Presence Of High Noise Levels Research Articles

A Scoping Review on Occupational Noise Mitigation Strategies and Recommendations for Sustainable Ship Operations

Environmental and occupational noise has the potential to result in health risks. The presence of high noise levels aboard ships can cause substantial hazards that affect the well-being of those employed in the maritime industry. The study and implementation of occupational noise reduction aboard ships are of the highest priority for ensuring the well-being of marine workers, compliance with regulatory standards, protection of the environment, and improvement of overall operational efficiency and safety within the maritime sector. A scoping study was conducted to collect and summarize the existing scientific literature about approaches to preventing occupational noise in vessel operations. We searched electronic databases for papers published up to June 2024. Initially, 94 articles were identified for screening, and the present research produced 16 studies, which were finally analyzed. Resultantly, noise control may begin with elimination, substitution, engineering, administrative, and hearing protection (ear plugs or muffs). Noise control innovation would be started with engineering techniques. Hearing protection devices (HPDs) could be used to reduce noise and as an instrument of communication between sailors. More research needs to be carried out in order to find the best ways for maritime vessels to reduce noise at work and to see how well they work in lowering the risks that come with noise for workers on board.

Model Selection Path and Construction of Model Confidence Set under High-Dimensional Variables

Model selection uncertainty has drawn a lot of attention from academics recently because it significantly affects parameter estimation and prediction. Scholars are currently addressing and quantifying uncertainty in model selection by concentrating on model combining and model confidence sets. In this paper, we present a new approach for building model confidence sets, which we call AMac. We provide a theoretical lower bound on the degree of confidence in the model confidence sets that AMac has built. Furthermore, we discuss how the implementation of current model confidence set construction methods becomes difficult when dealing with high-dimensional variables. To address this problem, we suggest building model selection paths (MSP) as a solution. We develop an algorithm for building MSP and show its effectiveness by utilizing the theories of adaptive lasso and lars. We perform an extensive set of simulation experiments to compare the performances of Mac and AMac methods. According to the results, AMac is more stable when there are fluctuations in noise levels. The model confidence sets built by AMac, in particular, achieve coverage rates that are closer to the desired confidence level, especially in the presence of high noise levels. To further confirm that MSP can successfully generate model confidence sets that maintain the given confidence level as the sample size increases, we conduct extensive simulation tests with high-dimensional variables. Ultimately, we hope that the strategies and concepts discussed in this work will improve results in subsequent research on the uncertainty of model selection.

Observing inter-well and intra-well oscillations in buckled nanomechanical systems enabled by image processing

The scanning electron microscope (SEM) recordings of dynamic nano-electromechanical systems (NEMS) are difficult to analyze due to the noise caused by low frame rate, insufficient resolution, and blurriness induced by applied electric potentials. Here, we develop an image processing platform enhanced by the physics of the underlying system to track the motion of buckling NEMS structures in the presence of high noise levels. The algorithm is composed of an image filter, two data filters, and a nonlinear regression model, which utilizes the expected form of the physical solution. The method was applied to the recordings of a NEMS beam about 150 nm wide, undergoing intra- and inter-well post-buckling states with a transition rate of approximately 0.5 Hz. The algorithm can track the dynamical motion of the NEMS and capture the dependency of deflection amplitude on the compressive force on the beam. With the help of the proposed algorithm, the transition from inter-well to intra-well motion is clearly resolved for buckling NEMS imaged under SEM.

Adaptive anadromic regularization method for the Cauchy problem of the Helmholtz equation

In this research work, we investigate the Cauchy problem for the Helmholtz equation. Considering the completion data problem in a bounded cylindrical domain with Neumann and Dirichlet conditions on a part of the boundary. An immediate approximation of missing boundary data is obtained using a method that factorizes the boundary value problem. This factorization uses the Neumann to Dirichlet or Dirichlet to Neumann operators that satisfy the Riccati equation. Some singularities appear in the solution of the Riccati equation for a particular length of the waveguide of the Helmholtz equation. We elaborate a new numerical method called ‘adaptive anadromic regularization method’ that can solve these operators beyond the singularity. In addition, we introduce a scaling matrix technique to the linear matrix equations associated with the Riccati equations to generate normalized solutions. Our numerical procedure not only approximates missing boundary data, but also provides an error estimate that allows efficient time stepping. Numerical tests proved to confirm the theory even in the presence of high noise levels.

Moving Object Localization in Distributed MIMO With Clock and Frequency Offsets

This paper investigates the localization problem of the moving target in distributed multiple-input multiple-output (MIMO) systems, where the transmitters are subject to clock and frequency offsets. The position and velocity of the moving target are jointly determined by applying time delay (TD) and Doppler shift (DS) measurements. For this purpose, two efficient solution approaches are proposed to estimate the unknowns of the moving target, as well as the clock and frequency offsets. Firstly, the nonconvex localization problem is first relaxed into a convex semidefinite programming (SDP) form. Additionally, a two-stage closed-form solution (TSCFS) approach is proposed to determine the unknowns. The performance of the proposed methods is examined by the mean squared error (MSE) analysis. The simulated results show that the performance of the proposed solutions are able to approach the Cramér-rao lower bound (CRLB) accuracy at low noise levels. In contrast, the SDP solution performs better than the TSCFS in the presence of high noise levels or a small number of receivers.

A Deep Learning Approach for Reconstruction in Millimeter-Wave Imaging Systems

In millimeter-wave (MMW) imaging, the objects of interest are oftentimes modeled as 2-D binary (black and white) shapes with white pixels representing the reflecting interior of the object. However, due to the propagation of the scattered waves, the continuous-domain binary images are convolved with a so-called point-spread function (PSF) before being digitized by means of sampling. As the 2-D PSF is both nonseparable and nonvanishing in the case of MMW imaging, exact recovery is quite complicated. In this communication, we propose a deep-learning approach for image reconstruction. We should highlight that the wave scatterings are suitably represented with complex-valued quantities, while standard deep neural networks (DNNs) accept real-valued inputs. To overcome this challenge, we separate the real and imaginary parts as if we had two imaging modalities and concatenate them to form a real-valued input with a larger size. Fortunately, the network automatically learns how to combine the mutual information between these modalities to reconstruct the final image. Among the advantages of the proposed method are improved robustness against additive noise and mismatch errors of imaging frequency and object to antenna distance; indeed, the method works well in wideband imaging scenarios over a wide range of objects to antenna distances even in the presence of high noise levels without requiring a separate calibration stage. We test the method with synthetic data simulated with software as well as real recordings in the laboratory.

Processing ambient noise using wavelet analysis tools: the Iraqi Seismological broadband Network Data at Iraqi Meteorological Organization and seismology (IMOS)

The purpose of this study is to reduce the noise of passive seismic signals recorded across the Iraqi Seismological Network Data at Iraqi Meteorological Organization and seismology (IMOS). The seismic records from six broadband stations of IMOS are largely affected by human activities in the populated cities of Iraq. We utilized several well-developed amplitude power spectrum and wavelet analysis techniques to improve signal-to-noise ratio. The results obtained from this comparison show that continuous wavelet transform can largely improve signal with minimal changes in the waveform shape of interest, even in presence of high noise levels. In addition, has overwhelmingly strong vitality and gains substantial development in the field of signal processing. Several mother wavelets were tested and Morlet and Coiflet selected as the optimum waves based on the signal characteristics and the noise reduction objective (maximizing SNR or reducing errors). Hence, the quality of the final seismic signal is crucial for the moment tensor analysis. However, traditional filters including discrete wavelet packet transform and 1-D wavelet analysis methods can be affective to reduce distortion in the signal.

Deep Learning for Denoising: An Attempt to Recover the Effective Magnetic Resonance Sounding Signal in the Presence of High Level Noise

Magnetic resonance sounding (MRS) measurements commonly suffer from a notably low signal-to-noise ratio (SNR). In recent years, many denoising methods have been developed, which have demonstrated the useful capability to improve the SNR. However, when MRS measurements are implemented on the sites with high noise levels, i.e., the human living environment, the conventional methods are helpless to recover the effective MRS signal, which is submerged in extensive environmental noise. In addition, the conventional methods that depend on signal models and the corresponding prior assumptions commonly rely on manual experience, which brings obstacles to the automation and efficiency of signal processing. To solve the above problems, we attempt to apply an intelligent denoising framework with a novel neural network as the basic tool for deep learning, called Dn-ResUnet in this article. The network extracts the features of the MRS signal through the encoder and decoder layers of the Dn-ResUnet structure, in which residual learning is adopted to accelerate the training process and improve denoising performance. Once the training is completed, the deep learning realizes adaptive denoising with no need for 1) prior assumptions of the MRS signal and noise; 2) optimal filter parameter tuning; or 3) expensive time cost. The comparison experiments demonstrate that the Dn-ResUnet model provides superior noise cancellation performance, especially it can replace the conventional methods to recover the effective MRS signals in noise levels down to an SNR of −30 dB. In addition, the noise attenuation tests are performed on synthetic and real data. The results show that the framework of deep learning yields a convincing performance in MRS signal denoising.

Handling noisy data in sparse model identification using subsampling and co-teaching

In this paper, a novel algorithm based on sparse identification, subsampling and co-teaching is developed to mitigate the problems of highly noisy data from sensor measurements in modeling of nonlinear systems. Specifically, sparse identification is combined with subsampling, a method where a fraction of the data set is randomly sampled and used for model identification, as well as co-teaching, a method that mixes noise-free data from first-principles simulations with the noisy measurements to provide a mixed data set that is less corrupted with noise for model training. The proposed method is bench-marked against sparse identification without subsampling as well as subsampling but without co-teaching using two examples, a predator-prey system and a chemical process, both of which are modeled as nonlinear systems of ordinary differential equations. It was shown that the proposed method yields better models in terms of prediction accuracy in the presence of high noise levels.

Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.

Inverse problem techniques for multiple crack detection in 2D elastic continua based on extended finite element concepts

Two efficient methods are presented to detect multiple cracks in 2D elastic bodies, based on the insights from Extended Finite Element. Adetection mesh is assigned to the cracked body and the responses are measured at the nodes. A finite element model with the same mesh is used to represent the uncracked state of the physical body. In the first method which is called Crack Detection based on Residual Error (CDRE), the residual error norm is calculated based on the uncracked body stiffness matrix and the cracked body responses. The contour of the error norm would show the crack pattern; the method is computationally efficient. In the second method that is coined as Crack Detection based on Stiffness Residual (CDSR), the crack locations are found based on the difference between the stiffness matrix of the cracked body and the uncracked body. The stiffness matrix of the cracked body is found by solving a dynamic inverse problem based on a modified Tikhonov regularization. The efficiency and accuracy of the identification method are enhanced by predicting the crack pattern by the CDRE method. Several examples are presented to show the accuracy and robustness of the methods in the presence of high noise levels.

DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK

Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials.

Combined effects of noise and air temperature on human neurophysiological responses in a simulated indoor environment

The aim of the present study was to evaluate the combined effects of noise and air temperature on the human body neurophysiological responses. This study was conducted on 35 male students, who were exposed to four different air temperatures (18 °C, 22 °C, 26 °C, and 30 °C) and two noise levels (55 dBA and 75 dBA) in eight sessions in a simulated indoor environment. The mean values of accuracy and time of response to stimuli in N-back test as well as neurophysiological responses were measured. In the studied experiment configurations, with increasing air temperature and noise, the working memory and neurophysiological responses were disturbed. The results indicated the significant effect of noise on working memory, as compared with that of air temperature. The effects of air temperature on heart rate, respiratory rate as well as theta and alpha bands were more significant than the impact of noise. The combined effects of noise and air temperature were more significant than the influence of each of them alone. In the presence of high noise levels, the increase in air temperature did not worsen the response accuracy. However, in the presence of high noise level, the rise in air temperature aggravated the mean value of neurophysiological responses. Overall, noise has a greater effect on working memory, while the air temperature can disturb neurophysiological responses in a more profound way.

Probabilistic evaluation of detection capability of eddy current testing to inspect pitting on a stainless steel clad using multiple signal features

This study proposes a probability of detection (POD) model to quantitatively evaluate the capability of eddy current testing to detect flaws on the inner surface of pressure vessels cladded by stainless steel and in the presence of high noise level. Welded plate samples with drill holes were prepared to simulate corrosion that typically appears on the inner surface of large-scale pressure vessels. The signals generated by the drill holes and the noise caused by the weld were examined using eddy current testing. A hit/miss-based POD model with multiple flaw parameters and multiple signal features was proposed to analyze the measured signals. It is shown that the proposed model is able to more reasonably characterize the detectability of eddy current signals compared to conventional models that consider a single signal feature.

A Hybrid Algorithm for Recognition of Power Quality Disturbances

An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Denoising and filtering are widely used in routine seismic-data-processing to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to learn simultaneously a sparse representation of data in the time-frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any non-seismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Our method properly handles a variety of colored noise and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that potential applications of our approach are not limited to these applications or even to earthquake data, and that our approach can be adapted to diverse signals and applications in other settings.

CognitiveEMS

This paper presents our preliminary results on development of a Cognitive assistant system for Emergency Medical Services (CognitiveEMS) that aims to improve situational awareness and safety of first responders. CognitiveEMS integrates a suite of smart wearable sensors, devices, and analytics for real-time collection and analysis of in-situ data from incident scene and delivering dynamic data-driven insights to responders on the most effective response actions to take. We present the overall architecture of CognitiveEMS pipeline for processing information collected from the responder, which includes stages for converting speech to text, extracting medical and EMS protocol specific concepts, and modeling and execution of an EMS protocol. The performance of the pipeline is evaluated in both noise-free and noisy incident environments. The experiments are conducted using two types of publicly-available real EMS data: short radio calls and post-incident patient care reports. Three different noise profiles are considered for simulating the noisy environments: cafeteria, people talking, and emergency sirens. Noise was artificially added at 3 intensity levels of low, medium, and high to pre-recorded audio data. The results show that the i) state-of-the-art speech recognition tools such as Google Speech API are quite robust to low and medium noise intensities; ii) in the presence of high noise levels, the overall recall rate in medical concept annotation is reduced; and iii) the effect of noise often propagates to the final decision making stage and results in generating misleading feedback to responders.

Fast and accurate pseudo multispectral technique for whole-brain MRI tissue classification

Numerous strategies have been proposed to classify brain tissues into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). However, many of them fail when classifying specific regions with low contrast between tissues. In this work, we propose an alternative pseudo multispectral classification (PMC) technique using CIE LAB spaces instead of gray scale T1-weighted MPRAGE images, combined with a new preprocessing technique for contrast enhancement and an optimized iterative K-means clustering. To improve the accuracy of the classification process, gray scale images were converted to multispectral CIE LAB data by applying several transformation matrices. Thus, the amount of information associated with each image voxel was increased. The image contrast was then enhanced by applying a real time function that separates brain tissue distributions and improve image contrast in certain brain regions. The data were then classified using an optimized iterative and convergent K-means classifier. The performance of the proposed approach was assessed using simulation and in vivo human studies through comparison with three common software packages used for brain MR image segmentation, namely FSL, SPM8 and K-means clustering. In the presence of high SNR, the results showed that the four algorithms achieve a good classification. Conversely, in the presence of low SNR, PMC was shown to outperform the other methods by accurately recovering all tissue volumes. The quantitative assessment of brain tissue classification for simulated studies showed that the PMC algorithm resulted in a mean Jaccard index (JI) of 0.74 compared to 0.75 for FSL, 0.7 for SPM and 0.8 for K-means. The in vivo human studies showed that the PMC algorithm resulted in a mean JI of 0.92, which reflects a good spatial overlap between segmented and actual volumes, compared to 0.84 for FSL, 0.78 for SPM and 0.66 for K-means. The proposed algorithm presents a high potential for improving the accuracy of automatic brain tissues classification and was found to be accurate even in the presence of high noise level.

Iterative Bayesian denoising based on variance stabilization using Contourlet Transform with Sharp Frequency Localization: application to EFTEM images

BackgroundDue to the presence of high noise level in tomographic series of energy filtered transmission electron microscopy (EFTEM) images, alignment and 3D reconstruction steps become so difficult. To improve the alignment process which will in turn allow a more accurate and better three dimensional tomography reconstructions, a preprocessing step should be applied to the EFTEM data series.ResultsExperiments with real EFTEM data series at low SNR, show the feasibility and the accuracy of the proposed denoising approach being competitive with the best existing methods for Poisson image denoising. The effectiveness of the proposed denoising approach is thanks to the use of a nonparametric Bayesian estimation in the Contourlet Transform with Sharp Frequency Localization Domain (CTSD) and variance stabilizing transformation (VST). Furthermore, the optimal inverse Anscome transformation to obtain the final estimate of the denoised images, has allowed an accurate tomography reconstruction.ConclusionThe proposed approach provides qualitative information on the 3D distribution of individual chemical elements on the considered sample.

Frame synchronization for pulsed jammed satellite telecommand links

SummaryA new issue of the satellite telecommand synchronization and channel coding sublayer protocol includes LDPC‐coded communication link transmission units (CLTUs) that contain a 64‐bit start sequence. The novel data structures allow operation at lower signal‐to‐noise ratios than before and offer improved protection against jamming attacks. This paper considers the corresponding CLTU frame synchronization process. We derive practical algorithms to locate the start sequence in the presence of high noise levels and pulsed jamming. The different algorithms are compared in terms of implementation complexity and performance under various jamming conditions. It is shown that among the considered frame synchronizers, those involving a full search over the entire observation window provide the desired accuracy, ie, they guarantee a frame synchronization error probability that is significantly smaller than the codeword error rate, for codeword error rates near a target value of 10−4. Among these synchronizers, the full‐search hard‐decision–directed correlation‐based algorithm has the lowest complexity.