Analytic height correlation function of rough surfaces derived from light scattering

We derive an analytic expression for the height correlation function of a rough surface based on the inverse wave scattering method of Kirchhoff theory. The expression directly relates the height correlation function to diffuse scattered intensity along a linear path at fixed polar angle. We test the solution by measuring the angular distribution of light scattered from rough silicon surfaces, and comparing extracted height correlation functions to those derived from atomic force microscopy (AFM). The results agree closely with AFM over a wider range of roughness parameters than previous formulations of the inverse scattering problem, while relying less on large-angle scatter data. Our expression thus provides an accurate analytical equation for the height correlation function of a wide range of surfaces based on measurements using a simple, fast experimental procedure.Comment: 6 pages, 5 figures, 1 tabl

Classical Analogue of the Ionic Hubbard Model

In our earlier work [M. Hafez, {\em et al.}, Phys. Lett. A {\bf 373} (2009) 4479] we employed the flow equation method to obtain a classic effective model from a quantum mechanical parent Hamiltonian called, the ionic Hubbard model (IHM). The classical ionic Hubbard model (CIHM) obtained in this way contains solely Fermionic occupation numbers of two species corresponding to particles with \up and \down spin, respectively. In this paper, we employ the transfer matrix method to analytically solve the CIHM at finite temperature in one dimension. In the limit of zero temperature, we find two insulating phases at large and small Coulomb interaction strength, $U$, mediated with a gap-less metallic phase, resulting in two continuous metal-insulator transitions. Our results are further supported with Monte Carlo simulations.Comment: 12 figure

Nano-structural, Electrical and Mechanical Characterization of Zirconium Oxide Thin Films as a Function of Annealing Temperature and Time

Zr thin films were deposited by DC magnetron sputtering technique on Si substrate and then post-annealed at different temperatures (150-750 °C in steps of 150 °C) and times (60 and 180 min) with flow of oxygen. X-ray diffraction (XRD) method was used for study of crystallographic structure. These results showed an orthorhombic structure for annealed films at 150 and a mixed structure of monoclinic and tetragonal for annealed films at higher temperatures (300-750 ºC). XRD result also showed that an increase in annealing temperature and time caused increasing of crystalline size. EDAX and AFM tech-niques were employed for investigation of chemical composition and surface morphology of samples, re-spectively. The results showed a granular structure for all samples, while the O / Zr ratio, grains size and surface roughness were increased with increasing of annealing temperature and time. A two probe instru-ment was used for electrical properties investigation, while hardness of films was measured by nano-indentation test. These results showed that increasing of annealing temperature and time caused increas-ing of electrical resistance and decreasing of hardness in the films. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3513

Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Anderson Transition in Disordered Graphene

We use the regularized kernel polynomial method (RKPM) to numerically study the effect disorder on a single layer of graphene. This accurate numerical method enables us to study very large lattices with millions of sites, and hence is almost free of finite size errors. Within this approach, both weak and strong disorder regimes are handled on the same footing. We study the tight-binding model with on-site disorder, on the honeycomb lattice. We find that in the weak disorder regime, the Dirac fermions remain extended and their velocities decrease as the disorder strength is increased. However, if the disorder is strong enough, there will be a {\em mobility edge} separating {\em localized states around the Fermi point}, from the remaining extended states. This is in contrast to the scaling theory of localization which predicts that all states are localized in two-dimensions (2D).Comment: 4 page