High Noise Levels Research Articles

An Experimental Investigation Of µUAV Rotor Wingtip Vortices Through High Spatial Resolution PIV

The interest for µ UAVs platforms with VTOL capabilities dramatically rose over the last decade due to the fast-paced advancements in electronics and control. However, the vortical structures shedded from the rotary wings -and their interaction-, which are responsible for high power usage and high noise level emissions, are yet to be fully understood. In this work, an experimental PIV campaign was conducted on a commercially available propeller with a 230mm diameter. The operating angular velocity was chosen to be 5750RPM, producing a chord Reynolds number of about Re_{c,75%} ≃ 11000, generating 3.42N of thrust while requiring 55W of power. The resolution of the PIV setup was able to reliably fit 8 measurement points in the vortex core when using narrowest field-of-view (FOV), while the widest FOV comprises a measurement area of about 0.5D×0.57D. Time averaged as well as phase averaged measurements were performed. The evolution and interaction of the wingtip vortices and shear layers shedded in the flow field is captured with an azimuthal angle step increment of ∆Ψ = 5º. The thrust produced by the multirotor configuration was indeed found to be approximately 5% lower than the one produced by the isolated propeller in hovering conditions. Additionally, the PIV setup successfully met the target spatial accuracy requirements: induced velocities are accurately captured in every stage of the wingtip vortex evolution.

Evaluation of Sound Pollution Near Several Government and Private Hospitals in Najaf City

One notable aspect of the environment is sound pollution, which varies in intensity depending on where it comes from, it has varied degrees of impact on the nearby buildings and items like Hospitals are exposed to noise. Additionally, because most hospitals are situated on major thoroughfares, industrial activities and commercial activities. As a result, these structures are frequently subjected to high levels of environmental noise due to the work and care that hospitals give for their patients, which necessitates relaxation and tranquility. The purpose of this study is to determine how much noise pollution in the area around multiple hospitals in Najaf City is caused by various activities. An equipment known as the Sound degree Meter (IEC 61672-1 type 2) was utilized to measure the degree of noise pollution , three times a day—Sunday, Tuesday, and Thursday—during a period of four weeks, the months of June and July were used to measure the noise levels. These measurements were also made in the vicinity of the hospitals—three times in the morning, afternoon, and evening. According to the World Health Organization and Iraqi Determinants of Noise, the average noise levels measured outside the hospital on certain days were significantly higher than the environmental noise limit required. Ultimately, several recommendations for lowering the noise level in this vicinity were made.

Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level

Physical environment plays a key role in determining human health risks. Exposure to toxins, weather extremes, degraded air and water quality, high levels of noise and limited accessibility to green areas can negatively affect health. Furthermore, adverse environmental exposures are often correlated with each other and with socioeconomic status, thereby compounding disadvantages in marginalized populations. Moreover, despite their importance in determining human health risks, the role of multiple environmental exposures is not well studied, and only a few resources contain aggregate environmental exposure data and only for selected areas of the contiguous US. To fill these gaps, we took a cumulative approach to measuring the environment by generating a composite Multi-Exposure Environmental Index (MEEI) as a US Census Tract-level summary of key environmental factors with known health effects. This measure quantifies multiple environmental exposures in the same area that can result in additive and synergistic effects on health outcomes. This information is crucial to better understand and possibly leverage environmental determinants of health for informed policy-making and intervention.

Neighborhood repartition-based oversampling algorithm for multiclass imbalanced data with label noise

The classification of imbalanced data remains one of the most significant topics in contemporary data analysis. Existing classification algorithms tend to favor majority classes, leading to false predictions and difficulties in addressing class overlap or label noise. These challenges are particularly evident in multiclass settings, where the mutual imbalance relationships among classes become more complex. Despite this, the vast majority of research in this field has concentrated on binary problems, while the more difficult multiclass problems are relatively underexplored. In this paper, we propose a novel data-sampling technique, a Multiclass Neighborhood Repartition-based Oversampling (MC-NRO) algorithm. The innovation of this method lies in it considers local data characteristics of each class to constrain the oversampled neighborhood. MC-NRO calculates the mutual potential of different classes to precisely optimize the subregions for generating new instances. By selecting different repartition neighborhoods to meet the needs of specific domain, it can detect outliers and label noise, expand the decision boundary of minority class, and avoid class overlap through data cleaning. The experimental results demonstrate that MC-NRO outperforms other advanced oversampling strategies, ranking first on average across the three evaluation metrics, and exhibits robustness, especially in datasets with high noise levels. More importantly, MC-NRO is highly versatile and can be flexibly applied to various classifiers, and is particularly suitable for processing naturally complex (i.e., not affected by noise) datasets.

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

Intelligent fault diagnosis of rolling bearings in strongly noisy environments using graph convolutional networks

SummaryRolling bearings often function under complex and non‐stationary conditions, where significant noise interference complicates fault diagnosis by obscuring fault characteristics. This paper presents an innovative fault diagnosis technique using graph convolutional networks (GCN) to address these challenges. Vibration signals are first transformed into the frequency domain through fast Fourier transform (FFT), creating a detailed graph where nodes and edges encapsulate fault signals. The GCN method then extracts complex node features from this graph, enabling a classifier, comprising a fully connected layer and Softmax function, to accurately identify fault types. Experimental results demonstrate the superior performance of the proposed GCN‐based fault diagnosis method, achieving an accuracy of 99.79%. This significantly surpasses traditional machine learning methods (85.4%), deep learning models (92.3%), and other graph neural network approaches (94.1%). Notably, the method shows exceptional resilience to noise, maintaining high accuracy even with 20% added noise, underscoring its robustness for practical industrial applications. The transformation of vibration signals into the frequency domain using FFT, followed by constructing a detailed graph structure, enables the GCN to effectively capture and represent intricate fault characteristics, thus enhancing accurate fault classification. These findings highlight the method's practical applicability and potential for deployment in advanced industrial settings characterized by high noise levels and complexity.

The effect of noise reduction on work-related stress: a quasi-experimental study on weaving workers exposed to high levels of noise

Introduction: The textile industry is one industry in the world with a high risk in its production process. The operation of weaving machines generates high levels of noise, which can cause various adverse effects on workers' health, especially work-related stress. IT Co. Ltd., one of the largest textile companies in Solo, Central Java, since 1975, has not been able to overcome the problem of the high noise intensity, especially in the weaving production room. Methods: This study aims to determine the effect of reducing noise exposure on work-related stress by installing sound-absorbing materials from coconut fiber waste. The study was carried out in 2 phases. The first phase measured noise intensity and work stress before the intervention. The second phase was carried out for 12 measurements daily after installing sound-absorbing material from coconut fiber waste. Results: Installing sound-absorbing material from the coconut fiber waste on the second to the thirteenth day positively reduced the average noise intensity and work-related stress score compared to the first day before the intervention. Conclusion: The study showed a significant difference in the average noise intensity and work-related stress before and after installing the sound-absorbing materials on 13 days of measurement.

Exploring single-shot propagation and speckle based phase recovery techniques for object thickness estimation by using a polychromatic X-ray laboratory source.

Propagation and speckle-based techniques allow reconstruction of the phase of an X-ray beam with a simple experimental setup. Furthermore, their implementation is feasible using low-coherence laboratory X-ray sources. We investigate different approaches to include X-ray polychromaticity for sample thickness recovery using such techniques. Single-shot Paganin (PT) and Arhatari (AT) propagation-based and speckle-based (ST) techniques are considered. The radiation beam polychromaticity is addressed using three different averaging approaches. The emission-detection process is considered for modulating the X-ray beam spectrum. Reconstructed thickness of three nylon-6 fibers with diameters in the millimeter-range, placed at various object-detector distances are analyzed. In addition, the thickness of an in-house made breast phantom is recovered by using multi-material Paganin's technique (MPT) and compared with micro-CT data. The best quantitative result is obtained for the PT and ST combined with sample thickness averaging (TA) approach that involves individual thickness recovery for each X-ray spectral component and the smallest considered object-detector distance. The error in the recovered fiber diameters for both techniques is , despite the higher noise level in ST images. All cases provide estimates of fiber diameter ratios with an error of 3% with respect to the nominal diameter ratios. The breast phantom thickness difference between MPT-TA and micro-CT is about 10%. We demonstrate the single-shot PT-TA and ST-TA techniques feasibility for thickness recovery of millimeter-sized samples using polychromatic microfocus X-ray sources. The application of MPT-TA for thicker and multi-material samples is promising.

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise

Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise originating from the snapping shrimp present throughout this region. This study presents the development and application of a neural-network based detector for marine-mammal vocalizations in long term acoustic data recorded by us at ten locations in Singapore waters. The detector’s performance is observed to be impeded by the high shrimp noise activity. To counteract this, we investigate several techniques to improve detection capabilities in shrimp noise including the use of simple nonlinear denoisers and a machine-learning based denoiser to suppress the noise level. These are shown to enhance the detection performance significantly. Using the robust detectors developed, we discuss some of the vocalizations detected over three years of our acoustic recorder deployments.

Detecting wire breaks in post-tensioned tendons of wind turbines: A signal energy spectrum analysis approach

In the wind industry, recent advancements have led to the research and development of wind turbines featuring hybrid towers, a novel combination of steel tube and precast concrete segments. These concrete modules, which absorb the high stresses caused at the intersection between the tower and its foundation, are commonly joined with post-tensioned tendons. This study focuses on the identification of wire breaks in post-tensioned tendons through the application of acoustic emission testing. Measurements involve the evaluation of wire breaks under controlled laboratory conditions and the analysis of the dynamic operational environment of a functioning wind turbine. The investigation encompasses the development and assessment of a methodology based on principles of discrete Fourier transformation and Parseval’s theorem for analyzing the frequency-dependent energy distribution of acoustic signals. This analytical approach extends to the examination of wire breaks generated under varying stress levels as well as the operational noise within the actual wind turbine. The findings demonstrate a notable concentration of energy within the frequency range of 5-20 kHz in the wire break signals. In contrast, the operational noise signals show a distinct pattern, presenting an extreme value distribution within the 0-2 kHz range. The developed methodology exhibits robustness, even in the presence of high background noise levels, establishing itself as a valuable tool for wire break detection. Additionally, its potential for integration with machine learning algorithms offers the prospect of automated wire break detection, enabling further advancements in structural health monitoring for wind turbines.

Evaluation and implication of noise and vibration levels to the operators and proxy population around selected block molding industries in Ibadan, Nigeria

Introduction: Block-making industries owned by private individuals are cited in cities to utilize the existing market opportunity of supplying concrete blocks to developers. The study aimed to measure noise and vibration levels at selected block-molding industries and assess their health implications. Methods: The study design involves twenty-five block molding industries randomly selected in the Ibadan metropolis. These industries voluntarily agreed to participate in the research, and measurements were done between January and May 2022. The noise and vibration levels were measured using a digital multi-function environmental meter, Model DT-8820, and a vibration meter, Model VM-6360, respectively. Results: An overall mean noise level of 101.81 dB(A), 85.62 dB(A), 76.40 dB(A), 70.21 dB(A), 65.91 dB(A), and 63.61 dB(A) at 0, 10, 20, 30, 40 m, respectively, away from the source to residential buildings were obtained. Results indicate that the noise level at 0 m and 10 m exceeded the occupational noise level standard. The results obtained for the vibration levels on the hand of the operators ranged from 42.2 ms-2 to 59.7 ms-2 and these exceeded the occupational vibration level standard. This may indicate that the operators of the block-moulding machine may be exposed to various adverse health detriments due to high noise and vibration levels at their workplace. Conclusion: The study recommends using safety gadgets such as hearing protection and anti-vibration gloves for workers in these industries. Moreover, environmental education and awareness should be carried out, and residential structures should be situated at least 20 meters from the block industries.

Awareness, attitudes and perceptions of students towards leisure noise in Durban, South Africa.

Young adults are exposed to high noise levels in leisure venues, which increases their risk of hearing loss, and can affect their quality of life. The aim of this study was to describe the young adults' awareness, attitudes and perceptions towards leisure noise at a university in South Africa. A descriptive cross-sectional study design with quantitative methods of data was considered for this study. Students from first to fourth years in the Education Department of a local university in Durban, South Africa, who were aged 18 years old - 25 years old were invited to participate in an online survey. Of the 462 participants, most had a general awareness on noise and hearing loss but lacked knowledge on the negative effect of loud noise, with 95.2% using personal listening devices, followed by visiting restaurants and gyms, and 48.3% being unsure if noise can damage hearing permanently. They were unaware of methods to reduce their exposure to noise. A significant relationship between awareness of noise and attitudes (p= 0.029) indicated that the higher the level of awareness regarding leisure noise, the better their attitude and behaviour, thus the lower the risk of hearing loss. The results highlight the need for implementing the World Health Organization (WHO) noise regulations and providing education for this age group to prevent irreversible hearing loss through exposure to leisure noise.Contribution:A national study is recommended to increase research evidence.

Damage identification in sandwich structures using Convolutional Neural Networks

The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

Power saw noise levels during steel stud cutting tasks on commercial construction sites: a tool characterization from a worker exposure standpoint.

Construction framers who cut and install steel studs as part of their daily tasks are exposed to hazardous noise levels during their work shift in large part due to the power saws they use to cut steel studs. This investigation characterized the sound pressure levels of power saws used to cut steel studs on active construction sites. Further, the length of time it took to cut various studs on a construction site was investigated to understand worker exposure times to saw noise. In general, power saws used on the study sites to cut steel studs had a mean A-weighted equivalent continuous sound pressure level (LAeq) of 107.2 dB and a C-weighted peak sound pressure level (LCpeak) of 120.1 dB. Three of the saws-the chopsaw, the cut-off saw, and the grinder-had similar noise levels, whereas the cordless circular saw had higher noise levels. It took an average of 13.2 s to cut each stud, and workers in the study used power saws to cut steel studs for an average of 371.5 s per day. This average exposure time at the average recorded sound pressure levels (SPLs) suggests these saws can increase the risk of occupational noise-induced hearing loss, according to National Institute for Occupational Safety and Health (NIOSH) recommendations.

GC-STCL: A Granger Causality-Based Spatial-Temporal Contrastive Learning Framework for EEG Emotion Recognition.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial-temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial-temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger-Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.

Improved image denoising through fractional anisotropic diffusion and resolution-tailored differentiation in the Fourier domain

Fractional-order anisotropic diffusion-based denoising of images has been implemented in the literature through fractional differentiation in the Fourier domain. Furthermore, Fourier transform of the schemes on half-integer or integer mesh points has been used for the differentiation of images. In this paper, differentiation in the Fourier domain is proposed using schemes on fractional mesh points, aiming to enhance the performance of the algorithm. This can be regarded as employing fractional schemes at various resolutions, governed by a parameter of resolution within the range of (0.5, 1). Variations in resolution affect the pixel neighborhood by incorporating additional information from interpolated pixels. Experiments conducted on a reference image dataset, using three quantitative measures, demonstrate that the proposed method surpasses the method from the literature for higher values of the parameter of resolution. The improvement is particularly noticeable at higher noise levels, where the proposed method consistently outperforms the method from the literature across all values of the parameter of resolution. As a side effect, the proposed method is less time-consuming than the original method, as it requires higher time steps within the numerical scheme. Experiments and stability analysis show that the proposed method reduces the number of iterations by three to four times compared to the original method.

A Military Audio Dataset for Situational Awareness and Surveillance

Audio classification related to military activities is a challenging task due to the high levels of background noise and the lack of suitable and publicly available datasets. To bridge this gap, this paper constructs and introduces a new military audio dataset, named MAD, which is suitable for training and evaluating audio classification systems. The proposed MAD dataset is extracted from various military videos and contains 8,075 sound samples from 7 classes corresponding to approximately 12 hours, exhibiting distinctive characteristics not presented in academic datasets typically used for machine learning research. We present a comprehensive description of the dataset, including its acoustic statistics and examples. We further conduct a comprehensive sound classification study of various deep learning algorithms on the MAD dataset. We are also releasing the source code to make it easy to build these systems. The presented dataset will be a valuable resource for evaluating the performance of existing algorithms and for advancing research in the field of acoustic-based hazardous situation surveillance systems.

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Abstract Traditional vision-based inertial odometry (VIO) suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units (IMUs). First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error (RMSE) by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising

Synchrotron transmission X‐ray microscopy with absorption near edge structure (TXM‐XANES) is a powerful tool for investigating the structure and composition of materials at nano‐ to meso‐scales. It is, however, often challenged by high levels of noise that obscure critical details at the single‐pixel level. To address this issue, a deep learning‐based algorithm is developed for suppressing the image noise, grounded in self‐supervised learning principles. In contrast to traditional image denoising methods, this approach successfully enhances the visibility of fine details while significantly reducing the noise in the X‐ray images. Through this advancement, the potential of the approach for improving the accuracy and interpretability of the TXM‐XANES data is demonstrated, thereby enabling more precise detection of nanoscale phenomena such as inhomogeneous cation redox and metal segregation in battery cathode materials. This technique offers an effective new avenue for harnessing the full potential of synchrotron TXM‐XANES imaging, paving the way for a range of exciting new studies in materials science and beyond.