Simulated Noise Research Articles

Ensemble noise properties of the European Pulsar Timing Array

Abstract The null hypothesis in Pulsar Timing Array (PTA) analyses includes assumptions about ensemble properties of pulsar time-correlated noise. These properties are encoded in prior probabilities for the amplitude and the spectral index of the power-law power spectral density of temporal correlations of the noise. Because multiple realizations of time-correlated noise processes are found in pulsars, these ensemble noise properties could and should be modelled in the full-PTA observations by parameterising the respective prior distributions using the so-called hyperparameters. This approach is known as the hierarchical Bayesian inference. In this work, we introduce a new procedure for numerical marginalisation over hyperparameters. The procedure may be used in searches for nanohertz gravitational waves and other PTA analyses to resolve prior misspecification at negligible computational cost. Furthermore, we infer the distribution of amplitudes and spectral indices of the power spectral density of spin noise and dispersion measure variation noise based on the observation of 25 millisecond pulsars by the European Pulsar Timing Array (EPTA). Our results may be used for the simulation of realistic noise in PTAs.

Research on the Reform of Architectural Acoustics Education Based on Noise Measurement and Simulation

This paper aims to enhance students' comprehensive practical abilities in the field of architectural acoustics through teaching reforms, especially in terms of how to effectively apply acoustic knowledge to the entire process of site planning and architectural design. In response to the disconnect between theory and practice in traditional architectural physics courses, this paper proposes the introduction of a combined experimental teaching model that integrates field noise measurement and simulation. Through this model, students can not only master the basic theories and principles of architectural acoustics but also deeply understand the impact of noise on the functional quality and environmental quality of architectural spaces through on-site noise monitoring and digital simulation technologies. The course content covers noise control and management from the early stages of building planning, design optimization, to later stages of building renovation, with a focus on the application of architectural acoustic simulation techniques in practical design. This approach not only helps students improve their practical application of architectural acoustics knowledge but also cultivates their ability to address noise issues effectively during the design process, ultimately promoting the organic integration of architectural design and environmental sustainability. The teaching reform proposal put forward in this paper seeks to enhance students' ability to solve practical problems by combining theory and experimentation and provide theoretical and technical support for acoustic optimization in future architectural designs.

Simultaneous Confidence Regions for Image Excursion Sets: a Validation Study with Applications in fMRI.

Functional Magnetic Resonance Imaging (fMRI) is commonly used to localize brain regions activated during a task. Methods have been developed for constructing confidence regions of image excursion sets, allowing inference on brain regions exceeding non-zero activation thresholds. However, these methods have been limited to a single predefined threshold and brain volume data, overlooking more sensitive cortical surface analyses. We present an approach that constructs simultaneous confidence regions (SCRs) which are valid for all possible activation thresholds and are applicable to both volume and surface data. This approach is based on a recent method that constructs SCRs from simultaneous confidence bands (SCBs), obtained by using the bootstrap on 1D and 2D images. To extend this method to fMRI studies, we evaluate the validity of the bootstrap with fMRI data through extensive 2D simulations. Six bootstrap variants, including the nonparametric bootstrap and multiplier bootstrap are compared. The Rademacher multiplier bootstrap-t performs the best, achieving a coverage rate close to the nominal level with sample sizes as low as 20. We further validate our approach using realistic noise simulations obtained by resampling resting-state 3D fMRI data, a technique that has become the gold standard in the field. Moreover, our implementation handles data of any dimension and is equipped with interactive visualization tools designed for fMRI analysis. We apply our approach to task fMRI volume data and surface data from the Human Connectome Project, showcasing the method's utility.

Design and Evaluation of Noise Simulation Algorithm Using MATLAB Ray Tracing Engine for Noise Assessment and Prediction

The Malaysian Department of Occupational Safety and Health (DOSH) reported that noise-induced hearing loss (NIHL) accounted for 92% of occupational diseases in 2019. To address this, accurate risk assessment is crucial. The current noise evaluation methods are complex and time-consuming, relying on manual calculations and field measurements. An easy-to-use, open-source noise simulator that directly compares the output with national standards would help mitigate this issue. This research aims to develop an advanced noise evaluation tool to assess and predict unregulated workplace noise, providing tailored safety recommendations. Using a representative plant layout, the Sound Pressure Level (SPL) is calculated using MATLAB’s ray tracing propagation model. The model simulates all possible transmission paths from the source to the receiver to derive the resultant SPL. A noise simulation application featuring a graphical user interface (GUI) built with MATLAB’s App Designer (version: R2024a) automates these computations. The simulation results are validated against the DOSH’s safety standards in Malaysia. Additional safety metrics, such as the recommended maximum exposure time and the required Noise Reduction Rating (NRR) for hearing protection, are calculated based on the SPLs for hazardous locations. The simulation algorithm’s functionality is validated against manual calculations, with an average deviation of just 3.06 dB, demonstrating the model’s precision. This tool can assess and predict indoor noise levels, provide information on optimal exposure limits, and recommend necessary protective measures, ultimately reducing the risk of NIHL in factory environments. It can potentially optimise plant floor operations for existing and new facilities, ensuring safer shift operations and reducing worker noise hazard exposure.

Environmental impact of seawater desalination plant: industrial noise calculation and simulation

Environmental impact of seawater desalination plant: industrial noise calculation and simulation

A Vibration Signal-Based Active Noise Control Method for Liquid-Filled Pipelines.

Pulsation noise in the piping system generated by the excitation of the pump source seriously affects the reliability of the pipeline system and mechanical equipment. The active noise control can effectively suppress the low-frequency noise in the liquid-filled pipeline. Active control methods with intrusive secondary sources generally use dynamic pressure sensors or hydrophones to collect signals, which destroy the structure of the pipe. In this paper, we propose an active noise control method utilizing signals acquired by accelerometers, which adopts offline modeling of the secondary path and the notch narrowband FxLMS algorithm for controlling the secondary source actuation. The feasibility of this method is verified by LabVIEW simulation and active noise control test of the liquid-filled pipeline. The test results show that this method can achieve more than 4 dB reduction for low-frequency (10~200 Hz) line spectrum noise under most operating conditions.

A laser tracking attitude dynamic measurement method based on real-time calibration and AEKF with a multi-factor influence analysis model

Abstract To address the limitations of six-degree-of-freedom (6-DOF) visual tracking systems in terms of low dynamic performance and the inability to perform measurements under light occlusion, this paper proposes a novel laser tracking attitude dynamic measurement method integrating vision and inertial measurement unit (IMU). A tailored real-time calibration model is developed to enable precise alignment of the vision and IMU coordinate systems during dynamic operations. To overcome the limitations of extended Kalman filter (EKF) that rely on static noise assumptions in complex dynamic measurement scenarios, this study proposes an adaptive mechanism based on multi-factor influence analysis. Through dynamic monitoring of visual observation noise, an adaptive EKF algorithm is designed to enhance performance under varying conditions. When integrated into a vision/IMU fusion system, the algorithm significantly enhances the system’s sensitivity to variations in visual sensor observation noise caused by changes in lighting and motion states. This capability allows rapid adjustment of filtering parameters, leading to substantial improvements in filtering accuracy and stability. Simulation results demonstrate that the proposed algorithm outperforms traditional EKF in fusion measurement accuracy when tested in simulated noise environments featuring sinusoidal and random jump signals. Furthermore, the fusion measurement accuracy is mainly influenced by the data density of the visual sensor and the measurement distance. Finally, the proposed method was validated on a precision turntable. Experimental results reveal that at a 3 m measurement distance and within a 0°–25° angular range, when the turntable rotates at a constant speed of 5°/s along a single axis, the maximum absolute deviation in repeatability of the fused attitude measurements was below 0.11°, and the system is capable of achieving a high measurement frequency of 100 Hz.

Calibration and simulation of ionization signal and electronics noise in the ICARUS liquid argon time projection chamber

The ICARUS liquid argon time projection chamber (LArTPC) neutrino detector has been taking physics data since 2022 as part of the Short-Baseline Neutrino (SBN) Program. This paper details the equalization of the response to charge in the ICARUS time projection chamber (TPC), as well as data-driven tuning of the simulation of ionization charge signals and electronics noise. The equalization procedure removes non-uniformities in the ICARUS TPC response to charge in space and time. This work leverages the copious number of cosmic ray muons available to ICARUS at the surface. The ionization signal shape simulation applies a novel procedure that tunes the simulation to match what is measured in data. The end result of the equalization procedure and simulation tuning allows for a comparison of charge measurements in ICARUS between Monte Carlo simulation and data, showing good performance with minimal residual bias between the two.

Deep Learning Training Data for Phase Unwrapping of SAR Interferograms

Phase unwrapping is an essential process in synthetic aperture radar interferometry that restores phase signals constrained within the range of (-π, π) to their true phase values. Traditional algorithm-based methods can introduce significant errors due to rapid and steep phase gradient or noise, which negatively impact terrain elevation and surface displacement analyses. To overcome these limitations, deep learningbased phase unwrapping techniques have been proposed; however, there has been insufficient previous studies due to the lack of accurate training and test data. This paper aims to share the training data generated using the phase unwrapping simulation method with locally-different phase noise. The data were generated by the simulation of topographic phases and phase noise, atmospheric and orbital distortions. Additionally, data augmentation for phase variation and noise levels was applied to address data imbalance issues. The shared data consists of two types: one with a constant phase noise level for each patch, and another that simulates locally different phase noise based on augmented coherence data. This data is primarily effective for unwrapping topographic phase components and holds significance as the first phase unwrapping training data of synthetic aperture radar interferograms shared in Korea. We expect this resource to serve as foundational data for future phase unwrapping technology research, including applications for upcoming satellites like KOMPSAT-6 and water resource satellites.

Edge-Aware Dual-Task Image Watermarking Against Social Network Noise

In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. To address the fundamental trade-off between the visual quality of the watermarked image and the robustness of watermark extraction, we explore the role of structural features and propose a novel edge-aware watermarking framework. Our primary innovation lies in the edge-aware secret hiding module (EASHM), which achieves adaptive watermark embedding by aligning watermarks with image structural features. To realize this, the EASHM leverages knowledge distillation from an edge detection teacher and employs a dual-task encoder that simultaneously performs edge detection and watermark embedding through maximal parameter sharing. The framework is further equipped with a social network noise simulator (SNNS) and a secret recovery module (SRM) to enhance robustness against common image noise attacks. Extensive experiments on three public datasets demonstrate that our framework achieves superior watermark imperceptibility, with PSNR and SSIM values exceeding 40.82 dB and 0.9867, respectively, while maintaining an over 99% decoding accuracy under various noise attacks, outperforming existing methods by significant margins.

Assessment of changes in environmental pollution by road noise using a scalar measure

The article addresses the issue of assessing the impact of road rebuilding on traffic noise pollution. To assess noise hazards, parameters expressed on the decibel scale were used, and a new measure was proposed - a scalar reference that compares the sound level value to the recommended threshold. This measure is based on Weber Fechner's law, which relates to human perception of changes in sound levels. It was derived through the decibel algebra applied to measurement results and is called the “coefficient of exceedance of the recommended sound level”. Its usefulness was verified by analyzing the results of measurements of traffic and noise parameters before and two years after the reconstruction of a section of the national road in Kielce. An assessment was made of traffic volume, vehicle speed, and road vehicle noise. The analysis evaluated the absolute values, variability and uncertainty of results obtained for the entire year, Fridays and Sundays. Significant differences in traffic parameter values were observed between the lanes entering and leaving the city on weekdays and weekends. The analysis showed a 28% increase in traffic volume following the road reconstruction. The current measure, which compares the difference in noise levels before and after the road reconstruction, indicates that while noise levels have decreased, they still exceed the normative values. For the same parameters, the median coefficient of exceedance decreased by approximately 17%, and the maximum coefficient of exceedance decreased by approximately 15%. The diagnostic usefulness of the coefficient of exceedance was further assessed using noise simulations based on the Cnossos-EU model. These simulations showed the high sensitivity of the proposed scalar noise measure to changes in vehicle speed and traffic volume. The simulations also indicated that to meet the Polish noise normative values, traffic volume would need to reduced by 50%, and the vehicle speed would need to be capped at 50 km/h. Additionally, the simulations suggested that even more stringent traffic restrictions would be necessary to meet the World Health Organization's noise recommendations.

SegcsvdWMH: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large-scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task. We present segcsvdWMH, a convolutional neural network-based tool developed to provide reliable and accurate WMH quantification across diverse clinical datasets. segcsvdWMH was developed using a large dataset consisting of over 700 fluid-attenuated inversion recovery MRI scans from seven multisite studies, spanning a wide range of clinical populations, WMH burdens, and imaging protocols. Model training incorporated anatomical information through a novel hierarchical segmentation approach, together with extensive data augmentation techniques to improve performance across varied imaging conditions. Benchmarked against three widely available segmentation tools, segcsvdWMH demonstrated superior accuracy, achieving mean Dice score improvements of 7.8% ± 9.7% over HyperMapp3r, 21.8% ± 8.6% over SAMSEG, and 43.5% ± 7.1% over WMH-SynthSeg across four diverse test datasets. segcsvdWMH also maintained consistently high Dice scores across these test datasets (mean DSC = 0.86 ± 0.08), and exhibited strong, stable correlations with periventricular, deep, and total WMH ground truth volumes (mean r = 0.99 ± 0.01). Additionally, segcsvdWMH was robust to low and moderate levels of simulated MRI spike noise artifacts and maintained strong performance across a range of binary segmentation thresholds and WMH burden levels. These findings suggest that segcsvdWMH may provide more accurate and robust WMH segmentation performance for heterogeneous clinical datasets characterized by varying degrees of CSVD severity.

Older adults’ recognition of medical terminology in hospital noise

Word identification accuracy is modulated by many factors including linguistic characteristics of words (frequent vs. infrequent), listening environment (noisy vs. quiet), and listener-related differences (older vs. younger). Nearly, all studies investigating these factors use high-familiarity words and noise signals that are either energetic maskers (e.g., white noise) or informational maskers composed of competing talkers (e.g., multitalker babble). Here, we expand on these findings by examining younger and older listeners’ speech-in-noise perception for words varying in both frequency and familiarity within a simulated hospital noise that has important non-speech information. The method was inspired by the real-world challenges aging patients can face in understanding less familiar medical terminology used by healthcare professionals in noisy hospital environments. Word familiarity data from older and young adults were collected for 800 medically related terms. Familiarity ratings were highly correlated between the two age groups. Older adults’ transcription accuracy for sentences with medical terminology that vary in their familiarity and frequency was assessed across four listening conditions: hospital noise, speech-shaped noise, amplitude-modulated speech-shaped noise, and quiet. Listeners were less accurate in noise conditions than in a quiet condition and were more impacted by hospital noise than either speech-shaped noise. Sentences with low-familiarity and low-frequency medical words combined with hospital noise were particularly detrimental for older adults compared to younger adults. The results impact our theoretical understanding of speech perception in noise and highlight real-world consequences of older adults’ difficulties with speech-in-noise and specifically noise containing competing, non-speech information.

Modeling and Simulation of Eddy Current Dissipation Magnetic Acceleration Noise of Space Inertial Sensors.

The magnetic acceleration noise (MAN) that stems from the eddy current dissipation of a test mass (TM) serves as an important source of noise for space inertial sensors. Given the problem that the eddy current dissipation magnetic acceleration noise (ECDMAN) of a cubic TM defies analytical solutions, an analytical model of ECDMAN for a spherical TM, which has the same volume as the cubic TM, is systematically derived on the basis of the principles of electromagnetism and the fluctuation-dissipation theorem, and this model can be used as an approximate analytical model for the evaluation of this noise term. Based on the approximate analytical model, with the TM of the LISA Pathfinder (LPF) as the research object, this paper obtains a modification coefficient using the approach of combining the analytical method with the finite element method (FEM), and establishes a semi-analytical model of ECDMAN for the cubic TM. Using the parameters of the LPF's TM, the calculation error of the semi-analytical model is reduced by about 4.64% compared with the approximate analytical model. Finally, a generalized modeling approach for the semi-analytical model of ECDMAN is put forward, which is applicable to TMs with different parameters and can realize the real-time and rapid evaluation of ECDMAN during in-orbit experiments.

Statistical biases correction in channelized Hotelling model observers

Channelized Hotelling model observers are efficient at simulating the human observer visual performance in medical imaging detection tasks. However, channelized Hotelling observers (CHO) are subject to statistical biases from zero-signal and finite-sample effects. The point estimate of the d' value is also not always symmetric with exact confidence interval (CI) bounds determined for the infinitely trained CHO. A method for correcting these statistical biases and CI asymmetry is studied. CHO d' values and CI bounds with hold-out and resubstitution methods were computed for a range of 200x200 pixels images from 20 to 10 000 images for 10, 40 and 96 channels from a set of 20 000 images with gaussian coloured simulated noise and simulated signal. The median of the non-central F cumulative distribution (F'), which is the CHO underlying statistical behaviour for the resubstitution method, was computed, and compared to d' values and CI bounds. A set of experimental data was used to evaluate F' median values. The F' median allows to get accurate corrected simulated d' values down to zero-signals. For small d' values, the variation of d' values with the inverse of number of images is not linear while the F' median allows a good correction in such conditions. The F' median is also inherently symmetric with regards to the confidence interval. With experimental data, F' median values in a range of about 1 to 10 d' values were within -0.8% to 4.7% of linearly extrapolated values at an infinite number of images. The F' median correction is an effective simultaneous correction of the zero-signal statistical bias and finite-sample statistical bias, and of confidence interval asymmetry of channelized Hotelling observers.&#xD.

Gen TSO: A General JWST Simulator for Exoplanet Times-series Observations

Gen TSO is a noise calculator specifically tailored to simulate James Webb Space Telescope (JWST) time-series observations of exoplanets. Gen TSO enables the estimation of signal-to-noise ratios (S/Ns) for transit or eclipse depths through an interactive graphical interface, similar to the JWST Exposure Time Calculator (ETC). This interface leverages the ETC by combining its noise simulator, Pandeia, with additional exoplanet resources from the NASA Exoplanet Archive, the Gaia DR3 catalog of stellar sources, and the TrExoLiSTS database of JWST programs. The initial release of Gen TSO allows users to calculate S/Ns for all JWST instruments for the spectroscopic time-series modes available as of the Cycle 4 GO call. Additionally, Gen TSO allows users to simulate target acquisition on the science targets or, when needed, on nearby stellar targets within the visit splitting distance. This article presents an overview of Gen TSO and its main functionalities. Gen TSO has been designed to provide both an intuitive graphical interface and a modular API to access the resources mentioned above, facilitating planing and simulation of JWST exoplanet time-series observations. Gen TSO is available for installation via the Python Package Index and its documentation can be found at pcubillos.github.io/gen_tso.

Efficient Solution Strategies for Cabin Noise Assessment of Wave-Resolving Aircraft Fuselage Models

For the purpose of high-fidelity aircraft cabin noise simulations during early design phases, we examine three efficient solving approaches for the fully coupled finite element model of an aircraft fuselage segment. Obtaining an efficient solution with respect to consumed computational time and resources is challenging within a conventional simulation pipeline, as large-scale and complex vibroacoustic models demand crucially high computational costs with increasing frequency. In this contribution, we adopt (1) frequency and domain specific discretization, (2) domain-decomposition techniques, and (3) model order reduction with rational Arnoldi Krylov subspace methods for an aircraft fuselage model. The three approaches have shown remarkable advantage thereby reducing the solving time as well as the memory requirement that are essential when solving large-scale models. While the discretization and the model order reduction approaches accelerate the solving process by efficiently handling the complexity of the system to be solved, domain-decomposition techniques further handle the aspect of reducing the overall memory consumption. Finally with the help of active research aircraft segment models, we implement and showcase the achieved efficiency.

Bayesian analysis of multi-fidelity modeling in the stochastic simulations

Multi-fidelity surrogate modeling has received intensive attention owing to its strong applicability to engineering design problems. Generally, unknown parameters in a surrogate model are estimated using simulation data, introducing parameter estimation uncertainty into the model prediction. This uncertainty can be further aggravated by the presence of random noise in stochastic simulations. To tackle this issue, this paper develops a novel Bayesian meta-modeling method for multi-fidelity simulations in stochastic situations. Utilizing prior information about the parameters and Bayesian posterior inference, a new closed-form predictive distribution is derived within the autoregressive cokriging framework. This formula explicitly accounts for both parameter estimation uncertainty and measurement error in response without requiring time-consuming Markov chain Monte Carlo methods. Numerical experiments conducted on the classic Borehole function and a stochastic customer order scheduling problem illustrate the performance of the proposed method. Results demonstrate that the proposed method surpasses existing methods and maintains robust predictive performance under heterogeneous stochastic circumstances with varying noise levels.

Polycyclic Aromatic Hydrocarbons in exoplanet atmospheres: A detectability study

Abstract In this paper, we explore the detectability of polycyclic aromatic hydrocarbons (PAHs) under diverse planetary conditions, aiming to identify promising targets for future observations of planetary atmospheres. Our primary goal is to determine the minimum detectable mass fractions of PAHs on each studied planet. We integrate the one-dimensional self consistent model petitCODE with petitRADTRANS, a radiative transfer model, to simulate the transmission spectra of these planets. Subsequently, we employ the PandExo noise simulator using the NIRSpec PRISM instrument aboard the JWST to assess the observability. Then, we conduct a Bayesian analysis through the MULTINEST code. Our findings illustrate that variations in C/O ratios and planet temperatures significantly influence the transmission spectra and the detectability of PAHs. Our results show that planets with [Fe/H]=0 and 1, C/O=0.55, and temperatures around 1200 K are the most promising for detecting PAHs, with detectable mass fractions as low as 10−7, or one thousandth of the ISM abundance level. For colder planets with lower metallicities and C/O ratios, as well as hotter planets with carbon-rich atmospheres, PAHs can be detected at abundances around 10−6. These results aid our strategy for selecting targets to study PAHs in the atmospheres of exoplanets.

GPU-accelerated semicoherent hierarchical search for stellar-mass binary inspiral signals in LISA

Searching for gravitational waves from stellar-mass binary black holes with LISA remains a challenging open problem. Conventional template-bank approaches to the search are impossible due to the prohibitive number of templates that would be required. This paper continues the development of a hierarchical semicoherent stochastic search, extending it to a full end-to-end pipeline that is then applied to multiple mock LISA data streams which include simulated noise. Particle swarm optimization is used as a stochastic search algorithm, tracking multiple maxima of a semicoherent search statistic defined over source parameter space. The pipeline is accelerated by the use of graphical processing units (GPUs). No prior information from observations by ground-based detectors is used; this is necessary in order to provide advance warning of the merger. We find that the pipeline is able to detect sources with signal-to-noise ratios as low as ρ∼17, we demonstrate that these searches can directly seed coarse parameter estimation in cases where the search trigger is loud. An example of how the false-alarm probability can be estimated for this type of GW search is also included. Published by the American Physical Society 2024