The Intrinsic Magnetization of Antiferromagnetic Textures

Antiferromagnets (AFMs) exhibit intrinsic magnetization when the order parameter spatially varies. This intrinsic spin is present even at equilibrium and can be interpreted as a twisting of the homogeneous AFM into a state with a finite spin. Because magnetic moments couple directly to external magnetic fields, the intrinsic magnetization can alter the dynamics of antiferromagnetic textures under such influence. Starting from the discrete Heisenberg model, we derive the continuum limit of the free energy of AFMs in the exchange approximation and explicitly rederive that the spatial variation of the antiferromagnetic order parameter is associated with an intrinsic magnetization density. We calculate the magnetization profile of a domain wall and discuss how the intrinsic magnetization reacts to external forces. We show conclusively, both analytically and numerically, that a spatially inhomogeneous magnetic field can move and control the position of domain walls in AFMs. By comparing our model to a commonly used alternative parametrization procedure for the continuum fields, we show that the physical interpretations of these fields depend critically on the choice of parametrization procedure for the discrete-to-continuous transition. This can explain why a significant amount of recent studies of the dynamics of AFMs, including effective models that describe the motion of antiferromagnetic domain walls, have neglected the intrinsic spin of the textured order parameter.Comment: 12 pages, 7 figure

A flexible space-variant anisotropic regularisation for image restoration with automated parameter selection

We propose a new space-variant anisotropic regularisation term for variational image restoration, based on the statistical assumption that the gradients of the target image distribute locally according to a bivariate generalised Gaussian distribution. The highly flexible variational structure of the corresponding regulariser encodes several free parameters which hold the potential for faithfully modelling the local geometry in the image and describing local orientation preferences. For an automatic estimation of such parameters, we design a robust maximum likelihood approach and report results on its reliability on synthetic data and natural images. For the numerical solution of the corresponding image restoration model, we use an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM). A suitable preliminary variable splitting together with a novel result in multivariate non-convex proximal calculus yield a very efficient minimisation algorithm. Several numerical results showing significant quality-improvement of the proposed model with respect to some related state-of-the-art competitors are reported, in particular in terms of texture and detail preservation

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Radial Basis Functions: Biomedical Applications and Parallelization

Radial basis function (RBF) is a real-valued function whose values depend only on the distances between an interpolation point and a set of user-specified points called centers. RBF interpolation is one of the primary methods to reconstruct functions from multi-dimensional scattered data. Its abilities to generalize arbitrary space dimensions and to provide spectral accuracy have made it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc. The present thesis discusses three applications of RBF interpolation in biomedical engineering areas: (1) Calcium dynamics modeling, in which we numerically solve a set of PDEs by using meshless numerical methods and RBF-based interpolation techniques; (2) Image restoration and transformation, where an image is restored from its triangular mesh representation or transformed under translation, rotation, and scaling, etc. from its original form; (3) Porous structure design, in which the RBF interpolation used to reconstruct a 3D volume containing porous structures from a set of regularly or randomly placed points inside a user-provided surface shape. All these three applications have been investigated and their effectiveness has been supported with numerous experimental results. In particular, we innovatively utilize anisotropic distance metrics to define the distance in RBF interpolation and apply them to the aforementioned second and third applications, which show significant improvement in preserving image features or capturing connected porous structures over the isotropic distance-based RBF method. Beside the algorithm designs and their applications in biomedical areas, we also explore several common parallelization techniques (including OpenMP and CUDA-based GPU programming) to accelerate the performance of the present algorithms. In particular, we analyze how parallel programming can help RBF interpolation to speed up the meshless PDE solver as well as image processing. While RBF has been widely used in various science and engineering fields, the current thesis is expected to trigger some more interest from computational scientists or students into this fast-growing area and specifically apply these techniques to biomedical problems such as the ones investigated in the present work

Influence of surface energy anisotropy on nucleation and crystallographic texture of polycrystalline deposits

This paper aims to elucidate the role of interface energy anisotropy in orientation selection during nucleation of new grains in a polycrystalline film growth. An assessment of (heterogeneous) nucleation probability as function of orientation of both the bottom grain and of the nucleus was developed (using the concepts of classical nucleation theory). Novel solutions to the generalized Winterbottom construction were described in cases of very strong anisotropy and arbitrary orientations. In order to demonstrate the effect on the film crystallographic texture, a 2D Monte Carlo algorithm for anisotropic polycrystalline growth was used to simulate growth of films with columnar microstructure. The effect of strength of anisotropy, the deposition rate and initial texture were investigated. Results showed that with larger strength of anisotropy, the nucleation rate is less dependent on the driving force, but more dependent on the initial texture. With certain initial textures, the anisotropic nucleation may even be either impossible or having probability close to one irrespective of the driving force. Depending on the conditions, the anisotropic nucleation could hasten the evolution towards the interface-energy minimizing texture or retard it. Based on these insights, a hypothesis was offered to explain a peculiar texture evolution in electrodeposited nickel.Comment: 18 pages, 14 figures, 1 table Submitted in September 2023 to Computational Materials Science, Elsevie

Degradation of carbon-based materials under ablative conditions produced by a high enthalpy plasma jet

A stationary experiment was performed to study the degradation of carbon-based materials by immersion in a plasma jet. In the experiment, graphite and C/C composite were chosen as the target materials, and the reactive plasma jet was generated by an air plasma torch. For macroscopic study of the material degradation, the sample’s mass losses were measured as function of the exposure time under various temperatures on the sample surface. A microscopic analysis was then carried out for the study of microscopic aspects of the erosion of material surface. These experiments showed that the mass loss per unit area is approximately proportional to the exposure time and strongly depends on the temperature of the material surface. The mass erosion rate of graphite was appreciably higher than the C/C composite. The ablation rate in the carbon matrix region in C/C composite was also noticeably higher than that in the fiber region. In addition, the latter varied according to the orientation of fibers relatively to the flow direction. These tests indicated an excellent ablation resistance of the C/C composite, thus being a reliable material for rocket nozzles and heat shielding elements of the protection systems of hypersonic apparatuses from aerodynamic heating