Filter Strength Research Articles

A novel dynamic scale factor designed for recovering global TWS changes

For time-variable satellite gravity solutions of GRACE and GRACE-FO in terms of spherical harmonics coefficients, the Scale Factor (SF) is often used to recover the close to “true” signal of Terrestrial Water Storage (TWS) anomalies. However, the conventional SF method has some limitations that may hinder its effectiveness, including: (1) their dependency on input hydrological models that may lead to divergent estimations of SFs; (2) the arbitrary choice of filter strength, which may not be representative in different regions; (3) limited consideration of SF in the temporal dynamics (the conventional SF was fixed value) for monthly varied TWS. Here, we propose a new Dynamic SF method to overcome these limitations and increase accuracy of the restored global TWS changes. This method involves: (1) the Bayesian Three-Cornered Hat (BTCH) method is applied to merge three sets of hydrological products into an optimal hydrological dataset to be used for estimating a unique SF, (2) the anisotropic DDK3 filter (found to be numerically optimal) is applied to suppress the correlated noise, and (3) an iterative Kalman filter process is formulated and implemented to estimate monthly Dynamic SF corresponding to monthly global TWS fields. The Dynamic SF outperformed an ordinary SF method in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), and the Signal to Noise Ratio (SNR), which were found to be improved by 30.8%, 32.2%, and 41.3%, respectively. Moreover, the recovered TWS using the Dynamic SF method showed a good agreement with the GRACE/GRACE-FO RL06 mascon solutions of the CSR and that of JPL regarding the long-term trend, seasonal, and interannual variations.

Biotic and abiotic factors drive multi-trophic interactions among spiders at different spatial scales in urban greenspaces

Abstract Urbanization is often detrimental to biodiversity, yet urban greenspaces can be managed to provide habitat for many arthropods. Understanding how anthropogenic filters influence processes of community assembly in urban ecosystems will inform conservation of species such as spiders, which provide natural pest control. Spiders are abundant in urban areas, but the relative importance of biotic and abiotic factors for structuring spider communities in urban greenspaces is unclear. We initiated the Cleveland Pocket Prairie Project in the legacy city of Cleveland, Ohio, where vacant lots and urban prairies were established across eight inner-city neighborhoods. In each greenspace, spiders were collected along with landscape and local environmental data in July 2017. Using a path analysis approach, we investigated the relative importance and strength of landscape and local environmental filters for influencing the structure of spider communities during mid-summer within this system. We found that spider community assembly was influenced by multiple abiotic and biotic drivers across spatial scales related to landscape composition, soil texture and quality, prey breadth, and habitat management designs. Web-building and active hunting spiders responded differently to these landscape and local drivers, highlighting the need to incorporate a functional perspective when studying community assembly. These findings suggest that a multi-scale approach to conservation management is needed to support biodiversity and associated biological control services in urban ecosystems.

Deterrence Perceptions, Self-Control Ability and the Moral Filter: Conceptualizing and Testing a Model of a Subsidiary Relevance of Deterrence

ABSTRACT The idea of differential deterrence suggests that deterrent effects vary across individuals and settings. However, the pertinent research is poorly integrated and lacks a coherent theoretical conceptualization. This is why the present article seeks to (a) reconcile the empirical findings obtained for the most frequently investigated moderators of the magnitude of the deterrent effect, (b) introduce a novel model of a subsidiary relevance of deterrence, and (c) provide a first test of the new model. A narrative review of the previous research on differential deterrence forms the basis of theoretical efforts to build a model of subsidiary deterrent effects that is consistent with the available evidence. The proposed model synthesizes the conditioning roles of the strength of the moral filter and the level of trait self-control. In detail, it is assumed that the total strength of the moral filter governs whether controls affect behavior (because it determines whether crime is actually being pondered) and that, given a permeable moral filter, deterrence matters particularly for individuals with lower self-control ability. Following the presentation of the new model, we report the results of its first empirical examination. Analyzing a self-report survey on adolescent shoplifting activity yields supportive findings. There is evidence of a moderated moderation relationship according to which a poor capacity for self-control increases the association between the perceived risk of getting caught and the level of shoplifting delinquency mainly under conditions of a weak moral filter.

A model of radiofrequency and plasma coupling for compact ion sources and design

Inductive coupling of radiofrequency power to plasma is a complicate process, since it depends from the density of plasma itself, because ionization is a chain reaction process, and, at low density a capacitive coupling may mix with inductive coupling (with no Faraday screen). Plasma temperature Te , density ne and vector potential are closely coupled, giving nonlinear and singular systems of Partial Differential equations, which require slow iterative solutions, motivating the consideration of a 2D model, also as rapid design and first approximation tool. Plasma conductivity and heating depend on collision rate, which includes also the so-called stochastic collisions (mainly electron collisions with walls), proportionally more important at low gas density ng . Conductivity is also affected by static and radiofrequency magnetic fields; results for skin depth and stochastic collision estimates are reported. The transport of Te and ne inside source can be controlled by a magnetic filter B f . Considering a 5 cm radius H- ion source as example, solution of ne and Te are reported as a function of filter strength B fa, applied rf power and wall status; solver convergence methods andkey plasma observables are briefly discussed. Due to small dimension, a filter strength B fa in the order of 8 mT is needed to achieve electron temperature lower than 2 eV (for negative ion production) at extraction.

Towards a new generation of environmentally-friendly ceramic foam filters: contribution of graphene nanoadditives in calcium aluminate-rich coatings

In this contribution, the widely used non-ecological production process of ceramic foam filters was modified into an environmentally friendly procedure, in which no hazardous materials are involved. Firstly, a large number of carbon-bonded ceramic foam filters were prepared by this approach. In the next step, a thin coating consisting of graphene-doped, carbon-bonded calcium aluminate was applied over these filters. Reference samples as well as coated samples were analyzed in detail with a focus on the phase composition, microstructural analysis, and mechanical as well as thermal properties. The results confirmed that the application of the developed coating improved the cold crushing strength of the ceramic foam filters. In addition, by application of graphene nanoadditives, it is also possible to vary the surface microroughness which will subsequently enable improved efficiency of inclusions elimination from metal melts during the filtration process.

Effect of α-Al2O3 content on microstructures, mechanical properties and purification efficiency on molten steel of MgO-based filters

In this study, five sets of MgO-based ceramic filters were prepared using porous MgO powder and α-Al2O3 micro-powder as raw materials. The effect of α-Al2O3 content on the microstructures, mechanical properties and purification efficiency of MgO-based filters were investigated via SEM, EDS, and immersion test in molten steel, etc. The results indicate that the reactive sintering between α-Al2O3 and MgO promoted the formation and growth of neck connections of magnesium aluminate spinel between grains, increased the density of the filter skeleton, and decreased the pore size inside the skeleton. The higher density and smaller pore size of the skeleton, as well as the neck connection of spinel and crack deflection effect caused by magnesium aluminate spinel, comprehensively improved the strength and thermal shock resistance of MgO-based ceramic filters. Furthermore, the results from the immersion test in molten steel revealed that an appropriate amount of high-temperature liquid phase, smaller pore size, and in-situ generated spinel were beneficial for the filters to adsorb dissolved aluminum, dissolved oxygen, alumina inclusions, and secondary spinel inclusions in the steel. The filter with 20 wt.% α-Al2O3 had the best overall performance with a compressive strength of 2.35 MPa, a compressive strength after thermal shock test of 1.91 MPa and an efficiency of 77.3% in reducing the total oxygen content of the steel.

An optimal algorithm for bilateral filtering

Noise is unavoidable in digital signal processing, therefore studying effective denoising method is very important. Bilateral filter is one of them as effective filter methods. But the parameters of conventional bilateral filter are often fixed, thus we can’t realize adaptive parameters according to contents of videos or pictures. A video denoising algorithm based on both spatial domain and time domain is proposed in this paper to improve the video quality based on bilateral filter. This algorithm can adaptively estimate noise intensity, edge intensity and apply motion detection, which will be employed to adaptively adjust the bilateral filter strength. Peak signal to noise ratio (PSNR) is elevated significantly by the proposed algorithm after experimental verification..

Microhabitat variation affects the understory community assembly of a neotropical rainforest

Deterministic and stochastic processes combine with biotic interactions in community assembly. Stochasticity and biotic interactions are thought to operate on fine scales, and determinism on coarse scales. Would determinism be detected on a fine scale in an understory community of a tropical rainforest? We measured the altitude, index of convexity (IC), slope, and canopy openness, counted the individuals and species and calculated Simpson's dominance index of plants with height ≥ 30 cm and DBH < 5 cm for each of 50 randomly drawn 3 × 3 m plots from a one-hectare grid. We used redundancy analysis and spatial modeling with principal coordinates of neighbor matrices to assess how much of the community variation each variable explained. The same was done for each set of plots with 20%, 60%, and 20% respectively of the lower, intermediate, and higher values of the microrelief variables. Most of the community variation remained unexplained, but the effect of the microrelief variables was significant. IC had the highest contribution and was four times higher in the plots with 20% higher values, stressing the increasing importance of determinism towards the higher extremes of the variable's local range. Species richness decreased and dominance increased in convex plots (high IC), and the most abundant species differed between concave and convex plots. Microrelief variation can act as a local filter with a growing influence towards the extremes of the local range of microenvironmental variables in the community space. Niche-based processes rely on the strength of the environmental filter.

Repeatability and reproducibility of apparent exchange rate measurements in yeast cell phantoms using filter-exchange imaging

ObjectivesDevelopment of a protocol for validation and quality assurance of filter-exchange imaging (FEXI) pulse sequences with well-defined and reproducible phantoms.Materials and methodsA FEXI pulse sequence was implemented on a 7 T preclinical MRI scanner. Six experiments in three different test categories were established for sequence validation, demonstration of the reproducibility of phantoms and the measurement of induced changes in the apparent exchange rate (AXR). First, an ice–water phantom was used to investigate the consistency of apparent diffusion coefficient (ADC) measurements with different diffusion filters. Second, yeast cell phantoms were utilized to validate the determination of the AXR in terms of repeatability (same phantom and session), reproducibility (separate but comparable phantoms in different sessions) and directionality of diffusion encodings. Third, the yeast cell phantoms were, furthermore, used to assess potential AXR bias because of altered cell density and temperature. In addition, a treatment experiment with aquaporin inhibitors was performed to evaluate the influence of these compounds on the cell membrane permeability in yeast cells.ResultsFEXI-based ADC measurements of an ice–water phantom were performed for three different filter strengths, showed good agreement with the literature value of 1.099 × 10–3 mm2/s and had a maximum coefficient of variation (CV) of 0.55% within the individual filter strengths. AXR estimation in a single yeast cell phantom and imaging session with five repetitions resulted in an overall mean value of (1.49 ± 0.05) s−1 and a CV of 3.4% between the chosen regions of interest. For three separately prepared phantoms, AXR measurements resulted in a mean value of (1.50 ± 0.04) s−1 and a CV of 2.7% across the three phantoms, demonstrating high reproducibility. Across three orthogonal diffusion directions, a mean value of (1.57 ± 0.03) s−1 with a CV of 1.9% was detected, consistent with isotropy of AXR in yeast cells. Temperature and AXR were linearly correlated (R2 = 0.99) and an activation energy EA of 37.7 kJ/mol was determined by Arrhenius plot. Furthermore, a negative correlation was found between cell density (as determined by the reference ADC/fe) and AXR (R2 = 0.95). The treatment experiment resulted in significantly decreased AXR values at different temperatures in the treated sample compared to the untreated control indicating an inhibiting effect.ConclusionsUsing ice–water and yeast cell-based phantoms, a protocol for the validation of FEXI pulse sequences was established for the assessment of stability, repeatability, reproducibility and directionality. In addition, a strong dependence of AXR on cell density and temperature was shown. As AXR is an emerging novel imaging biomarker, the suggested protocol will be useful for quality assurance of AXR measurements within a study and potentially across multiple sites.

Numerical Investigation of the Inviscid Taylor-Green Vortex Using an Adaptive Filtering Method for a Modal Discontinuous Galerkin Method

Implicit Large Eddy Simulation and under-resolved Direct Numerical Simulation bypass the complexity and uncertainty of turbulence modelling by using the numerical dissipation of the scheme as a subgrid scale model. High-order methods allow for more accurate capturing of smaller scale structures but suffer from energy pile-up in the higher modes which leads to instability in under-resolved applications. This work presents a filtered modal Discontinuous Galerkin method which adaptively determines the filter strength, avoiding unnecessary degradation of accuracy while maintaining stability. The method is applied to the inviscid Taylor-Green Vortex, a challenging test case which exhibits under-resolved turbulence for which few published results exist. This goal of this work is to present the adaptive filtering method which achieves robustness and accuracy despite a low number of degrees of freedom, as well as to publish a quantity of relevant data for the inviscid TGV problem.

Sampling technique for Fourier convolution theorem based k‐space filtering

AbstractThis research presents a sampling technique for Fourier convolution theorem (FCT) based k‐space filtering. One polynomial function and three transfer functions were selected: (1) Gaussian, (2) Bessel, (3) Butterworth, and (4) Chebyshev. The functions were sampled on the image grid, and they are called filtering functions. Each filtering function was multiplied by the Sinc function to obtain the “Sinc‐shaped convolving function.” The k‐space of the Sinc‐shaped convolving function is calculated by direct Fourier transform (FT) and it is featured by a central region. This central region of the k‐space is rectangular in its shape because it is consequential to the direct FT of the product between the filtering function and the Sinc function. Low‐pass and high‐pass filtering is obtained by inverse FT of the pointwise multiplication between the k‐space of the departing image and the k‐space of the Sinc‐shaped convolving function. A variety of cut‐off frequencies, bandwidth, sampling rates, and numbers of poles of the filters were verified as effective to filter the images. Filtering strength can be modified by fine‐tuning the size of the central rectangular k‐space region of the Sinc‐shaped convolving functions. K‐space analysis of departing images and filtered images provide additional evidence of effective filtering. Moreover, k‐space filtering was compared to Z‐space filtering using the extension of the FCT to Z‐space. The novelty of this research is the sampling technique used to determine the Sinc‐shaped convolving function. The sampling technique uses fine‐tuning of bandwidth and sampling rate to determine the strength of the k‐space filter.

IDAM: Iteratively Trained Deep In-loop Filter with Adaptive Model Selection

As a rapid development of neural-network-based machine learning algorithms, deep learning methods are being tentatively used in a much wider range than well-known artificial intelligence applications such as face recognition or auto-driving. Recently, deep learning models are investigated intensively to improve the compression efficiency for video coding, especially at the in-loop filtering stage. Although deep learning-based in-loop filtering methods in prior arts have already shown a remarkable potential capability in video coding, content propagation issue is still not well recognized and addressed yet. Content propagation is the fact that contents of reference frames are propagated to frames referring to them, which typically leads to over-filtering issues. In this article, we develop an iteratively trained deep in-loop filter with adaptive model selection (iDAM) to address the content propagation issue. First, we propose an iterative training scheme, which enables the network to gradually take into account the impacts of content propagation. Second, we propose a filter selection mechanism, i.e., allowing a block to select from a set of candidate filters with different filtering strengths. Besides, we propose a novel approach to design a conditional in-loop filtering method that can deal with multiple quality levels with a single model and serve the functionality of filter selection by modifying the input parameters. Extensive experiments on top of the latest video coding standard (Versatile Video Coding, VVC) have been conducted to evaluate the proposed techniques. Compared with VTM-11.0, our scheme achieves a new state-of-the-art, leading to {7.91%, 20.25%, 20.44%}, {11.64%, 26.40%, 26.50%}, and {10.97%, 26.63%, 26.77%} BD-rate reductions on average for {Y, Cb, Cr} under all-intra, random-access, and low-delay configurations, respectively. As far as we know, our proposed iDAM scheme provides the highest coding performance compared to all existing solutions. In addition, the syntax elements of the proposed scheme were adopted at the 76th meeting of Audio Video coding Standard (AVS) held this year.

Adaptive physics-informed trajectory reconstruction exploiting driver behavior and car dynamics

As more and more trajectory data become available, their analysis creates unprecedented opportunities for traffic flow investigations. However, observed physical quantities like speed or acceleration are often measured having unrealistic values. Furthermore, observation devices have different hardware and software specifications leading to heterogeneity in noise levels and limiting the efficiency of trajectory reconstruction methods. Typical strategies prune, smooth, or locally modify vehicle trajectories to infer physically plausible quantities. The filtering strength is usually heuristic. Once the physical quantities reach plausible values, additional improvement is impossible without ground truth data. This paper proposes an adaptive physics-informed trajectory reconstruction framework that iteratively detects the optimal filtering magnitude, minimizing local acceleration variance under stable conditions and ensuring compatibility with feasible vehicle acceleration dynamics and common driver behavior characteristics. Assessment is performed using both synthetic and real-world data. Results show a significant reduction in the speed error and invariability of the framework to different data acquisition devices. The last contribution enables the objective comparison between drivers with different sensing equipment.

Investigation of mechanical and thermo-mechanical strength of ceramic foam filters (CFFs)

During aluminium production, the molten metal will always contain varying amounts of impurities, e.g., non-metallic inclusions, and for high-quality products removing such inclusions is essential. This can be achieved by filtration using ceramic foam filters (CFFs). However, these filters are highly brittle materials subjected to strong mechanical and thermo-mechanical stresses during transport and operation, which occasionally leads to failure of the filter material. In the present study, the compression strength of five different Al2O3-based CFFs was measured at room temperature and elevated temperature (compressed at 730 °C), as well as while submerged in molten aluminium with varying melt compositions (pure aluminium and an aluminium-magnesium alloy). The compression strengths at room temperature were established to be in the range of 1.19–2.09 MPa depending on the filter type tested. In the case of the CFFs compressed at elevated temperature, a reduction in compression strength in the range of 9.2–58.6% was established to exist depending on filter type and heating duration, except in three of the filter/duration-combinations tested. Compression of CFF samples submerged in molten aluminium led to an even further reduction in compression strength in the range of 42.6–69.4% depending on filter type and duration of exposure. With an exposure time of only 5 min, no difference in compression strength was observed between the two aluminium melts.

Positivity-preserving entropy-based adaptive filtering for discontinuous spectral element methods

In this work, we present a positivity-preserving entropy-based adaptive filtering method for shock capturing in discontinuous spectral element methods. By adapting the filter strength to enforce positivity and a local discrete minimum entropy principle, the resulting approach can robustly resolve strong discontinuities with sub-element resolution, does not require problem-dependent parameter tuning, and can be easily implemented on general unstructured meshes with relatively low computational cost. The efficacy of the approach is shown in numerical experiments on hyperbolic and mixed hyperbolic-parabolic conservation laws such as the Euler and Navier-Stokes equations for problems including extreme shocks, shock-vortex interactions, and complex compressible turbulent flows.

Adaptive DDK Filter for GRACE Time-Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient and the power index of signal covariances, which are adaptively determined from the data themselves according to the generalized cross validation (GCV) criterion. The regularization coefficient controls the global constraint on the signal variances of all degrees, while the power index adjusts the attenuation of the signal variances from low to high degrees, namely local constraint. By tuning these two parameters for the monthly SCM, the adaptability to the data and the optimality of filtering strength can be expected. In addition, we also devise a half-weight polygon area (HWPA) of the filter kernel to measure the filtering strength of the anisotropic filter more reasonably. The proposed adaptive DDK filter and filtering strength metric are tested based on CSR GRACE temporal gravity solutions with their ECMs from January 2004 to December 2010. Results show that the selected optimal power indices range from 3.5 to 6.9, with the corresponding regularization parameters range from 1 × 1014 to 5 × 1019. The adaptive DDK filter can retain comparable/more signal amplitude and suppress more high-degree noise than the conventional DDK filters. Compared with the equivalent smoothing radius (ESR) of filtering strength, the HWPA has stronger a distinguishing ability, especially when the filtering strength is similar.

Production of ultra-steep dichroic filters with broad band optical monitoring.

The feasibility of using direct broad band optical monitoring control in the fabrication of the ultra-steep dichroic filters based on resonant structures is investigated. Using computational manufacturing and deposition experiments, the role of the errors self-compensation effect is clarified by comparing the results of direct broad band optical monitoring and time monitoring. The errors correlation strength of ultra-steep dichroic filter is analyzed and it shows that the correlation calculated by the current model is not strong. The relationship between errors correlation and errors self-compensation effect for the ultra-steep dichroic filter is discussed.

Detecting Central Nodes From Low-Rank Excited Graph Signals via Structured Factor Analysis

This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass property with a generic low-rank excitation model. We treat two cases depending on the low pass graph filter's strength. When the graph filter is strong low pass, i.e., it has a frequency response that drops sharply at the high frequencies, we show that the principal component analysis (PCA) method detects central nodes with high accuracy. For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor. We propose a two-stage decomposition algorithm to learn the structured factor model via a judicious combination of the non-negative matrix factorization and robust PCA algorithms. We analyze the identifiability conditions for the model which lead to accurate central nodes detection. Numerical experiments on synthetic and real data are provided to support our findings. We demonstrate significant performance gains over prior works.

Object Tracking in Satellite Videos: A Spatial-Temporal Regularized Correlation Filter Tracking Method With Interacting Multiple Model

Target occlusion is common in satellite videos, which makes object tracking difficult because most state-of-the-art trackers are not robust to occlusion, particularly complete occlusion. In this letter, we propose a novel correlation filter algorithm with an interacting multiple model (IMM) for object tracking in satellite videos that combines the strength of the correlation filter and the IMM. When the target is occluded, we utilize the IMM to predict target position. Therefore, the proposed tracker is robust to occlusion. The experimental results demonstrate that our tracker performs favorably when the target is occluded and achieves excellent performance compared with state-of-the-art methods.

EYEDi: Graphical Authentication Scheme of Estimating Your Encodable Distorted Images to Prevent Screenshot Attacks

Graphical authentication schemes have the advantage of being more memorable than conventional passwords. Although some image distortion methods have been proposed to prevent the risks of over-the-shoulder attacks (OSAs), these methods cannot prevent camera recording attacks, as the key images are the same each time. In this study, we propose a graphical authentication scheme that generates various distorted images, named Estimating Your Encodable Distorted images (EYEDi). EYEDi generates distorted images by applying several image processing filters to the original images. Moreover, EYEDi estimates the appropriate image processing filter strength based on the authentication data. To measure attack resistance, twenty participants performed three types of attacks (OSA, camera recording attack, and screenshot) 300 times, each using existing methods and EYEDi. The classification error rate of all three types of attacks showed that EYEDi had a lower classification error rate between the legitimate user and attackers. Especially for the screenshot attack, which is the most severe threat model, the existing method was completely broken through, while EYEDi prevented the attacks with a classification error rate of 10&#x0025;. This result shows that EYEDi can eliminate the screenshot attacker by using the difference in authentication times and a simple improvement in defense performance.