Showing papers on "Noise (signal processing) published in 2022"
TL;DR: Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
108 citations
TL;DR: An adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed, aiming at the determination of cyclic frequency set estimation method based on autocorrelation function of morphological envelope and the validity of the method is verified.
107 citations
TL;DR: In this article, the authors proposed noise subtraction and marginal enhanced square envelope spectrum (MESES) for detecting bearing defects in the centrifugal and axial pump in order to avoid time lag problem which generally occurs in two signals obtained at different time.
39 citations
TL;DR: The proposed VNCD adopts a new algorithmic framework by modifying the optimization function of the VNCMD to eliminate an upper bound determined by noise, which makes the V NCD more adaptive in practical applications and a novel initial frequencies estimation method based on optimizing a spectrum concentration index and a resampling technique.
28 citations
TL;DR: An output feedback control scheme with neural network based unknown dynamics compensation for DC motor systems and stability analysis reveals the tracking error can asymptotically converge to zero while facing time-variant unknown dynamics.
21 citations
TL;DR: A new algorithm to improve EWT is proposed to weaken the influence of extreme points in the complex Fourier spectrum on modal differentiation, and the cycle envelope spectral segmentation method is proposed.
19 citations
TL;DR: Results verify that detectability of Am-MUSIC-driven damage imaging is not limited by damage quantity, and the amelioration expands conventional MUSIC from phased array-facilitated nondestructive evaluation to health monitoring using built-in sparse sensor networks.
16 citations
TL;DR: Wang et al. as discussed by the authors proposed a sensor and actuator fault diagnosis method for small pressurized water reactors (SPWRs), with an innovative labeled fault dictionary established to map complex fault modes, using long short-term memory (LSTM) networks.
15 citations
TL;DR: In this article, the authors proposed a deep learning-based ECG denoising approach based on the periodicity of the ECG signals, where ECG cardiac cycles are stacked together to form a 2D signal which will be fed to a CNN model.
13 citations
TL;DR: In this paper, the authors proposed an adaptive technique based on Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN) to filtrate LS signals in a noisy environment.
13 citations
TL;DR: In this article, a focus variation microscopy sensor that can be integrated into various types of machine tools is presented, with dimensions of 78 mm diameter and 200 mm length, with a 20 mm travel range.
TL;DR: In this article, a multiple-damage-scattered wavefield model is developed, with which the signal representation equation is constructed in the frequency domain, avoiding computationally expensive pixel-based calculation.
TL;DR: In this paper, a single-sensor based output-only algorithm is proposed for real-time condition monitoring of mechanical vibrating systems through recursive singular spectrum analysis (RSSA): filtering, enhancement, fault detection, and modal identification.
TL;DR: The proposed method overcomes the initial-state dependence and randomness of the identification result in the algebraic solution of the traditional operational matrix, and reduces the effect of noise on the accuracy of parameter identification.
TL;DR: A fault diagnosis framework based on temporal convolutional network (TCN) integrating adaptive chirp mode decomposition (ACMD) and silhouette coefficient (SC) was proposed in this paper.
TL;DR: In this article, the authors proposed a novel detector, dubbed as PU-Net, that dynamically learns the PU activity patterns in a cognitive 5G smart city, where a network of unmanned aerial vehicles (UAVs) is deployed as flying base stations to serve the Internet-of-Things (IoT) users.
TL;DR: The proposed convolutional neural network is trained on real data containing a large range of source signatures to make the network robust and adaptive to signature variations, and its sensitivity to changing geology within a survey and on two different surveys on the Norwegian Continental Shelf is investigated.
Abstract: Estimating the far-field source signature has always been an important part of seismic processing. However, estimating the source signature from an air-gun array is difficult because of the...
TL;DR: In this paper, a generalized stochastic resonance (GSR) based instantaneous frequency estimation method is proposed for frequency modulated (FM) signal embedded in strong noise, which can enhance weak FM signals with time varying frequency.
TL;DR: The numerical and experimental results have demonstrated that the proposed reference-driven S-transform method is useful for time-varying parameter extraction with a multisensory system, useful to structural condition monitoring of bridges.
TL;DR: An adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size LMS (VSS-LMS) algorithm, which can guarantee stable work of the interior noise control.
TL;DR: In this article, an improved flexible analysis wavelet transform (FAWT) algorithm is proposed to improve the denoising effect of surface electromyography (sEMG) signals.
01 Jan 2022
TL;DR: In this article, a dynamic background activity filtering (DBA-filter) was proposed for event cameras based on an adaptation of the K-nearest neighbor (KNN) algorithm and the optical flow.
Abstract: Newly emerged dynamic vision sensors (DVS) offer a great potential over traditional sensors (e.g. CMOS) since they have a high temporal resolution in the order of \(\mu s\), ultra-low power consumption and high dynamic range up to 140 dB compared to 60 dB in frame cameras. Unlike traditional cameras, the output of DVS cameras is a stream of events that encodes the location of the pixel, time, and polarity of the brightness change. An event is triggered when the change of brightness, i.e. log intensity, of a pixel exceeds a certain threshold. The output of event cameras often contains a significant amount of noise (outlier events) alongside the signal (inlier events). The main cause of that is transistor switch leakage and noise. This paper presents a dynamic background activity filtering, called DBA-filter, for event cameras based on an adaptation of the K-nearest neighbor (KNN) algorithm and the optical flow. Results show that the proposed algorithm is able to achieve a high signal to noise ratio up to 13.64 dB.
TL;DR: In this article, the intrinsic characteristics of speech modulations are estimated to propose the instant modulation spectral features for efficient emotion recognition, which is based on single frequency filtering (SFF) technique and higher order nonlinear energy operator.
TL;DR: In this paper, a well velocity logs are handled as noisy signals, and decomposed into intrinsic mode functions (IMFs) for fast oscillations to slow oscillations via EMD, then regularity exponent is computed for each IMFs using wavelet leaders algorithm.
TL;DR: In this paper, a kernel recursive maximum correntropy with variable center (KRMCVC) algorithm is proposed for nonlinear signal processing, especially when data is disturbed by non-Gaussion and non-zero mean noises.
TL;DR: In this paper, the authors proposed a novel and straightforward algorithmic solution for locating noise in an ECG signal by applying the R-peak detection algorithm at different sampling rates, which is easy to follow, and the results demonstrate its performance.
TL;DR: In this article, the authors analyzed the noise type of FAIMS signal in detail, and three different signal processing algorithms, including median filtering (MF), discrete wavelet transform (DWT), and zero-phase digital filtering (ZDF), were evaluated for their performance in denoising the FAIMs signal.
Abstract: RATIONALE FAIMS has a great potential to become a portable technology for rapid detection of chemical and biological agents. However the ion current signals, measured at the exit of the planar FAIMS directly, may contain different types of noises. The peak information in the FAIMS spectrum, such as the compensation voltage value at the maximum peak intensity (CVP ) and the peak width at half maximum (Wh ), could not be accurately determined under the weak signal condition which significantly limits the achievable instrument sensitivity, and there are no existing solutions to the problem. METHODS This study analyzed the noise type of FAIMS signal in detail, and three different signal processing algorithms, including median filtering (MF), discrete wavelet transform (DWT) and zero-phase digital filtering (ZDF), were evaluated for their performance in denoising the FAIMS signal. RESULTS The results show that the standard deviation of CVp obtained from the signal denoised by using ZDF algorithm is at least 31.82% smaller as compared to using MF and DWT algorithms. The standard deviation of Wh is at least 45.45% smaller by using ZDF algorithm. Moreover, only ZDF algorithm can keep the percentage error for the CV value of the denoised signal to be within 0.50±0.47% of the true CV value, implying the effectiveness of ZDF algorithm in denoising while retaining the integrity of the signal. CONCLUSIONS The ZDF algorithm greatly reduces the analyte peak extraction error and improves the limit of detection in FAIMS measurements.
TL;DR: In this paper, the authors proposed a beam-space matrix reconstruction method to estimate the direction-of-arrival (DOA) of coherent signals with a relatively small computational complexity, which can eliminate the phase perturbation caused by the mutual impact between the coherent signals and improve the SNR.
TL;DR: The subjective method allows a better assessment of the evolution of the severity of the gear defects compared to the objective methods used, which remain limited in the case of very noisy signals.
TL;DR: In this article, it was shown that the thermal power generated by the experiments is often noisy, thus hiding the periodic signal arising from the bubbles' formation and release, which allowed the accurate determination of the surface tension of the target liquid.