Edge Of Domain Research Articles

SOLEDGE3X integrated core-edge modelling of tungsten sources, migration, and radiation in WEST plasmas

The W impurity species was introduced in the multi-component edge fluid code SOLEDGE3X, in its 2D transport version. To do so, several physical ingredients have been implemented. Firstly, W gross erosion is calculated using the Garcia-Rosales formula, and W re-deposition is estimated with either a Groth–Tskhakaya formulation or a Neural Network trained on re-deposition data from a kinetic model. This fluid approach for W transport is compared with kinetic results obtained with ERO2.0 in edge and SOL domains. Moreover, as W radiates mainly in the core region, which was thus far not resolved in SOLEDGE3X, a 1D model has been implemented for the core plasma region assuming poloidal symmetry. The impact of Oxygen impurities on W content is also addressed (including in particular the impact of W oxide layers on the PFCs). A few applications of the model implementing a Deuterium plasma with Oxygen and Tungsten, taking into account the sputtering of Tungsten by Oxygen are shown.

Impact of a free normal velocity boundary on the modelling of external MHD modes

Free normal-flow (NF) conditions at the plasma boundary are shown to be essential for three-dimensional magnetohydrodynamic (MHD) simulations to agree with linear stability theory. A comparative verification study is presented between two different formulations of the boundary conditions (BCs) for velocity perturbations: (i) fully consistent free NF implementation and (ii) fixed NF formulation, neglecting flow perturbations at the numerical boundary. Numerical results are compared with consolidated figures of merit from the linear theory of external kink modes. We consider two classes of initial equilibria presenting different numerical challenges: a uniform current channel surrounded by pure vacuum and a shaped Wesson-like equilibrium, with smooth (polynomial) radial dependency. Only the fully consistent free NF formulation is invariably accurate in modelling the plasma interface at the numerical boundary, without the need of enforcing a pseudovacuum region at the edge of the simulation domain, as in most analogous past studies. This study employs the cylindrical code SPECYL (Cappello & Biskamp, Nucl. Fusion, vol. 36, no. 5, 1996, p. 571) that solves a full-MHD model without pressure gradients, whose fully consistent resistive wall module with free NF BCs was recently successfully verified against the independent code PIXIE3D (Spinicci et al., AIP Adv., vol. 13, no. 9, 2023, p. 095111).

Monocular Depth and Ego-motion Estimation with Scale Based on Superpixel and Normal Constraints

Three-dimensional perception in intelligent virtual and augmented reality (VR/AR) and autonomous vehicles (AV) applications is critical and attracting significant attention. The self-supervised monocular depth and ego-motion estimation serves as a more intelligent learning approach that provides the required scene depth and location for 3D perception. However, the existing self-supervised learning methods suffer from scale ambiguity, boundary blur, and imbalanced depth distribution, limiting the practical applications of VR/AR and AV. In this article, we propose a new self-supervised learning framework based on superpixel and normal constraints to address these problems. Specifically, we formulate a novel 3D edge structure consistency loss to alleviate the boundary blur of depth estimation. To address the scale ambiguity of estimated depth and ego-motion, we propose a novel surface normal network for efficient camera height estimation. The surface normal network is composed of a deep fusion module and a full-scale hierarchical feature aggregation module. Meanwhile, to realize the global smoothing and boundary discriminability of the predicted normal map, we introduce a novel fusion loss which is based on the consistency constraints of the normal in edge domains and superpixel regions. Experiments are conducted on several benchmarks, and the results illustrate that the proposed approach outperforms the state-of-the-art methods in depth, ego-motion, and surface normal estimation.

Computational Mathematics for Electromagnetic Field Simulation

Computational science could be a key portion of making reenactments and investigations of electromagnetic areas superior, which is critical for numerous ranges, from restorative imaging to broadcast communications. This theoretical talks around the essential thoughts, computer methods, and fundamental issues that come up when utilizing scientific models to form precise considers of electromagnetic areas. Maxwell's conditions portray how electric and attractive areas carry on in space and time and are utilized to recreate electromagnetic areas. Since real-life circumstances are so complicated and distinctive, these conditions, which are made up of differential conditions with fractional subordinates, got to be fathomed numerically. Utilizing procedures like finite difference, limited component, and boundary component strategies, computational science breaks these issues down into shapes that can be fathomed. Limited contrast strategies utilize a framework to induce near to subordinates, whereas finite element strategies break the space up into littler pieces to accurately illuminate complicated geometries. Boundary component strategies center on fundamentally details along the domain's edges, giving fast answers for problems within the exterior world. Each strategy strikes a adjust between exactness, the speed of calculations, and the capacity to work with distinctive electromagnetic occasions. Overseeing the complexity of the coding system, accurately speaking to the highlights of the fabric, and dealing with nonlinearities within the conditions are a few of the issues that come up in computational electromagnetic field reenactment. To create simulations more exact and productive, analysts are continuously looking into unused math calculations, concurrent computer methods, and optimization methodologies.

Multiscale Analysis of Sandwich Beams with Polyurethane Foam Core: A Comparative Study of Finite Element Methods and Radial Point Interpolation Method.

This study presents a comprehensive multiscale analysis of sandwich beams with a polyurethane foam (PUF) core, delivering a numerical comparison between finite element methods (FEMs) and a meshless method: the radial point interpolation method (RPIM). This work aims to combine RPIM with homogenisation techniques for multiscale analysis, being divided in two phases. In the first phase, bulk PUF material was modified by incorporating circular holes to create PUFs with varying volume fractions. Then, using a homogenisation technique coupled with FEM and four versions of RPIM, the homogenised mechanical properties of distinct PUF with different volume fractions were determined. It was observed that RPIM formulations, with higher-order integration schemes, are capable of approximating the solution and field smoothness of high-order FEM formulations. However, seeking a comparable field smoothness represents prohibitive computational costs for RPIM formulations. In a second phase, the obtained homogenised mechanical properties were applied to large-scale sandwich beam problems with homogeneous and approximately functionally graded cores, showing RPIM's capability to closely approximate FEM results. The analysis of stress distributions along the thickness of the beam highlighted RPIM's tendency to yield lower stress values near domain edges, albeit with convergence towards agreement among different formulations. It was found that RPIM formulations with lower nodal connectivity are very efficient, balancing computational cost and accuracy. Overall, this study shows RPIM's viability as an alternative to FEM for addressing practical elasticity applications.

Neuron circuit made of a single locally-active memristor

Neuromorphic computing, inspired by the human brain’s architecture, is expected to break the physical limits of transistors and von Neumann bottleneck. The multiple internal state variables of higher-order memristors (second-order or above) possess dynamic complexity and adaptability, enabling them to mimick the characteristics of biological neurons, which are very important building blocks for neuromorphic computing. This paper presents a simple neuron circuit containing a single second-order current-controlled locally-active memristor (LAM). The pinched hysteresis loop and DC V–I curve of the proposed second-order LAM show good odd symmetry. Applying small signal analysis method, we obtain the small-signal equivalent circuit of the neuron circuit, showing a [Formula: see text] parallel structure and an edge of chaos kernel in its locally-active domain. Also, we draw a parameter classification of the neuron circuit, showing four symmetrical edge of chaos domains, which plays an important role in biphasic action potentials. Finally, we demonstrate that the simple neuron circuit can produce monophasic action potentials, biphasic action potentials and co-existing neuromorphic phenomena via subcritical Hopf bifurcation with different input, verifying the simple circuit is suitable as artificial neurons.

Experimental Validation of the Modified Holmén Vortex Identification Method

The iterative vortex identification method originally proposed by Holmén was modified to improve its performance and to add the ability to distinguish between counter-rotating vortices. Then, the resulting modified Holmén method (MHM) was evaluated using experimental data from five different experiments, including planar and three-dimensional flowfield data. The Q, λ2, and Γ2 criteria were used for comparison. No currently available vortex identification method is perfect (otherwise, proposing a new scheme would be a pointless exercise), and the MHM is no exception; however, the MHM was found to accurately identify vortical structures when the number of passes is chosen correctly while minimizing the experimental noise. In particular, it was found to be useful in capturing large vortical structures occurring in a noisy or high-shear region, cases where the Q and Γ2 criteria sometimes fail. On the other hand, the method is limited by the spatial resolution of the experimental data with respect to the structure’s size and its inability to resolve rotating structures near the edges of the measurement domain.

Edge-selective reconfiguration in polarized lattices with magnet-enabled bistability

The signature topological feature of Maxwell lattices is their polarization, which manifests as an unbalance in stiffness between opposite edges of a finite domain. The manifestation of this asymmetry is especially dramatic in the case of soft lattices undergoing large nonlinear deformation under concentrated loads, where the excess of softness at the soft edge can result in the activation of sharp indentations. This study explores how this mechanical dichotomy between edges can be tuned and possibly extremized by working with soft magneto-mechanical metamaterials. The magneto-mechanical coupling is obtained by endowing the lattice sites with permanent magnets, which activate a network of magnetic forces that can interact with – either augmenting or competing with – the elasticity of the material. Specifically, under sufficiently large deformation that macroscopically alters the equilibrium positions of the sites, the attractive forces between the magnets can trigger bistable reconfiguration mechanisms. The strength of such mechanisms depends on the landscapes of elastic reaction forces exhibited by the edges, which are different due to the polarization, and is therefore inherently edge-selective. We show that, on the soft edge, the addition of magnets simply enhances the softness of the edge. In contrast, on the stiff edge, the magnets activate snapping mechanisms that locally reconfigure the cells and produce a lattice response reminiscent of plasticity, characterized by residual deformation that persists upon unloading.

(Invited) Next Generation Designer Carbon Supports for Durable Polymer Electrolyte Fuel Cell Cathode Catalysts

The implementation of polymer electrolyte membrane fuel cells (PEMFC) in medium- and heavy-duty vehicles is a promising strategy to eliminate greenhouse gas emissions as the only byproduct is water. However, the durability of PEMFC catalysts, especially for the kinetically sluggish oxygen reduction reaction (ORR) that occurs at the cathode, remains a significant obstacle. Current state-of-the-art electrocatalysts are comprised of platinum-based nanoparticles anchored on high surface area carbon black supports (Pt/C). While these catalysts meet the U.S. Department of Energy’s near-term activity benchmarks [2], they suffer heavily from degradation mechanisms that can lead to catastrophic failure of PEMFCs. Hence, carbon support materials that promote durability are crucial to further development of ORR catalysts. Here, we report on the physical and electrochemical characterization of next-generation commercial FCX carbon supports that aim to mitigate these degradation mechanisms.We synthesized Pt/C electrocatalysts by decorating FCX carbons with spherical Pt nanoparticles approximately 2-3 nanometers in diameter. The surface and pore structure of the supports were extensively probed to determine their effects on anchoring of Pt nanoparticles and ultimately catalytic activity. After physical and electrochemical characterization of the catalysts, structure-to-property relationships were established, leading to a schematic for an “ideal” Pt/C electrocatalyst as shown in Figure 1. The hierarchical pore structure is crucial for mass transport, and Pt nanoparticles are housed on the edges of stable graphitic carbon domains. Lastly, a promising alternative to the traditional Pt/C synthesis pathway is shown. As degradation is more pronounced on the catalyst’s exterior surfaces, this novel electrocatalyst contains Pt nanoparticles inside the support’s mesopores to mitigate the loss of electrochemically active surface area [3]. The insights gained from this work can be applied to optimizing carbon support morphology and chemistry for durable and highly active ORR electrocatalysts.[1] M. Rezaei Talarposhti, T. Asset, S. T. Garcia, Y. Chen, S. Herrera, S. Dai, E. J. Peterson, K. Artyushkova, I. Zenyuk, P. Atanassov, ChemPhysChem, 21 (2020) 1331-1339.[2] D.A. Cullen, K.C. Neyerlin, R.K. Ahluwalia, R. Mukundan, K.L. More, R.L Borup, A.Z. Weber, D.J. Myers, A. Kusoglu, Nature Energy, 6 (2021) 462–474.[3] G.S. Harzer, A. Orfanidi, H. El-Sayed, P. Madkikar, H.A. Gasteiger, J. Electrochem. Soc. 165 (2018) F770-F779. Figure 1

ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains

Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.

Edge of Chaos in Integro-Differential Model of Nerve Conduction

In this paper, we consider an integro-differential model of nerve conduction which presents the propagation of impulses in the nerve’s membranes. First, we approximate the original problem via cellular nonlinear networks (CNNs). The dynamics of the CNN model is investigated by means of local activity theory. The edge of chaos domain of the parameter set is determined in the low-dimensional case. Computer simulations show the bifurcation diagram of the model and the dynamic behavior in the edge of chaos region. Moreover, stabilizing control is applied in order to stabilize the chaotic behavior of the model under consideration to the solutions related to the original behavior of the system.

Fingering convection in a spherical shell

We use $123$ three-dimensional direct numerical simulations to study fingering convection in non-rotating spherical shells. We investigate the scaling behaviour of the flow length scale, the non-dimensional heat and compositional fluxes $Nu$ and $Sh$ and the mean convective velocity over the fingering convection instability domain defined by $1 \leq R_\rho < Le$ , $R_\rho$ being the ratio of density perturbations of thermal and compositional origins and $Le$ the Lewis number. We show that the chemical boundary layers are marginally unstable and adhere to the laminar Prandtl–Blasius model, hence explaining the asymmetry between the inner and outer spherical shell boundary layers. We develop scaling laws for two asymptotic regimes close to the two edges of the instability domain, namely $R_\rho \lesssim Le$ and $R_\rho \gtrsim 1$ . For the former, we develop novel power laws of a small parameter $\epsilon$ measuring the distance to onset, which differ from theoretical laws published to date in Cartesian geometry. For the latter, we find that the Sherwood number $Sh$ gradually approaches a scaling $Sh\sim Ra_\xi ^{1/3}$ when $Ra_\xi \gg 1$ ; and that the Péclet number accordingly follows $Pe \sim Ra_\xi ^{2/3} |Ra_T|^{-1/4}$ , $Ra_T$ and $Ra_{\xi}$ being the thermal and chemical Rayleigh numbers. When the Reynolds number exceeds a few tens, we report on a secondary instability which takes the form of large-scale toroidal jets which span the entire spherical domain. Jets distort the fingers, resulting in Reynolds stress correlations, which in turn feed the jet growth until saturation. This nonlinear phenomenon can yield relaxation oscillation cycles.

Asymmetrical anchor way of manganese atoms on carbon domain edge for enhanced oxygen reduction reaction

Single atomic catalysts (SACs), especially the catalysts featured by well-developed M-N4 moieties, show excellent catalytic activity and spark enormous attention. However, the highly symmetric active sites may result in some degenerate electronic states for the d orbitals of the center metal atom, which suffer from the unsatisfactory adsorption-desorption behaviors during reaction. Herein, we develop an asymmetric anchor site by two types of nitrogen (sp-N and pyridinic N) created in the inherent edge-rich carbon domain of hydrogen-substituted graphdiyne (HsGDY), where atomic catalyst like manganese atoms can be asymmetrically anchored on the one side of hexagon carbon rings. The abundant elegant hexagon carbon rings on HsGDY not only facilitate the creation of edge sites, but also provide available space for the coordination of terminal ligands (OH) with Mn atoms. The as-synthesized Mn-N-HsGDY exhibits excellent ORR activity and an impressive long-term cyclability for over 3800 cycles in a rechargeable Zn-air battery.

Recent advances in digital image masking techniques Future challenges and trends: a review

Image steganography has advanced over recent decades, driven by a demand for secure, high-capacity data-hiding techniques capable of maintaining imperceptibility in digital images. This study provides a comprehensive review of steganographic techniques, highlighting recent improvements across spatial and frequency domains and innovations that integrate cryptographic methods, genetic algorithms, and machine learning to enhance security and payload capacity. The assessment reveals trade-offs between imperceptibility, capacity, and security in various methods, including least significant bit (LSB) substitution, frequency domain transformation, edge detection, and pixel intensity manipulation. This analysis identifies key research gaps in multi-criteria evaluation, PSNR reliability, and the need for enhanced methodologies in imperceptibility and robustness to withstand statistical and steganalysis attacks. By investigative these trends, this research emphasizes the importance of complementary image quality and data safety to improve effective and resilient stenographic systems.

Nonlinear dynamic analysis of aircraft CFRP sandwich wings under explosive blast loading: Introducing SVM-DNN algorithm to predict dynamical information

The aircraft wings are a crucial component, particularly during mechanical shock excitation. Thus, for the first time, an inventive adaptable model to simulate the nonlinear dynamics of the wings under explosive blast loading at the moment of the collision is described in this article. It is crucial to increase the structure's stability because of explosive blast loading. The mentioned structure is made of two composite face sheets and a carbon fiber-reinforced polymer (CFRP). Functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) is introduced to improve the stability of this kind of applicable structure, especially in air maneuvers. The analysis is conducted utilizing Reddy's third-order shear deformation of Reddy theory (TSDRT), incorporating Hamilton's principle and Von Kármán nonlinear geometric assumptions. The governing and boundary equations are accurately computed at the domain and boundary edges of the doubly curved structure. The coupled mesh-free radial point interpolation method (MRPIM) and Newton-Raphson method are applied for numerically simulating the current doubly curved structure by employing Newmark's temporal integration method with a constant-average acceleration approach. The present findings are cross-checked against the machine learning method and open-source results from the literature. Based on physical data, machine learning integrates supervised machine learning to predict the nonlinear vibration of the system. In this case, nonlinear transient bending properties are found using hybrid machine learning methods and solutions. The findings demonstrate the significance of GOEAM for nonlinear vibrations of the composite system. The current study's conclusions could be used as a benchmark and useful recommendations for future structural design techniques with enhanced features.

Denoising of Digital Images Using Spatial Domain Edge Detection Approach

Better results can be produced by the Hybridization of the Wavelet-based image denoising technique and sparse representation of edges. A novel method for spatial domain edge identification that produces a denoised image that has been tainted by additive white Gaussian noise without sacrificing the image's detail information. By combining bivariate shrinkage and local profile edge detection, a denoised image is produced. In this paper, the hybridization method is proposed by modifying the existing Wavelet Transform for image denoising leading to an increase in the PSNR and SSIM as compared to that given by existing Wavelet denoising techniques, maintaining the visual quality of an image. To modify the wavelet coefficients Bivariate Wavelet Shrinkage is used. The quality assessment is evaluated in terms of SSIM value and PSNR value.

EdiTor: Edge-guided Transformer for Ghost-free High Dynamic Range Imaging

Synthesizing the high dynamic range (HDR) image from multi-exposure images has been extensively studied by exploiting convolutional neural networks (CNNs) recently. Despite the remarkable progress, existing CNN-based methods have the intrinsic limitation of local receptive field, which hinders the model’s capability of capturing long-range correspondence and large motions across under/over-exposure images, resulting in ghosting artifacts of dynamic scenes. To address the above challenge, we propose a novel Ed ge-gu i ded T ransf or mer framework (EdiTor) customized for ghost-free HDR reconstruction, where the long-range motions across different exposures can be delicately modeled by incorporating the edge prior. Specifically, EdiTor calculates patch-wise correlation maps on both image and edge domains, enabling the network to effectively model the global movements and the fine-grained shifts across multiple exposures. Based on this framework, we further propose an exposure-masked loss to adaptively compensate for the severely distorted regions ( e.g. , highlights and shadows). Experiments demonstrate that EdiTor outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.

Multiple discrete crack initiation and propagation in Material Point Method

Cracks in MPM (CRAMP) is one of the most prominent discrete crack simulation methods in the Material Point Method (MPM) due to its simplicity and versatility. However, CRAMP is yet to include the capability to simulate concurrent crack initiations and propagations, as well as propagation to the edge of the material domain. The method proposed in this paper enables the simulation of multiple crack paths with CRAMP via the dynamic assignment of particles to separate grids while minimizing the number of necessary grids. It also proposes methods of evaluating crack initiation and propagation via the Rankine criterion. The proposed methods are then implemented in an in-house Convected Particle Domain Interpolation (CPDI) MPM developed at Aalto University.To verify the integrity of the CPDI algorithm, our CPDI code with the proposed method implemented simulated a CPDI vortex. Furthermore, six fracture-simulation verification test cases were carried out: (1) through-crack in an infinite plate; (2) mode-I propagation; (3) initiation; (4) initiation with large deformations; (5) merging; (6) multiple initial cracks; and (7) radially-cracked thick ring. All these verification tests show successful initiation, propagation, merging, crack opening, and agreement with the results from the literature, as well as the convergence of various parameters with the expected rates.

Climatologically invariant scale invariance seen in distributions of cloud horizontal sizes

Abstract. Cloud area distributions are a defining feature of Earth's radiative exchanges with outer space. Cloud perimeter distributions n(p) are also interesting because the shared interface between clouds and clear sky determines exchanges of buoyant energy and air. Here, we test using detailed model output and a wide range of satellite datasets a first-principles prediction that perimeter distributions follow a scale-invariant power law n(p) ∝ p-(1+β), where the exponent β = 1 is evaluated for perimeters within moist isentropic atmospheric layers. In model analyses, the value of β is closely reproduced. In satellite data, β is remarkably robust to latitude, season, and land–ocean contrasts, which suggests that, at least statistically speaking, cloud perimeter distributions are determined more by atmospheric stability than Coriolis forces, surface temperature, or contrasts in aerosol loading between continental and marine environments. However, the satellite-measured value of β is found to be 1.26 ± 0.06 rather than β = 1. The reason for the discrepancy is unclear, but comparison with a model reproduction of the satellite perspective suggests that it may owe to cloud overlap. Satellite observations also show that scale invariance governs cloud areas for a range at least as large as ∼ 3 to ∼ 3 × 105 km2, and notably with a corresponding power law exponent close to unity. Many prior studies observed a much smaller range for power law behavior, and we argue this difference is due to inappropriate treatments of the statistics of clouds that are truncated by the edge of the measurement domain.

Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Notice of Violation of IEEE Publication Principles A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm, by Om Verma Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012 This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.