Spatial Noise Research Articles

Retrofit Measures for Aircraft Noise Reduction: Simulation Benchmark and Impact Assessment

DLR, German Aerospace Center, has developed a low-noise retrofit to reduce noise generation of a conventional midrange transportation aircraft, i.e., measures to the airframe and engine exhaust nozzle. Selected measures have been installed onboard of DLR’s flying testbed Advanced Technology Research Aircraft and been subject to a flyover noise campaign. The aircraft in its original configuration and the modified aircraft have been tested and measured in flyover campaigns in 2016 and 2019, respectively. Initial simulation models have been derived from the results and previous component simulations and wind tunnel tests to account for the new reduction measures within DLR’s system noise prediction tool Parametric Aircraft Noise Analysis Module (PANAM). This paper presents the developed measures as selected for the flight test campaigns. An overview of the conducted flight tests is provided. The overall noise simulation process, including aircraft and engine design, flight simulation, and noise prediction, is presented. Simulation results obtained from this process are then compared to the measured levels from the flight campaigns. Estimated simulation uncertainties from PANAM can now be directly compared to differences between measured and simulated levels. Finally, a parameter study is conducted in order to assess the reduction potential of the novel retrofit measures along different simulated flight procedures. Spatial noise distribution in sound exposure level contours is assessed.

Enabling Electric Field Model of Microscopically Realistic Brain.

Modeling brain stimulation at the microscopic scale may reveal new paradigms for a variety of stimulation modalities. We present the largest map of distributions of the extracellular electric field to date within a layer L2/L3 mouse primary visual cortex brain sample, which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm 3 volume). We used the map to identify microscopic perturbations of the extracellular electric field and their effect on the activating thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Result: Our immediate result is a reduction of the predicted stimulation field strength necessary for neuronal activation by a factor of approximately 0.7 (or by 30%) on average, due to microscopic perturbations of the extracellular electric field-an electric field "spatial noise" with a mean value of zero. Although this result is largely sample-specific, it aligns with experimental data indicating that existing macroscopic theories substantially overestimate the electric fields necessary for brain stimulation. Currently, there is a discrepancy between macroscopic volumetric brain modeling for brain stimulation and experimental results: experiments typically reveal lower electric intensities required for brain stimulation. This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. The immediate result is a prediction of lower electric field intensities necessary for brain stimulation, with an average reduction factor of 0.7.

Unsupervised Clustering-based 3D Static Scene Construction Using LiDAR Channel and Azimuth Angle

Abstract. Cameras are typically used at road intersections to collect data to perform object detection but struggle in low-light and harsh weather. On the other hand, Light Detection and Ranging (LiDAR) is used as a key technology in 3D vision systems. It gives the 3D point cloud, which includes accurate depth information, but its resolution is cost-dependent, with higher resolutions being more expensive. Deep learning-based method requires large, labelled dataset which increases the cost, time and accuracy depending on the model trained on labelled dataset. To perform object detection in point cloud data is a challenging task due to the incomplete representations, data sparsity and unavailability of training data. To overcome this by using an unsupervised approach, it is important to identify the static scene and then detect the moving object. This work incorporated a novel approach to construct static scene using azimuth angle and laser channel information using an unsupervised clustering approach. This work incorporates two modules I) data collection using VLP-16 LiDAR, and II) static scene construction using DBSCAN (density-based spatial clustering and noise) clustering-based approach. Data is collected at a 4-legged intersection and pre-processed to extract aggregated distances corresponding to the unique pair of azimuth angle and laser channel. DBSCAN is used to perform clustering on the aggregated distances, based on the highest silhouette score and lowest intra distance between points in cluster, static points are identified, and static scene constructed. The qualitative evaluation of method demonstrates that the algorithm effectively and accurately filters out background points.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Experience-based noise model of infrared detectors for system dimensioning and simulation

We show that infrared detectors frequently exhibit non-Gaussian spatial noise, which makes it difficult to compare their performance. The power-law-like behavior we show can be very detrimental to certain detection missions, hence the need to characterize this noise. We demonstrate that a simple mixture of Gaussian and Student processes corresponds to the observed example, and that the parameter determination procedure described here reaches its theoretical limit.

Road traffic noise assessment method for Hong Kong and associated GIS-based assessment tools

Hong Kong, known for its compact and complex urban environment, has long faced unique challenges in modeling road traffic noise for planning and assessment purposes. The Environmental Protection Department (EPD) of the Hong Kong Special Administrative Region Government (HKSARG) identified and established an enhanced prediction methodology called Road Traffic Noise Assessment Method - Hong Kong (RONOSS-HK). RONOSS-HK aims to provide better treatment on source geometry, source terms, noise propagation, considerations for new-energy vehicles and special noise reduction of the Innovative Noise Mitigation Designs (INMD) to cater for the complicated spatial noise situations in Hong Kong. To facilitate the implementation of RONOSS-HK, the EPD has also developed and introduced a GIS-based assessment tool which allows users to utilize GIS data readily available in HKSARG databases and other spatial information in the public domain for accurate road traffic noise prediction using RONOSS-HK methodology. Additionally, the EPD is in the process of further developing a web-based 3D software to serve as an alternative comprehensive assessment tool for applying RONOSS-HK in noise assessment. This paper introduces RONOSS-HK prediction methodology and the latest development of its associated GIS-based assessment tools.

Tree Rings Reveal ENSO in the Last Millennium

AbstractWe present new climate field reconstructions (CFR) of tropical Pacific ENSO sea surface temperatures (HadISST) for the boreal winter season using a circum‐Pacific tree‐ring network from known El Niño rainfall impact regions. We use two different CFR methods: Point‐by‐Point Regression (PPR) and reduced‐space Orthogonal Spatial Regression (OSR). Both methods produce reconstructions with high validation skill, but OSR is preferred because it has less spatial noise and is more efficient. Only the leading EOF of the SST field (EOF1) can be skillfully reconstructed by either method; EOF2 does not validate. The success of EOF1 reflects its importance for ENSO rainfall impacts over land; the failure with EOF2 is from the lack of these impacts. EOF1 allows for the reconstruction of many ENSO indices, including the ENSO Longitudinal Index (ELI). We also find evidence in our reconstructions for a recent increase in ENSO activity.

SpotGF: Denoising spatially resolved transcriptomics data using an optimal transport-based gene filtering algorithm

Spatially resolved transcriptomics (SRT) combines gene expression profiles with the physical locations of cells in their native states but suffers from unpredictable spatial noise due to cell damage during cryosectioning and exposure to reagents for staining and mRNA release. To address this noise, we developed SpotGF, an algorithm for denoising SRT data using optimal transport-based gene filtering. SpotGF quantifies diffusion patterns numerically, distinguishing widespread expression genes from aggregated expression genes and filtering out the former as noise. Unlike conventional denoising methods, SpotGF preserves raw sequencing data, thereby avoiding false positives that can arise from imputation. Additionally, SpotGF demonstrates superior performance in cell clustering, identifying potential marker genes, and annotating cell types. Overall, SpotGF has the potential to become a crucial preprocessing step in the downstream analysis of SRT data. The SpotGF software is freely available at GitHub. A record of this paper's transparent peer review process is included in the supplemental information.

CT image reconstruction: integrating iterative methods with Ml-EM algorithm and deep learning models

Computed Tomography (CT) imaging faces limitations, including low spatial resolution and noise, particularly in low-radiation-dose imaging. To address these challenges, researchers are exploring CT image reconstruction from sinogram data. Sinograms represent X-ray absorption throughout the body, and sophisticated image reconstruction methods, including machine learning algorithms and generative adversarial networks (GANs), can improve precision and resolution without increasing patient radiation exposure. This study proposes an iterative reconstruction approach that combines filters from deep learning models (Convolutional Neural Networks and UNet) with the Maximum Likelihood Expectation Maximization (ML-EM) algorithm and the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model. Our method aims to enhance image quality and reconstruction speed. Experimental results show significant improvements in image quality and resolution, with the proposed method (DL-MLEM-IR-UNET-ESRGAN) achieving an average SSIM of 0.9980 and PSNR of 53.2119, outperforming other methods. Additionally, our method reduces reconstruction time, with an average runtime of 131 seconds.

Evaluation of the incident management system of a bigger city during the winter flood 2023/24 in northern Germany: Organized (ir)responsibility in the pre-disaster phase

Incident management systems operate within complex and uncertain conditions during disasters. Therefore, researching their activities is challenging as there is no clear right or wrong. The winter 2023/2024 flood event in the city of Oldenburg, Germany, provided an opportunity to evaluate crisis management under high uncertainty, although no disaster has eventually occurred. Three stakeholder groups of the Incident Management System were examined through After-Action Reviews: The Emergency Operation Center (EOC), specialist advisors, and the Area Command (AC). Data on performance shaping factors (descriptions and judgements) were collected and analyzed using the Countenance Evaluation Model as a general framework in combination with a Systems-Thinking approach. The study aimed to identify variables within and between groups as well as factors in the environment that either hindered or facilitated work transactions. It emerged that the work organization in bodies of public administration is at odds with the uncertain, fast-paced, and problem-oriented structure of Incident Management System operations, causing stress and adding workload to employees. Additionally, the separation of strategic and tactical decision making led to coordination issues due to divergent time structures and task logics of EOC and AC. Furthermore, although specialist advisors played a crucial role in risk assessments and setting the direction of risk mitigation activities, it became clear that this person related resource is scarce and difficult to embed into the Incident Command System. A final finding is the importance of the larger Emergency Operation Center working environment. The spatial constraints and noise levels caused stress.

Multilevel approximation of Gaussian random fields: Covariance compression, estimation, and spatial prediction

The distribution of centered Gaussian random fields (GRFs) indexed by compacta such as smooth, bounded Euclidean domains or smooth, compact and orientable manifolds is determined by their covariance operators. We consider centered GRFs given as variational solutions to coloring operator equations driven by spatial white noise, with an elliptic self-adjoint pseudodifferential coloring operator from the Hörmander class. This includes the Matérn class of GRFs as a special case. Using biorthogonal multiresolution analyses on the manifold, we prove that the precision and covariance operators, respectively, may be identified with bi-infinite matrices and finite sections may be diagonally preconditioned rendering the condition number independent of the dimension p of this section. We prove that a tapering strategy by thresholding applied on finite sections of the bi-infinite precision and covariance matrices results in optimally numerically sparse approximations. That is, asymptotically only linearly many nonzero matrix entries are sufficient to approximate the original section of the bi-infinite covariance or precision matrix using this tapering strategy to arbitrary precision. The locations of these nonzero matrix entries can be determined a priori. The tapered covariance or precision matrices may also be optimally diagonally preconditioned. Analysis of the relative size of the entries of the tapered covariance matrices motivates novel, multilevel Monte Carlo (MLMC) oracles for covariance estimation, in sample complexity that scales log-linearly with respect to the number p of parameters. In addition, we propose and analyze novel compressive algorithms for simulating and kriging of GRFs. The complexity (work and memory vs. accuracy) of these three algorithms scales near-optimally in terms of the number of parameters p of the sample-wise approximation of the GRF in Sobolev scales.

A real-world evaluation of the clinical benefits of improved sound processor technology among Chinese cochlear implant users: A focus on Cochlear Nucleus 7.

Real-world evidence is increasingly used to support clinical and regulatory decisions globally and may be a useful tool to study the unique needs of cochlear implant users in China. The ability to recognize and understand speech in noise is critical for cochlear implant users, however, this remains a challenge in everyday settings with fluctuating competing noise levels. The Cochlear™ Sound Processor, Nucleus® 7 (CP1000), includes Forward Focus, a spatial noise algorithm aimed to improve speech-in-noise performance, and Made for iPhone/iPod/iPad functionality. We conducted a prospective, single-center, open-label, within-participant, real-world evidence investigation in participants with cochlear implants. The primary objective of this study, conducted in China, was to compare speech perception in spatially separated dynamic noise with the Nucleus 7 to the recipients' current older Cochlear Sound Processor, including the Freedom and Nucleus 5 sound processors. A follow-up study monitored participants from the initial study up to 12-months post the fitting of their Nucleus 7 and investigated hearing ability, satisfaction, and usability of the device via a questionnaire. Forty participants were included in the initial study (age-range 3 to 49 years) and 29 continued to the follow-up study (age-range 5 to 28 years). The participants were heterogeneous in terms of age, cochlear implant experience, and duration of hearing loss. Nucleus 7 significantly improved participant speech recognition performance in noise by 7.54 dB when compared with the participants' current older sound processor (p<0.0001). Overall satisfaction with Nucleus 7 was 72%. Satisfaction in different hearing contexts ranged from 93.1% for understanding a 1:1 conversation in a quiet setting, 62.1% for understanding on the phone, to 34.5% hearing in complex noisy situations. The study demonstrated the benefits of the Nucleus 7 sound processor across different hearing environments in a Chinese population and showed improved hearing ability, usability, and satisfaction in a real-world every-day environment.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic and ex-vivo phantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

Lévy noise-induced effects in a long Josephson junction in the presence of two different spatial noise distributions

We analyze the impact of Lévy-distributed stochastic fluctuations on the average switching time and voltage drop across a current-biased long Josephson tunnel junction. We compare the system’s response for two spatial configurations of time-dependent noise, i.e., homogeneous and distributed along the junction length. The response of the Josephson junction is explored by varying the characteristic parameter of the Lévy source, i.e., the α stability index and the noise intensity. These findings offer an effective tool to characterize a Lévy component possibly embedded in an unknown noise signal.

Objective method of measuring resolution of image intensifier tubes

Image intensifier tubes (IITs) are the most important modules of night vision devices used in huge numbers by military forces worldwide. Resolution is the most important parameter of IITs that presents information about ability of these devices to produce output images preserving information about details of the observed scenery. Despite its importance, it is still a common practice to measure resolution subjectively, by an observer looking at image of a resolution target created by a tested IIT. A series of attempts have been carried out to develop objective methods for accurate resolution measurement of IITs but with limited success. Accuracy of these methods varies depending on the tested IIT. This paper presents detailed analysis of proposed methods for objective resolution measurement. This analysis has shown that significant variability of accuracy of these methods is caused by one main drawback: the methods do not take into account influence of the spatial noise effect on human perception of image of the resolution target. Thus, an improved method taking into account spatial noise and its impact on target detection has been proposed. The method has been validated through experimental verification that shows accuracy improvements compared to other objective methods. This new approach improves accuracy of measurement of resolution of IITs to a level that can be accepted at professional test stations. In this way, this new method has potential to replace the standard subjective method to measure resolution of IITs and fix the biggest flaw of the standard test stations: measurement subjectivity.

Buffering effects of nonspecifically DNA-bound RNA polymerases in bacteria

RNA polymerase (RNAP) is the workhorse of bacterial gene expression, transcribing rRNA and mRNA. Experiments found that a significant fraction of RNAPs in bacteria are nonspecifically bound to DNA, which is puzzling as these idle RNAPs could have produced more RNAs. Whether nonspecifically DNA-bound RNAPs have any function or are merely a consequence of passive interaction between RNAP and DNA is unclear. In this work, we propose that nonspecifically DNA-bound RNAPs buffer the free RNAP concentration and mitigate the crosstalk between rRNA and mRNA transcription. We verify our theory using mean-field models and an agent-based model of transcription, showing that the buffering effects are robust against the interaction between RNAPs and sigma factors and the spatial fluctuation and temporal noise of RNAP concentration. We analyze the relevant parameters of and find that the buffering effects are significant across different growth rates at a low cost, suggesting that nonspecifically DNA-bound RNAPs are evolutionarily advantageous. Published by the American Physical Society 2024

Polarization analysis and filtering of undersampled 3C seismic data in the frequency-slowness domain

Polarization filters have emerged as a useful tool for coherent noise denoising in multicomponent seismic data. By exploiting the polarization properties of seismic waves, these filters can significantly improve wave-type separation, which is a crucial step in seismic data processing. However, their effectiveness in land seismic data in complex areas can be impacted by the presence of scattering noise, and their applicability is limited by irregular spatial sampling, which hinders velocity information for wave-type discrimination. To address these limitations, we introduce a novel two-step method for distinguishing and separating polarized wave types in shot gathers captured from a spatially undersampled linear array of 3C vector sensors. The first step involves conducting a polarization analysis in the frequency-slowness domain using elliptical elements derived from the linear Radon transform of the seismic data. The second step isolates specific polarized waves using the 3C frequency-slowness polarization filter (3C-FSPF), a technique that uses tailored taper functions based on polarization analysis. Our method stands out from single-station filters that cannot use velocity information for wave-type discrimination and existing multistation filters that require regularly sampled data. To evaluate our method, comprehensive tests are conducted on synthetic and real data. The results consistently demonstrate the effectiveness of 3C-FSPF in separating diverse polarized wave types under conditions of irregular and sparse spatial sampling and noise. Our findings underscore the potential of this method for advancing exploration geophysics by enhancing the quality of seismic data, particularly in land regions with complex near-surface structures.

Study on the Impact of Drainage Noise in Residential Bathrooms Based on Finite Element Simulation

Residential bathroom drainage noise is a primary source of indoor noise that directly affects quality of life and physical and mental health. Therefore, based on the acoustic theory and the finite element simulation technology, this paper proposes a method to simulate the drainage noise characteristics and its impact range jointly using the flow and acoustic fields. The pressure at the pipe wall caused by the internal flow field of the bathroom drainage pipe is calculated by the Fluent software. Simulations are carried out with the Virtual Lab software to predict the drainage noise characteristics and spatial distribution and to analyse the influence of factors such as the door position, riser position, and the partition wall material on the noise distribution. The results show that drainage noise has prominent high-frequency characteristics, the position of the bathroom drainage pipes and doors affects the spatial noise distribution, and the sound insulation performance of a par- tition wall with ordinary fired bricks in the bathroom is slightly better than that of ordinary concrete bricks, lightweight aggregate concrete blocks or fly ash blocks. This paper provides a theoretical basis and practical reference for reducing the impact of residential drainage noise and creating a healthy and comfortable indoor acoustic environment.

StarDICE II: Calibration of an Uncooled Infrared Thermal Camera for Atmospheric Gray Extinction Characterization.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission is a significant source of systematic uncertainty. We propose a solution relying on an uncooled infrared thermal camera to evaluate gray extinction variations. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to temperature-induced effects, compromising their spatial and temporal precision. Moreover, these systems cannot provide scene radiance in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements were conducted inside a climatic chamber, with a FLIR Tau2 camera imaging a thermoregulated blackbody source. The results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel dispersion of 0.09 W m-2 sr-1 and residual spatial noise of 0.03 W m-2 sr-1. We emphasize that the accuracy of scene radiance retrieval can be systematically affected by the camera's close thermal environment, especially when the ambient temperature exceeds that of the scene.

Factors in the Effective Use of Hearing Aids among Subjects with Age-Related Hearing Loss: A Systematic Review.

Objectives: Investigate factors contributing to the effective management of age-related hearing loss (ARHL) rehabilitation. Methods: A systematic review was conducted following PRISMA guidelines. The protocol was registered in PROSPERO (CRD42022374811). Articles were identified through systematic searches in the Scopus, PubMed, Web of Science, and Cochrane databases in May 2024. Only articles published between January 2005 and May 2024 were included. Studies were assessed for eligibility by two independent researchers and evaluated using the Crowe Critical Appraisal Tool v1.4 (CCAT). Results: Of the 278 articles identified, 54 were included. Three factors explain effective HA use. First, hearing aid signal processing, with directional microphones and noise reduction, improves user comfort and understanding regarding noise. Second, there is hearing aid fitting, with the NAL prescription rules as the gold standard, and bilateral, high-level HA performance for spatial localization and noise comprehension. Third, there is a patient-centered approach, using patient-related outcome measures (PROMs), questionnaires, counseling, and regular follow-up to involve patients in their therapeutic rehabilitation. Conclusions: Reaching a consensus on acoustic parameters is challenging due to variability in audiological results. Involving patients in their rehabilitation, addressing their needs and expectations, and offering individualized care are crucial.