Spatial Noise Research Articles

Real-Time Construction Site Noise Mapping System Based on Spatial Interpolation

Because construction noise is one of the major factors affecting the health and safety of workers and nearby residents, it must be managed appropriately. Thus, this study aimed to map real-time noise information generated during construction using a sensor network and spatial interpolation. An optimal sensor was selected in terms of cost and performance by comparing noise measurements with an officially certified sensor in a semianechoic chamber. Field constraints that required or avoided placing the sensors were identified to secure satisfactory noise-estimation performance without interfering with fieldwork. A noise-estimation model was developed based on spatial interpolation, and its performance was evaluated in an experimental environment. The experiment was conducted in two different cases: the first case with only one noise source and the second case by adding another extra noise source. For each case, the optimal sensor combinations were found by considering the noise-estimation performance and the number of sensors installed. As a result, for the first case, the model showed an accuracy of 96.90% and a root-mean square error (RMSE) of 2.19, whereas the second case showed 97.40% and 2.31%, respectively. The noise information estimated by the model was visualized through simulation software for better field applicability. Detailed spatial and numerical noise information using the proposed system will help practitioners intuitively understand the real-time tendency of noise propagation, contributing to the determination of hot spots where reduction measures should be implemented. This information can also be used to more accurately identify workers continuously exposed to noise above dangerous levels and evaluate their actual cumulative noise exposure causing possible hearing damage.

Spatial sigma-delta modulation for coarsely quantized linear, circular, and spherical arrays

Quantization noise shaping, also known as sigma-delta modulation, is widely used in analog-to-digital converters. Such converters oversample a signal in time, quantize it to one or a few bits of precision, and then use analog feedback to shape quantization noise outside the band of interest, producing a high-precision output. Similarly, if a sensor array is spatially oversampled so that its elements are much less than one-half wavelength apart, then quantization noise can be propagated between channels to shape its spatial distribution. A beamformer acts as a spatial filter, removing shaped quantization noise to produce a high-precision output even with coarsely quantized sensors. Spatial sigma-delta modulation has been explored primarily for radio-frequency applications using linear arrays. In this presentation, we apply spatial noise shaping to dense acoustic sensor arrays, including circular and spherical arrays. For immersive audio applications, we demonstrate how noise-shaping feedback can be used to push quantization noise into higher-order Ambisonic modes.

Individual variability in speech recognition with compression and beamforming at positive signal-to-noise ratios

Individuals with hearing loss often struggle to communicate in noisy conditions. Aggressive hearing aid (HA) processing, such as fast-acting wide dynamic range compression (WDRC), may reduce speech recognition under some circumstances, and there is concern about disproportionate reductions in listeners with poor working memory. We investigated how speech recognition is affected when WDRC is combined with highly directional beamforming intended to reduce noise from lateral and rear locations. We also sought to determine how listener factors—working memory, age, and hearing loss—affect such interactions in typical listening conditions, including positive signal-to-noise ratios (SNR) and different spatial noise locations. To-date 22 listeners with mild to moderately severe sensorineural hearing loss repeated low-context sentences (0°) mixed with six-talker interference at + 3 and + 8 dB SNRs, distributed equally in two ( + /−90°) or three ( + /−90°/180°) locations. Wearable HAs presented four signal processing combinations, matched for audibility: microphone directionality (omnidirectional or beamformer) and WDRC (fast- or slow-acting). Data show higher speech recognition with beamforming (re omnidirectional), especially at + 3 dB SNR, regardless of WDRC and spatial locations. Relative contributions of listener factors will be assessed in mixed models in the context of combined signal distortions from HA processing and interferers. [Work supported by NIH-K01DC018324.]

Speech recognition in the presence of speech maskers in children

Listening to a target talker in the presence of other talkers follows a protracted developmental period. Such listening skills are critical for speech-language development and have clinical value. The purpose of the present study was to examine speech-in-speech recognition ability using digits in children. The major advantages of using digits are that it overcomes the biggest challenge of test administration, and digits are among the few first words children learn. Data were collected from 32 normal-hearing children (4–12 years) using full bandwidth speech materials. The test had four conditions like the Listening in Spatial Noise test. (1) Low cue: The target (male talker) and two male maskers were presented from the front. (2) Talker advantage: The target and two female maskers were presented from the front. (3) Spatial advantage: The target was presented from the front, and two male maskers were presented, one each from ±90°. (4) The target was presented from the front, and two female maskers were presented, one from ±90°. Results demonstrate developmental effects, good test-retest reliability, and feasibility of the computerized version of the test. Furthermore, results will be discussed in the context of various conditions and potential clinical applications.

Effect of temporal and spatial noise colour in insect outbreak frequency

Effect of temporal and spatial noise colour in insect outbreak frequency

PeyeMMV: Python implementation of EyeMMV’s fixation detection algorithm

This article presents a new Python module called PeyeMMV. The module implements the two-step spatial dispersion fixation detection algorithm imported in both EyeMMV and LandRate MATLAB toolboxes. This algorithm is based on spatiotemporal criteria, belongs to the family of I-DT based algorithms and can be used as a spatial noise filtering approach during fixation identification process. PeyeMMV module could be utilized in order to extract fixation clusters among raw gaze data as well as to generate a basic plot that visualizes both gaze data and fixation centers positions. The module could be easily imported in every Python script or module.

A step closer to automation: kilovoltage and Megavoltage Planar Imaging Quality Assurance, baseline, tolerance and action levels definition and exploration

PurposeTo establish automated quality assurance (QA) procedures for the kilovoltage (kV) and the Megavoltage (MV) imagers of two linear accelerators (LINACS) using a commercial software. MethodsSNC Machine™ phantoms and software were used and the baseline values, tolerance and action levels for various image quality parameters were defined. Scaling, spatial resolution, contrast, uniformity and noise were considered, explored and evaluated utilizing the appropriate phantoms and the accompanying software. kV and MV planar radiographic images, for 6MV and 10MV beams were obtained for each LINAC. For both kV and MV QA tasks, the baseline values for spatial resolution, contrast, uniformity and noise were defined. ResultsSubsequent measurements performed were highly reproducible and within tolerance and action levels, while noise showed variations. The calculated tolerance and action levels for noise were looser compared to the other image quality metrics. ConclusionsAn automated QA workflow of the kV and MV planar radiographic mode of LINAC imagers’ was established and appears to be time effective.

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen.

Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual-energy CT. To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual-energy CT of the abdomen and compare it to single-energy (SE) CT. An oval 25cmx35cm custom-made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5mGy) and two iodine concentrations (8 and 2mg/mL), using three typical abdominal scan protocols: dual-energy (DE), SE 80kV (SE-80kV) and SE 120kV (SE-120kV). Reconstructions were performed with three strengths of IR (ASiR-V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono-energetic images between 40 and 80keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d') were determined for the reconstructions following the recommendations of AAPM Task Group 233. Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (-29%, -21%) for (AR50%, TF-M), while for DE-70keV were (-28%, -43%). There was less reduction in mean frequency (fav ) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (fpeak ) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in fav ). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d' than IR. The effect of energy was large: d' increased with lower keV, and SE-80kV had higher d' than SE-120kV. Using DE with DLR could provide higher d' than SE-80kV at the higher dose but not at lower dose. DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d' was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low keV and DLR had superior detectability to SE DLR at the high dose but was not better than SE-80kV at low dose.

AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability.

This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability.The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3.Section 4 discusses system calibration, including geometric calibration and uniformity calibration.A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality.Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.

Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in the form of ion images. Unsupervised clustering of ion images facilitates the interpretation in the spectral domain, by identifying groups of ions with similar spatial distributions. Unfortunately, many current methods for clustering ion images ignore the spatial features of the images, and are therefore unable to learn these features for clustering purposes. Alternative methods extract spatial features using deep neural networks pre-trained on natural image tasks; however, this is often inadequate since ion images are substantially noisier than natural images. We contribute a deep clustering approach for ion images that accounts for both spatial contextual features and noise. In evaluations on a simulated dataset and on four experimental datasets of different tissue types, the proposed method grouped ions from the same source into a same cluster more frequently than existing methods. We further demonstrated that using ion image clustering as a pre-processing step facilitated the interpretation of a subsequent spatial segmentation as compared to using either all the ions or one ion at a time. As a result, the proposed approach facilitated the interpretability of MSI data in both the spectral domain and the spatial domain. The data and code are available at https://github.com/DanGuo1223/mzClustering. Supplementary data are available at Bioinformatics online.

Analysis of Spatial and Temporal Variation of Noise Level at Intersections of a Mid-Sized City in India

This study aims to determine spatial and temporal noise variation and acoustical climate at intersections. Data monitoring comprises measurement of peak and off-peak hour variations at 19 intersections between February 2020 to September 2021 in Kanpur, India. Kanpur was proposed as a smart city in 2015 under National Smart Cities Mission. This work has been part of a National Level Project "IMPRINT" to document ambient noise levels for a Tier 2 city with highly heterogeneous traffic. Noise variations are primarily influenced by traffic flow, geometric characteristics, classified volume, and honking events. Frequent stoppage of vehicles for boarding and alighting of passengers also significantly affect noise levels. The results suggest that spatial variation of noise is larger (more than15 dBA) than temporal variation (less than 5 dBA). Noise risk zones are identified based on noise level alongside intersections. Zones are classified as safe, tolerable, low risk, moderate risk, high risk, and extremely high risk zones. Noise level crosses tolerable limit at most study locations, and some locations fall in high-risk zones. The study has highlighted influence of different parameters on spatial and temporal noise variation at intersections and remedial action plans for traffic noise abatement.

Recent Technological Advances in Spatial Active Noise Control Systems

This article provides a broad overview of the recent advances in the field of active noise control techniques to reduce unwanted noise over a certain spatial region of interest. By both commercial and technological advances in local active noise control systems extending the size of the quiet zone seems to be a crucial step to develop the next generation of active control systems for a more personalized and quieter audio products. In this review article, the advances over the past decade in design and development of spatial active noise control techniques to enlarge the controlled sound zone is reviewed. The focus is specifically on the adaptive control techniques and the methods proposed in the frequency domain to control the sound field. The study has paid a specific attention into the most important performance measures in designing a spatial active noise control system such as convergence rate, stability and robustness of the algorithm, and the size of quite zone and how it can be enlarged by configuring the loudspeaker and microphone array geometries. Finally, the authors will discuss the current and future challenges that should be overcome to improve the effectiveness of the recently proposed methods to expand the silence zone.

On a nonlinear SPDE derived from a hydrodynamic limit in a Sinai-type random environment

With the recent developments on nonlinear SPDEs, where smoothing of rough noises is needed, one is naturally led to study interacting particle systems whose macroscopic evolution is described by these equations and which possess an in-built smoothing. In this article, our main results are to derive regularized versions of the ill-posed one-dimensional SPDE ∂tρ=1 2ΔΦ(ρ)−2∇(W′Φ(ρ)), where the spatial white noise W′ is replaced by a regularization Wε′, as quenched and annealed hydrodynamic limits of zero-range interacting particle systems in ε-regularized Sinai-type random environments. Some computations are also made about annealed mean hydrodynamic limits in unregularized Sinai-type random environments with respect to independent particles.

On the Scale‐Dependence of Fault Surface Roughness

AbstractDefining roughness as the ratio of height to length, the standard approach to characterize amplitudes of single fault, joint and fracture surfaces is to measure average height as a function of profile length. Empirically, this roughness depends strongly on scale. The ratio is approximately 0.01 at a few mm but 10× smaller at a few tens of meters. Surfaces are rougher at small scales. However, these conclusions are metric‐dependent. If instead height is averaged over wavelength, roughness is nearly Brown spatial noise, having almost scale‐independent apparent surface height to wavelength ratio. The small deviation from scale‐independence is of the opposite sense than found using the standard metric; surfaces are slightly rougher at long wavelengths. Some natural surfaces may be Brownian within the measurement uncertainties. These contradictions are curiosities of surfaces that have Hurst exponents between 0.5 and 1, as natural fault surfaces do. The wavelength‐based analysis of roughness and how it changes with scale are straight‐forward; a normalized Fourier transform approximately preserves amplitude and its scale dependence in the wavelength domain. Among the conclusions from reconsideration of scale dependence are that the scale dependence is weak and much smaller than that of other fault and shear zone properties. Background and aftershock seismicity, jogs and step‐overs indicate strong localization (smoothing) with slip and scale. The lack of strong scale dependence to surface roughness suggests it is not the dominant control on brittle shear zone evolution.

Application of proper orthogonal decomposition in particle image velocimetry in ocean engineering

Particle image velocimetry (PIV) plays a vital role in flow measurement. Eliminating the effects of overexposure and shadow in particle images is an urgent problem to be solved in the detailed flow measurement of ocean engineering. Seek a robust denoising method for bad test environments, and apply it to the PIV experiments to achieve dual denoising at the image preprocessing and data postprocessing. This study verified the superiority of the Proper Orthogonal Decomposition (POD) filter in pre- and postprocessing compared with traditional mathematical filters. Subsequently, the POD filter is applied to a practical project: the wake field measurement of bulk carrier. Regarding preprocessing, the POD filter is a valuable tool for objectively filtering typical high-frequency spatial noise from the energy perspective based on preserving the original information of particles to the greatest extent, effectively alleviating the impact of overexposure and shadow areas. Regarding postprocessing, the POD filter can fill the data gap by considering the surrounding flow characteristics. At the same time, the flow's spatial distribution and time evolution characteristics can be obtained by POD. The flow reconstructed by selecting the appropriate order model can clearly show the evolution characteristics of large-scale coherent structures, which may provide a deep understanding of complex flow field mechanisms.

High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system

Recent advances in deep learning realized accurate, robust detection of various types of objects including pedestrians on the road, defect regions in the manufacturing process, human organs in medical images, and dangerous materials passing through the airport checkpoint. Specifically, small object detection implemented as an embedded system is gaining increasing attention for autonomous vehicles, drone reconnaissance, and microscopic imagery. In this paper, we present a light-weight small object detection model using two plug-in modules: (1) high-resolution processing module (HRPM ) and (2) sigmoid fusion module (SFM). The HRPM efficiently learns multi-scale features of small objects using a significantly reduced computational cost, and the SFM alleviates mis-classification errors due to spatial noise by adjusting weights on the lost small object information. Combination of HRPM and SFM significantly improved the detection accuracy with a low amount of computation. Compared with the original YOLOX-s model, the proposed model takes a two-times higher-resolution input image for higher mean average precision (mAP) using 57% model parameters and 71% computation in Gflops. The proposed model was tested using real drone reconnaissance images, and provided significant improvement in detecting small vehicles.

Pilot study to determine whether reduced-dose photon-counting detector chest computed tomography can reliably display Brody II score imaging findings for children with cystic fibrosis at radiation doses that approximate radiographs.

The Brody II score uses chest CT to guide therapeutic changes in children with cystic fibrosis; however, patients and providers are often reticent to undergo chest CT given concerns about radiation. We sought to determine the ability of a reduced-dose photon-counting detector (PCD) chest CT protocol to reproducibly display pulmonary disease severity using the Brody II score for children with cystic fibrosis (CF) scanned at radiation doses similar to those of a chest radiograph. Pediatric patients with CF underwent non-contrast reduced-dose chest PCD-CT. Volumetric inspiratory and expiratory scans were obtained without sedation or anesthesia. Three pediatric radiologists with Certificates of Added Qualification scored each scan on an ordinal scale and assigned a Brody II score to grade bronchiectasis, peribronchial thickening, parenchymal opacity, air trapping and mucus plugging. We report image-quality metrics using descriptive statistics. To calculate inter-rater agreement for Brody II scoring, we used the Krippendorff alpha and intraclass correlation coefficient (ICC). Fifteen children with CF underwent reduced-dose PCD chest CT in both inspiration and expiration (mean age 8.9years, range, 2.5-17.5years; 4 girls). Mean volumetric CT dose index (CTDIvol) was 0.07 ± 0.03mGy per scan. Mean effective dose was 0.12 ± 0.04mSv for the total examination. All three readers graded spatial resolution and noise as interpretable on lung windows. The average Brody II score was 12.5 (range 4-19), with moderate inter-reader reliability (ICC of 0.61 [95% CI=0.27, 0.84]). Inter-rater reliability was moderate to substantial for bronchiectasis (0.52), peribronchial thickening (0.55), presence of opacity (0.62) and air trapping (0.70) and poor for mucus plugging (0.09). Reduced-dose PCD-CT permits diagnostic image quality and reproducible identification of Brody II scoring imaging findings at radiation doses similar to those for chest radiography.

Distribution Retrieval of Both Depth and Reflectivity in 3-D Objects via Using Modulated Single Pixel Imaging

3-D objects are illustrated in terms of depth and reflectivity, and these parameters are usually detected by different devices. A modulated single pixel imaging (MSI) method is introduced for simultaneous detections of both depth and reflectivity in 3-D targets. Two modulations for spatial resolution and noise immunity are realized by operations of a digital-micro-mirror device (DMD) and emitted laser amplitude. Laser intensities are modulated at a high frequency up to 100 MHz and demodulated for their amplitude and phase shift. Modulated laser is reflected by the imaged object and collected by a single photodetector. The imaging model is termed as a minimization problem and solved by a proposed modified log-barrier method at optimal iteration steps. The proposed method yields faster convergence and fewer steps of about a half of that used in the typical line-searching method. Two mirrors were positioned in parallel to fabricate a 3-D phantom. The distributions of both depth and surface reflectivity of these mirrors are well reconstructed. Both simulated and experiment data verified the efficacy of proposed MSI on 3-D objects.

Reducing laser beam fluence and intensity fluctuations in symmetric and asymmetric compressors

Abstract All space–time coupling effects arising in an asymmetric optical compressor consisting of two non-identical pairs of diffraction gratings are described analytically. In each pair, the gratings are identical and parallel to each other, whereas the distance between the gratings, the groove density and the angle of incidence are different in different pairs. It is shown that the compressor asymmetry does not affect the far-field fluence and on-axis focal intensity. The main distinctive feature of the asymmetric compressor is spatial noise lagging behind or overtaking the main pulse in proportion to the transverse wave vector. This results in a degraded contrast but reduces beam fluence fluctuations at the compressor output. Exact expressions are obtained for the spectrum of fluence fluctuations and fluence root mean square that depends only on one parameter characterizing compressor asymmetry. The efficiency of small-scale self-focusing suppression at subsequent pulse post-compression is estimated.

FPN Modeling and Model-Based Correction for Shutter-Less Uncooled Bolometric Imagers

This article establishes a behavior model for analyzing the temperature-dependent fixed pattern noise (TD-FPN) induced by pixel passive and active resistance for uncooled bolometric thermal imagers. A model-based two-point correction method is also proposed to compensate for the TD-FPN by using the ratio of passive and active resistance. An infrared imaging system based on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times512$ </tex-math></inline-formula> readout chip is implemented to verify the proposed model and the model-based correction method. The comparison between the simulation and measurement results shows the model’s accuracy. The residuals after the model-based correction are within ±25 LSBs, which is considerably lower than the traditional method’s residuals. Due to the low residuals, the spatial noise equivalent temperature difference (S-NETD) is less than 100 mK within the temperature range of [−40 °C to 80 °C]. The proposed model and the correction method enable uncooled bolometric thermal imagers to achieve low-cost and wide-temperature-range shutter-less operation.