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The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

The main focus of this paper is the separation of underdetermined convolutive blind speech in a multi-speaker environment. We present a method based on mask prediction in the time-frequency domain. Firstly, depending on the sparsity of signals in the time-frequency (TF) domain, we extimate speakers’ masks by clustering the relative absolute and Hermitian angle features extracted from the frequency components of the mixtures. Speech separation algorithms that are based on the sparsity and disjoint orthogonality of the speech signals in the time-frequency domain are not efficient when more than one source is active. Hence, in this paper, the cluster centers are estimated mostly based on the TF units that probably have only one active source. The correlations between the estimated masks, belonging to adjacent frequency bins, are leveraged to solve the permutation problem. To increase the accuracy, we have zeroed the value of masks at the TF unit without any active source. Moreover, in clustering, we employ a weighting function to consider the parts of masks that probably contains just one active source. Finally, in order to decrease the musical noise of the separated signals and improve their quality, sparse filters in the time-domain are utilized to re-estimate the separated signals. Performance of the proposed method is evaluated by a number of simulated and real speech signals. The simulated experiments were performed using a public dataset and Roomsim simulator. Compared the proposed method with some conventional algorithms, we observed that our separation method is more accurate than other approaches.

$\ell _{2,1}$ -norm penalized compressive sensing (CS) is utilized to improve the performance of DOA estimation with small snapshots recently. However, the existing CS-based methods are not robust to the noise. In this article, we propose a CS-based DOA estimation using a novel weighted $\ell _{2,1}$ -norm penalty. A spatial filter which can roughly “clean up” or eliminate the signals coming from the directions of the true sources is constructed. Thus, the space spectrum of the spatial filter can work as a weighting matrix to adjust the sparse penalty automatically. A new weighted $\ell _{2,1}$ -norm penalty based on this spatial filter is then proposed for DOA estimation. Simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Monte Carlo integration is an efficient method to solve a high-dimensional integral in light transport simulation, but it typically produces noisy images due to its stochastic nature. Many existing methods, such as image denoising and gradient-domain reconstruction, aim to mitigate this noise by introducing some form of correlation among pixels. While those existing methods reduce noise, they are known to still suffer from method-specific residual noise or systematic errors. We propose a unified framework that reduces such remaining errors. Our framework takes a pair of images, one with independent estimates, and the other with the corresponding correlated estimates. Correlated pixel estimates are generated by various existing methods such as denoising and gradient-domain rendering. Our framework then combines the two images via a novel combination kernel. We model our combination kernel as a weighting function with a deep neural network that exploits the correlation among pixel estimates. To improve the robustness of our framework for outliers, we additionally propose an extension to handle multiple image buffers. The results demonstrate that our unified framework can successfully reduce the error of existing methods while treating them as black-boxes.

Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults.

Seismic interpolation can provide complete data for some multichannel processing techniques such as time lapse imaging and wave equation migration. However, field seismic data often contains random noise and noisy data interpolation is a challenging task. A traditional method applies interpolation and denoising separately, but this needs two workflows. Simultaneous interpolation and denoising combines interpolation and denoising in one workflow and can also get acceptable results. Most existing interpolation methods can only recover missing traces but fail to attenuate noise in sampled traces. In this study, a novel thresholding strategy is proposed to remove the noise in the sampled traces and meanwhile recover missing traces during interpolation. For each iteration, the residual is multiplied by a weighting factor and then added to the iterative solution, after which the sum in the transformed domain is calculated using the thresholding operation to update the iterative solution. To ensure that the interpolation and denoising results are robust, the exponential method was chosen to reduce the threshold values in small quantities. The curvelet transform was used as sparse representation and three interpolation methods were chosen as benchmarks. Three numerical tests results proved the effectiveness of the proposed method on removing noise in the sampled traces when the minimum threshold values are correctly chosen.

The generalized cross correlation (GCC) is an efficient technique for performing acoustic imaging. However, it suffers from important limitations such as a large main lobe width for noise sources with low frequency content or a high amplitude of side lobes for noise sources with high frequencies. Prefiltering operation of the microphone signals by a weighting function can be used to improve the acoustic image. In this work, two weighting functions based on PHAse Transform (PHAT) improvements are used. The first adds an exponent to the PHAT expression (ρ-PHAT), while the second adds the minimum value of the coherence function to the denominator (ρ-PHAT-C). Numerical acoustic images obtained with the GCC and those weighting functions are compared and quantitatively assessed thanks to a metric based on a covariance ellipse, which surrounds either the main lobe or the side lobes. The weighting function ρ-PHAT-C provides the smallest surface ellipses especially when the arithmetic of the GCC is replaced by the geometric mean (GEO). Experimental measurements are carried out in a hemi-anechoic room and a reverberant chamber where two loudspeakers were set in front of microphone array. The acoustic images obtained confirm that the ρ-PHAT-C with the GEO outperforms the GCC, GCC-PHAT, and GCC ρ-PHAT.

This paper presents a new A-weighting filter&rsquo, s design and explores the potential of using approximate multiplication for low-power digital A-weighting filter implementation. It presents a thorough analysis of the effects of approximate multiplication, coefficient quantization, the order of first-order sections in the filter&rsquo, s cascade, and zero-pole pairings on the frequency response of the digital A-weighting filter. The proposed A-weighting filter was implemented as a sixth-order IIR filter using approximate odd radix-4 multipliers. The proposed filter was synthesized (Verilog to GDS) using the Nangate45 cell library, and MATLAB simulations were performed to verify the designed filter&rsquo, s magnitude response and performance. Synthesis results indicate that the proposed design achieves nearly 70% reduction in energy (power-delay product) with a negligible deviation of the frequency response from the floating-point implementation. Experiments on acoustic noise suggest that the proposed digital A-weighting filter can be deployed in environmental noise measurement applications without any notable performance degradation.

Learning with labels noise has gained significant traction recently due to the sensitivity of deep neural networks under label noise under common loss functions. Losses that are theoretically robust to label noise, however, often makes training difficult. Consequently, several recently proposed methods, such as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to learn a weighting function that downweights samples that are likely to have corrupted labels under the meta-learning framework. However, obtaining such a set of clean samples is not always feasible in practice. In this paper, we analytically show that one can easily train MW-Net without access to clean samples simply by using a loss function that is robust to label noise, such as mean absolute error, as the meta objective to train the weighting network. We experimentally show that our method beats all existing methods that do not use clean samples and performs on-par with methods that use gold samples on benchmark datasets across various noise types and noise rates.

This paper presents new closed-form estimators for 3-D source localization using Doppler measurements collected by a moving sensor platform. Two estimators, based on bias compensation and instrumental variables, are developed with significant performance improvement over a previously proposed least-squares estimator (LSE). First, a refined variant of the LSE, called bias-compensated weighted LSE (BCWLSE), is presented utilizing a weighting matrix and a bias compensation step. A weighted instrumental variable estimator is then developed based on a modified pseudolinear equation for the LSE to avoid pseudolinear noise amplification that may deteriorate the BCWLSE performance at large noise. Comparative simulation studies are presented to demonstrate the performance advantages of the proposed estimators over the severely biased LSE and divergence-prone maximum likelihood estimator. The proposed estimators are observed to produce almost no bias and achieve a mean-squared-error performance close to the Cramer–Rao lower bound.

Augmented reality-based constructive jaw surgery has been facing various limitations such as noise in real-time images, the navigational error of implants and jaw, image overlay error, and occlusion handling which have limited the implementation of augmented reality (AR) in corrective jaw surgery. This research aimed to improve the navigational accuracy, through noise and occlusion removal, during positioning of an implant in relation to the jaw bone to be cut or drilled. The proposed system consists of a weighting-based de-noising filter and depth mapping-based occlusion removal for removing any occluded object such as surgical tools, the surgeon’s body parts, and blood. The maxillary (upper jaw) and mandibular (lower jaw) jaw bone sample results show that the proposed method can achieve the image overlay error (video accuracy) of 0.23~0.35 mm and processing time of 8–12 frames per second compared to 0.35~0.45 mm and 6–11 frames per second by the existing best system. The proposed system concentrates on removing the noise from the real-time video frame and the occlusion. Thus, the acceptable range of accuracy and the processing time are provided by this study for surgeons for carrying out a smooth surgical flow.

In order to predict and study the sound quality of automobile exhaust noise, Zwicker steady-state and time-varying method were applied to calculate the psychoacoustic objective parameter values in terms of the exhaust noise of sample cars at uniform velocity and accelerated velocity; Thereby, a prediction model of GA-BP sound quality based on psychoacoustic objective parameters was established. At the same time, wavelet analysis was used to decompose the accelerated signal; in order to overcome the shortcomings such as Heisenberg uncertainty, the RNR (regularization nonstationary regression technique) was applied to compute the WVD distribution (RNR-WVD), therefrom obtaining the coefficient matrices of different-band signals after wavelet decomposition, and then A weighting was carried out on the coefficient matrices, so as to establish a new sound quality parameter SQP-WRW (sound quality parameter base on wavelet and then proceed to RNR-WVD) as the input of GA-BP model, and therefrom a sound quality prediction model was established. The results indicate that the model based on SQP-WRW has higher precision for predicting the sound quality of acceleration signal, and it can better reflect the characteristics of acceleration signal and sound quality.

The increasing demand for spectral and energy efficient communication networks has drawn an upsurge of interest in frequency (RF) energy harvesting (EH) cognitive radio networks (CRNs), and the spectrum analyzer is the brain to provide different spectral access and energy harvesting opportunities in wireless-powered cognitive radio networks (WP-CRNs). The performance of detecting the existence of primary signals will be more crucial for the EH node. However, to the best of our knowledge, no dynamic threshold-setting method to achieve target detection performance, particularly in the practical non-Gaussian noise, is reported in the literature. Traditional assumption of additive white Gaussian noise (AWGN) fails to model the behavior of certain types of noise found in practice. In this paper we give a dynamic threshold-setting method to detect the existence of primary signals for the energy harvesting node in non-Gaussian noise background. Combined with the fractional lower order statistics (FLOS), we propose a weighting factor for dynamic threshold-setting to exploit the existence of the PU to harvest RF energy, namely weighting factor based fractional lower order moment (WF-FLOM) detector. Numerical simulation results verify that the proposed method is effective for the EH node, when the signal-to-noise ratio is low or the back ground noise varies.

Recently, a new complementary joint sparse representations (CJSR) method was proposed for monaural speech enhancement, which utilizes the relationships among speech, noise and mixture. One of the joint sparse representations (JSRs) uses the mapping relationship between mixture and speech while the other uses the mapping relationship between mixture and noise. However, since they only use the joint information and overcomplete dictionaries, there may be some confusion components between the estimated speech and noise. In this paper, a novel model with fusion process is proposed, which further using an additional composite dictionary composed of clean speech dictionary and noise dictionary as prior knowledge. When the estimated speech and noise are obtained by JSRs, they are further sparsely represented on the composite dictionary, respectively. Because that the composite dictionary has the discriminative property, the source confusion problem of previous methods is coped well. In order to take advantage of the complementary knowledge of the two estimated signals, we propose a weighting parameter based on the residuals of sparse representation on composite dictionary. If one of the residuals is smaller, the corresponding weighting is greater, and this weighting parameter varies with noise type and speaker flexibly and effectively. Experimental results show that the proposed algorithm has superior performance compared with other tested approaches.

In this paper, a blind phase noise estimation method for a coherent optical orthogonal frequency-division multiplexing (CO-OFDM) system is considered. Based on the subspace approach, a simple and robust method is proposed to adaptively estimate and track phase noise in CO-OFDM systems. The idea behind this novel technique is to estimate the singular vectors that correspond to the smallest singular values of the noise subspace. A weighting parameter, which is derived based on the forward–backward linear prediction technique, is subsequently constructed using the obtained singular vectors of the noise subspace to adaptively estimate the phase noise in the system. Also, a variable step size is introduced to ensure an improved performance as well as stable convergence. Simulation results are shown to demonstrate the effectiveness as well as the efficiency of the proposed algorithm, which is implemented in a polarization multiplexed CO-OFDM system stressed by polarization mode dispersion, chromatic dispersion, and other polarization-dependent losses along the fiber link.

In many chemical engineering applications, it is often difficult to get accurate first-principle models because of complexity of modern processes. Even if it is possible to do so, it is often time consuming and computationally expensive. Hence, there is a growing need to develop data-driven models. Gaussian process regression (GPR) model has been extensively applied in data-based modelling due to its good adaptability to deal with high dimensional, small samples, and nonlinear problems. The standard GPR algorithm assumes constant noise power throughout the sampling process. However, in process systems, the observation noise often varies so that different sample points are corrupted by different degrees of noise. Under these circumstances, the standard GPR algorithm may not work properly. To model Gaussian process with heteroscedastic noise, this paper introduces a weighting strategy into the standard GPR algorithm, and proposes three weighted GPR algorithms: the clustered GPR (C-GPR) algorithm, the partial weighted GPR (PW-GPR) algorithm and the weighted GPR (W-GPR) algorithm. Different from the standard GPR algorithm, three weighted algorithms put the weight on sampled data by calculating the noise variance for each data point. In addition, in order to optimize the proposed algorithms, this paper utilizes the particle swarm optimization (PSO) algorithm to estimate hyper-parameters of the GPR model, instead of using the traditional conjugate gradient (CG) method. The effectiveness of the three weighted GPR algorithms is verified by means of two numerical examples and a wet spinning coagulation process. Extensive simulation results demonstrate that the proposed algorithms optimized by the PSO algorithm can improve prediction accuracy of the GPR model.

To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.

Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.

The investigated method enables precise shaping of acoustic radiation of a vibrating plate, i.e. it allows one to relocate or create resonances and anti-resonances for selected frequencies, simultaneously altering their acoustic radiation efficiency in a desired manner. The method can be very beneficial for plates used as noise barriers, both in passive and active applications. The acoustic radiation shaping method involves mounting several additional ribs and masses to the plate surface at locations followed from an optimization process (sensors and actuators can also be included, if active control is considered). The optimization process requires a model of the vibroacoustic system, a cost function corresponding to the considered objective, and an optimization algorithm. In this paper, an introduction of A-weighting to the cost functions, which reflects a human perception of the noise radiated by or transmitted through the plate, is investigated. It follows from the analysis of obtained results that the introduction of A-weighting can provide even 8 dBA better passive noise attenuation.

Continuous recognition of ingested foods without user intervention is very useful for the pre-screening of obesity and diet-related disease. An automatic food recognition method that combines the two modalities of audio and ultrasonic signals (US) is proposed in this study. Under a noise-free environment, classification accuracy of an audio-only recognizer is generally higher than that of US-only recognizers, but the performance of US recognizers is unaffected by acoustic noise levels. In the recognition system presented herein, the likelihood score of the audio-US feature was given by a linear combination of class-conditional observation log-likelihoods for two classifiers, using the appropriate weights. We developed a weighting process adaptive to signal-to-noise ratios (SNRs). The main objective here involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. A feasibility test was conducted to verify the usefulness of the proposed method by conducting recognition experiments on seven types of food. The performance was compared with conventional methods that use in-ear and throat microphones. The proposed method yielded remarkable levels of recognition performance of 90.13% for artificially added noise and 89.67% under actual noisy environments, when the SNR ranged from 0 to 20 dB.

The search of heavy atoms is crucial to the de novo determination of protein structures. Typically, the difference Patterson map is calculated as a first step to solve substructure. However, the pseudo-peaks and noises inherent in such maps arising from the high symmetry and large size of protein structures accompanied with the data collection errors inevitably pose a challenge in accurate real space-based substructure determination. In order to mitigate such pseudo-peaks and noises and further improve signal-to-noise ratio (SNR) of the difference Patterson map, the noise and artifact suppression using resampling (NASR) method originally proposed in nuclear magnetic resonance is introduced into protein crystallography in this work to optimize the difference Patterson map. The NASR method makes use of the statistical learning theory, which in this work repeatedly samples a fixed portion of diffraction data (sub-dataset) randomly followed by a statistical analysis of the multiple calculated difference Patterson maps to discard pseudo-peaks and noises. Its feasibility is based on the fact that the true vector peaks of the heavy atoms keep static in the multiple random sub-datasets, whereas the pseudo-peaks and noises fluctuate remarkably. And the key of this method lies in the design of a weighting function to distinguish true vector peaks from pseudo-peaks and noises, as well as a proper selection of the parameters associated with the function. The introduced NASR method is both numerically and experimentally demonstrated to be feasible in suppressing spurious peaks and non-correlative noises intrinsic to the difference Patterson maps. As a result, the SNR of the difference Patterson maps can be enhanced to some extent to facilitate real space-based substructure determination. It is therefore anticipated that the proposed method may provide a meaningful insight into how to denoise the difference Patterson maps, which in turn assists in locating heavy atoms and further facilitates de novo protein structure determination.

We introduce a robust k-means-based clustering method for high-dimensional data where not only outliers but also a large number of noise variables are very likely to be present [4]. Although Kondo et al. [2] already addressed such an application scenario, our approach goes even further. Firstly, the introduced method is designed to identify clusters, informative variables, and outliers simultaneously. Secondly, the proposed clustering technique additionally aims at optimizing required parameters, e.g. the number of clusters. This is a great advantage over most existing methods. Moreover, the robustness aspect is achieved through a robust initialization [3] and a proposed weighting function using the Local Outlier Factor [1]. The weighting function provides a valuable source of information about the outlyingness of each observation for a subsequent outlier detection. In order to reveal both clusters and informative variables properly, the approach uses a lasso-type penalty [5]. The method has thoroughly been tested on simulated as well as on real high-dimensional datasets. The conducted experiments demonstrated a great ability of the clustering method to identify clusters, outliers, and informative variables.

CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: In its basic version, it does not have the concept of diffuse flux, and the common practice of convolving the CLEAN components with the CLEAN beam erases the potential for super-resolution; it does not output uncertainty information; it produces images with unphysical negative flux regions; and its results are highly dependent on the so-called weighting scheme as well as on any human choice of CLEAN masks for guiding the imaging. Here, we present the Bayesian imaging algorithm resolve , which solves the above problems and naturally leads to super-resolution. We take a VLA observation of Cygnus A at four different frequencies and image it with single-scale CLEAN, multi-scale CLEAN, and resolve. Alongside the sky brightness distribution, resolve estimates a baseline-dependent correction function for the noise budget, the Bayesian equivalent of a weighting scheme. We report noise correction factors between 0.4 and 429. The enhancements achieved by resolve come at the cost of higher computational effort.

Objective To evaluate the noise level in adult, pediatric and neonatal intensive care units of a university hospital in the city of Santa Marta (Colombia). Design A descriptive, observational, non-interventional study with follow-up over time was carried out. Materials and methods Continuous sampling was conducted for 20 days for each unit using a type i sound level meter, filter frequency in A weighting and Fast mode. We recorded the maximum values, the 90th percentile as background noise, and the continuous noise level. Results The mean hourly levels in the adult unit varied between 57.40 ± 1.14 and 63.47 ± 2.13 dBA, with a maximum between 71.55 ± 2.32 and 77.22 ± 1.94 dBA, and a background noise between 53.51 ± 1.16 and 60.26 ± 2.10 dBA; in the pediatric unit the mean hourly levels varied between 57.07 ± 3.07 and 65.72 ± 2.46 dBA, with a maximum of 68.69 ± 3.57–79.06 ± 2.34 dBA, and a background noise between 53.33 ± 3.54 and 61.96 ± 2.85 dBA; the neonatal unit in turn presented mean hourly values between 59.54 ± 2.41–65.33 ± 1.77 dBA, with a maximum value between 67.20 ± 2.13 and 77.65 ± 3.74 dBA, and a background noise between 55.02 ± 2.03 and 58.70 ± 1.95 dBA. Analysis of variance revealed a significant difference between the hourly values and between the different units, with the time of day exhibiting a greater influence. Conclusions The type of unit affects the noise levels in intensive care units, the pediatric unit showing the highest values and the adult unit the lowest values. However, the parameter exerting the greatest influence upon noise level is the time of day, with higher levels in the morning and evening, and lower levels at night and in the early morning.

Low frequency noise is one of the most harmful factors occurring in human working and living environment. Low frequency noise components from 20 to 250 Hz are often the cause of employee complaints. Noise from power stations is an actual problem for large cities, including Cairo. The noise from equipments of station could be a serious problem for station and for environmental area. The development of power stations in Cairo leads to appearing a wide range of gas turbines which are strong source of noise. Two measurement techniques using C-weighted along side the A-weighted scale are explored. C-weighting is far more sensitive to detect low frequency sound. Spectrum analysis in the low frequency range is done in order to identify a significant tonal component. Field studies were supported by a questionnaire to determine whether sociological or other factors might influence the results by using annoyance rating mean value. Subjects included in the study were 153 (mean = 36.86, SD = 8.49) male employees at the three electrical power stations. The (C-A) level difference is an appropriate metric for indicating a potential low frequency noise problem. A-weighting characteristics seem to be able to predict quite accurately annoyance experienced from LFN at workplaces. The aim of the present study is to find simple and reliable method for assessing low frequency noise in occupational environment to prevent its effects on work performance for the workers. The proposed method has to be compared with European methods.

Noise-induced hearing loss (NIHL) still remains as a serious occupational related health problem worldwide. A-weighted equivalent sound pressure level (SPL) LAeq has been widely used to assess the auditory risk of occupational noises in noise measurements standards. In addition, C-weighting is also used in the standards for detection of peak SPL of noise. However, both A-weighting and C-weighting have limitations on evaluation of high-level complex noise, which is often experienced in many military and industrial fields. In this study, we proposed a new adaptive weighting (F-weighting) for more accurate evaluation of complex noises. F-weighting is based on the blending of A-weighting and C-weighting through the weighting coefficients αA,T and αC,T. To determine αA,T and αC,T, two parameters, kurtosis (KT) and oscillation coefficient (OT) were introduced. Complex noise exposures in animal studies and noise signals measured in a mining facility were applied to validate the performance of F-weighting. The results show that F-weighting performs better than both A-weighting and C-weighting on the assessment of high-level complex noise. In addition, F-weighting based LFeq shows higher correlation with the hearing loss of the animal experimental data compared with A-weighted LAeq, C-weighted LCeq, and Non-weighted Leq. The proposed F-weighting could be a potential alternative weighting for the assessment of high-level complex noise in military and industrial applications.

A weighting function formulated from polynomial least squares (LS) is combined with statistical estimation theory to produce the statistical estimate, expected value, and variance of arbitrary points on the LS estimated trajectory polynomial from samples of the polynomial corrupted with statistical observation noise. The statistical noise variance is estimated as the sample average of the deviations of noisy samples from the estimated polynomial. This approach obviates the need to assume observation and/or state noises. Although state noise in the Kalman filter (KF) is actually fictitious and not really a noise at all but merely a threshold, such noise can actually exist but is not taken into accounted by Monte Carlo simulations of the KF. It along with all other unknown and unspecified noises (atmospheric, interference, etc.) that do exist are accounted for in the sample average. Perhaps most important is the estimated tracking variance at arbitrary points on the estimated polynomial. It is minimum at the centroid of horizontal axis samples and increases as the distance squared of a point from that centroid along the horizontal axis. Moreover, the weighting function solves the problem that existed in LS invented by Gauss two and a quarter centuries ago: The absence of the statistical tracking variance at arbitrary points on estimated polynomials. An example demonstrates the tracking variance at four points on an estimated polynomial: the centroid, last sample location, next sample location, and the point where the tracking variance exceeds the noise sample variance.

In this paper, we focus on the metrological aspects of spectroscopic Mueller ellipsometry—i.e. on the uncertainty estimation of the measurement results. With the help of simulated Mueller matrices, we demonstrate that the commonly used merit functions do not return the correct uncertainty for the measurand under consideration (here shown for the relatively simple case of the geometrical parameter layer thickness for the example system of a SiO2 layer on a Si substrate). We identify the non-optimal treatment of measured and sample- induced depolarization as a reason of this discrepancy. Since depolarization results from sample properties in combination with experimental parameters, it must not be minimized during the parameter fit. Therefore, we propose a new merit function treating this issue differently: It implicitly uses the measured depolarization as a weighting parameter. It is very simple and computationally cheap. It compares for each wavelength the measured Jones matrix elements to Cloude’s covariance matrix: ∼ ∑ λ j sim, λ † H meas, λ + j sim, λ . Moreover, an extension will be presented which allows us to include the measurement noise into this merit function. With this, reliable statistical uncertainties can be calculated. Except for some pre-processing of the raw data, there is no additional computational cost.

The conventional Active noise control (ANC) system obtains the greatest noise reduction around the error microphone and minimizes the mean square error signal. However, the impression of auditory sensation may sometimes be less than the numerical value when a person actually listens to noise with ANC. This is because of the complicated characteristic of psychoacoustic properties. The noise control filter optimized based on the mean square error (conventional ANC) does not necessarily give optimal results for the human auditory system. To mitigate this problem, ANC systems considering psychoacoustic properties have been proposed and their effectiveness has also been demonstrated for magnetic resonance imaging (MRI) noise and synthesized random noise. In this ANC system, noise weighting is incorporated into the conventional ANC structure. In this paper, we examine the effectiveness of the ANC system considering psychoacoustic properties based on A weighting and lTV-R 468 noise weighting for nonstationary noise through some experimental results and a subjective evaluation.

Susceptibility weighted imaging (SWI) involves post-processing of gradient echo images which are sensitive to the spatial variations in magnetic susceptibility. The aim of this study is to develop an automated filtering scheme to enhance the contrast-to-noise ratio (CNR) and blooming on SWI. Here, the high-pass filtering for SWI processing is designed by applying a weighting function to the neighboring phase differences to enhance the susceptibility-related (SuR) contrast. This is accomplished by summing the neighboring phase differences, weighted with a scaled and shifted error function of the phase difference. Besides using the filter weights of this weighted high-pass (WHP) filter to minimize the filtering artefacts using a filter scale parameter, the CNR is further increased by introduction of the neighborhood-based noise compensation weights into the filtering process. These weights are deduced from the channel phase distribution, conditioned on the channel magnitude and noise variance. Using in vivo SWI data acquired at 1.5 T (16 nos.) and 3.0 T (30 nos.), the magnitude SWI processed using the noise compensated WHP (WHPC) filter is shown to provide an average CNR improvement of 68.40% over that of a homodyne high-pass (HHP) filter. Two tailed t-tests performed separately for different field strengths, show significant differences (p 0.001) between mean separations of phase masks generated from the WHPC and HHP filtered phase images. In conclusion, the WHPC filter, tuned by the mean separation of the phase mask, enhances the SuR contrast of magnitude SWI for evaluation of mild cognitive impairments, brain tumor and hemorrhagic stroke.

Purpose The purpose of this study was to develop a novel patient-specific pixel-based weighting factor dual-energy (PP-DE) algorithm to effectively suppress bone throughout the image and overcome the limitation of the conventional DE algorithm with constant weighting factor which is restricted to regions with uniform patient thickness. Additionally, to derive theoretical expressions to describe the dependence of the weighting factors on several imaging parameters and validate them with measurement. Methods A step phantom was constructed consisting of slabs of solid water and bone materials. Thicknesses of bone ranged [0-6] cm in one direction and solid water [5-30] cm in the other direction. Projection images at 60 and 140 kVp were acquired using a clinical imaging system. Optimal weighting factors were found by iteratively varying it in the range [0-1.4], where bone and soft-tissue contrast-to-noise ratio (CNR) reached zero. Bone and soft-tissue digitally reconstructed thicknesses were created using computed tomography (CT) images of a Rando phantom and ray tracing techniques. A weighting factor image (ω) was calculated using digitally reconstructed thicknesses (DRTs) and precalculated weighting factors from the step phantom. This ω image was then used to generate a PP-DE image. The PP-DE image was compared to the conventional DE image which uses a constant weighting factor throughout the image. The effect of the misaligned ω image on PP-DE images was investigated by acquiring LE and HE images at various shifts of Rando phantom. A rigid registration was used based on mutual information algorithm in Matlab. The signal-to-noise ratios (SNR) were calculated in the step phantom for the PP-DE image and compared to that of conventional DE technique. Analytical expressions for theoretical weighting factors were derived which included various effects such as beam hardening, scatter, and detector response. The analytical expressions were simulated in Spektr3.0 for different bone and solid water thicknesses as per the step phantom. A tray of steel pins was constructed and used with the step phantom to remove the scattered radiation. The simulated theoretical weighting factors were validated by comparing to those from the step phantom measurement. Results Optimal weighting factor values for the step phantom varied from 0.633 to 1.372 depending on region thickness. Thicker regions required larger weighting factors for bone cancellation. The PP-DE image of the Rando phantom favorably cancelled both ribs and spine, whereas in the conventional DE image, only one could be cancelled at a time. The misaligned ω image was less effective in removing all bones indicating the importance of alignment as part of the PP-DE algorithm implementation. The SNRs for the PP-DE image was larger than those of the conventional DE images for regions which required smaller weighting factors for bone suppression. Comparisons of measured and simulated weighting factors demonstrated a 3% agreement for all bone overlapped regions except for the thickest region with 30 cm of solid water overlapped with 6 cm bone where the signal was lost due to excess attenuation. Conclusions A novel PP-DE algorithm was developed which can create higher quality DE images with enhanced bone cancellation and improved noise characteristics compared to conventional DE technique. In addition, theoretical weighting factor expressions were derived and validated against measurement.

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.

I explore the possibility of resurrecting an old, non-Bayesian computational approach for inferring the source direction of a gravitational wave from the output of a two-detector network. The method gives the beam pattern response functions and time delay, and performs well even in the presence of noise and unexpected signal forms. I further suggest an improvement to this method in the form of a weighting algorithm that usefully improves its accuracy beyond what can be achieved with simple best-fit methods, validating the new procedure with several small-scale simulations. The approach is identified as complimentary to -- rather than in competition with -- the now-standard Bayesian approach typically used by the LIGO network in parameter determination. Finally, I briefly discuss the possible applications of this method in the world of three-or-more detector networks and some directions for future work., Comment: 13 pages, 2 figures, to appear in European Physical Journal Plus

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Bimodal tomography introduces a weighting factor $\alpha$ to incorporate X-ray data into projection images acquired from scanning transmission electron microscope (STEM) for achieving an atom-specific three-dimensional (3D) reconstruction of an object on the nanoscale. Currently its value is chosen by computing reconstructions for a large range of $\alpha\in(0,1)$ and comparing them to a hand-segmented ground truth with the mean square error (MSE). Since this is infeasible for an industrial application, in this paper we propose an image quality metric to quantify the quality of tomograms in terms of cross-atomic contamination and noise for selecting the weighting factor without a ground truth. Numerical results demonstrate that our framework can determine close-to-optimal weighting factor within an accuracy of $\pm 0.03$ . Moreover, approximating the shape of the minimum by a parabola effectively reduces the computational time by 90%.

Noise is considered as the most pervasive pollutant, it constitutes the most disturbing among the city pollutants and is a serious urban crisis in Ado Ekiti metropolis. Noise pollution was monitored at five different locations in Ado-Ekiti, the capital town of Ekiti State of Nigeria. The study was done using a Virtual Instrument; a Sound Pressure Level mobile application installed on a mobile phone. The equivalent noise level using A-weighting was taken every fifteen minutes in each location between 6:00 am and 9:00 pm and the LAeq,T of each location was calculated. Maximum LAeq,6h of 105.40 dB was obtained in marketplace between 6:00 am and 12:00 pm while the minimum value of LAeq,6h obtained for a residential area in the same period is 69.51 dBA. The residents are exposed to average noise levels of 75 - 98 dB (A) or more every day which is higher than the recommended value of 60 dBA by WHO. This is very dangerous to the health of the people in these areas, and the results obtained in this study are useful as reference and guideline for future regulations on noise limit to be implemented for urban areas in Nigeria.

This paper deals with active noise control (ANC) in an enclosure on a broadband frequency range. It aims to compare two control strategies: (i) an adaptive control approach namely FxLMS control, (ii) a robust H∞ control, in the SISO (single input single output) case. Only feedback scheme is considered and disturbance sources are assumed to be unknown. For the aim of a fair comparison focusing on the same frequency range of attenuation, a new FxLMS algorithm with a weighting filter is proposed. Finally, achievable performances of each control are analyzed through simulation and experimental results driven on an instrumented setup.

In this paper, we propose an active contour model and its corresponding algorithms with detailed implementation for image segmentation. In the proposed model, the local and global region fitting energies are described by the combination of the local and global Gaussian distributions with different means and variances, respectively. In this combination, we increase a weighting coefficient by which we can adjust the ratio between the local and global region fitting energies. Then we present an algorithm for implementing the proposed model directly. Considering that, in practice, the selection of the weighting coefficient is troublesome, we present a modified algorithm in order to overcome this problem and increase the flexibility. By adaptively updating the weighting coefficient and the time step with the contour evolution, this algorithm is less sensitive to the initialization of the contour and can speed up the convergence rate. Besides, it is robust to the noise and can be used to extract the desired objects. Experiment results demonstrate that the proposed model and its algorithms are effective with application to both the synthetic and real-world images.

This paper presents an evaluation on elastomeric mount used to isolate vibration from powertrain to chassis or structure vehicle. The assessments started with measurement of noise inside compartment, and exhaust noise. This is followed by the measurement of vibration on both sides of elastomeric mounts. The noise in the compartment and exhaust noise is measured according to BS 6086: 1981 and BS ISO 5130: 2007. The noise in the compartment and vibration is tested in three conditions. Firstly, engine is run-up with load (driving at second gear); secondly, without load; and thirdly, without load but hanging. A microphone is fixed at the ear of the mannequin. The fast response and A weighting sound level meter were used for measurement noise in the compartment and exhaust noise. The vibration is measured in terms of acceleration on both sides of each elastomeric powertrain mounts. Two accelerometer transducers are fixed on both sides of powertrain elastomeric mounts. One side was identified as a source of vibration and the other as receiver of vibrations. The results showed that the pattern of overall vibration level on source and receiver increased from 1050 RPM (idling) to 4000 RPM on all test conditions. Vibration transmitted to chassis or receiver structure was analyzed using transmissibility concept. By evaluating test condition of engine run-up without load, informed that the front and rear mounts showed a high level transmissibility contributing to structure-borne noise.

In this study, the authors propose multichannel weighted Euclidean (WE) and weighted cosh (WCOSH) cost function estimators for speech enhancement in the distributed microphone scenario. The goal of the work is to illustrate the advantages of utilising additional microphones and modified cost functions for improving signal-to-noise ratio (SNR) and segmental SNR (SSNR) along with log-likelihood ratio (LLR) and perceptual evaluation of speech quality (PESQ) objective metrics over the corresponding single-channel baseline estimators. As with their single-channel counterparts, the perceptually-motivated multichannel WE and WCOSH estimators are functions of a weighting law parameter, which influences attention of the noisy spectral amplitude through a spectral gain function, emphasises spectral peak (formant) information, and accounts for auditory masking effects. Based on the simulation results, the multichannel WE and WCOSH cost function estimators produced gains in SSNR improvement, LLR output and PESQ output over the single-channel baseline results and unweighted cost functions with the best improvements occurring with negative values of the weighting law parameter across all input SNR levels and noise types.

The paper presents an extensive review investigating the practical aspects related to the use of single- number ratings used in describing the sound insulation performance of partition wall panels and practical complications encountered in precise measurements in extensive frequency range from 50 Hz to 5 kHz. SWOT analysis of various single number ratings is described. A laboratory investigation on a double wall partition panel combination revealed the significant dependence of STC rating on transmission loss at 125 Hz attributed to 8 dB rule. An investigation conducted on devising alternative spectrums of aircraft noise, traffic noise, vehicular horn noise and elevated metro train noise as an extension to ISO 717-1 Ctr for ascertaining the sound insulation properties of materials exclusively towards these noise sources revealed that the single-number rating Rw + Ctr calculated using ISO 717-1 Ctr gives the minimum sound insulation, when compared with Rw + Cx calculated using the alternative spectrums of aircraft noise, traffic noise, etc., which means that material provides a higher sound insulation to the other noise sources. It is also observed that spectrum adaptation term Cx calculated using the spectrum of noise sources having high sound pressure levels in lower frequencies decreases as compared to ISO 717-1 Ctr owing to significant dependence of Ctr at lower frequencies.

Gaussian process regression (GPR) algorithm has good adaptability to deal with high dimensional, small sample, and nonlinear problems. The standard GPR algorithm assumes constant noise power throughout the sampling process. However, different sample points are often affected by different degrees of noise, which will reduce the modeling accuracy and increase uncertainty of the prediction in the standard GPR algorithm. In this paper, we introduce a weighting strategy to the standard GPR algorithm, and propose a weighted GPR (W-GPR) algorithm. Different from the standard GPR algorithm, the proposed W-GPR algorithm assigns a weight to each observation. In addition, in order to optimize hyper-parameters in the GPR modeling process, the particle swarm optimization (PSO) algorithm is used instead of the traditional gradient method. By means of a numerical example and one chemical process example, the W-GPR algorithm significantly reduces the uncertainty of the prediction, and improves the accuracy of the model. Further, simulation results demonstrate that the W-GPR model optimized by the PSO algorithm achieves a more accurate and reliable estimation result than the traditional gradient method.

In this paper, performance improved hole-filling algorithm including boundary noise removing pre-process which can be used for an arbitrary view synthesis with given two views is proposed. Boundary noise usually occurs because of the boundary mismatch between the reference image and depth map and common-hole is defined as the occluded region. These boundary noise and common-hole created while synthesizing a virtual view result in some defects and they are usually very difficult to be completely recovered by using only given two images as references. The spiral weighted average algorithm gives a clear boundary of each object by using depth information and the gradient searching algorithm is able to preserve details. In this paper, we combine these two algorithms by using a weighting factor to reflect the strong point of each algorithm effectively in the virtual view synthesis process. The experimental results show that the proposed algorithm performs much better than conventional algorithms.

Objective: The aim of this study was to develop a version of the threshold-equalizing noise (TEN) test for the diagnosis of dead regions for use with Etymotic ER-3A insert earphones. The use of such earphones is helpful when testing clients with asymmetric hearing loss or clients whose ear canals tend to collapse under the pressure of supraaural headphones. It can also be useful when ambient noise levels are problematic. Design: The spectral shape of the noise required to give equal masked thresholds at all frequencies, when expressed in dB HL, was derived by empirical measurements of the electrical output of audiometers using ER-3A earphones. To reduce the loudness of the noise and to minimize distortion generated in the audiometer or earphone, the noise was bandlimited between 354 and 6500 Hz. In addition, the noise was synthesized using a method that leads to a low crest factor (ratio of peak to root mean square value). This further reduced audiometer/earphone distortion, and allowed higher levels per ERBN; ERBNis the equivalent rectangular bandwidth of the auditory filter at 1 kHz, as determined using young normally hearing subjects. The test tone frequencies were limited to the range 500 to 4000 Hz. Subjects with normal or near-normal hearing were tested using a noise level of 60 dB HL/ERBN, to assess whether the noise did lead to equal masked thresholds in dB HL for all audiometric frequencies from 500 to 4000 Hz. Thresholds in the TEN were measured using manual audiometry with a 2 dB final step size. Results: The mean-masked thresholds varied by 1.3 dB across frequency when expressed in dB HL, and were close to the noise level per ERBN. Conclusion: This version of the TEN test can be used with ER-3A insert earphones.

In order to make the AC contactor’s working noise measured under enviroment with strong backgroud noise, a kind of noise detecting system is developed based on LabVIEW. Firstly,a vibrating signal is obtained by acceleration transducer;Then a noise value is figured out by using sound radiation efficiency index curve and A weighting curve. The results indicate that the measuring precision is 2dB. The noise detecting system meets the precision demand of noise detecting.

With the fast development of the high speed train, the major noise sources of the high speed train emit the higher level noise. It is a hot issue to reduce the noise within the high speed train to provide quieter environment for the passenger. The internal noises inside four carriages of high speed trains were measured at three running speeds. Four sound descriptors such as A-weighted sound pressure level, loudness, room criterion, and RC Mark II were used to assess the noises inside high speed trains. It is found that the low frequency compo¬nents of sound pressure level within carriages are dominant. A-weighted sound pressure level underestimates the perceived intensity of low-frequency components. The specific loudness curves are more accurate to measure the perceived intensity of sound pressure level in the whole frequency range. The internal sound environment inside the carriage is judged as questionably acceptable or unacceptable and the components around the octave band center at 2000 Hz exceed the reference values. It is recommended that the noise in the appropriate frequency range should be reduced to enhance the sound quality within carriage.

Noise may be defined as any unwanted sound. Sound becomes noise when it is too loud, unexpected, uncontrolled, happens at the wrong time, contains unwanted pure tones or unpleasant. In addition to being annoying, loud noise can cause hearing loss, and, depending on other factors, can affect stress level, sleep patterns and heart rate. The primary object for determining subjective estimations of loudness is to present sounds to a sample of listeners under controlled conditions. In heating, ventilation and air conditioning (HVAC) systems only the ventilation fan industry (e.g., bathroom exhaust and sidewall propeller fans) uses loudness ratings. In order to find satisfaction, percent of exposure to noise is the valuable issue for the personnel who are working in these areas. The room criterion (RC) method has been defined by ANSI standard S12.2, which is based on measured levels of in HVAC systems noise in spaces and is used primarily as a diagnostic tool. The RC method consists of a family of criteria curves and a rating procedure. RC measures background noise in the building over the frequency range of 16-4000 Hz. This rating system requires determination of the mid-frequency average level and determining the perceived balance between high-frequency (HF) sound and low-frequency (LF) sound. The arithmetic average of the sound levels in the 500, 1000 and 2000 Hz octave bands is 44.6 dB; therefore, the RC 45 curve is selected as the reference for spectrum quality evaluation. The spectral deviation factors in the LF, medium-frequency sound and HF regions are 2.9, 7.5 and -2.3, respectively, giving a Quality Assessment Index (QAI) of 9.8. This concludes the QAI is useful in estimating an occupant's probable reaction when the system design does not produce optimum sound quality. Thus, a QAI between 5 and 10 dB represents a marginal situation in which acceptance by an occupant is questionable. However, when sound pressure levels in the 16 or 31.5 Hz octave bands exceed 65 dB, vibration in lightweight office construction is possible.

To analyze the influence of the amplitude of different frequency bands of vehicle interior noise on subjective evaluation, an orthogonal array table was designed with 12 factors and three levels using the design of experiment (DOE) method. Furthermore, the range analysis was presented, and the optimal combinations for different categories were found. The sound quality can be improved by increasing, decreasing, or keeping the sound pressure level (SPL) in a certain frequency band.