaker-dependent regularization is developed and optimized for use in this system. Hence, at high frequencies just loudspeakers close to the listener are used. This is shown to create a more robust solution than traditional crosstalk cancellation filter design when the system has undergone perturbations.

Real-Time Fast Fourier Transform-Based Notch Filter for Single-Frequency Noise Cancellation: Application to Electrocardiogram Signal Denoising.

Despite the considerable improvement of the common-mode rejection ratio of digital filtering techniques, the electrocardiogram (ECG) traces recorded by commercialized devices are still contaminated by residual power line interference (PLI). In this study, we address this issue by proposing a novel real-time filter adapted to single-frequency noise cancellation and automatic power line frequency detection. The filtering process is principally based on a point-by-point fast Fourier transform and a judicious choice of the analysis window length. Intensive experiments conducted on real and synthetic signals have shown that our filtering method offers very clean ECGs, due to the suppression of spikes corresponding to the PLI and the preservation of spikes outside the filter band. In addition, this method is characterized by its low computational complexity which makes it suitable for real-time cleaning of ECG signals and thus can serve for more accurate diagnosis in computer-based automated cardiac system.

A Causality-Constrained Frequency-Domain Least-Squares Filter Design Method for Crosstalk Cancellation

Crosstalk cancellation filters play a crucial role when reproducing binaural signals over loudspeakers. Least-squares design methods for crosstalk cancellation filters are commonly formulated either in the time domain or in the frequency domain. While the time-domain problem is too complex to be solved in a dynamic real-time setup, the frequency-domain problem is computationally cheap. However, it yields circular convolution artifacts, which degrade the performance. Using a unified framework, we derive a causality-constrained frequency-domain least-squares problem, which itself is an unconstrained problem. Our method does not yield circular convolution artifacts since the impulse responses are causal by construction. This construction is based on the fact that the real part and the imaginary part of a causal signal's spectrum are related via Hilbert transform. By approximating the Hilbert transform, we reduce the proposed method's computational complexity. The complexity reduces by two to three orders of magnitude while the performance remains close to that of the time-domain solution. We provide a detailed complexity analysis and compare our method to three state-of-the-art methods on a large dataset. Our investigations reveal that the causality-constrained method enables a flexible tradeoff between computational complexity and performance figures.

Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises.

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.

A harmonic-cancellation-based model to predict speech intelligibility against a harmonic masker.

This work aims to predict speech intelligibility against harmonic maskers. Unlike noise maskers, harmonic maskers (including speech) have a harmonic structure that may allow for a release from masking based on fundamental frequency (F0). Mechanisms, such as spectral glimpsing and harmonic cancellation, have been proposed to explain F0 segregation, but their relative contributions and ability to predict behavioral data have not been explored. A speech intelligibility model was developed that includes both spectral glimpsing and harmonic cancellation. The model was used to fit the data of two experiments from Deroche, Culling, Chatterjee, and Limb [J. Acoust. Soc. Am. 135, 2873-2884 (2014)], in which speech reception thresholds were measured for stationary harmonic maskers varying in their F0 and degree of harmonicity. Key model parameters (jitter in the masker F0, shape of the cancellation filter, frequency limit for cancellation, and signal-to-noise ratio ceiling) were optimized by maximizing the correspondence between the predictions and data. The model was able to accurately describe the effects associated with varying the masker F0 and harmonicity. Across both experiments, the correlation between data and predictions was 0.99, and the mean and largest absolute prediction errors were lower than 0.5 and 1 dB, respectively.

Peak tailing cancellation techniques for digital CR-(RC)n filter

The CR-(RC)n filter is used for Semi-Gaussian pulse shaping in analog spectroscopy. A new digital CR-(RC)n filter is presented based on the recursive models of the CR filter and RC filter. The spectroscopic signal reconstruction technique is proposed. This technique is applied to digital exponential decay signals obtained by the digitization of analog signals that represent the combined response of a FAST-SDD detector and the associated front-end circuit. Compared with the direct application of the digital CR-(RC)n filter, the technique eliminates the undershoot of the Semi-Gaussian pulse which is the primary cause of the low-energy peak tailing. And, the digital pole-zero cancellation filter follows digital CR-(RC)n filter to reduce the long tail of the shaped Semi-Gaussian pulse which leads to the high-energy peak tailing. Experimental results based on XRF signals of manganese sample show that the proposed techniques can cancel peak tailing due to the shaped Semi-Gaussian pulse improving FWTM/FWHM ratio and throughput capability.

Combined functional link adaptive filter for nonlinear acoustic echo cancellation

The usage of low-quality components in communicating devices may introduce nonlinearity in the audio signals. The degree of nonlinearity may vary over time due to the nonstationary nature of audio signals. In the presence of nonlinear audio signals, the performance of an acoustic echo cancellation (AEC) scheme is impacted by two major limitations. The first one is related to the modeling accuracy, and the second one is related to the adaptation performance. The issue of modeling accuracy exists because the available nonlinear AEC (NAEC) schemes consider the nonlinearity as time-invariant and employ a fixed architecture to model the echo path. The adaptation performance issue occurs due to the conflicting requirements of fast convergence and low steady-state error. There is a need for an architecture that not only models the nonlinearity efficiently but also provides better performance in terms of convergence. In this paper, three new architectures convex combination functional link adaptive filter (CC-FLAF), improved CC-FLAF (ICC-FLAF), and variable step size CC-FLAF (VSSCC-FLAF) are proposed to overcome the above limitations. The proposed architectures are based on the combination of adaptive filters and variable step size approach. Computer simulations demonstrate the effectiveness of proposed architectures, in improving the nonlinear modeling performance for NAEC applications.

A Collaborative Spline Adaptive Filter for Nonlinear Echo Cancellation

Acoustic echo causes the quality of communication to degenerate and results in loss of clarity. Though conventional linear adaptive filters have been applied successfully to eliminate linear acoustic echo, they can barely deal with the problem of nonlinear acoustic echo. In this paper, a collaborative spline adaptive filter is presented to eliminate nonlinear acoustic echo. The filter is composed of a linear adaptive filter in the upper branch and a nonlinear spline adaptive filter in the lower branch. The nonlinear one is a Hammerstein system consisting of a spline interpolation function and a subsequent linear adaptive filter. The two branches are collaboratively combined and a mixing parameter is adopted, which can be updated to adjust the proportion of the lower branch output signal. Experimental results show that the presented method can achieve a good performance in nonlinear echo cancelation regardless of whether the nonlinear degree of the echo path varies with time or not.

Motion artifact cancellation from a single channel SCG using adaptive forgetting factor recursive least square filter

This work proposes a novel filter for motion artifact cancellation from a single channel seismocardiography (SCG) data recorded by a tri-axis accelerometer. A real time adaptive forgetting factor recursive least square filter (AFFRLSF) was developed and embedded in our designed single channel SCG recorder system (SCSRS) for motion artifact cancellation, heartbeat signal extraction, and heart rate calculation. This SCSRS was placed on the chest wall of 24 subjects who were asked to perform standing, walking, jogging, and jumping movements on a treadmill. We recorded the single channel SCG signal and the standard Electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. The heartbeat signals were extracted and heart rates were calculated from the output of AFFRLSF. The graphs of the extracted heartbeat signals were very clear under all the standing, walking, jogging, and jumping motions. The results indicate an average correlation coefficient of up to 0.9878 between heart rates estimated from SCG and ECG of all the 24 subjects. This observation shows that the proposed AFFRLSF could be an effective method for motion artifact cancellation from recorded single channel SCG signals.

The wavelet transform-domain adaptive filter for nonlinear acoustic echo cancellation

The usage of low-quality components in communicating devices introduces acoustic nonlinearity. The presence of nonlinearity creates challenges in noise cancellation applications, especially the acoustic echo cancellation (AEC) that requires an adaptive filter of a very high order. However, the functional link adaptive filter (FLAF) algorithm models the acoustic nonlinearity efficiently but shows slow convergence performance due to a very high filter order. To improve the convergence performance of the FLAF, the wavelet transform-domain FLAF (WTD-FLAF) is proposed for nonlinear AEC (NAEC) applications. The convergence rate is improved by decomposing a higher-order adaptive filter into smaller-order subfilters. The convergence speed improvement is gained at the expense of increased computational complexity. A low complexity version of the WTD-FLAF, named as selective update WTD-FLAF (SU-WTD-FLAF) algorithm, is also presented. The SU-WTD-FLAF algorithm is based on the selective coefficient update approach. Computer simulations demonstrate that the convergence performance of the proposed algorithms outperforms the standard FLAF.

An Assessment of Impact of Adaptive Notch Filters for Interference Removal on the Signal Processing Stages of a GNSS Receiver

With the fast growing diffusion of real-time high-accuracy applications based on the global navigation satellite system (GNSS), the robustness of GNSS receiver performance has become a compelling requirement. Disruptive effects can be induced to signal processing stages of GNSS receivers due to the disturbances from radio frequency interference (RFI), even leading to a complete outage of the positioning and timing service. A typical RFI threat to GNSS signals is represented by portable jammers, which transmit swept-frequency (chirp) signals in order to span the overall GNSS bandwidth. The implementation in the receivers of adaptive notch filters (ANFs) for chirp cancellation has been extensively investigated and proved to be an efficient countermeasure. However, the performance of the ANF is strongly dependent on its configuration setup. Inappropriate parameter settings of the ANF for interference removal may induce severe distortion to the correlation process. In addition, an effective mitigation will still introduce a vestigial signal distortion contributed by the residual unmitigated chirp and the ANF operation itself, being not negligible for high-accuracy solutions. This article addresses the detailed analysis for assessing the effects of interference mitigation by notch filtering. A bias compensation strategy is proposed, wherein for each pseudorandom noise, the biases due to parameter settings of the notch filter are estimated and compensated. The impact of using the ANF operation on chirp signals at the acquisition and tracking stages of GNSS receivers is analyzed. On the basis of the three proposed metrics, the effects can be quantitatively estimated to depict a complete picture of the most influential parameters of the chirp and the ANF configurations, as well as the optimal achievable performance at the acquisition and tracking stages.

Low Energy Less Area Less Delay Fixed Point LMS Method for Adaptive Noise Cancellation Filter

Present paper is about the high speed low complexity implementation derived by its architecture using least mean square (LMS) adaptive filtering. Here straight form LMS adaptive filter has almost the similar critical path as it is a reverse from of the counter path hoiver it has a fast coverage and also a loir register complication. Here critical path evaluation tells that no pipelining is necessary for implementation of straight form LMS adaptive filtering in most of the practical cases requires a realized extremely small adaptive delay and very high sampling rate. Here based on these finding LMS adaptive filtering is divided into 3 structural proposal designs. a) There is no adaption delay b) Only one adaption delay c) Only two adaption delay. Here first one includes least area and least energy per sample (EPS).

Target detection and localization method for distributed monopulse arrays in the presence of mainlobe jamming

In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter is able to cancel MLJ and maintain the target echo. By applying the identical filter on sum-difference beams, MLJ can be cancelled, and the monopulse ratio can be maintained simultaneously. Hence, we simply detect and locate the target on the filtering output of sum-difference beams according to the monopulse principle. Monte Carlo simulations demonstrated that the proposed filter outperforms the conventional algorithms.

Physical configuration-based feedforward active noise control using adaptive second-order truncated Volterra filter

This paper presents a physical configuration-based feedforward active noise control scheme with an adaptive second-order truncated Volterra filter for point source cancellation in three-dimensional free-field acoustic environment. The inertial particle swarm optimization (PSO) algorithm is used as the parameter adjustment mechanism for tuning the coefficients of the adaptive Volterra filter. The first motivation of this paper is to provide a precise description of the relationship between the degree of cancellation and the physical distances between system components. The second motivation is to improve the cancellation performance in the presence of nonlinearities with the adaptive Volterra filter in light of the characteristics of sensing microphone and actuating loudspeaker. The reason for choosing the inertial PSO algorithm is that it can avoid the trap of local optima. The work thus presented makes two main contributions. The first is using the degree of cancellation as a function of the physical distances between system components to provide a quantitative analysis of system performance. The second is the application of the adaptive Volterra filter, which achieves improvements in the cancellation performance of the system under different physical configurations with a reasonable compromise with complexity. For consistency with the numerical analysis, several simulation experiments are conducted using MATLAB/Simulink.

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU.

Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.