Noise Components Research Articles

Linear Modeling of Doppler Wind Lidar Systems for Gust Load Alleviation Design

Preview control using wind estimates derived from Doppler wind lidar measurements is a promising technique for designing active gust load alleviation functions. Due to the relatively high noise levels in the lidar measurements, the associated estimator must use some type of smoothing to obtain a usable estimate for gust load alleviation. In the resulting wind estimate, this results in the loss of some of the information as well as a filtered and reduced component of the measurement noise. Taking the losses and noise into account during control synthesis should help ease the tuning procedure and improve the performance and robustness of the resulting controller. This paper proposes a method based on a maximum a posteriori interpretation of the wind estimation problem to consistently produce linear filters that closely approximate the behavior of the complete measurement chain (sensor and wind estimator) and that can be integrated into a linear control synthesis framework. Its characteristics are shown to closely match those of the real estimator over a range of types of turbulence using a standard set of system parameters.

Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties

Precise physical properties of the known transiting exoplanets are essential for their precise atmospheric characterization using modern and upcoming instruments. Leveraging the large volume of high-signal-to-noise-ratio photometric follow-up data from TESS, highly precise physical properties can be estimated for these systems, especially for those discovered using ground-based instruments prior to the TESS mission. In this work, I have used the publicly available TESS follow-up data for 28 transiting systems with 10 < V mag < 10.5, with an aim to update their known physical properties. The observed lightcurves have been analyzed by implementing a state-of-the-art critical noise treatment algorithm to effectively reduce both time-correlated and uncorrelated noise components, using sophisticated techniques like wavelet denoising and Gaussian-process regression. Compared with the previous studies, the estimated transit parameters are found to be more precise for most of the targets, including a few cases where a larger space-based instrument like Spitzer, Kepler, or CHEOPS has been used in the previous study. The large volume of transit observations used for each target has also resulted in a more accurate estimation of the physical properties, as this overcomes any error in parameter estimations from bias present in a smaller volume of data. Thus, comparing with the literature values, statistically significant improvements in the known physical properties of several targeted systems have been reported from this work. The large volume of transit-timing information from the analyses was also used to search for transit-timing variation trends in these targets, which has resulted in no significant detection.

Subspace projection attention network: Deep weak signal recovery from complicated downhole distributed acoustic sensing data

Distributed acoustic sensing (DAS) is a new, rapidly developing acquisition technology. It has many advantages, such as full well coverage, high sampling density, and strong tolerance compared with conventional geophones in harsh environments. However, DAS data contain various new types of noise with different characteristics, which brings great difficulties to weak signal recovery. It is also not conducive to subsequent inversion, imaging, and interpretation. Several traditional methods and their developments can only remove particular types of DAS noise with serious energy loss. At the same time, the denoising performance is strongly dependent on the threshold function or manual setting parameters. In recent years, deep learning has attracted much attention in DAS data processing. However, convolutional neural networks can only capture local information. The commonly used channel attention assigns weights to each channel to reflect the importance and neglects the features within the channels. The popular self-attention mechanism (SAM) can efficiently obtain the dependencies between different locations or different patches that are a long distance from the input. Thus, a novel subspace projection attention network (SPANet) is designed from a new projection perspective. In the novel network, some feature basis vectors are generated to guide the decoupled projection of the noise components from the signal components. Low-level surface features are projected under the guidance of high-level abstract features through SAM before feature fusion. The projection operation helps to find the optimal subspace that preserves the fine structure of the input as much as possible, better capturing the potential relevance in DAS data. Finally, experimental results of synthetic and field data illustrate that SPANet recovers the effective reflection signals well, whether in a shallow layer with strong noise or a deep layer with weak energy. Its superior performance compared with some traditional and network methods is fully verified and analyzed.

Noise Reduction Using Sparsity Constrained and Regularized Iterative Thresholding Algorithm and Dictionary

Dictionary-based methods are recognized for their ability to estimate clear speech from speech contaminated with noise. However, these techniques face a limitation in distinguishing between speech and noise components. The presented algorithm addresses this challenge by introducing an innovative solution through an iterative thresholding method. In this method, the thresholding is based on noise characteristics and remains independent of variations in noise power. To determine the stopping criteria for thresholding, the algorithm leverages the structure of the speech signal in time–frequency domains using the Gini Index. Remarkably, this technique adeptly extracts reliable signal components from a noisy time–frequency magnitude spectrum, performing well under non-varying and varying Signal-to-Noise Ratio (SNR) conditions. Importantly, it achieves this without the need for mask functions, voice activity detection techniques, noise or mixture dictionaries, or SNR information, which are essential components in other dictionary-based methods. Following the thresholding process, the clean speech is assessed using a dictionary-based approach to restore perceptual loss. In evaluating its performance, the algorithm is compared with traditional enhancement techniques concerning perceptual evaluation of speech quality and short-time objective intelligibility. The assessment is conducted under various background noise conditions, including babble, white, factory, and Volvo noises. The proposed algorithm exhibits superior performance, particularly in low and varying SNR scenarios.

HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation.

Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data.

Disentangling signal and noise in neural responses through generative modeling.

Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal , operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. Importantly, GSN improves estimates of the signal distribution, but does not provide improved estimates of responses to individual events. We validate GSN using ground-truth simulations and show that it compares favorably with related methods. We also demonstrate the application of GSN to empirical fMRI data to illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.

A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods.

Application of Singular Spectrum Analysis to InSAR time-series for constraining the postseismic deformation due to moderate magnitude earthquakes: the case of 2019 Mw 6 Mirpur earthquake, NW Himalaya

Summary Detection and separation of the subtle postseismic deformation signals associated with moderate magnitude earthquakes from Interferometric Synthetic Aperture Radar (InSAR) time-series is often challenging. Singular Spectrum Analysis (SSA) is a statistical non-parametric technique used to decompose and reconstruct signals from complex time-series data. We show that the SSA analysis effectively distinguished the postseismic signal associated with the 2019 Mw 6 Mirpur earthquake from periodic and noise components. The SSA derived postseismic deformation signal is smoother and fits better to an exponential model with a decay time of 34 days. The postseismic deformation is confined to the southeast of the rupture area and lasted for ∼90 days following the mainshock. Inversion of the postseismic deformation suggests an afterslip mechanism with a maximum slip of ∼0.07 m on the shallow, up-dip portions of the Main Himalayan Thrust. The 2019 Mirpur earthquake and afterslip together released less than 12 per cent of the accumulated strain energy since the 1555 Kashmir earthquake and implies continued seismic hazard in the future.

50 Hz Temporal Magnetic Field Monitoring from High-Voltage Power Lines: Sensor Design and Experimental Validation.

A low-cost, tri-axial 50 Hz magnetic field monitoring sensor was designed, calibrated and verified. The sensor was designed using off-the-shelf components and commercially available coils. It can measure 50 Hz magnetic fields originating from high-voltage power lines from 0.08 µT to 364 µT, divided into two measurement ranges. The sensor was calibrated both on-board and in-lab. The on-board calibration takes the circuit attenuation, noise and parasitic components into account. In the in-lab calibration, the output of the developed sensor is compared to the benchmark, a narrowband EHP-50. The sensor was then verified in situ under high-voltage power lines at two independent measurement locations. The measured field values during this validation were between 0.10 µT and 13.43 µT, which is in agreement with other reported measurement values under high-voltage power lines in literature. The results were compared to the benchmark, for which average deviations of 6.2% and 1.4% were found, at the two independent measurement locations. Furthermore, fields up to 113.3 µT were measured in a power distribution sub-station to ensure that both measurement ranges were verified. Our network, four active sensors in the field, had high uptimes of 96%, 82%, 81% and, 95% during a minimum 3-month interval. In total, over 6 million samples were gathered with field values that ranged from 0.08 µT to 45.48 µT. This suggests that the proposed solution can be used for this monitoring, although more extensive long-term testing with more sensors is required to confirm the uptime under multiple circumstances.

Double autocorrelation-based cyclicity evaluation for repetitive transients feature extraction

Abstract The vibration response caused by bearing local defects has impact and periodicity in waveform, which provides a standard for the frequency band selection in envelope analysis. However, most periodicity measurements without prior knowledge belong to sparsity evaluation, while the defects of sparsity index in nature are inevitable. Inspired by the periodic component extraction of autocorrelation function, a novel cyclicity measurement based on double autocorrelation calculation is proposed. With the help of normalization, this approach can distinguish periodic impulses from random impulses by using the periodic sub-maxima of the envelope autocorrelation. Considering the influence of the noise component on the autocorrelation of the periodic signal, the sub-maximums are maintained by threshold processing. On this basis, the re-autocorrelation is calculated to identify the periodic sub-maximum. Finally, as a non-prior index, a demodulation band selection is also proposed in combination with an impulsivity evaluation. The results of the proposed method are analyzed and verified by comparison with typical methods.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Abstract Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise—the dominant contributing noise component in high-resolution fMRI. NOise Reduction with DIstribution Corrected Principal Component Analysis (NORDIC PCA) is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. While investigating auditory functional responses poses unique challenges, we anticipated NORDIC to have a positive impact on the data on the basis of previous applications. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we did observe a reduction in the average response amplitude (percent signal change) within regions of interest, which may suggest that a portion of the signal of interest, which could not be distinguished from general i.i.d. noise, was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high-resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures

Hole spins in Ge/SiGe heterostructures have emerged as an interesting qubit platform with favourable properties such as fast electrical control and noise-resilient operation at sweet spots. However, commonly observed gate-induced electrostatic disorder, drifts, and hysteresis hinder reproducible tune-up of SiGe-based quantum dot arrays. Here, we study Hall bar and quantum dot devices fabricated on Ge/SiGe heterostructures and present a consistent model for the origin of gate hysteresis and its impact on transport metrics and charge noise. As we push the accumulation voltages more negative, we observe non-monotonous changes in the low-density transport metrics, attributed to the induced gradual filling of a spatially varying density of charge traps at the SiGe-oxide interface. With each gate voltage push, we find local activation of a transient low-frequency charge noise component that completely vanishes again after 30 hours. Our results highlight the resilience of the SiGe material platform to interface-trap-induced disorder and noise and pave the way for reproducible tuning of larger multi-dot systems.

Real-time and screen-cam robust screen watermarking

Unauthorized photography of screen-loaded information has become a low-cost and difficult-to-trace method for leakage. Many researchers have proposed screen-cam robust watermarking methods for digital images and documents to identify surreptitious photography. However, these methods are designed for specific types of data and cannot be used in real time, which limits their applicability. This study proposes a screen watermarking method that possesses screen-cam robustness and real-time watermark refreshing. Specifically, a resolution-adaptive watermark template was designed that comprises message watermark templates and two types of synchronization watermark templates. To achieve real-time watermark refreshing, the pregenerated watermark template is directly embedded into the back buffer, which represents the content to be loaded onto the screen. A simplified just noticeable difference model was employed to ensure watermark imperceptibility. For watermark extraction, the positions of the embedded synchronization watermark templates are calculated using a synchronization response index method. Based on these positions, we can perform automatic perspective correction and locate the message watermark template embedded region. Finally, the message watermark was extracted from the discrete Fourier transform domain of the noise component. Extensive experiments demonstrated that the watermark refresh rate meets practical requirements and that the proposed method is robust against image-processing attacks and screen-cam attacks.

INA-Net: An integrated noise-adaptive attention neural network for enhanced medical image segmentation

Skin cancer, a growing health concern, poses a significant threat. Automated segmentation of skin lesions is crucial for precise clinical diagnosis and treatment. However, challenges such as hair interference, vascular occlusion, and ambiguous lesion boundaries in dermoscopic images complicate accurate segmentation. Recent research has focused on capturing multiscale-enhanced global information across spatial and channel dimensions to improve the segmentation of ambiguous boundaries. Despite these advancements, performance remains suboptimal in the presence of complex disturbances. To address this, we propose INA-Net, an Integrated Noise-Adaptive Attention Neural Network for enhanced medical image segmentation. INA-Net proposes a lightweight noise-range attention module that integrates local and simulated noise features with global information to improve robustness against complex mixed noises. Additionally, our proposed Edge-aware Spatial Attention (ESA) and Multi-scale Channel Attention (MCA) enhance feature information and boundary perception. The proposed Dynamic Noise Encoding module and Fourier Wavelet Analysis (FW-Parser) further improve robustness by handling high-frequency noise components. INA-Net effectively manages hair interference, vascular occlusion, and ambiguous lesion boundaries, thereby providing critical support for accurate lesion identification. Evaluations on the ISIC2016, ISIC2018, and PH2 datasets demonstrate outstanding performance, with IOU scores of 87.53%, 84.74%, and 85.30%, respectively. These results surpass those of other models with comparable complexity and size.

A modified feedforward-feedback time-frequency domain hybrid algorithm for multichannel active road noise control system

Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue,a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper.The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost.

Distributed Acoustic Sensing Signal Model Under Static Fiber Conditions

This paper presents a statistical model for distributed acoustic sensor interrogation units that utilizes laser pulses transmitted into fiber optics. Interactions within the fiber lead to localized acoustic energy, resulting in backscatter, which is a reflection of the light. Explicit equations were used to calculate the amplitudes and phases of backscattered signals. The proposed model accurately predicts the amplitude signal spectrum and autocorrelation, aligning well with experimental observations. This study also explores the phase signal characteristics relevant to optical time-domain reflectometry (OTDR) system sensing applications, demonstrating consistency with the experimental results. The experiments were conducted using Python coding, enabling the analysis of the individual components of the Distributed Acoustic Sensing (DAS) system. The assumptions of the model include the static condition of the fiber, implying the absence of external forces or vibrations. Consequently, no external acoustic disturbances were considered. The backscattered signal comprises a random noise component resulting from intrinsic fiber imperfections, and a coherent component arising from the interplay between the laser pulse and the fiber. Keywords: distributed acoustic sensing, fiber optics, optical time-domain reflectometry, Rayleigh scattering.

A Comparison of Denoising Approaches for Spoken Word Production Related Artefacts in Continuous Multiband fMRI Data

Abstract It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing (denoising) techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of cerebrospinal fluid (CSF)/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production.

Diffraction by gratings: from the C-method to the stochastic C-method

The Chandezon, or C-method, is an efficient and versatile numerical method for modeling diffraction problems involving smooth surface relief gratings. For some grating profiles, the C-method is limited when the height-to-period ratio exceeds a factor of three. This is due to the formation of ill-conditioned matrices for inversion. Here, the stochastic C-method (SCM) is introduced as a solution that leverages stochastic differential equations to overcome these numerical difficulties. The SCM is developed by altering the physical model of the grating profile function to include an additive Brownian noise component. The inclusion of noise dramatically expands the applicability of the C-method and enriches the physical model. Numerical experiments show that the SCM achieves a precision on the order of 10−5 for diffracted/transmitted amplitudes on sinusoidal profiles with height-to-period ratios as high as 72. These results are in agreement with those obtained using multi-precision and the rigorous coupled wave analysis (RCWA).

Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method’s average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized.

Weighted squared envelope dispersion entropy as nonlinear measure for dynamic health monitoring of rotating machineries

In the context of nondestructive testing and evaluation, dispersion entropy (DisE) stands out as a promising dynamic nonlinear health monitoring measure for rotating machineries. However, in high-noise scenarios, transient impulses linked to rotating machinery faults often get submerged under the noise component present in the collected vibration signal. As a result, DisE not only fails to detect the presence of a fault at the earliest stage of inception but also performs poorly in tracking the progression of the incepted fault. Aiming at overcoming the limitations of DisE in dynamic health monitoring of rotating machineries, in this paper, impulses corresponding to a fault is extracted by suppressing the unnecessary noise component by weighting the squared envelope of the collected vibration signal. Due to the application of weighted squared envelope in calculating the DisE, the proposed measure is termed as weighted squared envelope dispersion entropy (WSEDisE). Effectiveness of WSEDisE in dynamic health monitoring of rotating machineries is verified by two different experimental run to failure data collected from rolling element bearings and spur gears. Experimental results show that WSEDisE not only overcomes the weaknesses of original DisE but also demonstrates better performance than conventional entropy-based methods such as permutation entropy (PE) and advanced DisE based method namely multiscale DisE (MDisE).