High-intensity Noise Research Articles

Numerical Investigation of Firing Characteristic of Stochastic Hodgkin-Huxley Neuron under Different Forcing Regimes

This paper investigates the influence of the ion channel noise, that is one of important internal neuronal noise sources, on the response of a Hodgkin-Huxley neuron in different stimulus regimes. Our results show that, in the case of dc current introduction into neuron dynamics, neuronal firings in excitable neuron emerge with growing firing rate due to increasing ion channel noise. Despite such a relationship between firing rate and channel noise, emergent behaviour is still spontaneous and irregular. However, neuronal firings in spiking neuron skip or terminate due to intermediate level of channel noise. This is known as inverse stochastic resonance phenomenon. We show that firing behaviour of such a spiking neuron is, interestingly, highly irregular around the revealed noise levels and this continues towards higher noise intensities. On the other hand, we examine the influence of channel noise on the neuronal response to a periodic signal primarily with subthreshold amplitude. We show that signal frequency has a significant effect on the response sensitivity related to channel noise intensity whereas, compared to dc current input, firing probability and regularity show a close relationship due to increasing noise. Finally, neuronal behaviour due to ion channel noise in the case of suprathreshold periodic forcing is analysed. Up to a certain level of channel noise, it does not seriously affect number of firings which has a nonlinear relationship with increasing signal frequencies. It is also possible to see inverse stochastic resonance effect at the high frequency regions with the help of relatively high noise. Another interesting finding is that channel noise does not affect the regularity at certain frequencies, yielding the presence of irregular response region at suprathreshold periodic inputs.

Image Denoising Using Adaptive Weighted Low-Rank Matrix Recovery

This paper introduces a new image denoising method using adaptive weighted low-rank matrix recovery to tackle the challenges of separating low-rank information from noise and improving performance affected by empirical hyperparameters. We start by using image nonlocal similarity to build a low-rank denoising model, then apply the Gerschgorin theory to precisely determine the rank of the low -rank matrix. With this rank estimation, we use adaptive weighting along with singular value decomposition and weighted soft-thresholding to solve the denoising model, resulting in the denoised image. Experiments show our algorithm surpasses traditional denoising methods in average PSNR and SSIM. Specifically, for images contaminated with high-intensity noise (with a variance of 100), our algorithm achieves average PSNR and SSIM values of 24.66dB and 0.7267, respectively. Additionally, our algorithm exhibits superior performance in denoising images with real noise and is also applicable to color image denoising.

Possibilistic C-means with novel image representation for image segmentation

Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. Compared to the traditional Fuzzy C-Means algorithm, the Possibilistic C-Means (PCM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PCM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. Considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based Possibilistic C-Means (VBPCM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPCM algorithm outperforms the conventional PCM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPCM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.

In Situ Operando Indicator of Dry Friction Squeal

In various applications, dry friction could induce vibrations. A well-known example is frictional braking systems in ground transportation vehicles involving a sliding contact between a rotating and a stationary part. In such scenarios, the emission of high-intensity noise, commonly known as squeal, can present human health risks based on the noise’s intensity, frequency, and occurrences. Despite the importance of squeal in the context of advancing urbanization, the parameters determining its occurrence remain uncertain due to the complexity of the involved phenomena. This study aims to identify a relevant operando indicator for predicting squeal occurrences. To this end, a pin-on-disc test rig was developed to replicate various contact conditions found in road profiles and investigate resulting squealing. Each test involves a multimodal instrumentation, complemented by surface observations. It is illustrated that the enhanced thermal indicator identified is relevant because it is sensitive to the thermomechanical and tribological phenomena involved in squealing.

Stochastic modeling of plant-insect interaction dynamics with MEMS-based monitoring and noise effects

The dynamics of plant-insect interactions play a crucial role in the ecosystem, influenced by complex molecular signaling pathways. This study extends existing deterministic models of plant-insect systems by incorporating stochastic elements and molecular interactions, particularly focusing on the roles of Botrytis Induced Kinase-1 (BIK1) and Phyto Alexin Deficient-4 (PAD4) proteins. The model evaluates the effects of constant inhibition, pulsed inhibition, and adaptive feedback control on plant biomass (y1), insect herbivore density (y2), PAD4 levels (y3), and BIK1 levels (y4). Additionally, we examine the impact of different noise types, including deterministic, Gaussian, and Lévy noise, on system variability and stability. Results indicate that our stochastic model is superior as it shows a significant reduction in BIK1 levels, particularly under higher noise intensities, which enhances PAD4 activity and improves plant defense mechanisms. Moreover, moderate noise intensity (σ=0.05) provides an optimal balance, sustaining PAD4 levels while effectively controlling insect herbivore populations. We also integrate MEMS-based feedback mechanisms, which dynamically adjust plant biomass and molecular signaling, further stabilizing the system’s response to environmental variability.

An Enhanced Spectral Amplitude Modulation Method for Fault Diagnosis of Rolling Bearings

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved.

Novel methods for the control of blast noise from explosive depth hardening

DNV Spadeadam undertakes crucial major hazards testing to improve safety across a wide range of industries, including energy and infrastructure. One common form of testing is Explosive Depth Hardening (EDH) in which plastic explosives up to 10 kg TNT are used to harden pre-cast railway crossings in order to support national transport infrastructure. The operations produce extremely high intensity noise and are carried out up to 10 times per day. The source characteristics of the explosion are not well understood and vary significantly with operational demands. This has implications on occupational and environmental blast noise impacts. Quantification of the source characteristics are important for the exposures of site personnel with regards to providing adequate hearing protection. Moreover, for the purpose of managing environmental noise impacts on local communities, the uncertainties around source directivity and strength make long-range blast noise prediction difficult. This paper presents the current research on novel blast noise mitigation undertaken in the laboratory, such as experimental water curtains, water sprays, and metamaterial designs. It is proposed to implement novel methods developed in the laboratory for use during EDH shots at full-scale, and the potential improvements to occupational and environmental noise impacts will be discussed.

PPARα suppresses low-intensity-noise-induced body weight gain in mice: the activated HPA axis plays an critical role.

As the second most risky environmental pollution, noise imposes threats to human health. Exposure to high-intensity noise causes hearing impairment, psychotic disorders, endocrine modifications. The relationship among low-intensity noise, obesity and lipid-regulating nuclear factor PPARα is not yet clear. In this study, male wild-type (WT) and Pparα-null (KO) mice on a high-fat diet (HFD) were exposed to 75 dB noise for 12 weeks to explore the effect of low-intensity noise on obesity development and the role of PPARα. 3T3-L1 cells were treated with dexamethasone (DEX) and sodium oleate (OA) to verify the down-stream effect of hypothalamic-pituitary-adrenal (HPA) axis activation on the adipose tissues. The average body weight gain (BWG) of WT mice on HFD exposed to noise was inhibited, which was not observed in KO mice. The mass and adipocyte size of adipose tissues accounted for the above difference of BWG tendency. In WT mice on HFD, the adrenocorticotropic hormone level was increased by the noise challenge. The aggravation of fatty liver by noise exposure occurred in both mouse lines, and the transport of hepatic redundant lipid to adipose tissues were similar. The lipid metabolism in adipose tissue driven by HPA axis accorded with the BWG inhibition in vivo, validated in 3T3-L1 adipogenic stem cells. Chronic exposure to low-intensity noise aggravated fatty liver in both WT and KO mice. BWG inhibition was observed only in WT mice, which covered up the aggravation of fatty liver by noise exposure. PPARα mediates the activation of HPA axis by noise exposure in mice on HFD. Elevated adrenocorticotropic hormone (ACTH) promoted lipid metabolism in adipocytes, which contributed to the disassociation of BWG and fatty liver development in male WT mice. Summary of PPARα suppresses noise-induced body weight gain in mice on high-fat-diet. Chronic exposure to low-intensity noise exposure inhibited BWG by PPARα-dependent activation of the HPA axis.

Effects of high-intensity chronic noise on spatial memory in male versus female rats.

The detrimental effects of high-intensity noise on the auditory system and emotional status, including the induction of anxiety, are well documented. Preclinical as well as epidemiological and clinical studies have solidly established differential responses between males and females to various stressful stimuli, including high-intensity white noise (HIWN). However, whether chronic exposure to noise affects cognitive functions and whether this effect is sex dependent has not been adequately addressed. In this study, we used two cognitive test paradigms, such as the Morris water maze (MWM) and the multi-branch maze (MBM), to test the effect of chronic HIWN on indices of spatial learning and memory in both male and female Wistar rats. Our findings indicate that daily (1 h) exposure to 100 dB of noise for 30 consecutive days induces different task-dependent responses in male versus female rats. For example, in the acquisition phase of MWM, female rats exposed to noise outperformed their male counterparts at twice the speed. Similarly, in the MBM test, noise-exposed female rats outperformed the male rats in reaching the nest box. It is clear from these studies that noise impairs cognitive functions twice as negatively in male rats as in female rats. Thus, sex-related differences in spatial learning and memory in response to HIWN must be taken into consideration when investigating the neurobiological components and/or treatment modalities.

Stochastic effects on plankton dynamics: Insights from a realistic 0-dimensional marine biogeochemical model

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system.

The Noise Exposure of Urban Rail Transit Drivers: Hazard Classification, Assessment, and Mitigation Strategies

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train’s mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit’s operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers’ hearing health, enhancing work efficiency, and ensuring driving safety.

A wavelength-division-multiplex depolarized Sagnac-based interferometer for noise suppression

The Sagnac-based interferometer (SI) has been developed over several decades and is widely recognized for its sensitivity and stability. Most SIs utilize polarization-maintaining (PM) fiber and polarizers to ensure polarization reciprocity, or depolarizers to mitigate the effects of polarization non-reciprocity. The depolarizing method was considered more promising from a marketability standpoint. Additionally, broadband optical sources were typically used in SIs to eliminate the Kerr effect and backscatter. However, broadband light source SIs are hindered by high excess relative intensity noise (excess RIN) generated by the self-interference of different spectral components. To enhance the performance of SIs, it is crucial to suppress the excess RIN. In this study, we demonstrate a wavelength-division-multiplex depolarized Sagnac-based interferometer and suppress the excess RIN by utilizing the overlap of two interfering lights, which shifts the primary frequency of noise outside the detection range. Experimental results show that the noise equivalent displacement at a light intensity of 42 μW achieves 170.18 fm/√Hz, which encompasses both shot noise and non-optical noise, indicating successful suppression of excess RIN.

Identification of blood at simulated crime scenes using silver nanoparticles with SERS.

The analysis of substances and samples obtained from a crime scene is very important in solving forensic cases. To determine the variables involved in a crime and to expedite the investigation process, the rapid analysis of body fluids in small quantities and within environments containing diverse components is particularly necessary. For this reason, it is of great importance to analyze biological fluids with rapid, noncontaminating, nondestructive, low-cost, and accurate techniques. In recent years, with advancements in laser technology, spectroscopic methods have been introduced as analytical techniques in forensic medicine and chemical studies. This study focuses on surface-enhanced Raman spectroscopy (SERS) to demonstrate the detection of blood samples in simulated crime scenes. To minimize the background signal from fluorescent biomolecules in blood, dilution was performed with two different components and Raman analysis was performed for four different concentrations of blood. In general, a decrease in noise in the spectra was observed as the blood was diluted. Crime scenes consisting of pure blood, blood diluted with ethanol and distilled water (1:2, 1:4, and 1:8), a blood-mineral water mixture, a blood-cherry juice mixture, and silver nanoparticle-added mixtures were simulated, and their spectra were examined. Chemometric analyses of the data were performed. Despite high noise and low peak intensities, blood-identifying signals were detected when examining different blood concentrations. It was observed that silver nanoparticles provided high enhancement of blood peaks thanks to their strong plasmonic properties.

The Effect of Using Earmuffs on Hearing Loss Complaints and Subjective Stress: A Quasi-Experimental Study among Weaving Workers in Surakarta, Indonesia

Introduction: Noise pollution from machine operations is one of industry's long-standing issues, especially in the textile manufacturing industry. High noise levels produced by weaving machine operation can harm employees' health, most notably hearing loss and stress at work. Since 1975, IT Co. Ltd., one of the biggest textile corporations in Surakarta, Indonesia, has struggled to find a solution to the issue of excessive noise, particularly in the weaving manufacturing facility. This study aims to determine the effect of employing earmuffs as a type of intervention to alleviate hearing loss complaints and subjective stress on weaving workers who are exposed to high-intensity noise. Methods: A time series design was used in this quasi-experimental study. This study’s participants were divided into 2 groups. The first group was weaving workers who used earmuffs as ear protection (Intervention Group), and the second group did not use earmuffs (Control Group). Data collection was carried out for 6 working days to see whether there was a significant effect of using earmuffs on hearing loss complaints and subjective stress. Results: From the second to the sixth day, using earmuffs as ear protection did not significantly reduce hearing loss complaints. However, it showed significant findings that increased the average score of subjective stress. Conclusion: Using earmuffs should protect the ears from noise exposure and its effects on workers' health. However, it has been shown that using earmuffs can increase the average score of hearing loss complaints and subjective stress.

Simultaneous PIV/LIF Measurements Of Velocity And Oxygen In An Advecting Air-Water Channel Flow

We investigate oxygen transfer across an air-water interface using an integrated Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) system. This setup allows for the simultaneous measurement of velocity and dissolved oxygen (DO) concentration fields, alongside the visualization of the air-water interface. The imaging process begins with zero DO concentration and continues until oxygen saturation in the water is achieved. Calibration of the oxygen LIF intensity field is performed using these images, benchmarked against measurements from an optical oxygen point probe, to ensure accurate conversion to a physically relevant unit. The air channel generates an air flow with a center-line mean velocity of 8.84 m/s, corresponding to a Taylor Reynolds number of 100. This high-velocity air flow results in the formation of wavy structures on the water interface creating situations similar to those found in nature (such as rivers and oceans). The wavy structures create reflections from the air-water interface; these reflections are mitigated during post-processing using a Wavelet-Fast Fourier Transform (W-FFT) method to remove high intensity noise. Additionally, the wind shear generated by the air flow induces high turbulence intensity and vortices near the air-water interface, significantly enhancing the dissolution of oxygen into the water. This turbulence and the associated vortical structures play a crucial role in the mixing process and the overall oxygen transfer efficiency. The study presents instantaneous dissolved oxygen structures overlapped with velocity structures in a flow with deformable interface, providing some insights into the oxygen transfer and mixing processes.

Voronoi diagrams and Delaunay triangulation for modelling animal territorial behaviour.

We explore the use of movable automata in numerical modelling of male competition for territory. We used territorial dragonflies as our biological inspiration for the model, assuming two types of competing males: (a) faster and larger males that adopt a face-off strategy and repulse other males; (b) slower and smaller males that adopt a non-aggressive strategy. The faster and larger males have higher noise intensity, leading to faster motion and longer conservation of motion direction. The velocity distributions resemble the Maxwell distributions of velocity, expected in Brownian dynamics, with two probable velocities and distribution widths for the two animal subpopulations. The fast animals' trajectories move between visually fixed density folds of the slower animal subpopulation. A correlation is found between individual velocity and individual area distribution, with smaller animals concentrated in a region of small velocities and areas. Attraction between animals results in a modification of the system behaviour, with larger animals spending more time being surrounded by smaller animals and being slowed down by their interaction with the surroundings. Overall, the study provides insights into the dynamics of animal competition for territory and the impact of attraction between animals.

Perancangan Sistem Alarm Kebisingan untuk Kapal Penangkap Ikan Berbasis Arduino Uno dengan Sensor KY-037

Fishermen are highly susceptible to hearing impairment due to prolonged exposure to the high-intensity noise of boat engines. While numerous studies have investigated the noise intensity levels of boat engines, no research has been conducted on noise warning alarm systems for fishing boats. Therefore, this study aims to develop a sound noise alarm system based on Arduino Uno with a KY-037 sensor for fishing boats, which is expected to reduce the potential for hearing impairment among fishermen. This research employs a quantitative descriptive approach, with data collected through experimental methods and analyzed by comparing noise level data at various engine speeds. The results of the noise level measurements of boat engines at the Fish Landing Base (PPI) in Cikidang, Pangadaran Regency, indicate that the majority of fishermen are safe from hearing impairment due to engine noise, as most boats there are small, with a Gross Tonnage (GT) capacity of under 5 GT. The risk of hearing impairment emerges for boats with a capacity of 11 GT running at a maximum engine speed of 1600 rpm, producing noise levels up to 84 dB. Thus, it is recommended that crew members use earplugs.

Evaluating the performance of optically pumped magnetometer magnetoencephalography in measuring inter-trial and inter-region phase-locking value

Magnetoencephalography (MEG) records the brain magnetic field signals. Optically pumped magnetometer (OPM) is a novel technology for MEG measurement in its infancy, it is essential to demonstrate its performance on the key neuroimaging features, such as phase-locking value. In this paper, the inter-trial phase-locking value (ITPL) and inter-region phase-locking value (IRPL) were analyzed by simulation and measurement experiments using OPM-MEG, the superconducting quantum interference device (SQUID) data was applied for the compare. The simulation results show that OPMs could offers good characterization on phase-locking value under higher noise intensity. In experiments, the similarity ratios of ITPL and IRPL between OPM-MEG and SQUID-MEG are >0.90. The significantly increased frequencies of IRPL in pure tone and somatosensory stimulation are at about 10–20 Hz and 8–13 Hz respectively, with Spearman correlation coefficients >0.7. The results provide experimental evidence of the ability of OPM-MEG for measuring phase synchronization and functional connectivity on sensor signals.

Lossy Compression of Single-channel Noisy Images by Modern Coders

Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented.

Detection and characterization of ship underwater radiated narrowband noise

Underwater radiated noise (URN) emitted by ships is a high-intensity noise that interferes with acoustic transmissions. It is composed of broadband cavitation noise, tonal noise and its harmonics that can be observed up to several tens of kHz. The in-situ detection of ship noise and the identification of its tonal components are therefore useful for adaptive underwater communication techniques and for channel sensing. To this end, we present a detection scheme to identify the presence of a vessel and identifying its narrowband components. For presence detection, we develop a Convolution Neural Network (CNN) model whose input is a Detection Envelope Modulation On Noise (DEMON) analysis for a given observation window. The model is trained with our collected recordings of ship data and ambient noise. After identifying the ship’s URN, our tonal detector relies on the stability and stationarity of the ship’s tonal lines, as opposed to the randomness of the ambient noise. Cross-correlation and spectral entropy are used as detection metrics. To reduce the sensitivity to the tested environment, the detection thresholds are set adaptively. The results show a favorable trade-off between precision and recall compared to benchmark methods. We share our database of URN of labeled ships.