Showing papers on "Noise (signal processing) published in 2021"
TL;DR: A new type of RIS is proposed, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS.
Abstract: Reconfigurable Intelligent Surface (RIS) is a promising solution to reconfigure the wireless environment in a controllable way. To compensate for the double-fading attenuation in the RIS-aided link, a large number of passive reflecting elements (REs) are conventionally deployed at the RIS, resulting in large surface size and considerable circuit power consumption. In this paper, we propose a new type of RIS, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS. Therefore, for a given power budget at the RIS, a strengthened RIS-aided link can be achieved by increasing the number of active REs as well as amplifying the incident signal. We consider the use of an active RIS to a single input multiple output (SIMO) system. However, it would unintentionally amplify the RIS-correlated noise, and thus the proposed system has to balance the conflict between the received signal power maximization and the RIS-correlated noise minimization at the receiver. To achieve this goal, it has to optimize the reflecting coefficient matrix at the RIS and the receive beamforming at the receiver. An alternating optimization algorithm is proposed to solve the problem. Specifically, the receive beamforming is obtained with a closed-form solution based on linear minimum-mean-square-error (MMSE) criterion, while the reflecting coefficient matrix is obtained by solving a series of sequential convex approximation (SCA) problems. Simulation results show that the proposed active RIS-aided system could achieve better performance over the conventional passive RIS-aided system with the same power budget.
223 citations
01 Jun 2021
TL;DR: NBNet as mentioned in this paper proposes a non-local attention module to explicitly learn the basis generation as well as subspace projection, which achieves state-of-the-art performance on PSNR and SSIM with significantly less computational cost.
Abstract: In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reduction by image-adaptive projection. Specifically, we propose to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space. Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space. Our key insight is that projection can naturally maintain the local structure of input signal, especially for areas with low light or weak textures. Towards this end, we propose SSA, a non-local attention module we design to explicitly learn the basis generation as well as subspace projection. We further incorporate SSA with NBNet, a UNet structured network designed for end-to-end image denosing based. We conduct evaluations on benchmarks, including SIDD and DND, and NBNet achieves state-of-the-art performance on PSNR and SSIM with significantly less computational cost.
110 citations
TL;DR: Wang et al. as mentioned in this paper designed an end-to-end signal prior-guided layer separation and data-driven mapping network with layer-specified constraints for single-image low-light enhancement.
Abstract: Due to the absence of a desirable objective for low-light image enhancement, previous data-driven methods may provide undesirable enhanced results including amplified noise, degraded contrast and biased colors. In this work, inspired by Retinex theory, we design an end-to-end signal prior-guided layer separation and data-driven mapping network with layer-specified constraints for single-image low-light enhancement. A Sparse Gradient Minimization sub-Network (SGM-Net) is constructed to remove the low-amplitude structures and preserve major edge information, which facilitates extracting paired illumination maps of low/normal-light images. After the learned decomposition, two sub-networks (Enhance-Net and Restore-Net) are utilized to predict the enhanced illumination and reflectance maps, respectively, which helps stretch the contrast of the illumination map and remove intensive noise in the reflectance map. The effects of all these configured constraints, including the signal structure regularization and losses, combine together reciprocally, which leads to good reconstruction results in overall visual quality. The evaluation on both synthetic and real images, particularly on those containing intensive noise, compression artifacts and their interleaved artifacts, shows the effectiveness of our novel models, which significantly outperforms the state-of-the-art methods.
101 citations
TL;DR: Recommendations are put forward for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms based on the presence of a new interferometric signal in multilook synthetic aperture radar (SAR) interferogram that cannot be attributed to the atmospheric or Earth-surface topography changes.
Abstract: This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.
97 citations
TL;DR: The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection, and this method also has practical application value for engineering rotating machinery.
Abstract: In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.
90 citations
17 Aug 2021
TL;DR: In this article, the authors provide a comprehensive overview of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection for terahertz (THz)-band communications.
Abstract: Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advances in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress establishes a foundation for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques, emphasizing ultramassive multiple-input–multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, vital for overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.
85 citations
TL;DR: Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance and the experimental result showed that the proposed stationary wavelet transform based ECGDenoising technique outperformed the other ECG Denoising techniques as more ECGs signal components are preserved than other denoised algorithms.
Abstract: Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.
77 citations
TL;DR: A novel denoising method based on ensemble empirical mode decomposition (EEMD) and grey theory, named EEMD-Grey, is proposed and can effectively remove noise and retain useful information.
65 citations
TL;DR: It is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation and shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the Dae-NN.
64 citations
TL;DR: An alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework, which derives conditions when perfect recovery is possible and complement them with a stable recovery algorithm and guarantees extend to measurements affected by bounded noise, which includes round-off quantization.
Abstract: Shannon's sampling theorem, at the heart of digital signal processing, is well understood and explored. However, its practical realization still suffers from a fundamental bottleneck due to dynamic range limitations of the underlying analog–to–digital converters (ADCs). This results in clipping or saturation for signal amplitudes exceeding their maximum recordable voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework . The key observation is that applying a modulo operation to the signal before the ADC prevents saturation; instead, one encounters a different type of information loss. Such a setup can be implemented, for example, via so-called folding or self-reset ADCs, as proposed in various contexts in the circuit design literature. The key challenge for this new type of information loss is to recover a bandlimited signal from its modulo samples. We derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The required sampling density is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our guarantees extend to measurements affected by bounded noise, which includes round-off quantization. Numerical experiments validate our approach. For example, it is possible to recover functions with amplitudes orders of magnitude higher than the ADC's threshold from quantized modulo samples up to the unavoidable quantization error. Applications of the unlimited sampling paradigm can be found in a number of fields such as signal processing, communication and imaging.
64 citations
TL;DR: A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data and shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal.
Abstract: Permanent magnet synchronous motor (PMSM) is one of the common core power components in modern industrial systems. Early fault diagnosis can avoid major accidents and plan maintenance in advance. However, the features of early faults are weak, and the symptoms are not obvious. Meanwhile, the fault signal is often overwhelmed by noise. Accordingly, fault diagnosis for early faults is difficult, and the diagnostic accuracy is generally low. A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data. The wavelet threshold denoising and minimum entropy deconvolution methods are used to improve the signal-to-noise ratio. The complementary ensemble empirical mode decomposition method is used to extract signal eigenvalues, and Bayesian networks are applied to identify the early, middle, and permanent faults. Experimental data carried out with Tyco ST8N80P100V22E medium PMSM are used to train the fault diagnostic model and validate the proposed fault diagnostic methodology. Result shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal. The influence of load on diagnostic accuracy is also investigated, and it indicates that the accuracy with acoustic emission signal is higher than that with vibration signal under different loads.
TL;DR: The effectiveness of arc-fault detection is significantly improved by this technique because it can acquire sufficient and accurate arc signatures and it does not need to predefine various thresholds.
Abstract: Protection devices are extensively utilized in direct current (DC) systems to ensure their normal operation and safety. However, series arc faults that establish current paths in the air between conductors introduce arc impedance to the system. Consequently, they can result in a decrease of current, and thus conventional protection devices may not be triggered. Undetected series arc faults can cause malfunctions and even lead to fire hazards. Therefore, a series arc-fault detection system is essential to DC systems to operate reliably and efficiently. In this paper, a series arc-fault detection system based on arc time-frequency signatures extracted by a modified empirical mode decomposition (EMD) technique and using a support vector machine (SVM) algorithm in decision making is proposed for DC systems. The oscillatory frequencies from the arc current are decomposed by the EMD with an analysis of the Hurst exponent ( ${H}$ ) to reject interference from the power electronics noise. ${H}$ analyzes the trend of a signal and the intrinsic oscillations of the signal are those with values of ${H}$ larger than 1/2. Comparing to traditional filters or wavelet transforms, this method does not require knowledge of the frequency range of the interference which varies from system to system. The capability and applicability of the proposed technique are validated in a photovoltaic system. The effectiveness of arc-fault detection is significantly improved by this technique because it can acquire sufficient and accurate arc signatures and it does not need to predefine various thresholds.
TL;DR: The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.
Abstract: In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features. Following this, the extracted features are decompressed to retrieve the seismic signal. Skip connections are used between the encoder and decoder parts, allowing the proposed algorithm to extract high‐order features without losing important information. Besides, dropout layers are used as regularization layers. The dropout layers preserve the most meaningful features belonging to the seismic signal and discard the remaining features. The proposed algorithm is an unsupervised approach that does not require prior information about the clean signal. The input patches are divided into 80% for training and 20% for testing. However, it is interesting to find that the proposed algorithm can be trained with only 30% of the input patches with an effective denoising performance. Four synthetic and four field examples are used to evaluate the proposed algorithm performance, and compared to the f−x deconvolution and the f−x singular spectrum analysis. The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.
TL;DR: In this article, a new denoising method for ship radiated noise based on Spearman variational mode decomposition (SVMD), spatial-dependence recurrence sample entropy (SdrSampEn), improved wavelet threshold denoing (IWTD), and Savitzky-Golay filter (SG) is proposed.
Abstract: Ship radiated noise denoising is the basis and premise of underwater acoustic signal processing. To obtain better denoising effect, a new denoising method for ship radiated noise based on Spearman variational mode decomposition (SVMD), spatial-dependence recurrence sample entropy (SdrSampEn), improved wavelet threshold denoising (IWTD) and Savitzky-Golay filter (SG) is proposed. Firstly, SVMD is proposed, ship radiated noise is decomposed a series of intrinsic mode functions (IMFs) by SVMD, and the SdrSampEn value of every IMF is counted. Then, according to the SdrSampEn value, these IMFs are divided into noise-dominated IMFs and real signal-dominated IMFs. Noise-dominated IMFs are denoised by IWTD, and real signal-dominated IMFs are denoised by SG. Finally, the processed IMFs are reconstructed, and the noise-reduced signal is acquired. The proposed method has three main advantages: (i) compared with empirical mode decomposition (EMD), variational mode decomposition (VMD) as a new non-recursive decomposition algorithm, overcomes the defect of mode mixing; (ii) the proposed SVMD method overcomes the problem that VMD needs to preset the number of decomposition levels K; (iii) real signal-dominated IMFs have also been denoised and the method improves signal-to-noise ratio (SNR) by 2 dB to 4 dB. The denoising experiments with the Lorenz signal and the Chen signal show that the proposed method can improve the SNR by 8 dB to 13 dB. Applying the proposed method to denoise ship radiated noise from the official website of National Park Administration ( https://www.nps.gov/glba/learn/nature/soundclips.htm ), the results show that the proposed method makes chaotic attractor phase waveform clearer and smoother, and can effective restrain marine environmental noise in ship radiated noise.
TL;DR: Experimental results show that the method proposed in this paper can extract the characteristic frequency of faulty bearing under stronger noise interference.
TL;DR: Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.
Abstract: A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery. However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This article focuses on the noise effect compensation and online parameter identification for the widely used equivalent circuit model. A novel degree of freedom (DOF) eliminator is proposed and combined with the Frisch scheme in a recursive fashion, for the first time, to coestimate the noise statistics and unbiased model parameters. A computationally tractable numerical solver is further proposed for the DOF eliminator to improve the real-time performance. Simulations and experiments are performed to validate the proposed method from theoretical to practical perspective. Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.
TL;DR: In this paper, a second-order stochastic resonance (SR) method enhanced by fractional-order derivative is developed to enhance weak fault characteristics for mechanical fault detection by using strong background noise, which is able to utilize the dependence among the values of a mechanical state variable.
Abstract: Stochastic resonance (SR), as a noise-enhanced signal processing tool, has been extensively investigated and widely applied to mechanical fault detection. However, mechanical degradation process is continuous where the current value of a mechanical state variable, e.g., vibration, is highly dependent on its previous values, and the widely used SR methods in mechanical fault detection, mainly focusing on integer-order SR, neglect the dependence among the values of the mechanical state variable and are unable to utilize such a dependence to enhance weak fault characteristics embedded in a signal that records the values of the mechanical state variable as time varies. Inspired by fractional-order derivative that characterizes memory-dependent properties and reflects the high dependence between current and previous values of the state variable of a system, a second-order SR method enhanced by fractional-order derivative is developed to enhance weak fault characteristics for mechanical fault detection by using strong background noise, which is able to utilize the dependence among the values of a mechanical state variable to enhance weak fault characteristics embedded in a signal. Numerical analyses show that output signal-to-noise ratio (SNR) versus fractional order in the second-order bistable SR system induced by fractional-order derivative depicts a typical feature of SR. Even the second-order bistable SR system induced by fractional-order derivative is similar to the optimal moving filter by fine-tuning the system parameters and scaling factor. Experimental data including a bearing with slight flaking on the outer race and a gear with scuffing from wind turbine drivetrain are used to validate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to not only suppress multiscale noise embedded in a signal but also enhance the benefits of noise to mechanical fault detection. In addition, the comparison with other advanced signal processing methods demonstrates that the proposed method outperforms the integer-order SR methods, even kurtogram and maximum correlated kurtosis deconvolution in extracting weak fault characteristics of machinery overwhelmed by strong background noise.
TL;DR: In this paper, the second-order synchroextracting transform (SET2) was proposed to improve the TF resolution and reconstruction accuracy for nonstationary signals with time-varying instantaneous frequency (IF) characteristics.
Abstract: Synchrosqueezing transform (SST) is a currently proposed novel postprocessing time–frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF representation. However, so far, how to improve the TF resolution while ensuring the accuracy of signal reconstruction is still an open question, particularly for the vibration signal with time-varying instantaneous frequency (IF) characteristics, due to the fact that the vibration signals of mechanical equipment usually contain many types of noise generated by harsh operating conditions, and the SST will mix these noise into the real signal. Our first contribution is using the Gaussian modulated linear chirp (GMLC) signal model to represent the general nonstationary signals. The GMLC signal model can more accurately represent the time-varying nonstationary signal, compared with the SST signal model composed of linear phase function and constant amplitude. Our second contribution in this work is proposing a method to improve the TF resolution and reconstruction accuracy for nonstationary signals with time-varying IF, which we coined the second-order synchroextracting transform (SET2). In SET2, we apply the GMLC to deduce the nonstationary signal model and then only use the energy at the IF to characterize the TF distribution, which improves the TF while reducing the impact of noise on the real signal.
TL;DR: A novel weak fault feature extraction and diagnosis method, composed of a multi-channel continuous wavelet transform (MCCWT), by which the original temporal signals can be more easily transformed into a new representation with several channels and fewer network parameter requirements than those required by the traditional methods.
Abstract: Because the fault characteristic frequencies of a rolling bearing are submerged in strong noise when it fails early, the fault feature in the original signal is relatively weak to allow the diagnosis of the bearing. Consequently, the method to extract a weak fault feature is becoming a challenging research topic in fault diagnosis. Traditional diagnostic networks are typically trained by the time series or the frequency spectrum of the acquired discrete signal fragment, whereas the connection of the local fragments (quasi-periodicity) is neglected, resulting in low diagnostic accuracy for the bearing under strong noise conditions. To solve this problem, a novel weak fault feature extraction and diagnosis method, composed of two parts, is proposed in this paper. The first part is a multi-channel continuous wavelet transform (MCCWT), by which the original temporal signals can be more easily transformed into a new representation with several channels and fewer network parameter requirements than those required by the traditional methods. The second part is a convolution-feature-based recurrent neural network (CFRNN) that is based on a traditional recurrent neural network (RNN). In the latter, a recurrent unit combining several residual blocks and a long short term memory (LSTM) block is proposed to mine the temporal features and the local vibration characteristics simultaneously. The efficiency of the proposed diagnosis method is validated respectively by the datasets collected by simulating fault bearings with strong noise and using real fault bearings containing faults at an early stage.
TL;DR: This paper presents a framework using sailfish optimization algorithm and Gini index as a criterion to adaptively select the optimum VMD parameters for each fault signal, and results indicate high efficiency of the proposed method in extracting fault feature and in exclusion of noise effect.
TL;DR: The main aim of this paper is to present the investigation carried out to suppress the noise found in EEG signals of depression, and to compare the effectiveness of the physiological signal denoising approaches based on discrete wavelet transform and wavelet packet transform combined with VMD with other approaches.
TL;DR: A new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function, and shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions.
Abstract: In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our investigation focuses on joint channel effects of attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve the success of GAN for channel modeling, we modify the loss function, design the condition vector of input, and address the mode collapse for the long-haul transmission. The effective architecture, parameters, and training skills of GAN are also displayed in the article. The results show that the proposed method can learn the accurate transfer function of the fiber channel. The transmission distance of modeling can be up to 1000 km and can be extended to arbitrary distance theoretically. Moreover, GAN shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions. Comparing the complexity of GAN with the split-step Fourier method (SSFM), the total multiplication number is only 2% of SSFM and the running time is less than 0.1 seconds for 1000-km transmission, versus 400 seconds using the SSFM under the same hardware and software conditions, which highlights the remarkable reduction in complexity of the fiber channel modeling.
TL;DR: The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical Mode decomposition and ensemble empiricalMode decomposition techniques.
Abstract: Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non...
TL;DR: This work presents a new analysis of gravitational wave data that contain both a signal and glitches by simultaneously modeling the compact binary signal in terms of templates and the instrumental glitches using sine-Gaussian wavelets.
Abstract: Transient non-Gaussian noise in gravitational wave detectors, commonly referred to as glitches, pose challenges for detection and inference of the astrophysical properties of detected signals when the two are coincident in time. Current analyses aim toward modeling and subtracting the glitches from the data using a flexible, morphology-independent model in terms of sine-Gaussian wavelets before the signal source properties are inferred using templates for the compact binary signal. We present a new analysis of gravitational wave data that contain both a signal and glitches by simultaneously modeling the compact binary signal in terms of templates and the instrumental glitches using sine-Gaussian wavelets. The model for the glitches is generic and can thus be applied to a wide range of glitch morphologies without any special tuning. The simultaneous modeling of the astrophysical signal with templates allows us to efficiently separate the signal from the glitches, as we demonstrate using simulated signals injected around real O2 glitches in the two LIGO detectors. We show that our new proposed analysis can separate overlapping glitches and signals, estimate the compact binary parameters, and provide ready-to-use glitch-subtracted data for downstream inference analyses.
TL;DR: By using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures, and it is demonstrated that the increase inData retention does not attenuate the ERP signal.
TL;DR: A waveform-based feature extraction method is developed based on the wavelet packet decomposition (WPD) to capture the information contained in original AE signals, which covers all features for reconstructed signals in the frequency domain.
TL;DR: An edge based novel approach is proposed by combining convolutional denoising autoencoder (CDAE) and long short-term memory (LSTM) for ECG signal compression that is efficient by achieving compression ratio of 64 with better reconstruction quality score of 15.61 which is higher than state-of-the-art methods.
TL;DR: A novel permutation entropy-based improved uniform phase empirical mode decomposition (PEUPEMD) method that was superior to the comparative methods in decomposing accuracy and mode mixing suppression and verified the feasibility via comparing it with EMD, UPEMD, CEemDAN and ICEEMDAN methods.
TL;DR: The study finds that the multi-resolution capability of the CWT technique allows it to render more accurate and richer details of the signals, and shows that the classification accuracy can be improved by using shallower networks modified from the VGG-16 network to mitigate the overfitting issue.
TL;DR: The proposed fault diagnosis method for SWR significantly improves the performance of LF detection and localization under strong shaking and strand noises and an integrated signal-processing method specifically designed for addressing the two problems.
Abstract: Because of its flexibility, high strength, and durability, steel wire rope (SWR) is widely used in irrigation works, bridges, harbors, tourism, and many industrial fields as a vital component. Thus, it can cause accidents and economic losses if local flaws (LFs) of the SWR in service are not detected in time. This article points out two major problems in magnetic flux leakage (MFL) imaging-based nondestructive testing for fault diagnosis of SWR and proposes an integrated signal-processing method specifically designed for addressing the two problems. In this article, the MFL signals are collected by a detector that is formed by a set of permanent magnets and a Hall sensor array. Based on these multichannel MFL signals obtained from the Hall sensor array, we use the principle of multichannel signal fusion to determine rich information from all MFL signals. We solve the strand noise problem by an oblique-directional resampling and filtering method, which avoids severe attenuation in the LF signal. Moreover, the shaking noise is effectively removed by the proposed antishaking filtering based on the median filter. According to our simulation and experiment, the proposed fault diagnosis method for SWR significantly improves the performance of LF detection and localization under strong shaking and strand noises.