Reconstruction Of Edges Research Articles

New results in high-resolution X-ray fluorescence spectroscopy

We present some recent results dealing with resonant inelastic X-ray scattering (RIXS) on atomic targets in the 3-5 keV enegy region. In this so-called tender spectral region, the K-shell fluorescence branching ratios become reasonably large, but a full vacuum enclosure is still preferable to avoid detection efficiency loss due to the sizeable arms of high resolution crystal spectrometers. By squeezing energy resolution in the fluorescence decay channel, one may improve the spectral resolution of photoabsorption, enable separation of multielectron excitation and relaxation channels, and completely eliminate the need to scan across the selected energy range of the photon probe in order to acquire the photoabsorption spectrum. On the other hand, the spectra may be untrivially modified by effects such as interference of absorption-emission paths or structured relaxation modes, and a more elaborated modelling is needed to understand the emitted signal. We illustrate these aspects by presenting four cases: the reconstruction of Ar KM and Ar KL absorption edges from a series of highly resolved emission spectra recorded at different probe energies, the reconstruction of the Xe L3 edge from a single X-ray emission spectrum, and the analysis of the radiative Ar K-MM Auger decay preceeded by the resonant or nonresonant photon absorption.

All-hexahedral meshing and remeshing for multi-object manufacturing applications

All-hexahedral meshing and remeshing algorithms giving support to finite element modeling of manufacturing processes require continuous development for improving its overall robustness and applicability. This paper draws from a previous all-hexahedral algorithm presented by the authors and proposes new developments related to the construction of adaptive core meshes and processing of multi-objects that are typical of manufacturing applications.Along with the aforementioned improvements there are other developments that will also be presented due to their effectiveness in increasing the robustness of all-hexahedral algorithms. These include identification and simplification of boundary features, reconstruction of vertices and edges with minimum element distortion, smoothing of nodal points along edges and topology based mesh repair procedures to ensure completeness of edge representation.The presentation is enriched with examples taken from pure geometry and metal forming applications, and a resistance projection welding industrial test case consisting of four different objects is included to show the capabilities of selective remeshing of objects while maintaining contact conditions and local geometrical details that are critical for electro-thermo-mechanical numerical simulations.

Nonconvex Compressed Sampling of Natural Images and Applications to Compressed MR Imaging

There have been proposed several compressed imaging reconstruction algorithms for natural and MR images. In essence, however, most of them aim at the good reconstruction of edges in the images. In this paper, a nonconvex compressed sampling approach is proposed for structure-preserving image reconstruction, through imposing sparseness regularization on strong edges and also oscillating textures in images. The proposed approach can yield high-quality reconstruction as images are sampled at sampling ratios far below the Nyquist rate, due to the exploitation of a kind of approximate l0 seminorms. Numerous experiments are performed on the natural images and MR images. Compared with several existing algorithms, the proposed approach is more efficient and robust, not only yielding higher signal to noise ratios but also reconstructing images of better visual effects.

Structure and electronic spectroscopy of steps on GaAs(110) surfaces

Steps on GaAs(110) surfaces, with step-normal vectors parallel to [001], are studied by scanning tunneling microscopy and spectroscopy. Two possible orientations of the steps occur, with outward normal vectors of [001] or [ 00 - 1 ], which in simple bulk-terminated form have Ga (cations) or As (anions) on their edges, respectively. The latter type of step in n-type or undoped material is found to retain its bulk-terminated form. A band of states is observed extending out from the valence band, associated with the dangling bonds of the terminating As atoms. It is argued that compensation of the dangling bonds on the step edges is the driving force for the step structure, producing reconstruction of the step edges in certain cases.

Nature of Graphene Edges: A Review

Graphene edges determine the optical, magnetic, electrical, and electronic properties of graphene. In particular, termination, chemical functionalization and reconstruction of graphene edges leads to crucial changes in the properties of graphene, so control of the edges is critical to the development of applications in electronics, spintronics and optoelectronics. Up to date, significant advances in studying graphene edges have directed various smart ways of controlling the edge morphology. Though, it still remains as a major challenge since even minor deviations from the ideal shape of the edges significantly deteriorate the material properties. In this review, we discuss the fundamental edge configurations together with the role of various types of edge defects and their effects on graphene properties. Indeed, we highlight major demanding challenges to find the most suitable technique to characterize graphene edges for numerous device applications such as transistors, sensors, actuators, solar cells, light-emitting displays, and batteries in graphene technology.

Effect of edge reconstruction and passivation on zero-energy states and magnetism in triangular graphene quantum dots with zigzag edges

We present the results of ab-initio density functional theory based calculations of the stability and reconstruction of zigzag edges in triangular graphene quantum dots. We show that, while the reconstructed pentagon-heptagon zigzag edge structure is more stable in the absence of hydrogen, ideal zigzag edges are energetically favored by hydrogen passivation. Zero-energy band exists in both structures when passivated by hydrogen, however in case of pentagon-heptagon zigzag, this band is found to have stronger dispersion, leading to the loss of net magnetization.

Mechanism and free-energy barrier of the type-57 reconstruction of the zigzag edge of graphene

Recent ab initio calculations without spin [P. Koskinen et al., Phys. Rev. Lett. 101, 115502 (2008)] predict that the zigzag edges of graphene should be reconstructed, albeit with an energy barrier to be overcome. After verifying that spin-polarized calculations give qualitatively the same result, we study the mechanism and the free energy of the reconstruction of the zigzag edges from a periodic hexagon structure (zz) to an alternate pentagon-heptagon structure [zz(57)] using the empirical long-range carbon bond order potential II (LCBOPII) potential. The zz(57) edges are stabilized by an almost triple bond similar to that of the armchair edges, and we propose a way to account for this quantum mechanical effect in the LCBOPII potential. Aside from that, the reconstructed edge is flat as a result of tensile edge stress. The reconstruction occurs spontaneously in molecular dynamics simulations at high temperature, leading to the identification of a reaction coordinate for the reconstruction that allows us to calculate the free-energy barrier by using Monte Carlo simulations and umbrella sampling. At room temperature, we find a free-energy barrier of $0.83$ eV for the first transformations of two hexagons to a pentagon-heptagon pair that is higher than the one for a fully reconstructed edge and increasing with temperature.

Real-time fuzzy regression analysis: A convex hull approach

In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the “ standard” regression.

Non-local regularization of inverse problems

This article proposes a new framework to regularize imaging linear inverse problems using an adaptive non-local energy. A non-local graph is optimized to match the structures of the image to recover. This allows a better reconstruction of geometric edges and textures present in natural images. A fast algorithm computes iteratively both the solution of the regularization process and the non-local graph adapted to this solution. The graph adaptation is efficient to solve inverse problems with randomized measurements such as inpainting random pixels or compressive sensing recovery.Our non-local regularization gives state-of-the-art results for this class of inverse problems. On more challenging problems such as image super-resolution, our method gives results comparable to sparse regularization in a translation invariant wavelet frame.

Recursive framework for joint inpainting and de-noising of photographic films

We address the problem of inpainting noisy photographs. We present a recursive image recovery scheme based on the unscented Kalman filter (UKF) to simultaneously inpaint identified damaged portions in an image and suppress film-grain noise. Inpainting of the missing observations is guided by a mask-dependent reconstruction of the image edges. Prediction within the UKF is based on a discontinuity-adaptive Markov random field prior that attempts to preserve edges while achieving noise reduction in uniform regions. We demonstrate the capability of the proposed method with many examples.

Adsorption and Activation of CO over Flat and Stepped Co Surfaces: A First Principles Analysis

The adsorption and activation of CO over flat Co{0001}, corrugated Co{1120}, and stepped Co{1012} and Co{1124} surfaces have been analyzed using periodic density functional theory calculations. CO strongly chemisorbs on all these surfaces but does not show a strong dependence on the surface structure. The calculated structure of adsorbed CO on Co{0001} at 1/3 monolayer (ML) of coverage was found to be in good agreement with the experiment. The barrier for CO dissociation over Co{0001} was found to decrease with decreasing CO coverage, taking on a value of 232 kJ/mol at 1/4 ML and 218 kJ/mol at 1/9 ML. The presence of the "zigzag" channel on Co{1120} enhances the reactivity slightly by reducing the barrier for CO dissociation to 195 kJ/mol. In contrast, the stepped Co{1012} and Co{1124} surfaces are much more active than the flat and corrugated surfaces. Both stepped surfaces provide direct channels for CO dissociation that do not have barriers with respect to gas-phase CO. In general the activation barriers lower as the reaction energies become more exothermic. Reconstruction of the step edges that occur in the product state, however, prevents a linear correlation between the reaction energy and the activation energy.

Incremental reconstruction of sharp edges on mesh surfaces

Limited by the regular grids in computing, many modelling approaches (e.g. field-based methods) sample 3D shape insensitive to sharp features therefore exhibit aliasing errors, by which a lot of sharp edges and corners are lost on the reconstructed surface. An incremental approach for recovering sharp edges on an insensitive sampled triangular mesh is presented in this paper, so that shape approximation errors are greatly reduced. Either chamfered or blended sharp edges on an input triangular mesh could be successfully reconstructed by the signals inherent in the mesh. As a non-iterative method, our approach could be finished in a very short time comparing to those diffusion-based sharp-feature reproducers. The region embedding sharp features is first identified through normal variations. The positions of vertices in the sharp-feature embedded region are then predicted progressively from outer to the inner of sharp regions so that sharp edges could be recovered in the sense of region shrinking.

Edge reconstruction in the fractional quantum Hall regime

The interplay of electron-electron interaction and confining potential can lead to the reconstruction of fractional quantum Hall edges. We have performed exact diagonalization studies on microscopic models of fractional quantum Hall liquids, in finite size systems with disk geometry, and found numerical evidence of edge reconstruction under rather general conditions. In the present work we have taken into account effects like layer thickness and Landau level mixing, which are found to be of quantitative importance in edge physics. Due to edge reconstruction, additional nonchiral edge modes arise for both incompressible and compressible states. These additional modes couple to electromagnetic fields and thus can be detected in microwave conductivity measurements. They are also expected to affect the exponent of electron Green's function, which has been measured in tunneling experiments. We have studied in this work the electric dipole spectral function that is directly related to the microwave conductivity measurement. Our results are consistent with the enhanced microwave conductivity observed in experiments performed on samples with an array of antidots at low temperatures, and its suppression at higher temperatures. We also discuss the effects of the edge reconstruction on the single electron spectral function at the edge.

Reconstruction of fractional quantum Hall edges.

We study the interplay of electron-electron interaction, confining potential and effects of finite temperature at the edge of a quantum Hall liquid. Our exact diagonalization calculation indicates that edge reconstruction occurs in the fractional quantum Hall regime for a variety of confining potential, including ones that correspond to a "sharp" edge. Our finite temperature Hartree-Fock calculation for integer quantum Hall edges indicates that reconstruction is suppressed above a certain temperature. We discuss the implication of our results on recent edge tunneling and microwave absorption experiments.

Facial expression transformation based on sketch image and multiscale edges

AbstractThis paper proposes a new method of transforming facial expression based on facial sketch image and multi‐scale edges. Multiscale edges, which are defined as local maxima of high‐frequency signal of facial images, are generally located on the edges of facial parts. Facial sketch images defined as line drawings of edges of facial parts are extracted by morphological processing, the generalized symmetry operator, the rectangle filter, and a geometrical template. Therefore, it is possible to transform the binary sketch image into gray‐tone multiscale edge images which are called characterized sketch images. The expression of the characterized facial image can be transformed easily based on FACS (Facial Action Coding System). By reconstruction based on Mallat's method of reconstruction of multiscale edges, facial images with different expressions can be obtained easily. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(9): 67–75, 2001

Reconstruction of baseline JPEG coded images in error prone environments

In this paper, a two-stage method for the reconstruction of missing data in the transmission of baseline JPEG coded images in error prone environments is proposed. In the first stage, we estimate the values of the missing DC coefficients. As effects of errors in estimating the missing DC values will appear as a number of stripes across the image, a technique for removing such stripes is also developed. In the second stage, the data of missing blocks is reconstructed by exploiting the correlation between adjacent blocks. Simulation results intricate that our reconstruction method performs very well. The two key contributions of our method are that it does not assume nondifferential encoding of the DC coefficients, and that it performs well in the reconstruction of diagonal edges.

Implantation of the nail plate in plastic surgery

A series of cases are presented to illustrate the possible uses of the nail plate as an alternative support in reconstruction of some free edges (nasal ala, eyelid, auricle of the ear), and laminar bone parts, such as the floor of the orbit. This technique was used experimentally by the authors many years ago and has subsequently been used in clinical practice.

Brick-wall structured segmentation for interpolative vector quantization of images

Abstract A new variable block size image segmentation scheme based on the modified split-and-merge (MSM) algorithm is devised in this paper. It is shown that the position of the sampling points obtained from the MSM algorithm are located at the edge boundaries and the distance between two consecutive sampling points indicates the local activity between them. An image is partitioned using the properties of the selected sampling points. The proposed method is applied for coding of monochrome image using interpolative vector quantization (IVQ) technique. Simulation results show that the proposed method can achieve 0.8 dB PSNR improvement at a bit rate lower than the fixed block size scheme. Besides, perfect reconstruction of edges is resulted and hence excellent reconstructed image visual quality is obtained.

A class of robust entropic functionals for image restoration

This paper considers the concept of robust estimation in regularized image restoration. Robust functionals are employed for the representation of both the noise and the signal statistics. Such functionals allow the efficient suppression of a wide variety of noise processes and permit the reconstruction of sharper edges than their quadratic counterparts. A new class of robust entropic functionals is introduced, which operates only on the high-frequency content of the signal and reflects sharp deviations in the signal distribution. This class of functionals can also incorporate prior structural information regarding the original image, in a way similar to the maximum information principle. The convergence properties of robust iterative algorithms are studied for continuously and noncontinuously differentiable functionals. The definition of the robust approach is completed by introducing a method for the optimal selection of the regularization parameter. This method utilizes the structure of robust estimators that lack analytic specification. The properties of robust algorithms are demonstrated through restoration examples in different noise environments.

Robust estimation techniques in regularized image restoration

The performance of conventional restoration algorithms is highly degraded by the presence of outliers in the nominal noise process. A novel restoration approach that combines the properties of regularized and robust estimation schemes is introduced. Moreover, we address the potential of robust stabilizing functionals in preserving the detailed structure. Initially, the least-squares part of the regularized optimization scheme is modified according to the notion of <i>M</i>-estimation. An iterative algorithm is introduced for the derivation of the corresponding nonlinear estimate. The global convergence of this algorithm is rigorously studied. The robust regularized criterion provides nonlinear estimates, which do not suffer from artifacts due to the presence of outliers. In addition, the presence of the stabilizing functional enables the efficient suppression of such outliers. For the evaluation of the regularization parameter, several optimality measures are considered. Moreover, an adaptive structure for this parameter, which leads to high-quality estimates without significantly affecting the computational complexity or the global convergence of the algorithm, is introduced. To further account for the effective preservation of the detailed structure, the concept of <i>M</i>-estimators is embedded in both composite functionals involved in the regularized criterion. It is demonstrated that the corresponding error-penalizing strategy allows the reconstruction of sharp edges, while preserving the robust characteristics of the restoration algorithm. The capabilities of the robust regularized algorithms are demonstrated through restoration examples.