Metamaterials, man-made composites scaled smaller than the wavelength, have demonstrated a huge potential in their applications in acoustics, opening up for sub--wavelength acoustic absorbers, acoustic invisibility, perfect acoustic mirrors and acoustic lenses for hyper focusing, acoustic illusions and enabling new degrees of freedom in the control of the acoustic field. The zero, or even negative, refractive sound index of metamaterials offers possibilities in control of the acoustic pattern and sound at sub--wavelength scales. Despite the tremendous growth of the research on acoustic metamaterials during the last decade, the potential of metamaterial-based technologies in aeronautics is still not fully explored and its utilization is still in its infancy. Thus the principal concepts mentioned above could very well provide means to develop devices that would allow the mitigation of the impact of the civil aviation noise on the community. This paper gives a review of the state of the art of the most relevant works on acoustic metamaterials, analyzing them against their potential applicability in aeronautics, and in this process identifying possible implementation areas and interesting metabehaviors. It also identifies some technical challenges and possible future directions for research with the goal of unveiling the potential of metamaterials technologies in aeronautics.

It is unclear which noise exposure time window and noise characteristics during nighttime are most detrimental for sleep quality in real life settings. We have conducted a field study with 105 volunteers wearing a wrist actimeter to record their sleep during seven days, together with concurrent outdoor noise measurements at their bedroom window. Actimetry recorded sleep latency increased by 5.6 minutes (95% confidence interval: 1.6 to 9.6 minutes) per 10 dB(A) increase in noise exposure during the first hour after bedtime. Actimetry assessed sleep efficiency was significantly reduced by 2-3 percent per 10 dB(A) increase in measured outdoor noise (Leq, 1h) for the last three hours of sleep. For subjectively reported sleepiness, noise exposure during the last hour prior to wake up was most crucial with an increase in the sleepiness score of 0.31 units (95% CI: 0.08 to 0.54) per 10 dB(A) Leq,1h. Associations for estimated indoor noise were not more pronounced than for outdoor noise. Considering noise events in addition to equivalent sound pressure levels (Leq) only marginally improved the statistical models. Our study provides evidence that matching the nighttime noise exposure time window to the individual’s diurnal sleep-wake pattern results in a better estimate of detrimental nighttime noise effects on sleep. We found that noise exposure at the beginning and the end of the sleep is most crucial for sleep quality.

In recent years, urbanization and industrialization have significantly increased the levels of outdoor noise pollution, affecting the quality of life in residential areas. In Uzbekistan, rapid development in urban centers has led to heightened concerns about the impact of noise on residents' health and well-being. Noise pollution can result from various sources, including traffic, construction, and industrial activities, causing disturbances and potential health issues such as stress, sleep disorders, and cardiovascular problems. Addressing this issue is crucial for creating a healthier living environment. This article explores effective strategies for mitigating the impact of outdoor noise on residential buildings in Uzbekistan.

Wind energy in Europe is aimed to grow at a steady, high pace, but opposition from residents to local wind farm plans is one of the obstacles to further growth. A large body of evidence shows that local populations want to be involved and respected for their concerns, but in practice this is a complex process that cannot be solved with simple measures such as financial compensation. The visual presence and the acoustic impact of a wind farm is an important concern for residents. Generally environmental noise management aims to reduce the exposure of the population, usually based on acoustics and restricted to a limited number of sources (such as transportation or industry) and sound descriptors (such as Lden). Individual perceptions are taken into account only at an aggregate, statistical level (such as percentage of exposed, annoyed or sleep-disturbed persons in the population). Individual perceptions and reactions to sound vary in intensity and over different dimensions (such as pleasure/fear or distraction). Sound level is in fact a weak predictor of the perceived health effects of sound. The positive or negative perception of and attitude to the source of the sound is a better predictor of its effects. This article aims to show how the two perspectives (based on acoustics and on perception) can lead to a combined approach in the management of a wind farm aimed to reduce annoyance, not primarily of sound level. An important aspect in this approach is what the sound means to people: is it associated with the experience of having no say in plans, does it lead to anxiety or worry, is it appropriate? The available knowledge will be applied to wind farm management: planning as well as operation.

Low-frequency audible noise generated by the magnetostriction effect inherent to the operation of power transformers has become a major drawback, especially in cases where the electrical substation is located in urban areas subject to strict environmental regulations that imposes sound pressure limits, differing for day and night periods. Such regulations apply a +5 dB penalty if a tonal component of noise is present, which is clearly the case of magnetostriction noise, typically concentrated at twice the industrial frequency (50 Hz or 60 Hz, depending on the country). The strategy used to eliminate the tonal characteristics, therefore contributing to establish compliance with the applicable regulation and to alleviate the discomfort it causes to the human ear, consisted in superimposing to the substation noise a masking sound synthesized from “sounds of nature” with suitable intensities, to flatten the noise spectrum while enhancing the soundscape. The masking system (heavy-duty speakers powered by a microprocessor platform) was validated at an already judicialized urban scenario. Measurement results confirmed that the masking solution was capable of flattening the tonal frequencies, whose beneficial effect yielded the cancellation of the public civil action filed by the neighbors. The proposed solution is ready to be replicated to other scenarios.

This study was undertaken to investigate and analyze noise pollution in a large Chinese governmental hospital’s medical intensive care unit and compare to the WHO guidelines.This cross-sectional study was conducted in a MICU at a public governmental teaching hospital in Fujian province between July and August of 2017. A WENSN® WS1361 Integrated Sound Level Meter (China) was used for continuous every five seconds one week noise levels recording. After this measurement, the decibel meter was used for recording different location of isolation rooms and open bays, including occupied and unoccupied patient, and recording sound events occurs in the ICU to identify sources of noise. Peak and average noise levels were obtained from the meter, and data were downloaded from the WS1361 into a laptop computer. The measured mean equivalent sound pressure levels (L) and standard Aeq deviation over one week period were 66.64±7.57 dB(A), with acute spikes reaching 119.7 dB(A), the average sound level for a 24 hour period in a work day was 68.03±5.07 dB(A). These are higher than the current daytime environmental noise limit of 40-45 decibels in China and WHO. Mean work day noise was significantly louder than weekend time, there was a significant difference in work days and weekend (t=16.85;P=0.000).There was a statistical difference between the day time and night time shifts (t=34.67;P=0.000). The isolation rooms were significantly quieter than the open-bay rooms(t=46.15; p=0.00), sound levels in the occupied and unoccupied rooms also had significant difference(t=17.26; P=0.000).Two types of noise resources, including twenty kinds sources were identified and measured, mean noise levels ranged from of 61.33 to 79.21 dB(A). This study shows noise levels in intensive care units were exceeded the recommended. The study of the influence of noise on patient and staff is needed, and noise reduction strategies must be conduct in ICU.

This study measures the noise levels in a baseball stadium and analyzes baseball fans’ attitude of effect of recreational noise exposure on their hearing. In the baseball stadium, noise levels were measured in four seating sections using a sound level meter during the games. The LAeq average of the 16 measures produced 91.7 dBA, showing a significantly high noise level in the red and navy sections. As a function of frequency by LZeq analysis, the noise levels were significantly higher in low frequencies than other frequencies. For the survey sample, 688 randomly selected participants completed a 16-question survey on their noise exposure during the game and on the potential risk of hearing loss. Despite the very high noise levels, 70% of the respondents preferred sitting in either the red or the navy section to be closer to the cheerleaders and to obtain a good view. Most respondents reported that they did not consider wearing earplugs, and one-third experienced hearing muffled speech after the game. We conclude that the noise levels in baseball stadiums are high enough to cause hearing damage and/or tinnitus later, but expect these results to improve public education regarding safe noise exposure during popular sports activities.

Currently, narrowband Power line communication (PLC) is considered as an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and \(\alpha\) stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by \(\alpha\) stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by MCA model.

The railway industry focus in the past years was to research, find and develop methods to mitigate noise and vibration resulted from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for the professionals of the industry today. However, although there are many options in the market, their practical implementations depend upon general constraints that affect most technological application in the engineering world. The progression of these technologies have facilitated the selection of more adequate methods for each best case scenario, but further studies are ought to be made to proper assess if each one is fit for their purpose. Every method implementation must be analyzed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming to meet different benefits, such as environmental impact control and wear of the whole infrastructure. There are several situations and facilities in a railway project design that need noise and vibration mitigation methods and each design allocates different priorities for each one of them. Traditionally the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that expresses in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts, the main area of this study, there are two noises types that must be evaluated: impact noise and screeching noise. With respect to the second, it is similar to curve squeals and, being such, its mitigation methods are to be assigned as if it was to abate curve squeal in turnouts and crossings. The impact noise on the other hand, emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e. gaps) that composes these special components of a railway track. A life cycle analysis is therefore substantial for this reality and in this case will be applied to Squeal and Impact Noise on Special Trackwork. The evaluation is based on a valid literature review and the total costs were assumed by industry reports to maintain coherency. The period for a life cycle analysis is usually of 50 years, hence it was the value assumed. As for the general parameters, an area with high density of people was considered to estimate the values for a community with very strict limits for noise and vibration.

We derive the thermal noise spectrum of the Fourier transform of the electric field operator of a given wave vector starting from the quantum-statistical definitions and relate it to the complex frequency and wave vector dependent complex conductivity in a homogeneous, isotropic system of electromagnetic interacting electrons. We analyze separately the longitudinal and transverse case with their peculiarities. The Nyquist formula for vanishing frequency and wave vector, as well as its modification for non-vanishing frequencies and wave vectors follow immediately. Furthermore we discuss also the noise of the photon occupation numbers. It is important to stress that no additional assumptions at all were used in this straightforward proof.

In developing countries, noise exposure and its negative health effects have been little explored. The present study aimed to assess the noise exposure situation in adults living in informal settings in the Western Cape Province, South Africa. We conducted continuous one-week outdoor noise measurements at 134 homes in four different areas. These data were used to develop a land use regression (LUR) model to predict A-weighted day-evening-night equivalent sound level (Lden) from geographic information system (GIS) variables. Mean noise exposure during day (6:00-18:00) was 60.0 A-weighted decibels (dB(A)) (interquartile range 56.9-62.9 dB(A)), during night (22:00-6:00) 52.9 dB(A) (49.3-55.8 dB(A)) and average Lden was 63.0 dB(A) (60.1-66.5 dB(A)). Main predictors of the LUR model were related to road traffic and household density. Model performance was low (adjusted R²=0.130) suggesting that other influences than represented in the geographic predictors are relevant for noise exposure. This is one of the few studies on the noise exposure situation in low- and middle-income countries. It demonstrates that noise exposure levels are high in these settings.

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.

The study examines the noise, vibration, and harshness (NVH) management of electric vehicle (EV) powertrains, considering the challenges of the automotive industry's transition to electric drivetrains. The growing popularity of EVs brings new NVH challenges, as the lack of internal combustion engine (ICE) noise makes drivetrain noise more prominent. Key to managing NVH in EV powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces, and high-frequency noise generated by inverters significantly impact overall NVH performance. The article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. Methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics (MBD), finite element method (FEM), and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. The study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components.

This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different phase relationships between the linear and nonlinear branches, suggesting independent gain, and phase expansion characteristics with this topology. This model is used to assess the impact of diode resistance, capacitance, and ideality factor on the APD characteristics. This feature is showcased with two similar GaAs diodes to find the best fit for the considered HPA. The selected diode is characterized and modeled between 1-26.5 GHz. A comprehensive APD design and simulation workflow is reported. Before fabrication, the simulated APD is evaluated with the measured HPA to verify linearity improvements. The APD prototype achieves a large signal bandwidth of 6 GHz with 3 dB gain expansion and 8∘ phase rotation. This linearizer is demonstrated with a 17-21 GHz GaN HPA with 41 dBm output power and 35 % efficiency. Using a wideband 750 MHz signal, this APD improves the noise power ratio (NPR) by 6.5-8.2 dB over the whole HPA bandwidth. Next, the HPA output power is swept to compare APD vs. power back off for the same NPR. APD improves the HPA output power by 1-2 W and efficiency by approximately 5-9 % at 19 GHz. This efficiency improvement decreases by only 1-2 % when including the APD post-amplifier consumption, thus suggesting overall efficiency and output power improvements with APD at K-band frequencies.

The concept of using special electrical circuit design realize a "cold resistors", that is, an active resistor circuitry with lowered effective noise temperature, was first introduced about 80 years ago. Later on, various kinds of artificial resistors were applied in different research areas, such as gravitational wave detection, photo-amplifiers and quartz oscillators. Their proofs of concepts were experimentally proved. Unfortunately, the complete theory was not found even though several attempts had been published, sometimes with errors. In this paper, we describe a correct and complete circuit theoretical model of a cold resistor system. The results are confirmed by computer simulations. A design tools for this circuit is also shown.

In the last decade, the drone market has grown rapidly for both civil and military purposes. Due to their versatility, drones demand is constantly increasing, with several industrial players joining the venture to transfer urban mobility to the air. This has exacerbated the problem of noise pollution, mainly due to the relatively lower altitude of these vehicles and to the proximity of their routes to extremely densely populated areas. In particular, both the aerodynamic and aeroacoustic optimization of the propulsive system and of its interaction with the airframe are key aspects of the design of aerial vehicles for the success or the failure of their mission. The industrial challenge involves finding the best performance in terms of loading, efficiency and weight, and, at the same time, the most silent configuration. For this reason, research has focused on an initial localization of the noise sources and, on further analysis, of the noise generation mechanism, focusing particularly on directivity and scattering. The aim of the present study is to review the noise source mechanisms and the state-of-the-art technologies available in literature for its suppression, focusing especially on the fluid-dynamic aspects of low Reynolds numbers of the propulsive system and on the interaction of the propulsive-system flow with the airframe.

This study aims to tackle the challenge of high noise levels on balconies while preserving natural ventilation. Eight innovative balcony designs, incorporating elements like diffuser edges, undulating ceilings, Helmholtz resonators, grooves, or sound traps, were evaluated via finite element (FE) modeling. The extensive FE studies involved about 10 million elements per analysis case. Results showed that a standard balcony with a front parapet wall could achieve a noise reduction of around 10 dBA, with the jagged ledge design providing an additional 2 dBA improvement. Adding protruded ledge designs led to higher insertion loss (IL) by up to 9.4 dBA, especially at higher stories. Having wall-embedded Helmholtz resonators improved IL by up to 1.5 dBA. The jagged ledge design consistently exhibited positive IL across a broad frequency range from 200 Hz to 2 kHz. Combining beneficial balcony ceilings and wall ledges significantly reduced noise across lower and higher frequency ranges. Front jagged and full wavy ceilings offered positive IL throughout different storeys, proving their effectiveness against human vocal range. This study found that jagged ledge and front jagged ceiling designs, effective across various balcony heights, are viable options for improving balcony noise mitigation, providing valuable insights for architects and designers seeking practical solutions for outdoor noise reduction.

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

There are numerous hazards found on the farms. Most of them are ignored, which might cause the farmer to pay later in terms of his ill health, potential injuries or death. The current article discusses some of the common issues such as dust and air quality concerns; environmental (weather) stressors and psychological stressors; noise and hearing protection; and tractor safety and seatbelt use. And finally, the recommendations to overcome the hazards are discussed.

This article investigates the source of booming noise emanating from the tailgates of electric vehicles, along with proposed strategies to mitigate it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, operational deflection shapes (ODS) were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite element analysis software to propose measures for reducing booming noise. Experimental findings validated the model accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to dampen the booming noise resulting from tailgate motion. Under random force inputs, analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness.

This article describes an algorithm for estimation the variance of Gaussian noise. The data is smoothed using the Savitsky-Golay polynomial filter. Absolute differences between original and smoothed data are sorted in ascending order. The initial part of this sequence is selected for analysis. The result of calculation mean value of differences can be used to estimate the variance of the noise. By selecting points for analysis, the impact of cosmic ray noise and other artifacts can be reduced. The use of the proposed method for artificial and real spectra shows the ability to effectively estimate the noise variance. The algorithm contains no user-defined parameters.

In underwater optical wireless communications (UOWC), scattering of the propagating light beam results in both intensity and phase variations, which limit the transmission link range and channel bandwidth, respectively. Scattering of photons while propagating through the channel is a random process, which results in the channel-dependent scattering noise. In this work, we introduce for the first time an analytical model for this noise and investigate its effect on the bit error rate performance of the UOWC system for three types of waters and a range of transmission link spans. We show that, for a short range of un-clear water or a longer range of clear water, the number of photons experiencing scattering is high, thus leading to the increased scattering noise.

Audio recording in classrooms is a common practice in educational research, with applications ranging from detecting classroom activities to analysing student behaviour. Previous research has employed neural networks for classroom activity detection and speaker role identification. However, these recordings are often affected by background noise that can hinder further analysis, and the literature has only sought to identify noise with general filters and not specifically designed for classrooms. Although the use of high-end microphones and environmental monitoring can mitigate this problem, these solutions can be costly and potentially disruptive to the natural classroom environment. In this context, we propose the development of a neural network model that can specifically detect and filter out background noise in classroom recordings. This model would allow the use of lower quality recordings without compromising analysis capability, thus facilitating data collection in natural educational environments and reducing the costs associated with high-end recording equipment.

Purpose: The study is aimed to quantify the effects of social noise (personal music players (PMP), high-intensity noise exposure events) and road traffic noise exposures in the sample of Slovak university students living and studying in Bratislava. Methods: There were 1,003 university students (306 males and 697 females, average age 23.13±2) enrolled in the study; 347 lived in the student housing facility exposed to road traffic noise (LAeq =67.6 dB) and 656 in the control one (LAeq =53.4 dB). Respondents completed a validated ICBEN 5-grade scale “Noise annoyance questionnaire”. The exposure to PMP was objectified by the conversion of the subjective evaluation of the volume setting and duration. With the cooperation of the ENT specialist, we arranged audiometric examinations on the pilot sample of 41 volunteers. Results: From the total sample of 1,003 students, 794 (79.16 %) of them reported the use of PMP in the course of the last week; average time of 285 minutes. There was a significant difference in PMP use between the exposed (85.59 %) and the control group (75.76 %) (p=0.01). Among PMP users 30.7 % exceeded the LAV (lower action value for industry LAeq,8h = 80 dB). On a pilot sample of volunteers (n=41) audiometry testing was performed indicating a hearing threshold shift at higher frequencies in 22% of subjects. Conclusions: The results of the study on a sample of young healthy individuals showed the importance of exposure to environmental noise from different sources (transportation, neighborhood, construction, entertainment facilities, etc.) as well as social noise and the need for prevention and intervention.

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100~dB, with the mean difference of less than 2~dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. Spiking Neural Networks (SNNs) can utilize biological temporal coding features, such as noise-induced stochastic resonance and dynamical synapses to increase a model’s performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified Convolutional Neural Network analog, this paper demonstrates two key results: (1) SNNs solve the problem linearly inseparable in ANN with over 10% improvement in the optimal setting, and (2) in SNNs, the synaptic noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results.

This study aims to assess using a smartphone app (DecibelX), as a noise measuring alternative to the more costly traditional use of measuring noise levels with a Sound Level Meter (SLM). The study compares the accuracy of the app to readings taken with a SLM and dosimeter, and also evaluates the app’s performance for pure tone and narrow band noise. And a usability study identifies strengths and weaknesses related to usability of the app.

When limited area models of the hydrodynamics of the atmosphere and of the ocean are 1 run over an extended time, variability unrelated to external "drivers" emerge - this variability is 2 colloquially named "hydrodynamical noise" or just "noise". This article summarises what we have 3 learned in the past few years about the properties of such noise, and the implications for numerical 4 experimentation and analysis. The presence of this noise can be identified easily in ensembles of 5 numerical simulations, and it turns out that the intensity of the noise is closely linked to the scale- 6 dependent "memory". At the system level, this "memory" term as given by Hasselmann’s Stochastic 7 Climate model plays a key role. In the case of marginal seas, the process of baroclinic instability 8 modulated by tides and the formation of seasonal thermoclines are significant aspects. Some more 9 general aspects are discussed, such as the applicability of the Stochastic Climate model to systems 10 outside of atmospheric and oceanic dynamics, the irreversibility of tipping points and the challenges 11 of detecting changes beyond a noise level and of the attribution of causes of change.

Optical motion capture systems are state-of-the-art in motion acquisition, however as any measurement systems they are not error free -- noise is their intrinsic feature. The works so far mostly employ simple noise model, expressing the uncertainty as a simple variance. In the work we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. For the automated readout of the noise coefficients we solve the multidimensional regression problem using sophisticated metaheuristics in exploration-exploitation scheme. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion in our facility. We had also opportunity to observe the influence of camera failure to the overall performance.

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.

Expensive cameras meant for research applications are usually characterized by the manufacturers and detailed specifications [1] are available for them. Suppliers of inexpensive cameras usually do not provide such detailed information about their cameras. This data set provides the acquisition speed and noise characteristics acquired from a monochrome 1.2 megapixel CMOS camera, the QHY5L-II M [2]. The source code provided along with this data set [3] can also be used to acquire similar data for other QHY cameras. This enables the use of such cost-effective cameras for other scientific applications in other fields, beyond the designed use in Astronomy.

A novel characterization method for discrete saw filters vibrational sensitivity is presented. The proposed approach allows the characterization of filters under vibrations and the extraction of a behavioural model. Filters are assumed to be transducers so that external induced vibrational energy is partially transformed in a undesired simultaneous amplitude and phase modulation of the input RF signal. When the filter is mechanically excited with vibrations, it introduces spurious amplitude and phase modulation to the input signal that can potentially affect the link quality.

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

This paper presents a method for filtering and extracting noise from ECG signals using a Butterworth filter. The aim is to enhance signal qual- ity, enabling more accurate analysis in biomedical contexts, particularly in arrhythmia detection and other cardiac assessments.

The limits for recording weak signals for electrochemical seismic sensors are determined by their self-noise. At the same time, the noise nature is complex and not fully understood. In this work, for the first time, in addition to the frequency dependences of the noise amplitude, the difference in the phases of the noise currents of the cathodes of an electrochemical sensor has been studied. It has been established that at low frequencies the cathode currents change in an-tiphase. As the frequency increases, the phase difference becomes random. At the same time, the frequency at which the phase difference changes depend on the interelectrode distance; the clos-er the electrodes are located to each other, the greater it is. Physically, this effect is explained by the transition from noise caused by the integral flow of the working fluid to noise associated with vortex flows, the size of which depends on frequency. Thus, a new way of studying noise makes it possible to identify its physical nature and develop new methods of reduction.

The present paper is aimed at presenting an updated review of prediction methods for the aerodynamic noise of ducted rotor-stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and increasingly used, for both aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced, from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered as the best suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons.

How people recognize linguistic and emotional prosody in different listening conditions is essential for understanding the complex interplay between social context, cognition, and communication. The perception of both lexical tones and emotional prosody depends on prosodic features including pitch, intensity, duration, and voice quality. However, it is unclear which aspect of prosody is perceptually more salient and resistant to noise. This study aimed to investigate the relative perceptual robustness of emotional prosody and lexical tone recognition in quiet and in the presence of multi-talker babble noise. Forty young adults with normal hearing listened to monosyllables either with or without background babble noise and completed two identification tasks, one for emotion recognition and the other for lexical tone recognition. Compared with emotional prosody, lexical tones were more perceptually salient in multi-talker babble noise. Native Mandarin Chinese participants identified lexical tones more accurately and quickly than vocal emotions at the same signal-to-noise ratio. Lexical tone perception is also more robust against babble speech noise degradation than emotional prosody perception for native Mandarin Chinese listeners. Acoustic and cognitive dissimilarities between linguistic prosody and emotional prosody may have led to the phenomenon, which calls for further explorations into the underlying psychobiological and neurophysiological mechanisms.

The cartographic cum site-effectuated view of Shillong region of northeast India is presented here. Starting from the existing tectonics, the prevalent geological settings of the study area is comprehensively delineated. The seismic prone area is further overviewed in the context of site effects with accompaniment of available borehole information. The resonance frequency estimates form ambient noise survey along with receiver functions are outlined which implicates a heterogeneous subsurface. This further helps in segregating the region into two compelling profiles, thereby enabling us to get a deeper insight in the probable subsurface as well as heterogeneity. Eventually, the influence of topography over strata was also highlighted and interpreted as well.

Impulsive noise is the main limiting factor for transmission over channels affected by electromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenarios. In this work, we analyze some of the existing as well as some novel estimation algorithms. Their performance is compared, for the first time, for different channel conditions, including the Markov-Middleton scenario, where the impulsive noise switches between different noise states. Following a modern approach in digital communications, the receiver design is based on a factor graph model and implements a message passing algorithm. The correlation among signal samples as well as among noise states brings about a loopy factor graph, where an iterative message passing scheme should be employed. As it is well known, approximate variational inference techniques are necessary in these cases. We propose and analyze different algorithms and provide a complete performance comparison among them, showing that both Expectation Propagation, Transparent Propagation, and the Parallel Iterative Schedule approaches reach a performance close to the optimal, at different channel conditions.

Most popular statistical models in epidemic evolution focus on the dynamics of average relevant quantities and overlooks the role of small fluctuations on the model parameters. Models for Covid-19 are no exception. In this paper we show that the role of time-correlated fluctuations, far from being negligible, can in fact determine the spreading of an epidemic and, most importantly, the resurgence of the exponential diffusion in the presence of time-limited episodes in promiscuity behaviours.

High level of noise is a disturbance to the human environment. Noise in industries is also an occupational hazard because of its attendant effects on workers’ health. Noise presents health and social problems in industrial operations, and the source is related to the machineries used in the industries. One of the unique features of the noise associated with wood machinery is the level of exposure and duration. Equipment used in a factory can be extremely loud. They can produce noise at decibels high enough to cause environmental health and safety concerns. The mechanically driven transport and handling equipment, cutting, milling, shaping and dust extractor installations in the wood industry generate noise. The sources of noise pollution have increased due to non-compliance with basic safety practices. The increased use of locally fabricated machine in the industry has increased the level of noise and vibration. The effects of industrial noise pollution as discussed include: increase in blood pressure; increased stress; fatigue; vertigo; headaches; sleep disturbance; annoyance; speech problems; dysgraphia, which means reading/learning impairment; aggression; anxiety and withdrawal. As presented in this paper, noise control techniques include; sound insulation, sound absorption, vibration damping and Vibration isolation.

Breast cancer is a leading cause of death among women. Conventional screening methods, such as mammography, and ultrasound diagnosis are expensive and have significant limitations. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening system that has a potential to be a low cost and reliable breast cancer screening tool. It is based on modal analysis of the breast mass, and stereographic 3D image analysis to detect the stiffer abnormal tissues. However, camera sensor noise, especially Gaussian noise is a major source of Optical Flow (OF) error in this approach to tumor detection. This work studies the performance of different conventional filters, including the standard Gaussian filter tool to remove this noise and produce more robust screening results. A radical approach, Multiple Frame Noise Removal (MFNR) is proposed, for use in this type of medical image processing instead of a Gaussian filter or other typical image noise removal tools. Its a multiple frame noise removal method where Probability Density Function (PDF) of noise is extracted from the multiple images by characterizing the same pixel positions in multiple images. The noise becomes deterministic, and hence easily removed. The proposed algorithm was applied to a data set from 10 phantom breast tests with a prototype DIET system, and 10 in-vivo samples from healthy women. Comparisons were made to an optimal Gaussian filter form that is commonly used. Reductions in OF error using these digitally imaged data sets was used to compare performance. Refinement of the images for medical applications requires higher PSNR, which was successfully achieved by using MFNR algorithm. In this study, the algorithm was used to improve the imaging results of a DIET system. The conventional wisdom that states that noise removal and detail preservation are contrasting effects is

An ultra-low power (ULP), power gated static random access memory (SRAM) is presented for Internet of Things (IoT) applications, which operates in sub-threshold voltage ranges from 300mV to 500mV. The proposed SRAM has tendency to operate in low supply voltages with high static and dynamic noise margins. The IoT application involves battery enabled low leakage memory architecture in subthreshold regime which has low power consumption. Therefore, to improve power consumption along with better cell stability, a power gated 10T SRAM is presented. The proposed cell uses a power gated p-MOS transistor to reduce the leakage power or static power in standby mode. Moreover, due to the schmitt triggering and read decoupling of 10T SRAM the static and dynamic behavior in read, write and standby mode has shown enhanced tolerance at different process, voltage and temperature (PVT) conditions. The proposed SRAM shows better results in terms of leakage power, read static noise margin (RSNM), write static noise margin (WSNM), write-ability or write trip point (WTP), read-write energy and dynamic read margin (DRM). Further, these parameters are observed at 8-Kilo bit (Kb) and compared with already existing SRAM architectures. It is observed that the leakage power is reduced by 1/81×, 1/75× of the conventional 6T (C6T) SRAM and read decoupled 8T (RD8T) SRAM, respectively at 300mV VDD. On the contrary, RSNM, WSNM, WTP and DRM values are improved by 3×, 2×, 11.11% and 31.8% as compared to C6T SRAM, respectively. Similarly, proposed 10T has 1.48×, 25% and 9.75% better RSNM, WSNM and WTP values as compared to RD8T SRAM, respectively at 300mV VDD.

Motivated by important applications to the analysis of complex noise-induced phenomena, a problem of the constructive description of randomly forced equilibria for nonlinear systems with multiplicative noise is considered. Using apparatus of the first approximation systems, we construct an approximation of mean square deviations that explicitly takes into account the presence of multiplicative noise. This approximation is compared with the widely used approximation based on the stochastic sensitivity technique. The general mathematical results are illustrated with the example of the van der Pol oscillator with hard excitement.

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for building, implementing, and simulating quantum hardware.

Highly-doped graphene samples show the conductance reduced and the shot-noise power enhanced compared to standard ballistic systems in two-dimensional electron gas. These features can be understood within a model assuming incoherent scattering of Dirac electrons between two interfaces separating the sample and the leads. Here we find, by adopting the above-mentioned model for the edge-free (Corbino) geometry and by means of the computer simulation of quantum transport, that another graphene-specific feature should be observable when the current flow through a doped disk is blocked by high magnetic field. In case the conductance drops to zero, the Fano factor approaches the value of $F\approx{}0.56$, with a very weak dependence on the disk radii ratio. The role of finite source-drain voltages and the system behavior upon tuning the electrostatic potential barrier from a rectangular to parabolic shape are also discussed.

Text-based CAPTCHA remains the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs) are the mainstream approach for text-CAPTCHA recognition, which are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment, they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive-CAPTCHA with a CRNN module and trainable and configurable filtering networks, which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexity show that compared with the baseline model Deep-CAPTCHA, the number of parameters of our proposed model is reduced by about 70% and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

This study proposes a customized industrial nozzle for generating impulsive air jets. It is installed on an automatic machine where wine caps are stacked and shot consecutively into a rotating cylinder by an air jet. This process is very noisy; hence, this study aimed to investigate possible geometric variations of the nozzle that can reduce the emitted noise. First, the nozzle was tested in a laboratory to measure the air consumption at different supply pressures. Subsequently, 3D models of the nozzle and its variations were created and used for computational fluid dynamics simulations. Different boundary conditions were set, first to validate the model and compare it with the experimental test results, and then to simulate the real working conditions and determine the geometry that is less noisy while maintaining the velocity peak. Among the various possibilities, shortening the final ducts of the nozzle appears to be the most promising solution. These modified nozzles could be easily added to current machines to provide immediate benefits, and this study represents a promising start for action on other machines where this type of device is present.

Wolfenden and his coworkers observed the astronomical numbers for the catalytic proficiency of some enzymes. We connected that pinnacle of biological evolution to the universal thermodynamic evolution. We added or multiplied a random noise with chosen rate constants to explore the correlation between dissipation and enzyme efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. The turnover number kcat and catalytic efficiency kcat/KM are proportional to the overall entropy production – the main parameter from irreversible thermodynamics. For most enzymes with the Michaelis-Menten type cycle kinetics, the best increase in the forward kcat/KM follows after increasing the equilibrium constant of substrate-enzyme association. The Discussion section emphasizes the role of biological evolution in harvesting order (high enzyme efficiency) from disorder (high noise and dissipation). It also connects the applications of the maximum partial entropy production theorem in optimizing enzyme kinetics (D. Juretić "Bioenergetics - A Bridge across Life and Universe") with the present total entropy production role analysis. De novo enzyme design and various attempts to speed up the rate-limiting catalytic steps may profit from our theoretical insights.

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. Training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combining with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise sup-pression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. Results have been assessed with objective methods (mathematical metrics) and subjective (listening to a set of audios by humans). Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work involves refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical ap-plications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.

This paper exposes the initial part of a larger research on the impact of the automotive on human health and the quality of life and environment. After a brief introduction on the need for responsible and sustainable approaches and few questions on the subject, some considerations and statistical data are presented, based on a study of the literature on the impact of road traffic and vehicles noise and vibration on people's comfort and health and their quality of life. The research results show, based on official statistics, the significant harmful impact of noise and vibration from motor vehicles and road transport on people's health and quality of life, especially in urban areas during the day. Also, the significant increase in the number of electric and hybrid vehicles from one year to another, a reality and a necessity nowadays, and the awareness that electric vehicles are not perfectly quiet and comfortable, open new research opportunities and require the development of new standards, materials, tools, equipment and test methods in the field of NVH, in a sustained synergistic approach from all stakeholders, to meet the needs and demands of today's consumers, and to comply with existing regulations and standards on environmental protection and sustainable development.

This study deals with two different aspects of the high-strength low-alloyed 1100 MC steel. The first is associated with the remarkable heterogeneity in the surface state produced during sheet rolling with respect to the sheet width. The variable-thickness surface layer exhibits a microstructure different from that of the deeper bulk. Variation of the thickness of the thermally softened near-surface region strongly affects Barkhausen noise, as well. This technique can be considered a reliable tool for monitoring the aforementioned heterogeneity. It can also be reported that the opposite sides of the sheet are different with respect to the surface state, heterogeneity distribution, and corresponding Barkhausen noise. These aspects indicate the different conditions during hot rolling followed by rapid quenching on the upper and lower rollers. The second aspect is related to the remarkable asymmetry of Barkhausen noise emission with respect to two consecutive bursts. This asymmetry is due to the presence of remnant magnetisation in the sheet produced during manufacturing. The remnant magnetisation is coupled to the magnetic field produced by the excitation coil of the Barkhausen noise sensor and strongly contributes to the aforementioned asymmetry. As soon as sufficient removal of this remnant magnetisation is carried out in the vanishing magnetic field (demagnetisation), the aforementioned remarkable asymmetry is fully lost.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

Analyzing the records of Advanced LIGO and Virgo gravitational observatories, we found a specific time-domain asymmetry, inherent only to the signals of their gravitational detectors. Experiments with different periodic signals, Gaussian and non-Gaussian noises made it possible to conclude that the noise of gravitational detectors is an unusual mixture of signals. The gravitational-wave signals have been detected and recognized using a specialized Pearson correlation analyzer. It turned out that the detector signals include a significant (– 6 dB) component, which has the properties of records of reliably recognized gravitational waves. This allows one to argue that the gravitational noise is largely due to the processes of merging astronomical objects. Since the specific signal is registered by the detectors continuously, the field of gravitational oscillations of the sub-kilohertz band can be considered as detected. A method of analysis has also been developed to estimate the contribution of the gravitational noise component to the total signal energy. With its help it will be possible not only to pass to the radio-frequency estimation of the magnitude of gravitational disturbances but also, possibly, to construct a map of the gravitational noise of the sky.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented, as pedestrianization areas and the definition of Low Emissions Zones (LEZ). When the effectiveness of these types of measures is in doubt, smart city tools provide data that can be used to scientifically asses their impact. This article analyzes the situation of Madrid Central (Spain), a LEZ subject to controversy. We apply statistical and regression analyses to evaluate the effectiveness of this measure to reduce air pollution and outdoor noise. According to the results, this LEZ was able to significantly reduce NO$_2$, PM$_{2.5}$, and PM$_{10}$ concentration locally, having the same positive impact in the rest of the city. In terms of noise, this measure is able to mitigate background noise levels generated by the road traffic.

We discuss some of the fundamental practical limitations of the Fluctuation-Enhanced Sensing of odors and gases. We address resolution, measurement speed, reproducibility, memory and other problems such as humidity. Various techniques and ideas are presented to overcome these problems. Circuit solutions are also discussed.

The new WHO Environmental Noise Guidelines for the European Region have recommendations for limiting noise exposure associated with adverse health effects. The limits are said to be based on a systematic review of existing evidence. This paper gives a systematic assessment of the presented evidence with respect to aircraft noise annoyance and demonstrates that the new guidelines are based on an arbitrary selection of existing studies comprising an imperfect and faulty set of data not representative for the general airport population.

Optical communication systems pose significant challenges, including the effects of nonlinear noises. The nonlinearities, including Kerr-induced phase noise, become more problematic in m-QAM as the orden of the format increases becoming a highly densed set of data-symbols and therefore, requiring advanced signal processing for successful separation of symbols at the demodulation stage. Machine learning techniques have recently been applied to improve signal integrity in such scenarios. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in -90, -100, and -110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.

Background/Objectives: It is known that balance disorders involve occupational hazards. However, the inverse relationship (between certain occupations and an increased incidence of vertigo or dizziness) has been scarcely studied. The objective of this work was to analyze the occupation of a group of patients with vertigo, compared to economically active general population, and to evaluate the prevalence of occupational noise and/or vibration-exposure in both groups. Methods: A cross-sectional, observational, case-control study was carried out including 461 patients (220: Meniere’s disease; 73: vestibular migraine; 28: vestibular neuritis; 140: BPPV) (310 women and 151 men). These patients were compared to a control group from the general population, obtained from 6th EWCS-Spain (2015). Possible differences regarding sex, age, occupation, exposure to noise, and exposure to mechanical vibrations were analyzed. Results: Differences in distribution of occupations between patients with vertigo and general population were observed (Chi-square, p=6.142 e-30). Patients with vertigo were significantly more exposed to noise (p=8.19 e-12; OR=2,127, CI95% (1.720;2.630)) and vibrations (p=1.11 e-12; OR=2.288, CI95% (1.835;2.853)) than the control group. These differences were observed both between men and women. Conclusions: A relationship between occupational exposure to noise and/or vibrations and the presence of vertigo was observed. Protective and preventive measures could help prevent the occurrence of some diseases involving vertigo.

The acoustic environment in learning spaces significantly influences learning effectiveness and occupant well-being. Recent studies have shown that a series of architectural factors within physical learning environments can impact students' educational experiences. This research delves into the relationship between architectural characteristics and the perceived acoustic environment among 467 primary students in 19 learning spaces across 9 schools in Chile. The survey comprises a student questionnaire, a checklist evaluating space characteristics, and acoustic measurements of indoor noise levels during occupied conditions. Examined topics include Indoor Environmental Quality (IEQ) comfort perception, noise annoyance, hearing ability, and acoustic preference. Architectural characteristics considered encompass flexible and acoustic space design parameters. The results indicated that noise discomfort was a common issue, especially in traditional learning spaces without acoustic treatment. The type of learning space (traditional or flexible settings) has a greater impact on acoustic perception particularly concerning noise discomfort and acoustic preference. However, poorly acoustic spaces lacking absorbing materials or exhibiting lower spatial density (m2 per student) contribute to increased indoor noise levels affecting noise annoyance caused by children's talking inside. The study emphasizes the importance of considering both architectural design and acoustic conditioning for optimizing the acoustic experience in educational spaces.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

We propose a method that enables precise determination of the number of atoms in a Dick-noise-free optical lattice clock, by effectively addressing quantum projection noise. Our approach relies on conducting stability measurements at three distinct parameter sets, allowing us to differentiate between quantum projection noise, photon shot noise, and technical noise. Importantly, our method enables accurate extraction of the atom number, even in the presence of photon shot noise and technical noise. To validate the effectiveness of our approach, we employ numerical simulations and optimize the modulation parameters to achieve the smallest uncertainty possible. The numerical simulations show the validity of our method and demonstrate a measurement uncertainty in the atom number that is below 4% across a wide range of atom numbers, provided that the standard deviation of atom number fluctuation is kept below 0.23 times the average atom number.

Discretization in time and in the state space of the system leads to the necessity to solve the parameter identification problems for dynamic systems in a limited time (at a finite time interval) using observations obtained under the influence of unknown-but-bounded noise. Finding the solution in this case is more difficult compared to traditional identification problem setting which considers random independent zero-mean noise. For system parameter identification problem under unknown-but-bounded noise, a randomized stochastic optimization algorithm is given in the paper, estimates for the mean square values of the residuals for a finite observation interval are obtained. An example of application of the given method to the problem of tuning the parameters of a multi-mirror telescope is considered.

In forensic acoustics, a possible area of analysis is represented by unwanted sound that 1 is perceived as a source of intrusion or disturbance within a certain auditory context. This context 2 is defined as the "auditory scene" and refers to the set of sounds present in a specific environment. 3 The presence of unwanted sounds in the auditory scene can cause a wide range of negative effects, 4 including disturbance, discomfort, moral or immoral harm, and other types of negative impact on the 5 health and well-being of individuals exposed to noise. In 2022, the technical specification UNI/TS 6 11844:2022 dedicated to the measurement and analysis of intrusive noise was published. The standard 7 introduces the concept of intrusive noise and defines its calculation methods based on environmental 8 measurements. The purposes of this technical specification is to provide an objective support to 9 methods already in used in acoustic disputes, where the assessment of the annoyance of a noise is 10 often a subjective evaluation of the technician. This work delves into the application to some real 11 cases, identifying the potentiality and the limits of the standardized method.

These years, disease-causing and disabling diseases have caused great concern. Neurological, musculoskeletal disorders are diverse and affect people of a wide range of ages. And the lack of comprehensive diagnostic methods places a huge burden on healthcare systems and social economies. In this paper, the current status of clinical research on neuromuscular diseases is introduced, and the advantages of magnetic field measurement compared with clinical diagnostic methods are illustrated. The paper makes a comprehensive description about the related technology of superconducting quantum interference devices (SQUIDs), magnetic field detection noise suppression scheme, the development trend of sensor detection system, and the application and model establishment of neuromuscular magnetic field. The current researches and developments in the worldwide are compared simultaneously in this paper and finally the conclusions and outlook are put forward. This review will be very interesting and helpful to the scientific community.

The frequency synthesizer is a critical component in Cs beam clock technology. In this paper, we present a demonstration of a direct microwave frequency-synthesis chain for a cesium-beam atomic clock, which utilizes frequency multiplication and a dual-phase-locked loop mode. A detailed analysis of the frequency-synthesis chain is conducted, and a mathematical model is established. The phase settling time and system stability are simulated, measured, and verified. The experimental results for the phase settling time align with the simulation outcomes. The phase settling time can be adjusted within the range of 644.5 µs to 1.5 ms, and the absolute phase noise values are -63.7 dBc/Hz, -75.7 dBc/Hz, -107.1 dBc/Hz, and -122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively. Additionally, the Ramsey fringes are detected, and the Allan deviations of the 10 MHz output from the cesium-beam atomic clock are measured to be 2.99×10−12 at 1s and 8.02×10−14 at 10,000 s.

We propose a method of cooling nuclear spin systems of solid-state nanostructures by application of a time-dependent magnetic field synchronized with spin fluctuations. Optical spin noise spectroscopy is considered as the method of fluctuation control. Depending on the mutual orientation of the oscillating magnetic field and the probe light beam, cooling might be either provided by dynamic spin polarization in an external static field or result from population transfer between spin levels without build-up of a net magnetic moment (“true cooling”).

An algorithm for the treatment of images affected by both blurring and salt&pepper noise is proposed with a cost only proportional to the number of pixels. The methodology uses a discretization scheme for the Laplace operator multiplied by a suitable nonlinear term depending on the gradient. Even if this approach resembles a diffusion type algorithm, only one step of the procedure is applied, leading to significant time savings. The procedure is successfully tested on some standard black&white natural images.

Graphene quality for use in biosensors was assessed in fabricated chips by a set of methods that includes atomic force microscopy (AFM), Raman spectroscopy, and low-frequency noise. It is shown that local areas of residues on the graphene surface, which arisen due to the interaction of graphene with a photoresist at the initial stage of chips development, leads to a spread of chips resistance (R) within 1-10 kΩ and to an increase in the root mean square (RMS) roughness up to 10 times that can significantly impair reproducibility of graphene parameters in biosensors chips. It was observed that the control of the photoresist residues after photolithography (PLG) by AFM and subsequent additional cleaning allow reducing the spread of R values in chips to 1–1.6 kΩ and obtaining RMS roughness similar to the roughness in pristine graphene before PLG. Monitoring of the spectral density of low-frequency voltage fluctuation (SU), which provides integral information about the defect system and quality of the material, makes it possible to identify chips with low graphene quality and with inhomogeneously distributed compressive stresses areas by the type of frequency dependence SU (f).

Multirotors are gaining great importance in the layout of innovative and more agile mobility. In this framework, a possible solution to developing an aircraft complying with the stringent size requirements characterizing this type of application can be a coaxial rotor configuration. To exploit the several possibilities linked to coaxial rotors, a scaled experimental model is designed to evaluate the performances of the counter-rotating propellers system, concerning the distance between the two propellers. Both thrust and noise are considered as parameters of interest. Two brushless motors are deployed whereas the propellers' angular velocity, in terms of round per minute (rpm), is controlled by an external control system. Tests are conducted on both single isolated propellers as well as on the counter-rotating system: the two propellers and their respective motors have been characterized concerning the thrust. Furthermore, a comparison with a numerical model is performed. Noise evaluation on the single propeller has shown a motor contribution prevalence at a low rpm regime (1140-1500 rpm) and a propeller prevalence for angular velocities higher than 1860 rpm. By varying the distances between the propellers a sensitivity analysis is performed with the aim of identifying the optimum configuration taking into account both noise and thrust performances.

Digital medicine based on the integration of all medical data of a particular patient, has become a reality today, thanks to information technology. Traditional medical examinations can be supple-mented by assessment results of the oxidative-anti-oxidative (OAO) status of the body [1]. Elec-trochemical sensors are able to not only determine the integral indicators of the OAO system of the body, but also to depict details of the processes occurring in the system. The main obstacle to the widespread use of electrochemical sensors in medical diagnostics is the extremely small amount of the received information in comparison with tens of thousands of known human dis-eases. The problem can be eliminated only by rethinking the purpose of electrochemical measure-ment within the framework of thermodynamics of information processes and information theory. In the information paradigm of electrochemical analysis of biological fluids, a sample is considered as an electrochemical message created by a sensor. The purpose of electrochemical measurement is to obtain information in a volume sufficient to identify the sample composition within the range of possible concentrations of its components. The fundamentals of the thermodynamics of infor-mation processes are considered and conclusions that are of practical importance for the devel-opment of electrochemical sensors and analyzers are derived. It is shown that potentiostatic con-trol of the sensor is physically impacted by the electromechanical instability of the electrical double layer, which is the main source of sensor signal noise. Estimates are of a minimum amount of an-alytical signal information required for identification of a sample of a known composition, such as a biological fluid, are provided. Examples of highly informative analytical signals for flowing and stationary samples are presented. Problems related to the visualization of such signals are noted.

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

Plants are such important keys of biological part of our environment, supply the human life and creatures. Understanding how the plant’s functions react with our surroundings, helps us better to make plant growth and development of food products. It means the plant phenotyping gives us bio information which needs some tools to reach the plant knowledge. Imaging tools is one of the phenotyping solutions which consists of imaging hardware such as the camera and image analysis software analyses the plant images changings such as plant growth rates. In this paper, we proposed a preprocessing algorithm to eliminate the noise and separate foreground from the background which results the plant image to help the plant image segmentation. The preprocessing is one of important levels has effect on better image segmentation and finally better plant’s image labeling and analysis. Our proposed algorithm is focused on removing noise such as converting the color space, applying the filters and local adaptive binarization step such as Niblack. Finally, we evaluate our algorithm with other algorithms by testing a variety of binarization methods.

One of the most common limits to gas sensor performance is the presence of unwanted interference fringes or etalons arising, for example, from multiple reflections between surfaces in the optical path. Additionally, since the amplitude and the frequency of these interference depend on the distance and alignment of the optical elements, they are affected by temperature changes and mechanical disturbances, giving rise to a drift of the signal. In this work, we present a novel semi-parametric algorithm which allows the extraction of a signal, like the spectroscopic absorption line of a gas molecule, from a background containing arbitrary disturbances, without having to make any assumption on the functional form of these disturbances. The algorithm is applied first to simulated data and then to oxygen absorption measurements in presence of strong fringes.To the best of the authors' knowledge, the algorithm enables an unprecedented accuracy particularly if the fringes have a free spectral range and amplitude comparable to those of the signal to be detected. The described method presents the advantage of being based purely on post processing, and to be of extremely straightforward implementation if the functional form of the Fourier transform of the signal is known. Therefore it has the potential to enable interference-immune absorption spectroscopy. Finally, its relevance goes beyond absorption spectroscopy for gas sensing since it can be applied to any kind of spectroscopic data.

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

Steels studs are an inevitable part of drywall construction as they are lightweight and offer the required structural stability. However, the studs act as sound bridges between the plasterboards reducing the overall sound insulation of the wall. Overcoming this often calls for wider cavity walls and complex stud decoupling fixtures that increase the installation cost while reducing the floor area. As an alternative approach, this research reveals the potential of perforated studs to improve the acoustic insulation of drywall partitions. The acoustic and structural performance is characterized using a validated finite element model that acted as a prediction tool in reducing the number of physical tests required. The results established that an acoustic numerical model featuring fluid-structure-interaction can predict the weighted sound reduction index of a stud wall assembly at an accuracy of ±1 dB. The model was used to analyze six perforated stud designs and found to outperform the sound insulation of non-perforated drywall partitions by reducing the sound bridging. Overall, the best performing perforated stud design was found to offer improvements in acoustic insulation of up to 4 dB, while being structurally compliant.

A new attack against the Kirchhoff-Law-Johnson-Noise (KLJN) key distribution system is introduced. The attack is based on 1) Utilizing the dc-voltage-source - which we put at Alice’s end in our mathematical modeling of the attack-that could exist due to errors, imbalances, Electromagnetic interference and etc.2) On studying the number of samples per bit in the security key that the measured Alice/Bob voltages exceeds or falls below a threshold voltage, respectively. The threshold voltage is the average between dc voltages across low and high resistors- generated by a dc-voltage source. We count the number of samples the voltage at Bob’s end (containing both the noise and dc components) exceeds the threshold voltage and how many times it falls below the threshold to judge whether the resistor can be guessed as low or high for every cycle. Also for a pre-specified key-length we count the number of high resistance estimations per bit –at Bob’s end-according to the previous criterion and the non-successful estimations per bit to judge the final guessed resistor value at Alice’s end and Bob’s end, where if we have more bits with most of its measured samples are above the threshold voltage then we will predict Bob’s resistance as high resistance, otherwise we predict Bob’s resistance to be low resistance. The Simulation was conducted and the attack proved that it is successful unless the temperature increased dramatically to ranges more than a threshold temperature ~ Kelvin that increases when the number of samples per bit increases.

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

The presence of phase diffusion can lead to the loss of the advantages of quantum measurements. Squeezing is considered to be an effective method to reduce the detrimental effect of phase diffusion on the measurement. The Mach-Zehnder (MZ) interferometer has been exploited as a generic tool for precise phase measurement. Describing the reduction in quantum advantage caused by phase diffusion in an MZ interferometer that can be mitigated by squeezing is not easy to handle analytically, because the input state will change from a pure state to a mixed state after experiencing the diffusion noise in the MZ interferometer. We introduce a truncated MZ interferometer that can achieve the same potential phase sensitivity as the conventional MZ interferometer. This scheme can theoretically explain how phase diffusion reduces phase estimation and why squeezing counteracts in the presence of phase diffusion. Using the Gaussian property of the input state and the characteristic of Gaussian operation in the squeezing, the two orthogonal field quantities of the quantum state are squeezed and anti-squeezed to different degrees, and the analytic results are obtained. The truncated MZ interferometer is simpler to build and operate than the conventional MZ interferometer, and also mitigates the phase diffusion noise by the squeezing operation, thus making it useful for applications in precision metrology.

This study tests the utilization of Bluetooth noise-canceling headphones in improving the quality of eye exams in patients with hearing loss. University of Texas Medical Branch patients above 55 years of age were screened for hearing loss using SoundCheck audiometry. All 29 subjects (18 red-zone and 11 yellow-zone) answered pre-recorded Amsler grid and patient education questions that solicited precise responses. As controls, subjects were randomly administered half of the questions via headphones and half via a smartphone at normal speech volume (60 decibels). Points were awarded for responses demonstrating comprehension, and a post-exam survey was collected. Collectively, the mean score was 1.79 with headphones versus 0.96 with control on the Amsler grid segment and 1.90 with headphones versus 0.97 with control on education questions (p=0.001). Between the red and yellow zone hearing loss patients, the red-zone group answered significantly better in both Amsler (1.78 versus 0.50; p=0.0003) and education questions (1.88 versus 0.44; p<0.0001) with headphones. The yellow zone group answered better with headphones overall but failed to reach significance. Post-exam survey indicated that 28 of 29 patients (97%) preferred the headphones during ophthalmic exams. Patients with severe hearing loss demonstrated better comprehension with the noise-canceling headphones; thus these devices show great promise in improving effective, compassionate communication between providers and hearing-loss patients.

Environmental pollution by noise is one of the most serious health threats nowadays. The impact of noise on the human body depends not only on the sound level but also on its spectral distribution. Reliable measurements of the environmental noise spectrum are often hampered by the very high price of top quality measuring devices. This paper explores the possibility of using much cheaper audio recorders for frequency analysis. Comparative research were performed in laboratory and field conditions, which showed that, with some limitations, these devices can be useful for the needs of environmental noise frequency analysis. This fact gives an opportunity for reduce the cost of noise analysis experimental work.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.

Titanium’s accelerating usage in global markets is attributable to its distinctive combination of physical and metallurgical properties. The key to best utilizing titanium is to exploit these characteristics, especially as they complement one another in a given application, rather than to just directly substitute titanium for another metal. Titanium alloy are extensively used in aerospace applications such as components in aero-engines and space shuttles, mainly due to their superior strength to weight ratio. For these demanding applications functionality and reliability of components are of great importance. To increase flight safety, higher sensitivity inspections are sought for rotating parts. Increased sensitivity can be applied at the billet stage, the forging stage, or both. Inspection of the forging geometry affords the opportunity to apply the highest sensitivity due to the shorter material paths when compared to those required for billet inspections. Forging inspection is typically performed for titanium (Ti) rotating parts with immersion inspection and fixed-focus, single-element transducers. Increased gain is required with depth because the ultrasonic beam attenuates with distance and diverges beyond the focus position that is placed near the surface. The higher gain that is applied with depth has the effect of increasing the UT noise with depth. The relationships between the UT noise, selection of the examination technique and the smallest detectable defect are presented in this material.

Wildlands are increasingly lost to human development. Conservation scientists repeatedly call for protecting the remaining wildlands and expanding the land area protected in reserves. Despite these calls, conservation reserves can be eliminated through legislation that demotes their conservation status. For example, legislation introduced to the Congress of the United States recently would demote 29 Wilderness Study Areas (WSAs) from the protections afforded by their existing status. The legislation suggests that the 29 areas are not suitable for a promotion and future inclusion in the National Wilderness Preservation System based on decades-old local evaluations. Local evaluations, notwithstanding, it may be important to consider the value of lands from a national perspective. Without a national perspective, local evaluations alone may overlook the national significance of lands. With this in mind, I used five qualities of wildland value (wildness, light pollution, noise pollution, intactness of mammals, and intactness mammal carnivores of conservation concern) to compare the 29 WSAs to all national parks and wilderness areas located within the contiguous United States. The pool of 29 WSAs were similar to the pool of national parks and wilderness with respect to the five qualities assessed, and some of the WSAs were characterized by higher values than most of national parks and wilderness areas. This analysis demonstrates the national significance of the WSAs targeted for conservation status demotion. Such an approach could be used in future land management legislation and planning to ensure that a national perspective on conservation value is brought to bear on decisions facing federally-managed lands.

This paper presents a 612–1152 MHz Injection Locked Frequency Multiplier (ILFM). The proposed ILFM is only used for sending an input signal to the receiver in the I/Q mismatch calibration mode. Using the Phase-Locked Loop (PLL) to calibrate the receiver places a burden on this system due to the extra area required and power consumption. Instead of the PLL, to satisfy high frequency, low jitter, and low area, a Ring Oscillator is proposed. The free-running frequency of the ILFM is automatically digitally calibrated to reflect the frequency of the injected signal from the harmonics of the reference clock. To control the frequency of the ILFM, the load current is digitally tuned with 6-bit digital control signal. The proposed ILFM locks to the target frequency using a digitally controlled Frequency Locked Loop (FLL). This chip is fabricated using 1-poly 6-metal 0.18 µm CMOS and achieve the wide tuning range of 612–1152 MHz. The power consumption is 0.95 mW from a supply voltage of 1.8 V. The measured phase noise of the ILFM is −108 dBc/Hz at a 1 MHz offset.

A turbomachinery is essential part in the power generation cycle. But, it is main noise source to annoy workers and users and to make environmental problem. Thus it is important to reduce this noise for operating the power generation cycle. This noise is created by flow instability on rotor blade trailing edge. An airfoil that becomes a section of a rotor blade of a rotating machine is manufactured as a blunt trailing edge (TE) with a round or flatback shape rather than the ideal sharp TE shape for the purposes of producibility and durability. This increases the tonal noise and flow-induced vibration at low frequency owing to vortex shedding behind TE when compared with a sharp TE. In order to overcome this problem, this study investigates the oblique TE shape using numerical simulation. In order to do so, the flow was simulated using large eddy simulation (LES) and the noise was analysed by acoustic analogy coupled with LES result. Once the simulation results were verified using the flatback airfoil measurements of Sandia National Laboratories, numerical prediction was performed for airfoils modified to have oblique trailing edge angles of 60°, 45°, and 30° to analyse the flow and noise characteristics. From the simulation results for an airfoil having an oblique TE, it could be seen that the vortex shedding frequency moves in accordance with the oblique angle and that the vortex shedding noise characteristics change according to this angle when compared to the flatback TE airfoil. Therefore, it is considered that modifying the flatback TE airfoil to have an appropriate oblique angle can reduce noise and change the tonal frequency to a bandwidth that is suitable for mechanical systems.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

This study presents an advanced data augmentation framework to enhance diagnostics in centrifugal pumps using vibration data, with a focus on practical industrial applications. Initially, Gaussian noise and signal stretching were employed to generate synthetic data, addressing the common issue of insufficient defect data in industrial settings. These methods simulate varied operating conditions and different rotating speeds. Recognizing the limitations of traditional approaches, we further integrated advanced models—Long Short-Term Memory (LSTM), Autoencoder (AE), and Generative Adversarial Networks (GANs) to augment the dataset comprehensively. This enhanced approach increases the robustness and accuracy of machine-learning models for fault detection and classification. Experimental results show significant improvements in diagnostic algorithm performance, reducing false positives and increasing fault detection rates. The study offers a complete framework for incorporating both traditional and advanced data augmentation techniques into predictive maintenance protocols, ensuring reliable operation of centrifugal pumps in diverse industrial environments.

Measuring sound pressure is a complex and tedious task in industrial environments. Classical methods can be costly, inaccurate, and potentially dangerous for workers. Integrating robotics and computer vision into acoustic measurement processes offers an innovative solution to these problems. This paper uses a Doosan Robotic Vision System to investigate an innovative automatic acoustic sound pressure measurement solution. The Doosan robot, equipped with a scanning camera and microphone, captures acoustic data and produces a map of the machine’s acoustic radiation using computer vision algorithms. The measurement technique is accurate, efficient, and safe for workers. It can also be used for continuous monitoring of the industrial acoustic environment. The developed measurement method offers a promising way to automate acoustic measurement, which can be performed faster and with better accuracy to help industries improve their product sound quality and reduce noise levels. The method was compared to analytical predictions. A 3D acoustic mapping was conducted to visualize the spatial distribution of sound sources within the environment. The experimental results show a good correlation with the theory.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

As the lifespan of porous asphalt pavement extends, it invariably experiences performance degradation and an increase in tire/pavement noise compared with the original surface when it is newly built. To understand the medium-term performance of porous asphalt pavement during service and reveal the influence of different pavement performance indicators on tire/pavement noise generation, a seven-year continuous observation and data analysis study was conducted. Porous asphalt with different designed material parameters was taken into account, including thin-layer porous asphalt overlay with a maximum nominal aggregate size of 10mm (PUC-10), porous asphalt with a maximum nominal aggregate size of 13mm (PAC-13), and a two-layer porous asphalt incorporating both PUC-10 and PAC-13 (PUC-10+PAC-13). Using automated pavement technology testing equipment, key performance indexes, including damage rate (DR), rut depth (RD), international roughness index (IRI), and sideway force coefficient (SFC), were measured and calculated. The on-board sound intensity (OBSI) method was used to test noise levels in three types of porous asphalt pavement structures and on a dense graded easy compact asphalt concrete (ECA) for comparison. The progressions of the medium-term performances were analyzed according to the measured results. The correlations between tire/pavement noise levels and testing speed, testing lane, pavement structure, and road condition indicators were investigated by regression. The research results revealed that the DR and SFC of the three types of porous asphalt pavement displayed more significant attenuation in the service period, while the IRI and RD showed small changes. Among them, the SFC demonstrated significant attenuation over the first 4 years, decreasing between 13.8% and 17.0%. From the 3-7 years of operation, the transverse crack disease in porous asphalt pavement progressed rapidly. The three porous asphalt pavement structures tested exhibited commendable noise reduction capabilities. Compared to ECA, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. Compared to other pavement condition indicators, RD had a more significant impact on the OBSI value of the PUC-10 porous asphalt pavement. For the PUC-10+PAC-13 porous asphalt pavement, there was a significant linear relationship between the OBSI value and SFC, mainly due to polishing of the coarse aggregate surface. It leads to a decrease of the micro-texture, which lies in increasing tire/pavement noise at high-frequency ranges and decreasing SFC. This study will have guiding significance for the research of the attenuated law and durability improvement of porous asphalt tire/pavement noise.

Noise model selection criteria has a significant impact on identifying the stochastic noise proper-ties of any GNSS daily coordinate time series. The low-frequency random walk noise existing in these time series could lead to overestimation of the tectonic rate, so it is of great significance to accurately detect the random walk component. This study focuses on noise model estimation cri-terion (BIC_tp) derived from the AIC and the BIC by introducing 2π factors. It is more sensitive to abnormal steps (random jumps). Using observation data from 72 GNSS stations from 1992 to 2022 and simulated data, four combined noise models are used to explore the impacts of time se-ries lengths (ranging from2 to 24 years) and data loss (between 2% and 30%) on noise models and velocity estimation. The results show that as the time length increased, the selected optimal noise model, and the estimated uncertainty of the tectonic trend with different data gap, gradually con-verge. When the time length is short (less than 8 years), it could lead to the FNRWWN, FNWN, and PLWN models being mistakenly estimated as GGMWN models, thereby affecting the accura-cy of determining the station velocity parameters. When the time length is 12 years, the RW noise component is more probably detected, As the time length increases, the impact of RW on velocity uncertainty is weakened. Finally, we conclude that for a time series with a minimum time length of 12 years, both the selection of the optimal stochastic noise model and the estimation of the ve-locity parameters are reliable.