Types Of Noise Research Articles (Page 1)

We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider access to a noisy quantum processor with dynamics that features a global attractor state. In a standard setting, such noise can be detrimental to the quantum optimization performance. Our algorithm bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian in a way that transforms the noise attractor into higher-quality solutions. The transformation effectively changes the attractor into a higher-quality solution of the Hamiltonian based on the results of the previous step. The end result is that noise aids variational optimization, as opposed to hindering it. We present an improved Quantum Approximate Optimization Algorithm (QAOA) runs in experiments on Rigetti's quantum device. We report approximation ratios 0.9 - 0.96 for random, fully connected graphs on n = 82 qubits, using only depth p = 1 QAOA with NDAR. This compares to 0.34 - 0.51 for standard p = 1 QAOA with the same number of function calls.

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined composite loss function, specifically designed for hybrid noise removal in raw infrared images. Our method employs a residual network backbone integrated with an adaptive weighting mechanism and edge-preserving loss, enabling joint modeling of multiple noise types while safeguarding structural edges. Unlike reference-based CNN denoising methods requiring clean images, our solution leverages intrinsic gradient variations within image sequences for adaptive smoothing, eliminating dependency on ground-truth data during training. Rigorous experiments conducted on three public datasets have demonstrated the optimal or suboptimal performance of our method in mixed noise suppression and detail preservation (PSNR > 32.13/SSIM > 0.8363).

This systematic review and meta-analysis synthesises and critically appraises published literature on tibiofemoral articular noise following total knee arthroplasty (TKA) to determine the prevalence of noise among various implant designs. An electronic literature search was conducted using Embase, Medline and Epistemonikos. Inclusion criteria were studies reporting audible noise from the implant following TKA. Numbers and proportions of patients with articular noise were stratified according to TKA stabilisation mechanism: posterior-stabilised (PS), cruciate-retaining (CR), medial pivot (MP) or ultracongruent (UC). Pooled analysis was performed using a generalised linear mixed-effects model on studies that: (1) reported proportions with articular noise; (2) stratified by stabilisation mechanism; (3) reported the most recent or complete cohort and (4) excluded of clunk or crepitus. Fourteen eligible studies, published between 1996 and 2024 (5769 knees), reported noise for 50 subsets (stabilisation mechanism, follow-up, or other differences). Follow-up ranged from 6 to 91 months, and the prevalence of audible noise ranged from 3% to 76%. Only 5 of the 14 studies could be included in the meta-analysis: 11 subsets were pooled into three stabilisation mechanisms and revealed the prevalence of articular noise to be highest for PS (35%), followed by UC (28%), and then CR (27%), but none were on MP. Sensitivity analysis revealed the prevalence of articular noise to be significantly higher for PS implants (31%) than for CR implants (24%, p = 0.012), but not significantly different to UC implants (23%, n.s.). The prevalence of reported articular noise varied highly among studies, likely due to heterogeneity in patient demographics, follow-up period, implant design, type of noise and design of noise questionnaires. PS implants have considerably higher prevalence of noise compared to CR and UC implants. The clinical relevance of these findings is that surgeons and patients should be aware that 26%-35% of patients could experience articular noise following TKA, which is likely important to manage patient expectations. Systematic review protocol registration (Prospero: CRD42024607551). Level IV.

Enhanced multi-leak detection in pressurized pipelines using super-resolution matched-field processing.

NADH against noise-induced hearing loss: Evidence from models of "temporary" and "permanent" deafness.

The production of underwater noise from on-land detonations is of concern, especially near sensitive marine mammal habitats. Despite this, there is a lack of public experimental data to analyze the characteristics of this type of noise. This paper quantifies noise from near-water land detonations, based on measurements obtained at Bentinck Island Demolition Range, Vancouver Island. The measurements show that ground-to-water propagation is dominant and that air-to-water coupling via evanescent waves is also present but mostly perceptible only at close distances from the detonation. A simple wavenumber integration model is used to illustrate the depth dependency of the evanescent field.

Sensory and perceptual processing is inherently shaped by both internal and external noise sources. While noise is typically seen as disruptive, it can, under certain conditions, enhance the detection of weak sensory signals-a phenomenon known as stochastic resonance (SR). Research on SR has primarily focused on dissimilar noise sources such as static visual noise or high-frequency transcranial random noise stimulation (hf-tRNS), leaving it unclear whether different noise types produce similar enhancements or operate through shared mechanisms. Here, we investigated how three external noise sources-hf-tRNS, low-frequency tRNS (lf-tRNS), and dynamic visual noise (DVN)-affect visual contrast detection, adopting an individualized analytical approach to account for variability in internal noise across participants. DVN was specifically designed to mirror the spatiotemporal characteristics of tRNS, enabling systematic evaluation of cortical- and sensory-level noise within a unified framework. Across three experiments (n = 149 of either sex), all noise types improved detection performance at participant-specific optimal intensities, with the most pronounced effects observed for subthreshold stimuli and in participants with lower baseline sensitivity, consistent with a compensatory role of external noise when perceptual encoding is weak. Notably, these results provide the first evidence of SR-like benefits from lf-tRNS, challenging frequency-dependent assumptions about the efficacy of electrical noise stimulation. Comparable enhancements with DVN further establish it as a promising non-invasive tool for perceptual modulation. Together, these findings expand the SR framework by demonstrating that both electrical and sensory noise can facilitate vision, and highlight the importance of individualized approaches in neuromodulation and sensory enhancement.Significance Statement Contrary to the common view, certain forms of noise can enhance sensory processing, as described by stochastic resonance (SR). We demonstrate that both low-frequency transcranial random noise stimulation (lf-tRNS) and a novel form of dynamic visual noise (DVN), designed to match the spatiotemporal characteristics of tRNS, improve visual contrast detection similarly to high-frequency tRNS (hf-tRNS). Crucially, we account for variability in internal noise across participants, offering insights into the mechanisms underlying noise-induced modulation. Our findings provide the first evidence of SR-like effects from lf-tRNS and validate DVN as a non-invasive tool for modulating sensory performance. These results advance the SR framework and highlight the potential of strategically applied external noise to optimize neural output in both basic and translational neuroscience.

Suppressing complex mixed noise in seismic data poses a significant challenge for conventional methods, which often cause signal damage or leave residual noise. While sparse basis learning is a promising approach for this task, traditional data-driven learning methods are often insensitive to the physical properties of seismic signals, leading to incomplete noise removal and compromised signal fidelity. To address this limitation, we propose a physics-constrained sparse basis learning method for mixed noise suppression. Our method integrates local dip attributes—estimated and iteratively refined by a plane-wave destructor filter—as a physical constraint within the dictionary learning framework. This constraint guides the learning process to achieve high-fidelity signal reconstruction while effectively suppressing multiple noise types. Tests on complex synthetic and real data demonstrate that the proposed method outperforms conventional techniques and industry-standard workflows in attenuating mixed noise, including strong anomalous amplitudes, ground roll, and random and coherent components, thereby significantly enhancing the signal-to-noise ratio and imaging quality.

This paper proposes a method for detecting distorted frames in video footage recorded by an unmanned vehicle. The proposed detection method is performed by analyzing a sequence of video frames, utilizing the contrast aspect of the structural similarity index between previous and current frames. This approach allows for the detection of distortions in the video caused by various types of noise. The scientific novelty lies in the targeted adaptation of the SSIM component to the task of real interframe analysis in conditions of shooting from an unmanned vehicle, in the absence of a reference. The three videos were considered during the simulation. They were distorted by random significant impulse noise, Gaussian noise, and mixed noise. Every 100th frame of the experimental video was subjected to distortion with increasing density. An additional measure was introduced to provide a more accurate assessment of distortion detection quality. This measure is based on the average absolute difference in similarity between video frames. The developed approach allows for effective identification of distortions and is of significant importance for monitoring systems and video data analysis, particularly in footage obtained from unmanned vehicles, where video quality is critical for subsequent processing and analysis.

Gesture control systems based on mid-air haptics are increasingly used in infotainment systems in cars, where they can reduce drivers' distractions and improve safety. However, studies on vibrotactile adaptation show that exposure to mechanical vibration impairs the perception of subsequent stimuli of the same frequency. Given that moving vehicles generate different types of mechanical noise, it is crucial to investigate whether mid-air ultrasound stimuli are also affected by mechanical adaptation. Here, we directly addressed this question by testing participants' perception of ultrasound stimuli both before and after exposure to different mechanical vibrations. Across two experiments, we systematically manipulated the frequency (Experiment 1) and amplitude (Experiment 2) of the adapting mechanical stimulus and measured participants' detection threshold for different ultrasound test stimuli. We found that low-frequency mechanical vibration significantly impaired perception of low-frequency ultrasound stimuli. In contrast, high-frequency mechanical vibration equally impaired perception of both low- and high-frequency ultrasound stimuli. This effect was mediated by the amplitude of the adapting stimulus, with stronger mechanical vibrations producing a larger increase in participants' detection threshold. These findings show that mid-air ultrasound stimuli are significantly affected by specific sources of mechanical noise, with important implications for their safe use in the automotive industry.

Wind farm noise (WFN) exposure effects on sleep remain poorly understood. This study compared the probability of electroencephalographically (EEG) defined arousal from established sleep following WFN versus road traffic noise (RTN) onset. Sixty-eight adults were studied in a sleep laboratory on one night with repeated 20-s WFN and RTN exposures. Following ≥ 2 min of established sleep and ≥ 20-s between noise exposures, pre-recorded WFN or RTN samples were reproduced at sound pressure levels (SPLs) of 30, 40, and 50 dBA in random order. The primary outcome was the probability of EEG-defined arousal events (> 3 s EEG shifts to faster frequencies) following the onset of each noise exposure. Awakening responses (> 15 s EEG frequency shifts) were also evaluated. Noise type, SPL, and sleep stage effects on arousal and awakening response probabilities were evaluated using mixed effects logistic regression analyses. Of 68 participants, 62 (mean ± SD aged 49 ± 20 years, 35 females) had sufficient replicates of noise exposure data for analysis. Arousal response probabilities were low, particularly in deep sleep, but showed a significant noise type-by-SPL interaction (χ2 = 13, p = 0.001), with marginally but significantly lower WFN compared to RTN arousal probabilities at 40 dBA (mean [95% CI]: 2.1 [1.5, 2.9] vs. 3.2 [2.4, 4.2]%, p = 0.016) and 50 dBA (5.0 [4.0, 6.2] vs. 8.6 [6.9, 10.6]%, p < 0.001). Awakenings were infrequent (< 4% at 50 dBA) but showed similar effects. These findings show that acute WFN onset is marginally less sleep disruptive than road traffic noise events of equivalent SPL ≥ 40 dBA.

Bearing condition monitoring in real environments can be confronted with the presence of disturbing ambient acoustic noise, which can penalise the acoustic modality for fault diagnosis. In this context, performance of signal processing methods for detecting bearing faults, developed without added noise, can be greatly reduced depending on the type of disturbing ambient noise added. The purpose of this study is to examine the bearing failure diagnosis by combining the signal measurement with two different types of ambient noise, including automobile and train noise. The measurement signal is measured by a microphone located near the rotating machine, which rotates at 1000 and 3000 rpm. A healthy bearing and a damaged bearing will be considered. A sound signal processing method that combines filtering and detecting steps is used to diagnose the bearing's condition. The first step will evaluate autogram pre-processing versus kurtogram pre-processing. The second step will evaluate the power after filtering of the fault frequency identified. ROC performance curves will be obtained by varying the signal-to-noise ratio of the added noise.

We study the phenomenon of turbulence initiation in pipe flow under different noise structures by estimating the probability of initiating metastable transitions. We establish lower bounds on turbulence transition probabilities using linearized models with multiplicative noise near the laminar state. First, we consider the case of stochastic perturbations by Itô white noise; then, through the Stratonovich interpretation, we extend the analysis to noise types such as white and red noise in time. Our findings demonstrate the viability of detecting the onset of turbulence as rare events under diverse noise assumptions. The results also contribute to applied stochastic partial differential equation (SPDE) theory and offer valuable methodologies for understanding turbulence across application areas.

While hospital and emergent use environment sound levels and corresponding design inputs (including stakeholder and system requirements) for alarms and other auditory feedback mechanisms have historically been the subject of extensive research, sound levels and design input requirements for in-home medical devices and combination products have received arguably less attention. In-home settings are generally considered to be relatively quiet, so there is little expected interference with device auditory feedback. In actuality, home settings vary in loudness and the types of distractor and background noises that can be reasonably expected while a user is operating a device. This variation—coupled with a growing effort to address the needs of aging intended user groups—highlights the importance of understanding and researching in-home use environments and using that knowledge to develop appropriate and feasible device design input requirements. Data collected from an in-home actual-use study was combined with an analysis of age-related hearing loss, in-home ambient noise levels, and analyses of early lift-off use error data. A device prototype was tested with representative users in a representative use environment. The prototype was evaluated for audibility of the auditory feedback and reduction of early lift-off use errors. Collectively, these data were used as key inputs to develop design input requirements for the autoinjector auditory feedback.

We investigate the robustness of transcript-based estimators for properties of interactions against various types of noise, ranging from colored noise to isospectral noise. We observe that all estimators are sensitive to symmetric and asymmetric contamination at signal-to-noise ratios that are orders of magnitude higher than those typically encountered in real-world applications. While different coupling regimes can still be distinguished and characterized sufficiently well, the strong impact of noise on the estimator for the direction of interaction can lead to severe misinterpretations of the underlying coupling structure.

The imminent era of error-corrected quantum computing demands robust methods to characterize quantum state complexity from limited, noisy measurements. We introduce the Quantum Attention Network (QuAN), a classical artificial intelligence (AI) framework leveraging attention mechanisms tailored for learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting permutation invariance. Combined with our parameter-efficient miniset self-attention block, this enables QuAN to access high-order moments of bit-string distributions and preferentially attend to less noisy snapshots. We test QuAN across three quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and toric code under coherent and incoherent noise. QuAN directly learns entanglement and state complexity growth from experimental computational basis measurements, including complexity growth in random circuits from noisy data. In regimes inaccessible to existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types, highlighting AI’s transformative potential for assisting quantum hardware.

SUMMARY The precise picking of first arrivals in seismology is very important for characterizing and monitoring the earthquakes. Similarly, in exploration seismology, identifying the refraction phases is crucial for building accurate velocity models. However, it's identification poses significant challenges in the presence of noise, especially for smaller magnitude earthquakes. Here, we propose a method to identify the first arrival P-wave from local earthquake data, employing time-frequency mapping of raw seismograms using the Generalized S-Transform and then extracting optimal deep encoded features utilizing the convolutional neural network-based unsupervised deep learning approach without the need for labelling the data. The statistical and transformational metrics, generated from both the deep encoded features and the original waveform, are combined to create an enriched feature space. Quantum clustering is then applied to this combined feature space to identify patterns or clusters that distinguish useful waveform sections from noise. This waveform-level selective identification and segmentation facilitate the determination of first arrival times within the relevant sections. The effectiveness of this method is first validated on a suite of synthetic data contaminated with various level and types of noise, and then applied to the observed data from the STEAD global dataset and seismic stations from the Jammu and Kashmir Himalaya. The method demonstrates stable picking performance under noisy conditions when compared to STA/LTA, AIC Picker and the unsupervised deep learning with classic K-Means. It also shows a broadly similar trend to supervised models such as PhaseNet and EQTransformer, and is computationally efficient, even in low signal-to-noise ratio conditions.

Sparse linear regression is one of the classic problems in the field of statistics, which has deep connections and high intersections with optimization, computation, and machine learning. To address the effective handling of high-dimensional data, the diversity of real noise, and the challenges in estimating standard deviation of noise, we propose a novel and general graph-based square-root estimation (GSRE) model for sparse linear regression. Specifically, we use square-root-loss function to encourage the estimators to be independent of the unknown standard deviation of error terms and design a sparse regularization term by using the graphical structure among predictors in a node-by-node form. Based on the predictor graphs with special structure, we highlight the generality by analyzing that the model in this paper is equivalent to several classic regression models. Theoretically, we also analyze the finite sample bounds, asymptotic normality and model selection consistency of GSRE method without relying on standard deviation of error terms. In terms of computation, we employ the fast and efficient alternating direction method of multipliers. Finally, based on a large number of simulated and real data with various types of noise, we demonstrate the performance advantages of the proposed method in estimation, prediction and model selection. Supplementary materials for this article are available online.

Quantum-enhanced sensors, which surpass the standard quantum limit (SQL) and approach the fundamental precision limits dictated by quantum mechanics, are finding applications across a wide range of scientific fields. This quantum advantage becomes particularly significant when a large number of particles are included in the sensing circuit. Achieving such enhancement requires introducing and preserving entanglement among many particles, posing significant experimental challenges. In this work, we integrate concepts from Floquet theory and quantum information to design an entangler capable of generating the desired entanglement between two paths of a quantum interferometer. We demonstrate that our path-entangled states enable sensing beyond the SQL, reaching the fundamental Heisenberg limit (HL) of quantum mechanics. Moreover, we show that a decoding parity measurement maintains the HL when specific conditions from Floquet theory are satisfied—particularly those related to the periodic driving parameters that preserve entanglement during evolution. We address the effects of phase uncertainty, imperfect transmission, and other types of noises, showing that our method remains robust under realistic conditions. Finally, we propose a superconducting-circuit implementation of our sensor in the microwave regime, highlighting its potential for practical applications in high-precision measurements.

EstimateNoiseSEM: A novel framework for deep learning based noise estimation of scanning electron microscopy images.