Pattern Noise Research Articles

The effect of time regime in noise exposure on the auditory system and behavioural stress in the zebrafish

Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.

Vibroacoustic analysis of dental air turbine noise

ObjectiveThe objective of this study is to analyze the noise mechanism of dental air turbine handpiece with vibroacoustic simulation.Materials and methodsThe operational part of the Gentle Silence Lux 8000B (KaVo Dental GmbH) was disassembled and scanned. The scanned data were rendered to smooth irregularities and then virtually reassembled. The rendering was 3D mesh modeled for the analysis. And, the interior void space and exterior space was mesh modeled as air layer. As per simulation input informations, the material property of steel was provided for the handpiece components. Supplied air pressure of 0.22 MPa at the inlet and static temperature of 25 °C was provided as operating conditions. Twenty virtual microphones were arrayed to measure the noise. Vibroacoustic noise simulation was performed with ACTRAN 2021 (MSC software corporation).ResultsThe mean value of noise ranged from 49.88 to 66.38 dB while the peak value ranged from 69.53 to 81.64 dB depending on the microphone position. All microphones showed the similar noise pattern which had peak amplitude at around 4500 Hz. The calculated natural frequency of interior air layer was 4478.92 Hz and 7573.77 Hz.DiscussionThe simulated result showed similar tonal noise of dental handpiece suggesting air resonance phenomenon as a possible cause of dental handpiece noise.ConclusionVibroacoustic analysis of the air layer contained within the dental air turbine handpiece showed the resonance peak noise at 4478.92 Hz under the simulated conditions.

DnSwin: Toward real-world denoising via a continuous Wavelet Sliding Transformer

Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs. Recently, the Vision Transformer (ViT) has exhibited a strong ability to capture long-range dependencies, and many researchers have attempted to apply the ViT to image denoising tasks. However, a real-world image is an isolated frame that makes the ViT build long-range dependencies based on the internal patches, which divides images into patches, disarranges noise patterns and damages gradient continuity. In this article, we propose to resolve this issue by using a continuous Wavelet Sliding-Transformer that builds frequency correspondences under real-world scenes, called DnSwin. Specifically, we first extract the bottom features from noisy input images by using a convolutional neural network (CNN) encoder. The key to DnSwin is to extract high-frequency and low-frequency information from the observed features and build frequency dependencies. To this end, we propose a Wavelet Sliding-Window Transformer (WSWT) that utilizes the discrete wavelet transform (DWT), self-attention and the inverse DWT (IDWT) to extract deep features. Finally, we reconstruct the deep features into denoised images using a CNN decoder. Both quantitative and qualitative evaluations conducted on real-world denoising benchmarks demonstrate that the proposed DnSwin performs favorably against the state-of-the-art methods.

Domain-adaptive denoising network for low-dose CT via noise estimation and transfer learning.

In recent years, low-dose computed tomography (LDCT) has played an important role in the diagnosis CT to reduce the potential adverse effects of X-ray radiation on patients, while maintaining the same diagnostic image quality. Deep learning (DL)-based methods have played an increasingly important role in the field of LDCT imaging. However, its performance is highly dependent on the consistency of feature distributions between training data and test data. Due to patient's breathing movements during data acquisition, the paired LDCT and normal dose CT images are difficult to obtain from realistic imaging scenarios. Moreover, LDCT images from simulation or clinical CT examination often have different feature distributions due to the pollution by different amounts and types of image noises. If a network model trained with a simulated dataset is used to directly test clinical patients' LDCT data, its denoising performance may be degraded. Based on this, we propose a novel domain-adaptive denoising network (DADN) via noise estimation and transfer learning to resolve the out-of-distribution problem in LDCT imaging. To overcome the previous adaptation issue, a novel network model consisting of a reconstruction network and a noise estimation network was designed. The noise estimation network based on a double branch structure is used for image noise extraction and adaptation. Meanwhile, the U-Net-based reconstruction network uses several spatially adaptive normalization modules to fuse multi-scale noise input. Moreover, to facilitate the adaptation of the proposed DADN network to new imaging scenarios, we set a two-stage network training plan. In the first stage, the public simulated dataset is used for training. In the second transfer training stage, we will continue to fine-tune the network model with a torso phantom dataset, while some parameters are frozen. The main reason using the two-stage training scheme is based on the fact that the feature distribution of image content from the public dataset is complex and diverse, whereas the feature distribution of noise pattern from the torso phantom dataset is closer to realistic imaging scenarios. In an evaluation study, the trained DADN model is applied to both the public and clinical patient LDCT datasets. Through the comparison of visual inspection and quantitative results, it is shown that the proposed DADN network model can perform well in terms of noise and artifact suppression, while effectively preserving image contrast and details. In this paper, we have proposed a new DL network to overcome the domain adaptation problem in LDCT image denoising. Moreover, the results demonstrate the feasibility and effectiveness of the application of our proposed DADN network model as a new DL-based LDCT image denoising method.

Design and Analysis of Radio Chaotic Circuit on Communication Network

Data penetration during transmission is one of the key issues with communication technology. In this work, a protected significant communication system combining a variety of chaotic and direct-sequence/spread-spectrum DS/SS approaches is proposed. This is an excellent piece of work. The bit value in this system varies according to chaotic behavior, but it really is a multiple of the process of communication's fixed chip duration. Input signals of varying duration were frequency band by multiplication simultaneously with a pseudo noise (PN) pattern and then manipulated onto harmonic carriers using quadrature phase shift keying (QPSK). The tent map is used by the MATLAB software over a time range of 0 to 1200 seconds. Three sample periods were taken from the time domain duration for each of the four areas S1, S2, S3 and S4. The results of simulations and numerical simulations are presented to demonstrate the system's dependability and viability

Contrastive label correction for noisy label learning

Noisy label learning is an important process that facilitates the collection of noisy label data for training accurate deep neural networks. The latest label correction methods are effective approaches that focus on identifying label errors in datasets for training. However, existing approaches to identify noise generally rely on model predictions that are not scalable and are error-prone. In this study, a new noisy label learning framework is proposed by leveraging supervised contrastive learning for enhanced representation and improved label correction. Specifically, the proposed framework consists of a class-balanced prototype queue, a prototype-based label correction algorithm, and a supervised representation learning module. The proposed framework produces closely aligned representations and model predictions for instances from the same classes and facilitates label correction by aggregating noisy and prototype labels. Furthermore, a theoretical analysis of the proposed framework is provided from the perspective of an expectation–maximization (EM) algorithm. To demonstrate the efficacy of the proposed framework, experiments were performed on synthetic datasets with various noise patterns and levels. The experimental results demonstrate that the proposed framework achieves superior classification accuracy compared with other label correction frameworks.

Numerical prediction of propeller-induced noise in open water and ship behind conditions

In this article, the underwater noise of non-cavitating propellers is investigated using a boundary element method (BEM) coupled with the Ffowcs Williams–Hawkings (FW–H) formulation in the presence of the hull. The propeller is mounted on the vessel with an infinite plane assumed at the design draft to consider the free surface or tunnel wall effect. The hydrodynamic BEM solves the flow field for the analysis of propeller performance, then the time-dependent surface pressures are taken as the known noise sources for the FW–H equation in the form proposed by Farassat. The hull geometry and its disturbance to the medium are considered by solving the boundary integral equations, and the half-space medium is modeled by adding an additional term to the full space Green’s function for an image body beyond the infinite plane. The propeller performance in open water condition is first investigated to validate BEM predictions against experimental measurements. Along with the overall noise patterns in the flow field, decay rates of noise with the distance from the propeller and limited influence of the linear noise terms of the FW–H equation are discussed. Afterward, the propeller-induced hull pressure fluctuations are investigated, and the time history of acoustic pressures shows reasonable agreement with experimental measurements. Direct noise from the propeller, its scattered noise by the hull, and their sums as the total noise are discussed. This study identifies the dominating noise source around the hull, which could provide a basis for the noise control strategy for a ship propulsion system.

What Can We Learn from Depth Camera Sensor Noise?

Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, we deal with depth cameras and their noise patterns. We show that from sensor noise alone, the object’s depth and location in the scene can be deduced. Sensor noise can indicate the source camera type, and within a camera type the specific device used to acquire the images. Furthermore, we show that noise distribution on surfaces provides information about the light direction within the scene as well as allows to distinguish between real and masked faces. Finally, we show that the size of depth shadows (missing depth data) is a function of the object’s distance from the background, its distance from the camera and the object’s size. Hence, can be used to authenticate objects location in the scene. This paper provides tools and insights into what can be learned from depth camera sensor noise.

3D noise photon transfer curve.

A photon transfer curve (PTC) is used to determine fundamental detector noise parameters such as read noise, conversion gain, and fixed pattern noise. Here, the method for determining a PTC is expanded to include 3D noise parameters. 3D noise PTC provides more insight into detector noise and is treated as the next logical step to classical PTC. However, it induces several new challenges in analyzing the results, specifically the fitting of seven, or more, variance curves compared to the one (total variance) or two (temporal and fixed pattern variance) prior. Therefore, a general measurement model is created, which provides a new method to separate out all the classical terms, such as DSNU and PRNU, but can also handle high gain cameras with a noise factor. This method is then verified using Monte Carlo simulations and applied to a commercial machine vision camera. In addition, the effects of lens vignetting and non-uniformity correction (NUC) are explored, along with a comparison of the single pixel PTC.

The effect of axial cyclone inlet velocity and geometrical dimensions on the flow pattern, performance, and acoustic noise

This article studied the effect of the inlet velocity and various geometric dimensions on the flow pattern, collection efficiency, pressure drop, and acoustic noise of axial cyclones using computational fluid dynamics (CFD). The maximum values of tangential and axial velocity over inlet velocity were obtained for a guide vane revolution of 0.4 and a ratio of the swirler core diameter to the cyclone diameter of 0.3. It was found that the revolution of guide vanes also significantly affected the collection efficiency. In addition, the minimum values of the cut-off Stokes number, Euler number, and the overall sound pressure level (OASPL) were found to be 4.83 × 10−3, 4.014, 22.81 dB, respectively, while the minimal acoustic noise was observed for the guide vanes leading-edge exponent of 1.5. However, an increase in the Reynolds number did not alter the shape of the pressure and velocity contours but affected their magnitudes in the cyclone.

OFDM Radar and Communication Joint System Using Opto-Electronic Oscillator With Phase Noise Degradation Analysis and Mitigation

Orthogonal frequency division multiplexing (OFDM) signal is a superior dual-functional waveform for the integration of radar sensing and communication in intelligent transportation. But the sensitivity to phase noise is a serious issue introducing interference and causing performance degradation during demodulation. In this paper, we explore the essential mechanism of the action and generation of phase noise through theoretical analysis, where the OFDM demodulation process and power spectrum density (PSD) of phase noise is discussed in the frequency domain, and draw the conclusion that high-speed phase jitter will cause unrecoverable deterioration of OFDM demodulation. Therefore, a photonics-aided radar and communication integrated system based on Optoelectronic oscillator (OEO) is proposed. The positive feedback oscillation with long energy storage time make the phase noise pattern of OEO just suitable to against the phase noise sensitivity of OFDM. A proof-of-concept experiment is demonstrated at 24 GHz with 2 GHz bandwidth to verify the radar sensing and communication function. A two-dimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 4.4 km/h, a communication capacity of 6.4 Gbps is obtained. A quantitative performance comparison is also carried out. By using an ordinary microwave source and OEO separately, the demodulation constellation and error vector magnitude (EVM) under different subcarrier spacing is measured and compared. The result is corresponding to our analysis with the EVM decreasing from 12.5% to 4.7% under subcarrier spacing of 125 kHz.

Anisotropy with respect to the applied magnetic field of spin qubit decoherence times

Electron spin qubits are a promising platform for quantum computation. Environmental noise impedes coherent operations by limiting the qubit relaxation (T1) and dephasing (Tϕ) times. There are multiple sources of such noise, which makes it important to devise experimental techniques that can detect the spatial locations of these sources and determine the type of source. In this paper, we propose that anisotropy in T1 and Tϕ with respect to the direction of the applied magnetic field can reveal much about these aspects of the noise. We investigate the anisotropy patterns of charge noise, evanescent-wave Johnson noise, and hyperfine noise in hypothetical devices. It is necessary to have a rather well-characterized sample to get the maximum benefit from this technique. The general anisotropy patterns are elucidated. We calculate the expected anisotropy for a particular model of a Si/SiGe quantum dot device.

Balance label correction using contrastive loss

The training of accurate deep neural networks using noisy labels is attracting tremendous attention as of late. Several studies have been devoted to noisy-label-based learning using label correction methods. The latest label correction methods provide a theoretical guarantee and avoid the accumulation of irreversible errors. However, these methods often encounter class-biased problems: some classes (“easy”) are easier to correct than others (“hard”). It has been observed that the noisy samples with a lower class confidence are harder to correct, and the class confidence is measured by the average of the model predictions, resulting from the correctly predicted samples across all classes. To address this class-biased problem, we provide a theoretical analysis of the correction procedure by investigating model predictions. Furthermore, we propose a new balance label correction method by employing the statistics of contrastive loss as a dynamic balancing factor that focuses more on the lower confidence hard classes. To demonstrate the efficacy of the proposed method, we performed experiments on synthetic and real-world datasets with various noise patterns and levels. The experimental results demonstrated that the proposed method achieved a superior classification accuracy compared with other state-of-the-art noisy label learning frameworks.

Source Camera Device Identification from Videos

Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the VISION data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of 72.75 pm 1.1 % on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of 71.75% on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the VISION and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs.

Urban Rhapsody: Large‐scale exploration of urban soundscapes

AbstractNoise is one of the primary quality‐of‐life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low‐cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state‐of‐the‐art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high‐precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five‐year deployment of a one‐of‐a‐kind sensor network in New York City.

A noise statistical distribution analysis-based two-step filtering mechanism for optical coherence tomography image despeckling

Optical coherence tomography (OCT) inevitably suffers from speckle noises that originate from the coherent multiple-scattered photons. Such speckle noises, following different distribution patterns, can hide tissue microstructures and degrade the disease diagnosis accuracy. So far, various schemes have been proposed for despeckling in OCT images, yet few have evaluated the impacts of different noise patterns on despeckling effects. In this study, we evaluate the influences of statistical noise distributions on OCT despeckling and propose a noise distribution analysis-based despeckling method for OCT images. Specifically, we establish a noise model by dividing speckle noises into multiplicative and additive ones first, and then propose a two-step filtering mechanism, namely, augmented Lagrange function minimization and Rayleigh alpha-trimmed filtering (AR) method, to suppress such noises separately while maintain tissue microstructures. Simulations with both synthetic and practical OCT images are conducted to verify the effectiveness of the proposed AR method. Results show that the influence of multiplicative noises on OCT images are more significant, and the AR method is capable of suppressing both multiplicative and additive noises effectively, e.g. it improves the peak signal-to-noise ratio and structural similarity index measurements by 83.22 and 812.88 for typical retinal images, respectively, while retaining the important image structural details with less computational time.

Understanding the neutron noise induced by fuel assembly vibrations in linear theory

This paper investigates the underlying physical mechanisms involved in the monochromatic vibrations of fuel assemblies and their effects on the induced neutron noise throughout the core of nuclear reactors, in the framework of simplified benchmark configurations. Any vibrating fuel pin introduces noise sources at the frequency of vibrations, as well as at higher harmonics, the first one being the most significant of those. Depending on the harmonics considered, the position of the vibrating fuel pin, the size of the core and its macroscopic cross-sections, different noise responses are observed within the reactor core. Through the lens of a decomposition of the neutron noise into its point-kinetics component and its deviation from it, the spectrum of noise responses is explained and related to the spatial distribution of the amplitude and phase of the noise sources at the considered frequencies. At the frequency of vibration, possible out-of-phase behaviour of the induced neutron noise can be partially or totally shadowed by the in-phase point-kinetics component, the only exception being for central vibrations in symmetrical systems. At the frequency of the first higher harmonics, the structure of the induced neutron noise is more involved. Nevertheless, due to the compensation of the individual responses associated to the different components of the noise source at that frequency, point-kinetics has a significant contribution. The results of this work sheds new light on the complex spatial pattern of the neutron noise computed by realistic core simulators in case of vibrations of fuel assemblies.

On the Effect of Interferences on X-Band Radar Wave Measurements.

X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system’s capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making.

Exploring spatial patterns of environmental noise and perceived sound source dominance in urban areas. Case study: the city of Athens, Greece

The aim of the present study is to map spatial patterns related to noise pollution and the acoustic environment -in a broader context- in the urban area of Athens, Greece. The primary goal of this thesis is to present a comprehensive approach that combines elements of two basic methodologies related to acoustic environment studies: a) noise mapping and b) the soundscape approach. The main inputs are environmental noise measurements and perceptual sound source-related observations. The results feature three noise pollution maps (LAeq,30 sec, L10, and L90 indices) and three sound source maps which reflect the way in which the human ear perceives the presence of sounds. Additionally, the question of whether the spatial distribution of sound source dominance can be explained by the dispersion of environmental noise levels was examined using geographically weighted regressions (GWR). The GWR models showed that sound source-related observations are explained to a significant extent by all three indicators. Four important findings emerge from the analysis. Firstly, areas with high levels of noise pollution are characterized by high to moderate presence of technological and absence of anthropic and natural sounds. Secondly, regions, where there is a simultaneous presence of all sound sources, are characterized by moderate to low noise levels. Thirdly, the absence of technological sounds is observed in quiet areas. Finally, areas featuring a moderate presence of technological and natural sounds are mostly urban green spaces built-in proximity to the main road network.

To differ is human: A reflective commentary on “Noise. A Flaw in Human Judgment”, by D. Kahneman, O. Sibony & C.R. Sunstein (2021). London: William Collins

AbstractInconsistency in real‐world judgments can cause random unfairness, injustice and misallocation of resources. In their recent monograph Kahneman, Sibony, and Sunstein (2021) analyse judgment inconsistency or “Noise,” examine its sources and propose remedies. In this commentary on Kahneman et al., we reflect on the major concepts (such as “judgment,” “noise,” “error,” and “bias”) used in analysing inconsistency. We place this work in the broader context of applied cognitive psychology, relating it to error typologies and to dual‐systems views of thinking. We also compare Kahneman et al.'s heuristics based approach to the linear combination of attributes based approach of Social Judgment Theory (SJT), with particular reference to judgment noise. We conclude that the main contributions of Kahneman et al.'s book are (a) to raise awareness of the pervasiveness of judgment noise across a range of important real‐world areas, (b) to provide a taxonomy of types of noise in terms of system noise versus occasion noise, and level noise versus pattern noise, and (c) to outline useful ways of reducing noise, and thus overall levels of error.