Function Of Noise Level Research Articles

Development of hybrid active noise cancellation system for the children with autism spectrum disorder

Children with autism spectrum disorder often experience auditory hyperreactivity, where they find certain sounds intolerable and may exhibit negative behaviours in response to noise. While noise-cancelling headphones are commonly used to address these behavioural issues, they primarily focus on reducing noise levels without considering the diverse auditory perceptions of children with autism. Aural perception and electroencephalography tests were conducted to design a noise-control function in headphones that cater to their individual needs. Autistic children with auditory hyperreactivity listened to sounds of various frequencies and amplitudes and their subjective responses were analyzed. The results were utilized to determine suitable noise-attenuation targets based on a function of mean aural perception ratings and noise levels. Using these findings, a hybrid active noise cancellation system was developed and validated. The results showed that frequencies of 250 Hz and 8 kHz were rated by the majority of the children with autism as most unpleasant. The aural perception response improved when children used headsets tailored to their specific aural perception characteristics.

Microseismic network sensitivity in case of no seismic activity

Underground human activities, such as mining, shale gas, and oil exploitation, waste-water disposal, or geothermal plants, can cause earthquakes; therefore, they are monitored by local seismic networks. An ideal seismic network has a triangulated grid, with spacing equal twice the minimal depth and no associated industry noise. In real cases, the network sensitivity is biased by stations placed near noisy roads, factories, or in a private garden, none located at optimal nodes. The sensitivity is also a function of the detection algorithm type and setting. The goal of this case study is to suggest a work-flow for network sensitivity calculation in case of no seismic activity. In other words: how small are the earthquakes that such seismic networks would detect if they were present? Such network sensitivity is a function of stations noise level, station-source geometry, and setting of the detection algorithm. A brief theory and work-flow description is followed by two real-case demonstrations from Czech Republic, Europe: first, a proof-test on a well-studied seismically active area of West Bohemia/Vogtland and second, an application to an uprising geothermal project in Litoměřice, where no seismic activity was detected in years of monitoring.

Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays.

Neural circuits consist of many noisy, slow components, with individual neurons subject to ion channel noise, axonal propagation delays, and unreliable and slow synaptic transmission. This raises a fundamental question: how can reliable computation emerge from such unreliable components? A classic strategy is to simply average over a population of N weakly-coupled neurons to achieve errors that scale as . But more interestingly, recent work has introduced networks of leaky integrate-and-fire (LIF) neurons that achieve coding errors that scale superclassically as 1/N by combining the principles of predictive coding and fast and tight inhibitory-excitatory balance. However, spike transmission delays preclude such fast inhibition, and computational studies have observed that such delays can cause pathological synchronization that in turn destroys superclassical coding performance. Intriguingly, it has also been observed in simulations that noise can actually improve coding performance, and that there exists some optimal level of noise that minimizes coding error. However, we lack a quantitative theory that describes this fascinating interplay between delays, noise and neural coding performance in spiking networks. In this work, we elucidate the mechanisms underpinning this beneficial role of noise by deriving analytical expressions for coding error as a function of spike propagation delay and noise levels in predictive coding tight-balance networks of LIF neurons. Furthermore, we compute the minimal coding error and the associated optimal noise level, finding that they grow as power-laws with the delay. Our analysis reveals quantitatively how optimal levels of noise can rescue neural coding performance in spiking neural networks with delays by preventing the build up of pathological synchrony without overwhelming the overall spiking dynamics. This analysis can serve as a foundation for the further study of precise computation in the presence of noise and delays in efficient spiking neural circuits.

Maximum likelihood separation of anthropogenic and wind-generated underwater noise.

A method is presented for simultaneous estimation of the probability distributions of both anthropogenic and wind-generated underwater noise power spectral density using only acoustic data recorded with a single hydrophone. Probability density models for both noise sources are suggested, and the model parameters are estimated using the method of maximum likelihood. A generic mixture model is utilized to model a time invariant anthropogenic noise distribution. Wind-generated noise is assumed normally distributed with a wind speed-dependent mean. The mean is then modeled as an affine linear function of the wind-generated noise level at a reference frequency, selected in a frequency range where the anthropogenic noise is less dominant. The method was used to successfully estimate the wind-generated noise spectra from ambient noise recordings collected at two locations in the southern Baltic Sea. At the North location, 3 km from the nearest shipping lane, the ship noise surpasses the wind-generated noise almost 100% of the time in the frequency band 63-400 Hz during summer for wind speed 7 m/s. At the South location, 14 km to the nearest shipping lane, the ship noise dominance is lower but still 40%-90% in the same frequencies and wind speed.

A machine learning approach to identify stochastic resonance in human perceptual thresholds

BackgroundStochastic resonance (SR) is achieved when a faint signal is improved with the addition of the appropriate amount of white noise. Perceptual thresholds are expected to follow a characteristic performance improvement curve as a function of the white noise level added (i.e., thresholds are reduced with an optimal amount of added white noise, beyond which excessive white noise is no longer beneficial). Since SR exhibition in perceptual thresholds is defined by a shape rather than a statistical difference, the presence of SR is typically identified qualitatively. The current state-of-the-art is for blinded human judges to categorize the presence of SR by visually examining data. While categorizations are made with subject data intermixed within a balanced, simulated dataset, which accounts for false positives, this method is still subjective and prone to human error. New methodWe use a logistic regression (LR) trained on engineered features in order to quantitatively classify exhibition of SR. The LR was trained on datasets simulated from a model for SR performance enhancement. ResultsWe implemented the algorithmic classification process in 6 perceptual threshold test cases, informed by the literature and parameters were defined by experimental subject data.Comparison to Existing Method(s): We report algorithmic classifications of SR exhibition, considering the 6 test cases, that outperform existing subjective methods in accuracy (p < 0.05). ConclusionsWe demonstrate that algorithmic classification can effectively identify SR in perceptual thresholds, providing a rigorous, objective, and quantitative approach to identifying the presence of SR.

Deep transfer learning for blended source identification in galaxy survey data

We present BLENDHUNTER, a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SEXTRACTOR) as a function of noise levels and the separation between sources. We find that BLENDHUNTERoutperforms SEP by ∼15% in terms of classification accuracy for close blends (&lt; 10 pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends (≥10 pixel separation between sources) provided the network is trained on data with noise that has a relatively close standard deviation to that of the target images. The code and data have been made publicly available to ensure the reproducibility of the results.

Analog Read Noise and Quantizer Threshold Estimation From Quanta Image Sensor Bit Density

Estimation of the analog read noise level and quantizer threshold level to within a few hundredths of an electron can be obtained by measuring the output bit density, D, of a single-bit quanta image sensor (1bQIS) as a function of quanta exposure, H. Analysis of the D-H characteristics as a function of read noise and quantizer threshold levels is performed and a procedure for extraction of estimated read noise and quantizer threshold is suggested and demonstrated.

Ensemble Learning Approach With Class Rotation for Three-Dimensional Classification on Direction-of-Arrival Estimation

Over the past decade, neural networks have been widely used for direction-of-arrival (DoA) estimation owing to their high accuracy in noisy and reverberant environments. Classes of singular-model classifiers generally correspond to discretized DoA candidate angles, which in the case of a three-dimensional estimation, are bounded by the grid derived from uniform sampling over the unit sphere. Motivated by this, we propose an ensemble learning approach for classification tasks to improve estimation accuracy, as the ensemble criterion outperforms the individual criterion. First, individual networks that make up the ensemble differ slightly by grid rotation according to the Euler rotation theorem to complement discrete directional information. Score fusion was performed in the spherical harmonic domain for a more stable ensemble classification because the grid rotation also involves an angular mismatch. Moreover, to achieve a more accurate DoA estimation, interpolation over the fused scores was performed. Performance analysis and a comparison of state-of-the-art parametric and deep learning-based methods in several acoustic situations were conducted to determine the accuracy of the ensemble and to analyze the gradual angular error reduction as a function of noise and reverberation levels as more networks were added.

Predicting the domain and nature of instabilities in charging and discharging Li batteries

A pattern of sequential group-by-group charging or discharging, of Li batteries with phase-separation thermodynamics, was detected by numerical simulations and justified by several experiments published in literature. The present work is the first to quantitatively predict the main characteristics of the sequential symmetry breaking (SB) events, such as the average Li concentration and the fraction of the Li-poor part at the SB transition times. Using a reduced two-zone (Li-poor and Li-rich) model, that ignores gradients in the liquid phase potential (ϕl), we derive an approximate solution predicting the effect of non-uniformity in initial conditions or in imposedparameter (collectively referred to as 'noise') on the characteristics. We show that gradients in ϕl, although extremely small, operate like noise leading to break-up of the homogeneous solution. A bifurcation map, showing the lowest current boundary that ensures a homogeneous lithiation/ delithiation as a function of noise level, is constructed.

Conversational distance adaptation in noise and its effect on signal-to-noise ratio in realistic listening environments.

Everyday environments impose acoustical conditions on speech communication that require interlocutors to adapt their behavior to be able to hear and to be heard. Past research has focused mainly on the adaptation of speech level, while few studies investigated how interlocutors adapt their conversational distance as a function of noise level. Similarly, no study tested the interaction between distance and speech level adaptation in noise. In the present study, participant pairs held natural conversations while binaurally listening to identical noise recordings of different realistic environments (range of 53-92 dB sound pressure level), using acoustically transparent headphones. Conversations were in standing or sitting (at a table) conditions. Interlocutor distances were tracked using wireless motion-capture equipment, which allowed subjects to move closer or farther from each other. The results show that talkers adapt their voices mainly according to the noise conditions and much less according to distance. Distance adaptation was highest in the standing condition. Consequently, mainly in the loudest environments, listeners were able to improve the signal-to-noise ratio (SNR) at the receiver location in the standing condition compared to the sitting condition, which became less negative. Analytical approximations are provided for the conversational distance as well as the receiver-related speech and SNR.

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions.

We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

Seasonal comparison of underwater ambient noise observed in the deep area of the South China Sea

Seasonal statistics on underwater ambient noise in the deep area of the South China Sea (SCS) were analyzed on the basis of the experimental observation data. The depth dependence, correlation with environmental parameters, empirical expression and probability density function (PDF) of noise levels (NL) in summer were compared with those in winter. Average of NLs at receiver depths shallower than 800 m in summer are 6 dB greater than those in winter at <400 Hz, and the difference between them are less than 2 dB at >1 kHz. A parameterized fitting model was utilized to determine the noise spectrum levels at different wind speed (WS) conditions and was compared with the Wenz curves, which are consistent with the NLs in winter at >wind force 3. The correlation coefficient between NLs and WS or significant wave height (SWH) were described according to the reanalysis database of the National Center for Environmental Prediction (NCEP) and WAVEWATCH III. In addition, a four-parameter logarithm model was used to model the relationship between NLs and WS or SWH. Empirical expression between NLs and spectrum levels at 1 kHz was acquired in SCS. The Weibull and Burr distributions were applied for the evaluation of PDF of NLs in summer and winter. Low-frequency NLs were dominated by distant shipping noise and their PDF satisfied the Burr distribution. High-frequency NLs were dominated by breaking waves or wind agitation and their PDF satisfied the Burr and Gaussian distributions simultaneously. The experimental observation duration was closely related to the PDF of high-frequency NLs. Standard deviation, skewness and kurtosis of NLs were also analyzed. Finally, frequency correlation and spectra correlation matrices were utilized in analyzing the noise sources mechanism, and discussing the reason for the statistical difference of NLs in summer and winter. The NL statistical differences was due to oceanic environment variation and noise source mechanism simultaneously.

Analysis for Error Patterns of Consonant-Vowel Combinations in Hearing-Impaired Elderly

Purpose: Many previous studies related to speech recognition of hearing-impaired elderly have found that the elderly have difficulty in monosyllable recognition under noisy situations, but there is still lack of their specific information to perceive the monosyllables in terms of error pattern. The purpose of the present study was to measure and analyze the prominent errors of consonant (C)- vowel (V) combinations as a function of noise levels in the hearing-impaired elderly. Methods: A total of twenty older adults with sensorineural hearing loss participated. While randomly presenting 122 syllables which consisted of the Korean CV and VC combinations under quiet and two signalto-noise ratios (SNRs, +6 and +3 dB) conditions, the participants were asked to respond the syllables heard in a way of either discrimination or identification. Error rate (or percent incorrect) and error patterns of the syllables were analyzed in each condition. Results: Substitution error of the initial consonant showed the highest rate regardless of the background conditions. As the medial phoneme, the vowel errors were decreased as the level of noise increased. In the final consonant, additional error rate of /k/ was abruptly increased as SNR decreased. Furthermore, it was an interesting pattern that the initial consonants such as /s/, /l/, and /t*/ had a high substitution error rate when combined with /i/, /o/, and /ɛ/ vowels, respectively. Conclusion: Based on the current results, the hearing-impaired older adults seem to have their unique error patterns although there is individual difference. We expect that these results will be fundamental data to understand characteristics of speech perception in the older adults at the monosyllables level.

On the feasibility of measuring physiologic and self-reported sleep disturbance by aircraft noise on a national scale: A pilot study around Atlanta airport

Aircraft noise can disturb sleep and impair recuperation. Research is needed to develop exposure-response relationships that are representative of noise-exposed communities and can be used to inform noise mitigation policy in the United States. For a national field study on physiologic response to aircraft noise during sleep to be feasible, an inexpensive yet sound study methodology is needed. In the pilot study presented here, the methodology of using electrocardiography and actigraphy to monitor sleep was implemented around Atlanta Hartsfield-Jackson International Airport (ATL). The primary objective was to evaluate the quality and quantity of data that could be obtained with the following study approaches: recruiting participants by postal questionnaire, shipping them the physiologic and noise measurement equipment, and the unattended setup of the equipment and recording of data by the participants themselves. The secondary objective was to compare objective and subjective measures of sleep and health between groups exposed to different levels of nocturnal aircraft noise. We mailed 4080 questionnaires containing items on sleep, health and noise disturbance to residences around ATL that were exposed to at least 35 dB Lnight aircraft noise. From 407 questionnaire respondents, 34 participants completed five nights of unattended sleep measurements. Data of sufficient quality and quantity to investigate the effects of aircraft noise on sleep were obtained. Self-reported awakenings increased as a function of the highest maximum aircraft noise level occurring during the sleep period. Event-related physiologic awakenings increased as a function of the maximum noise level of individual aircraft noise events, although this effect was of only borderline statistical significance (p = 0.057) likely due to the low sample size of this pilot study. The approach used in the presented pilot study has been demonstrated to be feasible for the purpose of the larger-scale study among a representative population around multiple airports in the future.

A noise correction of the γ-index method for Monte Carlo dose distribution comparison.

Due to the increasing complexity of IMRT/IMPT treatments, quality assurance (QA) is essential to verify the quality of the dose distribution actually delivered. In this context, Monte Carlo (MC) simulations are more and more often used to verify the accuracy of the treatment planning system (TPS). The most common method of dose comparison is the γ-test, which combines dose difference and distance-to-agreement (DTA) criteria. However, this method is known to be dependent on the noise level in dose distributions. We propose here a method to correct the bias of the γ passing rate (GPR) induced by MC noise. The GPR amplitude was studied as a function of the MC noise level. A model of this noise effect was mathematically derived. This model was then used to predict the time-consuming low-noise GPR by fitting multiple fast MC dose calculations. MC dose maps with a noise level between 2% and 20% were computed, and the GPR was predicted at a noise level of 0.3%. Due to the asymmetry of the γ-test, two different cases were considered: the MC dose was first set as reference dose, then as evaluated dose in the γ-test. Our method was applied on six proton therapy plans including analytical doses from the TPS or patient-specific QA measurements. An average absolute error of 4.31% was observed on the GPR computed for MC doses with 2% statistical noise. Our method was able to improve the accuracy of the gamma passing rate by up to 13%. The method was found especially efficient to correct the noise bias when the DTA criterion is low. We propose a method to enhance the γ-evaluation of a treatment plan when there is noise in one of the compared distributions. The method allows, in a tractable time, to detect the cases for which a correction is necessary and can improve the accuracy of the resulting passing rates.

Performance Evaluation of Visual Noise Imposed Stochastic Resonance Effect on Brain-Computer Interface Application: A Comparison Between Motion-Reversing Simple Ring and Complex Checkerboard Patterns.

Adding noise to a weak input signal can enhance the response of a non-linear system, a phenomenon known as stochastic resonance (SR). SR has been demonstrated in a variety of diverse sensory systems including the visual system, where visual noise enhances human motion perception and detection performance. The SR effect has not been extensively studied in brain-computer interface (BCI) applications. This study compares the performance of BCIs based on SR-influenced steady-state motion visual evoked potentials. Stimulation paradigms were used between a periodically monochromatic motion-reversing simple ring and complex alternating checkerboard stimuli. To induce the SR effect, dynamic visual noise was masked on both the periodic simple and complex stimuli. Offline results showed that the recognition accuracy of different stimulation targets followed an inverted U-shaped function of noise level, which is a hallmark of SR. With the optimal visual noise level, the proposed visual noise masked checkerboard BCI paradigm achieved faster and more stable detection performance due to the noise-enhanced brain responses. This work demonstrates that the SR effect can be employed in BCI applications and can achieve considerable performance improvements.

Contribution of random noise in the ITER RNC diamond neutron detectors pulses to the counting rate uncertainty

The main goal of the ITER Radial Neutron Camera (RNC) is to provide the plasma neutron emission profile (neutron emissivity) through measurement of the uncollided 14 MeV and 2.5 MeV neutrons from deuterium-tritium (DT) and deuterium-deuterium (DD) fusion reactions. The system is based on an array of detectors (diamonds are among the candidate detectors) located in collimated lines of sight (LOS) viewing the plasma through the ITER Equatorial Port Plug #1. Tomographic reconstruction techniques are used to calculate the neutron emissivity.The contribution of random noise on diamond detector pulses to the count rate uncertainty is investigated in this paper. The energy spectrum of the neutrons impinging on the detector has been calculated by means of MCNP calculations for one line of sight of the RNC In-Port system. The spectrum has been convoluted with the diamond detector response functions and the Pulse Height Spectrum (PHS) has been obtained. Experiments have been carried out with the CAEN™ 5810D detector emulator, that is able to produce arbitrary pulse height distributions as those expected in the ITER RNC. Random noise has been added on each detector pulse of the distribution using the detector emulator. The resulting uncertainty produced on the count rate has been evaluated as a function of the random noise level.

Analysis of switching times statistical distributions for perpendicular magnetic memories

Abstract Magnetization switching by injected spin-polarized current or applied field pulses is considered for perpendicular magnetic nanosystems with uniaxial symmetry and subject to thermal fluctuations. Such systems can model both perpendicular spin-transfer-torque magnetic random access memories and magnetic grains for state-of-the-art perpendicular recording. It is shown that, in the limit of high energy barriers and large current/field amplitudes, the probability and cumulative distribution functions of switching times can be analytically computed as function of material, geometrical parameters and thermal noise level. The theoretical predictions are confirmed by macrospin and full micromagnetic simulations. By using the derived formulas, it is shown that analysis of Write-Error Rates (WERs) at typical levels of reliability required by magnetic recording technology can be easily performed.

Determination of vibration amplitudes from binary phase patterns obtained by phase-shifting time-averaged speckle shearing interferometry.

Speckle shearing interferometry (shearography) is a full-field strain measurement technique that can be used in vibration analysis. In our case, we apply a method that combines the time-averaging and phase-shifting techniques. It produces binary phase patterns, where the phase changes are related to the zeroes of a Bessel J0 function, typical of time-averaging. However, the contrast and resolution are better compared to traditional time-averaging. In a previous paper, we have shown that this is particularly useful in vibration testing performed under industrial conditions, because fringe patterns are noisier than in quiet laboratory environments. This paper goes a step further in proposing a processing method for estimating the vibration amplitude, for helping non-experts to identify vibration modes. Since shearography measures the spatial derivative of displacement, spatial integration is required. Prior to that, different processes like denoising, binarization, automated nodal line detection, and amplitude assignment are applied. We analyze the performance of the method on synthetic and experimental data, in the function of noise level and fringes density. Results on data acquired in an industrial environment illustrate the good performances of the proposed method.

SMART (SiMulAtion and ReconsTruction) PET: an efficient PET simulation-reconstruction tool

BackgroundPositron-emission tomography (PET) simulators are frequently used for development and performance evaluation of segmentation methods or quantitative uptake metrics. To date, most PET simulation tools are based on Monte Carlo simulations, which are computationally demanding. Other analytical simulation tools lack the implementation of time of flight (TOF) or resolution modelling (RM). In this study, a fast and easy-to-use PET simulation-reconstruction package, SiMulAtion and ReconsTruction (SMART)-PET, is developed and validated, which includes both TOF and RM. SMART-PET, its documentation and instructions to calibrate the tool to a specific PET/CT system are available on Zenodo.SMART-PET allows the fast generation of 3D PET images. As input, it requires one image representing the activity distribution and one representing the corresponding CT image/attenuation map. It allows the user to adjust different parameters, such as reconstruction settings (TOF/RM), noise level or scan duration. Furthermore, a random spatial shift can be included, representing patient repositioning. To evaluate the tool, simulated images were compared with real scan data of the NEMA NU 2 image quality phantom. The scan was acquired as a 60-min list-mode scan and reconstructed with and without TOF and/or RM. For every reconstruction setting, ten statistically equivalent images, representing 30, 60, 120 and 300 s scan duration, were generated. Simulated and real-scan data were compared regarding coefficient of variation in the phantom background and activity recovery coefficients (RCs) of the spheres. Furthermore, standard deviation images of each of the ten statistically equivalent images were compared.ResultsSMART-PET produces images comparable to actual phantom data. The image characteristics of simulated and real PET images varied in similar ways as function of reconstruction protocols and noise levels. The change in image noise with variation of simulated TOF settings followed the theoretically expected behaviour. RC as function of sphere size agreed within 0.3–11% between simulated and actual phantom data.ConclusionsSMART-PET allows for rapid and easy simulation of PET data. The user can change various acquisition and reconstruction settings (including RM and TOF) and noise levels. The images obtained show similar image characteristics as those seen in actual phantom data.