Types Of Noise Research Articles

Dark continuous noise from mutant G90D-rhodopsin predominantly underlies congenital stationary night blindness

Congenital stationary night blindness (CSNB) is an inherited retinal disease that causes a profound loss of rod sensitivity without severe retinal degeneration. One well-studied rhodopsin point mutant, G90D-Rho, is thought to cause CSNB because of its constitutive activity in darkness causing rod desensitization. However, the nature of this constitutive activity and its precise molecular source have not been resolved for almost 30 y. In this study, we made a knock-in (KI) mouse line with a very low expression of G90D-Rho (equal in amount to ~0.1% of normal rhodopsin, WT-Rho, in WT rods), with the remaining WT-Rho replaced by REY-Rho, a mutant with a very low efficiency of activating transducin due to a charge reversal of the highly conserved ERY motif to REY. We observed two kinds of constitutive noise: one being spontaneous isomerization (R*) of G90D-Rho at a molecular rate (R* s-1) 175-fold higher than WT-Rho and the other being G90D-Rho-generated dark continuous noise comprising low-amplitude unitary events occurring at a very high molecular rate equivalent in effect to ~40,000-fold of R* s-1 from WT-Rho. Neither noise type originated from G90D-Opsin because exogenous 11-cis-retinal had no effect. Extrapolating the above observations at low (0.1%) expression of G90D-Rho to normal disease exhibited by a KI mouse model with RhoG90D/WTand RhoG90D/G90D genotypes predicts the disease condition very well quantitatively. Overall, the continuous noise from G90D-Rho therefore predominates, constituting the major equivalent background light causing rod desensitization in CSNB.

Improving the generalization of image denoising via structure‐preserved MLP‐based denoiser and generative diffusion prior

AbstractImage denoising aims to remove noise from images and improve the quality of images. However, most image denoising methods heavily rely on pairwise training strategies and strict prior knowledge about image structure or noise distribution. While these methods exhibit significant results when handling known types of noise, their generalization performance diminishes when confronted with images containing unknown noise distributions. To address this issue, a two‐stage approach is introduced for enhancing the generalizability of image denoising. The proposed method does not rely on a large amount of paired data or prior knowledge of the noise type and level. Instead, it constructs a denoising pipeline with improved generalizability through an MLP‐based denoiser and generative diffusion prior. Specifically, in the first stage, an initial denoised image is predicted with a structure resembling that of the underlying clean image by introducing an MLP‐based U‐shaped denoising network aided by an implicit structural prior. In the second stage, the generalizability and quality of the denoiser are further enhanced by conditioning the result obtained from the previous stage on the pretrained denoising diffusion null‐space model. Extensive experimentation on multiple datasets demonstrates that this method exhibits better denoising performance and generalizability than other image denoising methods.

Sound and Noise Sources in Sonotubometry: An Investigation of Eustachian Tube Assessment.

This research aims to enhance the understanding of the acoustic processes occurring during sonotubometry, a method used to assess the Eustachian tube (ET) function. Recent advancements in digital signal processing enable a more comprehensive data analysis. In this project, a silicone model of the ET was developed to systematically study the existing noise and sound sources. These measurements were then compared with recordings from human subjects. Three distinct 'noise sources' were identified, which can influence the assessment of the ET opening using transmission measurements of the imposed signal: sound leakage from the speaker, a clicking noise at the initiation of ET opening, and rumbling/swallowing noise. Through spectral analysis, it was also possible to ascertain the spectral and temporal occurrence of these sound and noise types. The silicone model exhibited remarkable similarity to the healthy human ET, making it a robust experimental model for investigating the acoustics of sonotubometry. The findings underscore the significance of delving deeper into the analysed sound, as the noise occurring during sonotubometry can be easily misconstrued as an actual ET opening. Particularly, careful consideration is warranted when evaluating data involving clicking and swallowing noise.

Design and experiment of electromagnetic-hydraulic-rubber integrated vibration isolator

Low-frequency radiated noise, characterized by a distinctive “acoustic fingerprint” is generated when vibrations from marine machinery propagate through a ship’s hull into the ocean. This type of noise travels long distances with concentrated and stable energy, posing a significant threat to a ship’s acoustic stealth capabilities. Active-passive hybrid isolation is the primary method for reducing low-frequency radiated noise from ships; however, technical challenges remain, such as effectively integrating active and passive components, achieving high output force in a compact design, and addressing the poor linearity of actuator output at low frequencies. To address these issues, this paper presented an innovative electromagnetic-hydraulic-rubber integrated vibration isolator. Firstly, the study analyzed the dynamic characteristics of a two-degree-of-freedom isolation system and investigated the influence of active-passive hybrid vibration isolator parameters on control force and vibration reduction performance. Secondly, it was established that the magnetic circuit model and magnetic field strength expressions for the electromagnetic actuator using magnetic circuit analysis derive the analytical relationship between electromagnetic force, current amplitude, and frequency using the energy method. Subsequently, a mathematical model was developed for the rubber-hydraulic suspension component to examine its dynamic characteristics, hydraulic damping, and hydraulic force amplification transmission laws. Lastly, we organically combine the electromagnetic actuator with the rubber-hydraulic suspension and conduct a multi-physics joint simulation of the integrated vibration isolator using Comsol software to verify the effectiveness of the optimized design and vibration isolation control. Experimental research was carried out on the dynamic characteristics, output force properties, and fatigue characteristics of the integrated vibration isolator prototype. Results indicated that the established models and methods can achieve over 90% accuracy in predicting the performance of the electromagnetic actuator’s output force and exhibit good linearity within the 5–400 Hz range. The rubber-hydraulic suspension can achieve an amplification factor of up to 1.5 for the electromagnetic force while reducing the transmission of vibrations to the base. The research findings can enhance the low-frequency vibration isolation performance of marine machinery equipment and improve their acoustic stealth capabilities.

Hyperspectral Image Mixed Noise Removal via Double Factor Total Variation Nonlocal Low-Rank Tensor Regularization

A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of mixed noise removal approaches have been developed for HSI, and the methods based on spatial–spectral double factor and total variation (DFTV) regularization have achieved comparable performance. Additionally, the nonlocal low-rank tensor model (NLR) is often employed to characterize spatial nonlocal self-similarity (NSS). Generally, fully exploring prior knowledge can improve the denoising performance, but it significantly increases the computational cost when the NSS prior is employed. To solve this problem, this article proposes a novel DFTV-based NLR regularization (DFTVNLR) model for HSI mixed noise removal. The proposed model employs low-rank tensor factorization (LRTF) to characterize the spectral global low-rankness (LR), introduces 2-D and 1-D TV constraints on double-factor to characterize the spatial and spectral local smoothness (LS), respectively. Meanwhile, the NLR is applied to the spatial factor to characterize the NSS. Then, we developed an algorithm based on proximal alternating minimization (PAM) to solve the proposed model effectively. Particularly, we effectively controlled the computational cost from two aspects, namely taking small-sized double factor as regularization object and putting the time-consuming NLR model before the main loop with fewer iterations to solve it independently. Finally, considerable experiments on simulated and real noisy HSI substantiate that the proposed method is superior to the related state-of-the-art methods in balancing the denoising effect and speed.

No-Reference Image Quality Assessment Algorithm

The presented article focuses on the development of a local image quality assessment algorithm, specifically designed for analyzing contrast and overall visual data quality. The proposed algorithm aims to enhance the efficiency of image assessment, particularly in conditions of low contrast and the presence of various types of noise. The algorithm's methodology takes into account spectral ranges and provides precise local contrast assessment, making it applicable to a broad spectrum of tasks related to image analysis and enhancement. The developed approach has the potential to improve the quality of visual data by supporting crucial aspects of contrast perception and overall image quality.

Adaptive merging migration

Velocity errors and data noise are inevitable for seismic imaging of field data sets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we develop a new method of adaptive merging migration (AMM). Our method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. In addition, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM uses an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field data set to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on synthetic and field data sets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.

Construction and volumetric benchmarking of quantum computing noise models

The main challenge of quantum computing on its way to scalability is the erroneous behaviour of current devices. Understanding and predicting their impact on computations is essential to counteract these errors with methods such as quantum error mitigation. Thus, it is necessary to construct and evaluate accurate noise models. However, the evaluation of noise models does not yet follow a systematic approach, making it nearly impossible to estimate the accuracy of a model for a given application. Therefore, we developed and present a systematic approach to benchmarking noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model containing five types of quantum noise and optimize its parameters using a series of training circuits. We compare its accuracy to other noise models by volumetric benchmarks involving typical variational quantum circuits. The model can easily be expanded by adding new quantum channels.

Regularly Truncated M-Estimators for Learning With Noisy Labels.

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples; (b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels.

A self‐supervised scheme for ground roll suppression

AbstractIn recent years, self‐supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labelled data in the training stage, an unobtainable requirement for seismic data. However, current self‐supervised methods usually suppress simple noise types, such as random and trace‐wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self‐supervised procedure, namely, blind‐fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self‐supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll.

Objective and Subjective Measures of Spatial Hearing in Unilateral Cochlear Implant Users with Bilateral Profound Hearing Loss

Purpose: The ability to benefit from spatial separation between target and masker signals is important in multi-sound source listening environments. The goal of this study was to measure the spatial release from masking (SRM) in unilateral cochlear implant (CI) users with bilateral profound hearing loss. We also determined the relationships between the SRMs and the self-reported spatial hearing abilities.Methods: Fourteen unilateral CI users with bilateral profound hearing loss participated in this study. The target sentence was always presented to the front of the listener, and the nonfluctuating speech-shaped noise (SSN) or fluctuating speech noise was either co-located with the target (speech at 0°, noise at 0°, S0N0) or spatially separated at ± 90°. The SRM was quantified as the difference between speech recognition thresholds (SRTs) in the co-located and spatially separated conditions. The self-reported spatial hearing abilities were also measured using validated subjective questionnaires.Results: Overall, the SRTs were lower (better) with SSN than with fluctuating speech noise. When the noise was presented to the non-CI ear (speech at 0°, noise at non-CI ear, S0Nnonci), speech-in-noise recognition was the greatest due to head shadow or better-ear listening effect, resulting in the SRMs of approximately 5~6 dB regardless of noise type. When the noise was given to the CI ear (speech at 0°, noise at CI ear, S0Nci), some individuals exhibited positive SRMs (3~8 dB), while others showed negative SRMs, leading to little SRMs overall. When the SSN was given, subjects with less SRMs (less spatial separation benefits on the objective test) reported greater subjective spatial hearing difficulties.Conclusion: The spatial hearing of unilateral CI users varied by the position of the sound source. Listeners' spatial hearing abilities, which are unpredictable from clinical routine tests, need to be assessed by either objective or subjective measures.

The Effects of Noise on Children’s Cognitive Performance: A Systematic Review

Noise exposure may interfere with concentration, learning, and executive functions. In children, in particular, the non-auditory consequences of acoustically inadequate conditions are a serious concern. This systematic review examined the evidence from 26 studies investigating the effects of noise on primary school children’s cognitive and academic performance. The reviewed studies show that speech noise significantly impairs children’s verbal working memory. Meanwhile, non-speech environmental noise appears to notably affect academic performance, particularly in reading. Other types of non-speech noise may, instead, even improve the cognitive performance of children, although only for children with low attentional skills. However, only a few studies have compared the impacts of speech and non-speech noise or explored noise effects across various cognitive tasks. And finally, there’s a shortage of both cross-sectional and longitudinal studies assessing developmental effects. Overall, this scarcity limits the ability to draw robust conclusions about noise effects on children’s cognitive and academic abilities.

Impact of interface traps and noise analysis on dual material graded channel CGAA FET: A device reliability

This paper explores the effects of changing the device's parameters, such as the gate length (Lg), nanowire radius (r), oxide thickness (tox), and frequency (f), on the noise characteristics of dual material graded channel (DMGC) cylindrical gate all around (CGAA) FET while considering the presence and absence of interface trap charges. The performance analysis of the DMGC CGAA FET with that of the SMGC (single material graded channel) CGAA FET, is carried out while taking into account the impact of trap charges. It's noteworthy that the DMGC FET demonstrates significant advantages, with improvement in Ion/Ioff, reduction in DIBL, and SS when compared to the SMGC CGAA FET, both in the cases of without and with traps. The investigation also extends to analyse the analog/RF performance of the DMGC FET, for different type of trap charges. Furthermore, the power spectral densities of current noise (Sid) and voltage noise (Svg) are examined by varying the device dimensions and temperature. This also addresses the various sources of noise, including flicker noise (1/f), generation-recombination (G-R) noise, and diffusion noise. These types of noise have different prevalence depending on the operating frequency. At low frequencies, flicker and G-R noises dominate, while at higher frequencies, diffusion noise becomes the prominent factor impacting the device's performance. The research findings indicate that the optimal performance of the DMGC CGAA FET is achieved with smaller nanowire radius and thinner oxide thickness, regardless of trap charges. These findings offer valuable insights for improving the design and performance of DMGC CGAA FET in low power applications.

A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

AbstractRough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images.

Intelligent agent for hyperspectral image classification with noisy labels: a deep reinforcement learning framework

ABSTRACT Label accuracy plays an important role in supervised hyperspectral image classification problems. However, labelling can be prone to various types of noise, mainly including human errors and incorrect pseudo-label predictions. Learning from datasets with noisy labels is a critical challenge in practice. To address this issue, we first propose a deep reinforcement learning method named HyperRefine to tackle the label noise problem for hyperspectral image classification. The method utilizes an agent to learn the accuracy of each sample label and determine its suitability to be used in training the classifier model. The agent is trained using a reinforcement signal based on the reward obtained from a small validation set that reflects performance on the target task, where the validation set is generated from the training set by a momentum-based recruiter. Compared to complex hand-crafted methods of label noise cleaning, HyperRefine is easy to implement, yet highly effective. The robustness of the model to different types of label noises is significantly improved by further combining a novel label refinement approach. Experiments on artificially added label noise and pseudo-label noise hyperspectral datasets show that the proposed method can achieve promising performance.

Modeling and FDTD discretization of stochastic Maxwell's equations with Drude dispersion

We develop the stochastic Maxwell's equations with Drude model under both additive and multiplicative noises. The additive noises characterize the random fluctuations in the electric current and magnetic current densities in Maxwell's equations, while the multiplicative noise describes the external fluctuation of electric field in the Drude dispersion equation of motion. For the stochastic models with Drude dispersion, we derive the averaged global energy law considering both types of noises. The CN-FDTD and Yee-FDTD discretization schemes are developed for solving the stochastic system over staggered grids. We establish the discrete averaged global energy laws for the CN-FDTD and Yee-FDTD schemes. Numerical experiments show the discrete averaged global energy evolutions and the convergence rates of the schemes for the stochastic Maxwell's equations with Drude model. We numerically simulate and analyze the stochastic electromagnetic propagation within Drude materials under various sinusoidal sources, initial wave conditions and additive and multiplicative noises.

Effects of microtopography on patterns and dynamics of groundwater–surface water interactions

Groundwater–surface water interaction is greatly affected by surface topography and agricultural activities in managed agricultural regions. Such an interaction increases the complexity of hierarchical groundwater flow systems. However, the combined effects of microtopography and agricultural activities on groundwater flow patterns remain unclear. In this study, an integrated groundwater–surface water model was used to quantify the impact of microtopographic variations on groundwater flow patterns in an agricultural region in northwest China. Numerical experiments were conducted under different scenarios representing various degrees of microtopography. The results indicated that microtopography plays a pivotal role in the transfer of signals from spatiotemporally varying surface processes to the groundwater flow. Without consideration of the microtopography, the stationary fractional Gaussian noise–type infiltration processes in the vadose zone–saturated zone interface may be incorrectly simulated as non-stationary fractional Brownian motion. Furthermore, fractal analysis reveals that the scaling exponents of the pressure head are highly sensitive within the vadose zone and local flow subsystems. Microtopography plays an important role in shaping the groundwater response time (GRT) pattern, which results in a smaller average GRT but a larger core range. Contrarily, neglecting microtopography may lead to overestimation of the water table ratio (WTR), resulting in erroneous identification of groundwater patterns as topography-controlled types. This study highlights the importance of microtopography in the formation and evolution of nested groundwater flow systems and provides guidance for groundwater–surface water interaction simulations in managed regions.

Low-cost wind turbine aeroacoustic predictions using actuator lines

Aerodynamic noise is a limitation for further exploitation of wind energy resources. As this type of noise is caused by the interaction of turbulent flow with the airframe, a detailed resolution of the flow is necessary to obtain an accurate prediction of the far-field noise. Computational fluid dynamic (CFD) solvers simulate the flow field but only at a high computational cost, which is much increased when the acoustic field is resolved. Therefore, wind turbine noise predictions using numerical approaches remain a challenge. This paper presents a methodology that couples (relatively fast) wind turbine CFD simulations using actuator lines with a fast turn-around noise prediction method. The flow field is simulated using actuator lines and large eddy simulations. The noise prediction method is based on the Amiet–Schlinker’s theory for rotatory noise sources, considering leading- and trailing-edge noise as unique noise sources. A 2D panel code (XFOIL) calculates the sectional forces and boundary layer quantities. The resulting methodology for the noise prediction method is of high fidelity since the wind turbine geometry is accounted for in both flow and acoustics predictions. Results are compared with field measurements of a full-scale wind turbine for two operational conditions, validating the results of this research.

Seismic data denoising with two-step prediction strategy based on Neural Network

Seismic data denoising (SDD) plays an important role in obtaining high-quality data for subsequent seismic imaging and inversion. However, traditional SDD methods still present several disadvantages such as parameter selection difficulties, high computational costs, and a strong dependence on the experience of processing personnel. Deep learning (DL)-based SDD methods can overcome these issues to a certain extent, having the advantages of high computational efficiency and negligible dependency on the experience of processing personnel. However, existing DL-based SDD methods typically adopt a specific single-network architecture, which can be further improved in terms of training efficiency and denoising quality. In this paper, we propose a two-step prediction denoising method based on the combination of two identical or different network architectures for improving the accuracy and efficiency of DL-based SDD methods. Compared with existing SDD methods, the two-step prediction denoising method is more capable of extracting feature information at multiple scales because multiple network architectures are combined in a single denoising task, thus improving the training efficiency and denoising accuracy. In this study, we implemented the two-step prediction denoising method using an improved denoising convolutional neural network (DnCNN) and a nested U-shaped fully convolutional network (U-Net++). Several synthetic numerical examples based on three different types of noise were denoised, with the results indicating that the proposed method effectively improves the accuracy and efficiency of the DL-based SDD method. Finally, a test using field data was performed to demonstrate the real-world applicability of the novel two-step prediction denoising method.

An Efficient Inexact Gauss–Seidel-Based Algorithm for Image Restoration with Mixed Noise

A challenge in image restoration is to recover a clear image from the blurry observation in the presence of different types of noise. There are few works addressing image deblurring under mixed noise. To handle this issue, we propose a general model based on classical wavelet tight frame regularization. We utilize a convexity-preserving term to obtain a component-wise convex model under a mild condition. Indeed, to reduce the cost of solving subproblems, the inexact Gauss–Seidel-based majorized semi-proximal alternating direction method of multipliers (sGS-imsPADMM) with relative error control is developed. Besides, the global convergence of sGS-imsPADMM is demonstrated. Numerical results for the image restoration problems show that the proposed model and solving approach are superior to some state-of-the-art methods both in numerical analysis and visual quality.