Local Average Research Articles

A Simple Denoising Algorithm for Real-World Noisy Camera Images.

The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an adaptive local averaging filter was executed for each pixel to remove moderate sensor noise while preserving fine image details and object contours. In the second step, image sharpness was enhanced by means of an unsharp mask filter to generate output images that are close to ground-truth images (multiple averages of static camera images). The performance of this denoising algorithm was compared with five popular denoising methods: bm3d, wavelet, non-local means (NL-means), total variation (TV) denoising and bilateral filter. Results show that the two-step filter had a performance that was similar to NL-means and TV filtering. Bm3d had the best denoising performance but sometimes led to blurry images. This novel two-step filter only depends on a single parameter that can be obtained from global image statistics. To reduce computation time, denoising was restricted to the Y channel of YUV-transformed images and four image segments were simultaneously processed in parallel on a multi-core processor.

LAW-IFF Net: A semantic segmentation method for recognition of marine current turbine blade attachments under blurry edges

Challenges exist in the power generation efficiency and safety of marine current turbines (MCTs), as the MCT blades are often attached by foreign objects when operating underwater. It is essential for the stable operation of an MCT to recognize attachments timely and accurately. However, underwater imaging suffers from blurry edges due to light attenuation and scattering. It is challenging for accurate recognition through underwater images since blurry edges result in unclear edge features. To alleviate this problem, LAW-IFF Net is proposed in this paper, which mainly contains two parts. Firstly, this paper proposes to transform the local averages of feature maps into weight matrices, namely the locally average weighting (LAW) mechanism. It is intended to alleviate the edge gradient reduction caused by blurry edges. Secondly, the proposed improved feature fusion (IFF) mechanism aims to overcome the deviation caused by the feature fusion of different attention branches based on spatial attention. At the same time, the lightweight networks are combined with the proposed method to improve the computation speed and ensure the timeliness of recognition. Experimental results on the MCT dataset show the superiority of the proposed method in terms of accuracy and speed of attachment recognition in images with blurry edges. The experimental results on publicly available datasets show the applicability of the proposed method to other underwater images.

Fast Models for Predicting Pollutant Dispersion inside Urban Canopies

A fast pollutant dispersion model for urban canopies is developed by coupling mean wind profiles to a parameterisation of turbulent diffusion and solving the time-dependent advection–diffusion equation. The performance of a simplified, coarse-grained representation of the velocity field is investigated. Spatially averaged mean wind profiles within local averaging regions or repeating units are predicted by solving the three-dimensional Poisson equation for a set of discrete vortex sheets. For each averaging region, the turbulent diffusion is parameterised in terms of the mean wind profile using empirical constants derived from large-eddy simulation (LES). Nearly identical results are obtained whether the turbulent fluctuations are specified explicitly or an effective diffusivity is used in their place: either version of the fast dispersion model shows much better agreement with LES than does the Gaussian plume model (e.g., the normalized mean square error inside the canopy is several times smaller). Passive scalar statistics for a regular cubic building array show improved agreement with LES when wind profiles vary in the horizontal. The current implementation is around 50 times faster than LES. With its combination of computational efficiency and moderate accuracy, the fast model may be suitable for time-critical applications such as emergency dispersion modelling.

Complex amplitude field recovery of a scattering media obstructed object with multi-captured images.

An iterative-based method for recovering the complex amplitude field behind scattering media is presented in this Letter. This method compensates the random phase modulation of scattering media by using multiple captured scattered light fields. Complex amplitude reconstruction with local iterative averaging of scattered light fields, and double weighted feedback is efficiently applied. Two feasible types of system setups, with varying detector positions and wavelength, are proposed. Simulations and proof-of-concept experiments are employed to demonstrate the effectiveness of the proposed method in reconstructing complex amplitude of a hidden target.

Feasibility of structure inversions for gravity-mode pulsators

Context. Gravity-mode asteroseismology has significantly improved our understanding of mixing in intermediate mass stars. However, theoretical pulsation periods of stellar models remain in tension with observations, and it is often unclear how the models of these stars should be further improved. Inversions provide a path forward by directly probing the internal structure of these stars from their pulsation periods, quantifying which parts of the model are in need of improvement. This method has been used with success in the case of solar-like pulsators, but has not yet been applied to main-sequence gravity-mode pulsators. Aims. Our aim is to determine whether structure inversions for gravity-mode pulsators are feasible. We focus on the case of slowly rotating slowly pulsating B-type (SPB) stars. Methods. We computed and analyzed dipole mode kernels for three variables pairs: (ρ, c), (N2, c), and (N2, ρ). We assessed the potential of these kernels by predicting the oscillation frequencies of a model after perturbing its structure. We then tested two inversion methods, regularized least squares (RLS) and subtractive optimally localized averages (SOLA), using a model grid computed with the MESA stellar evolution code and the GYRE pulsation code. Results. We find that changing the stellar structure affects the oscillation frequencies in a nonlinear way. The oscillation modes for which this nonlinear dependency is the strongest are in resonance with the near-core peak in the buoyancy frequency. The near-core region of the star can be probed with SOLA, while RLS requires fine tuning to obtain accurate results. Both RLS and SOLA are strongly affected by the nonlinear dependencies on the structure differences, as these methods are based on a first-order approximation. These inversion methods need to be modified for meaningful applications of inversions to SPB stars. Conclusions. Our results show that inversions of gravity-mode pulsators are possible in principle, but that the typical inversion methods developed for solar-like oscillators are not applicable. Future work should focus on developing nonlinear inversion methods.

Phase diagram of strongly coupled fine particle (dusty) plasmas with limited Spacial extents: Theory and simulation

AbstractPhase separations in strongly coupled fine particle (dusty, complex) plasmas are discussed and phase diagrams are expressed in the ‐‐plane or in terms of strengths of the Coulomb coupling between fine particles and the screening by the ambient electron‐ion plasma. Two possibilities, separations into phases with different electrons, ion, and fine particle densities and ones into phases with a common electron density, are analyzed and it is shown that, when the spacial extent is limited as in usual experiments, only the latter is realized. Based on a model of fine particle plasma as a Yukawa system with non‐uniformity taken into account, molecular dynamics simulations are performed with constant electron density. Fine particles are moved as particles while position‐dependent local averages of ion and fine particle distributions are treated as continuum. Though the critical point is located at a smaller coupling by about a factor of two compared with the theoretical prediction, the results of simulations confirm the existence of such phase separations with a common electron density.

Approximation of Nonhomogeneous Random Field from Local Averages

In this article, we consider the extension of Shannon sampling series reconstruction theorem for nonhomogeneous random fields using local averages sampling, which helps improve certain earlier results. The upper bound of mean square truncation sampling approximation error is more precise, and we establish one approximation result in the almost sure sense.

A combinatorial proof of a sumset conjecture of Furstenberg

We give a new proof of a sumset conjecture of Furstenberg that was first proved by Hochman and Shmerkin in 2012: if log r/log s is irrational and X and Y are times r - and times s-invariant subsets of [0, 1], respectively, then dim _{text {H}}(X + Y ) = min (1, dim _{text {H}}X + dim _{text {H}}Y ). Our main result yields information on the size of the sumset lambda X + eta Y uniformly across a compact set of parameters at fixed scales. The proof is combinatorial and avoids the machinery of local entropy averages and CP-processes, relying instead on a quantitative, discrete Marstrand projection theorem and a subtree regularity theorem that may be of independent interest.

Crustal attenuation structure of the Tianshan tectonic belt and its spatiotemporal variations

The quality factor value (Q) of the crustal medium, which can describe the anelasticity within the Earth’s interior, is a sensitive indicator of changes in the crystalline structure induced by temperature and phase transformations. Although the velocity structure of the Tianshan region in Central Asia has been extensively studied, studies regarding its Q values are limited. These studies focus mainly on the crustal attenuation structure of the Tianshan region; however, their results are limited to the qualitative analyses of the local areas or averages over large areas. Therefore, in this study, we conducted seismic attenuation tomography to create a Q map of the crust underneath the Tianshan tectonic belt (TTB) at a resolution of 0.8° × 0.8° using data from 24,273 near-source waveforms recorded by 51 observation stations of the Xinjiang regional seismic network from 2009 to 2020. The regional distribution of the static and sliding-average values (QS) was calculated. The average value (Q0) of TTB was approximately 523. Additionally, Qs exhibited considerable lateral variations that strongly correlate with the surface tectonics of the TTB region. Furthermore, the velocity and attenuation structures of the TTB were positively correlated. The main part of the TTB exhibited high velocities and Q (indicating low attenuation), whereas the areas adjoining the Tarim and Junggar basins (at the South and North of the TBB, respectively) and their margins exhibited low velocities and Q (indicating high attenuation). This suggested that the attenuation structure of the TTB was highly consistent with its velocity and density structures. Since 1900, most earthquakes in the TTB having magnitudes ≥6.0 earthquakes have occurred at the junctions of high- and low-Q-value areas, or in areas with low Q values. According to the Qs values in different periods, the average Qs of the entire TTB only varied between 500 and 540. However, the average Qs in the middle TTB region portrays an upward trended over time (from 494 in 2010 to 554 in 2020). The average Qs of the southwestern TTB region has been relatively stable, varying between 490 and 530. The Q0 of the southwestern TTB region was lower than that observed in the middle TTB region in most of the time. This observation is more consistent with the tectonic activity recorded in the southwestern TTB region (with greater intensity) than that observed in the middle Tianshan. In addition, the number of earthquakes with magnitudes ≥4.0 correlated positively with the regional average Qs in the middle and southwestern Tianshan. Notably, the higher the regional average Q value, the larger the number of moderate earthquakes. This correlation suggests that in earthquake-prone regions, the accumulation and release of stress influence the opening or closure of crustal fractures, resulting in noticeable changes in the Q values. The findings of our study provide novel insights into the mechanisms of earthquakes and their correlation with the structure of the Earth’s crust.

Efficient Bayesian inversion of borehole geophysical measurements with a gradient‐based Markov chain Monte Carlo method

AbstractWell logs are geophysical measurements of rock properties acquired continuously along a borehole. Because of the physics underlying their operation, borehole logging instruments perform local spatial averages of rock properties in the vicinity of the borehole, yielding well logs that are often affected by tool design, layer boundaries, borehole environmental conditions and mud‐filtrate invasion. Such environmental effects are ubiquitous and can lead to errors in petrophysical–property estimations from well logs if not accounted for in the interpretation. Separate well‐log inversion mitigates environmental effects by matching well logs with their numerical simulations. The latter simulations rely on specific assumptions about the relative geometry of the borehole and the rocks penetrated by the well. For vertical wells penetrating horizontal layers, it is common to assume piecewise‐constant layer properties that we collectively refer to as the earth model; some of these properties have a direct relationship to well logs (e.g. resistivity, gamma ray, density and acoustic slowness). Well‐log inversion is traditionally approached with deterministic methods such as the Levenberg–Marquardt algorithm to minimize the differences between well logs and their numerical simulations, often without accounting for the relationship between measurement noise and model uncertainty. Bayesian inversion, on the other hand, yields the posterior probability distribution of estimated properties and intrinsically quantifies their uncertainty. However, Bayesian Well‐log inversion usually involves the implementation of Markov chain Monte Carlo sampling, which requires a prohibitive number of forward simulations and is therefore not suitable for rapid petrophysical and/or elastic and mechanical evaluations of rocks. We introduce an efficient Bayesian Well‐log inversion method using a gradient‐based Markov chain Monte Carlo method. Gradient‐based Markov chain Monte Carlo is a group of relatively new Markov chain Monte Carlo algorithms that combine gradient updates with Hessian‐based sampling. gradient‐based Markov chain Monte Carlo draws samples efficiently from the posterior probability distribution using the gradient information to guide samples towards high‐probability regions and the Hessian to approximate the posterior probability distribution locally. We verify the gradient‐based Markov chain Monte Carlo inversion method with the inversion of synthetic well logs, including gamma ray, resistivity, density, neutron porosity, photoelectric factor and compressional/shear‐wave slowness. Results show that gradient‐based Markov chain Monte Carlo decreases the computational cost by more than 90% compared to conventional Markov chain Monte Carlo sampling. Next, gradient‐based Markov chain Monte Carlo inversion is applied to a field example from the Central North Sea, where conventional interpretation methods yield shale concentration, sandstone porosity and water saturation with errors up to 19.2%, 14.9% and 48.8%, respectively. Gradient‐based Markov chain Monte Carlo inversion products satisfactorily reproduce the available measurements with only a modest increase in computational cost compared to deterministic inversion approaches.

A Compact Derivation of Quantum Potential in de Broglie–Bohm Theory

In the stochastic electrodynamics, various quantum effects on particles are supposed to emerge from the interactions of the particles with the fluctuating background field. We have adopted the same supposition and further supposed that the quantum potential arises from the velocity fluctuations of particles around their local averages. Based on these suppositions we have derived the expression of the quantum potential in the framework of de Broglie–Bohm theory, which is a reformulation of Schrödinger’s. Thus we are allowed to use the wave functions and Schrödinger operators explicitly so that we have been able to derive the quantum potential in simpler and more compact manner as compared with the stochastic electrodynamics’s. A new hydrodynamical version of Schrödinger equation is also presented, which has been found in the course of our study.

Self-guided molecular dynamics simulation based on concerted movement.

Self-guided molecular/Langevin dynamics (SGMD/SGLD) simulation methods were developed to enhance conformational sampling through promoting low frequency motion of molecular systems and have been successfully applied in many simulation studies. The low frequency properties are derived effectively from local averages over the most recent trajectories. Using low frequency momentums and low frequency forces, SGMD/SGLD achieve accelerated diffusion and enhanced energy barrier crossing. In addition to the local frequency motion, we find concerted movement is important for rare event like protein folding and assembly. This work presents a new self-guided simulation method that accelerates conformational search through enhancing concerted movement. We abbreviate this method as SGMDc or SGLDc. Concerted motion is derived from spatial averages over chemical bonded atoms and/or nearby atoms. Combined with the local average scheme, one can derive concerted low frequency properties and use them as guiding forces to achieve accelerated conformational evolvement. Using peptide folding and pore formation in membrane, we demonstrate that SGMDc/SGLDc can accelerate rare events significantly.

Abstract TP187: The Mortality Cost Of Air Pollution And Carbon Dioxide Related To Mobile Stroke Unit Operation

Background: Mobile stroke units (MSU) have reduced time to thrombolysis, improved outcomes in ischemic stroke, but their sustainability and external costs have not been thoroughly evaluated. Fossil fuel combustion produces carbon dioxide (CO 2 ) and air pollution, which contribute to global climate crisis and excess mortality. Aim: To estimate the mortality cost of MSU due to increment of CO 2 and air pollution from fuel burned by MSU. Methods: Fuel cost of annual operation of MSU, obtained through correspondence, was used to obtain annual volume of gasoline burned by MSU. Estimate of excess global mortality between 2020 and 2100 from CO 2 -related climate change per increment of CO 2 was obtain from literature on the Mortality Cost of Carbon. Assuming the annual city-wide fuel consumption by road vehicles (publicly available) is directly proportional to excess mortality attributable to local traffic-related fine particulate matter (PM 2.5 ) air pollution (obtained from the literature), I estimated the increment of excess mortality related to air pollution produced by MSU operations. Results: Annual operation of one MSU burned 14.552 EUR of fuel, or 10.3 ton of gasoline, producing 24.7 ton of CO 2 . Such an increment of CO 2 in 2020 is expected to cause 0.0056 excess deaths (95% CI -0.0042, 0.0168) globally between 2020 and 2100 by contribution to climate crisis. In Berlin, mobile sources burn about 1.250.000 tons of liquid fossil fuel annually; and ~744 of annual deaths are attributable to traffic-related air pollution [2.17% (95% CI 1.55%, 2.79%) of total mortality]. Assuming the emission factor of MSU is the same as the local vehicle average, MSU-related air pollution leads to 0.0061 excess deaths (95% CI: 0.0044, 0.0079) per MSU-year. Conclusion: Fossil fuel combustion related to MSU operation is estimated to cause 0.0117 excess annual deaths (95% CI 0.0002, 0.0247) due to contribution to local air pollution and global climate crisis.

MCSfM: Multi-Camera-Based Incremental Structure-From-Motion.

Fully perceiving the surrounding world is a vital capability for autonomous robots. To achieve this goal, a multi-camera system is usually equipped on the data collecting platform and the structure from motion (SfM) technology is used for scene reconstruction. However, although incremental SfM achieves high-precision modeling, it is inefficient and prone to scene drift in large-scale reconstruction tasks. In this paper, we propose a tailored incremental SfM framework for multi-camera systems, where the internal relative poses between cameras can not only be calibrated automatically but also serve as an additional constraint to improve the system robustness. Previous multi-camera based modeling work has mainly focused on stereo setups or multi-camera systems with known calibration information, but we allow arbitrary configurations and only require images as input. First, one camera is selected as the reference camera, and the other cameras in the multi-camera system are denoted as non-reference cameras. Based on the pose relationship between the reference and non-reference camera, the non-reference camera pose can be derived from the reference camera pose and internal relative poses. Then, a two-stage multi-camera based camera registration module is proposed, where the internal relative poses are computed first by local motion averaging, and then the rigid units are registered incrementally. Finally, a multi-camera based bundle adjustment is put forth to iteratively refine the reference camera and the internal relative poses. Experiments demonstrate that our system achieves higher accuracy and robustness on benchmark data compared to the state-of-the-art SfM and SLAM (simultaneous localization and mapping) methods.

Clinical course of pediatric gunshot wounds involving the spine and spinal cord: the Miami experience.

Civilian gunshot wounds (GSWs) involving the skeletal spine and spinal cord in pediatric patients are fortunately rare. Nevertheless, their presentation mandates judicious evaluation, and their clinical outcomes remain poorly defined. Thus, the authors aimed to characterize the clinical course of this traumatic presentation in the pediatric population based on their institutional experience. A retrospective review of a level I trauma center database was performed for the period 2011-2021. Clinical data were included for patients aged ≤ 18 years who had presented with radiographic and clinical evidence of a GSW to the spine and had at least one documented follow-up at least 6 months after injury. The primary outcomes of the study were the categorization of gunshot injuries and the results of neurological and functional examinations. A total of 13 patients satisfied the study selection criteria. The mean patient age was 15.7 ± 1.6 years, and all presentations were assault in nature. Most of the patients were male (n = 12, 92%) in gender, Black in race (n = 11, 85%), and from zip codes with a median household income below the local county average (n = 10, 77%). All patients presented with a minimum Glasgow Coma Scale score of 14. Examination at presentation revealed American Spinal Injury Association Impairment Scale (AIS) grade A in 3 cases (23%), grade B in 2 (15%), grade C in 1 (8%), grade D in 2 (15%), and grade E in 5 (38%). Gunshot injury involved all regions of the spine, most commonly the cervical and thoracic spine (n = 6 for each, 46%). In terms of skeletal injury, the most common injuries were to the facet (n = 10, 77%) and the pedicle (n = 8, 62%), with evidence of intracanal injury in 9 patients (69%). Neurosurgical intervention was pursued in 1 patient (8%). Overall, 7 patients (54%) experienced a complication during admission, and the median length of hospitalization was 12 days (range 1-88 days) without any mortality events. Within 90 days from discharge, 2 patients (15%) were readmitted to the hospital for further care. The mean follow-up was 28.9 months (range 6-74 months), by which only 1 patient (8%) had an improved AIS examination; all other patients remained at their initial AIS grade. Pediatric GSWs involving the spine are typically nonfatal presentations, and their long-term functional outlook appears contingent on clinical examination findings at initial presentation. Although neurosurgical intervention is not necessary in most cases, judicious evaluation of radiographic and clinical examinations by a neurosurgical team is strongly recommended to optimize recovery.

A Multienergy Computed Tomography Method Based on a Blind Decomposition Model for Multivoltage X-Ray Transmission Images

Multienergy computed tomography (CT) can be implemented based on traditional CT systems to reduce the hardware cost. However, prior knowledge of the X-ray spectra or material composition and multiple scans are required in most such implementation methods, which imposes an extra burden. To avoid this, a method based on a blind decomposition model of multivoltage X-ray transmission images is proposed in this paper. The model considers X-ray scattering as an important factor resulting in errors of theoretical and measured polychromatic X-ray transmission. Since X-ray scatter has low-frequency characteristics, it is estimated via local averaging of the errors and subtracted from the errors. The solution algorithm for the model is derived with the Karush–Kuhn–Tucker conditions. In addition, image segmentation of the projection or reconstructed image is not needed. Samples representing different imaging situations are used in the experiments. Beam hardening artifacts are obviously reduced in the reconstructed images obtained with the proposed method. The relative errors of the X-ray energies of different component materials are calculated for the reconstructed images. For the first sample, the errors are less than 1% in the reconstructed images obtained with the proposed method and are seen to be the best among all compared methods. For the second sample, the errors are less than 10% in most reconstructed images obtained with the proposed method, whereas they are larger than 10% in most reconstructed images obtained with the compared methods. These results demonstrate the effectiveness of the proposed method.

Local Averaging for Consensus Over Communication Links With Random Dropouts

This letter proposes a mechanism for consensusability of multi-agent systems which communicate through packet loss channels. A distinguishing feature of our approach is that we assume that each agent may act on an estimate of the state of its neighbours when packet losses occur. A system using the proposed compensation method subject to nonidentical Markovian dropouts is analysed and conditions for consensusability are proposed. Simulation results are provided which verify the proposed conditions and show that it allows for consensusability over a wider range of dropout probabilities than methods previously documented in the literature.

Exact classical limit of the quantum bouncer

In this paper, we develop a systematic approach to determine the classical limit of periodic quantum systems and we applied it successfully to the problem of the quantum bouncer. It is well known that, for periodic systems, the classical probability density does not follow the quantum probability density. Instead, it follows the local average in the limit of large quantum numbers. Guided by this fact, and expressing both the classical and quantum probability densities as Fourier expansions, here we show that local averaging implies that the Fourier coefficients approach each other in the limit of large quantum numbers. The leading term in the quantum Fourier coefficient yields the exact classical limit, but subdominant terms also emerge, which we may interpret as quantum corrections at the macroscopic level. We apply this theory to the problem of a particle bouncing under the gravity field and show that the classical probability density is exactly recovered from the quantum distribution. We show that for realistic systems, the quantum corrections are strongly suppressed (by a factor of \(\sim 10^{-10}\)) with respect to the classical result.

Estimating the Spectral Density at Frequencies Near Zero

Estimating the spectral density function f(w) for some w ∈ [ − π , π ] has been traditionally performed by kernel smoothing the periodogram and related techniques. Kernel smoothing is tantamount to local averaging, that is, approximating f(w) by a constant over a window of small width. Although f(w) is uniformly continuous and periodic with period 2 π , in this article we recognize the fact that w = 0 effectively acts as a boundary point in the underlying kernel smoothing problem, and the same is true for w = ± π . It is well-known that local averaging may be suboptimal in kernel regression at (or near) a boundary point. As an alternative, we propose a local polynomial regression of the periodogram or log-periodogram when w is at (or near) the points 0 or ± π . The case w = 0 is of particular importance since f(0) is the large-sample variance of the sample mean; hence, estimating f(0) is crucial in order to conduct any sort of inference on the mean. Supplementary materials for this article are available online.

Clean two-dimensional Floquet time crystal

We consider the two-dimensional quantum Ising model, in absence of disorder, subject to periodic imperfect global spin flips. We show by a combination of exact diagonalization and tensor-network methods that the system can sustain a spontaneously broken discrete time-translation symmetry. Employing careful scaling analysis, we show the feasibility of a two-dimensional discrete time-crystal (DTC) pre-thermal phase. Despite an unbounded energy pumped into the system, in the high-frequency limit, a well-defined effective Hamiltonian controls a finite-temperature intermediate regime, wherein local time averages are described by thermal averages. As a consequence, the long-lived stability of the DTC relies on the existence of a long-range ordeblack phase at finite temperature. Interestingly, even for large deviations from the perfect spin flip, we observe a non-perturbative change in the decay rate of the order parameter, which is related to the long-lived stability of the magnetic domains in 2D.