Andres Yanez

Universidad de Cádiz | UCA · Department of Computer Engineering

The use of thin AlA capping layers (CLs) on InAs quantum dots (QDs) has recently received considerable attention due to improved photovoltaic performance in QD solar cells. However, there is little data on the structural changes that occur during capping and their relation to different growth conditions. In this work, we studied the effect of AlA c...

From simple averaging to more sophisticated registration and restoration strategies, such as super-resolution (SR), there exist different computational techniques that use a series of images of the same object to generate enhanced images where noise and other distortions have been reduced. In this work, we provide qualitative and quantitative measu...

Nowadays, despite the huge amount of digitized information, the biggest drawback to use machine learning in text mining is the lack of availability of a set of tagged data due to mainly, that it requires a great user effort that it is not always viable. In this paper, with the aim of reducing the great workload required to manually processing the c...

Game-based learning has proven to be effective for enhancing student motivation and learning outcomes. In this study, the authors first designed and then tested a 3D virtual world-based video game to support students in learning a foreign language. Two data mining clustering techniques are used to analyse the impact of the game on learning processe...

Super-resolution (SR) methodologies allow the construction of high resolution images from several noisy low-resolution images. This methodology can be applied to overcome the inherent resolution limitation and improve the performance in digital imaging of scanning transmission electron microscopes (STEM). Here we apply SR to column resolved images...

Super-resolution (SR) methodologies allow the construction of high resolution images from several noisy low-resolution images. This methodology can be applied to overcome the inherent resolution limitation and improve the performance in digital imaging of scanning transmission electron microscopes (STEM). Here we apply SR to column resolved images...

During image acquisition of crystalline materials by high-resolution scanning transmission electron microscopy, the sample drift could lead to distortions and shears that hinder their quantitative analysis and characterization. In order to measure and correct this effect, several authors have proposed different methodologies making use of series of...

During image acquisition in scanning transmission electron microscopy the sample drift effect introduces distortions, expansions, compressions and shears which prevent analysis and characterization of materials. In this work, we introduce a method that uses only a fast-acquisition STEM image in order to determine the drift angle using Fourier harm...

Video games are effective tools to engage students in foreign language learning. Research has shown that not all students benefit from video games in the same way nor at the same pace. We have created a 3D video game, based on a virtual world platform, that aims at fostering students' writing competence. It requires learners to play collaboratively...

In this paper, stress fields at the surface of the capping layer of self-assembled InAsP quantum wires grown on an InP (001) substrate have been determined from atomistic models using molecular dynamics and Stillinger-Weber potentials. To carry out these calculations, the quantum wire compositional distribution was extracted from previous works, wh...

In this paper we describe a methodology developed at the University of Cadiz (Spain) in the past few years for the extraction of quantitative information from electron microscopy images at the atomic level. This work is based on a coordinated and synergic activity of several research groups that have been working together over the last decade in tw...

This paper explains how to determine the optimal configuration of photonic crystal structures capable of carrying out a certain optical task. In this work we show how genetic algorithms can be applied to reliably finding an optimal topology of three-dimensional photonic crystals. The fitness, representing the performance of each potential configura...

The distribution of Bi atoms in epitaxial GaAs{sub (1-x)}Bi{sub x} is analyzed through aberration-corrected Z-contrast images. The relation between the atomic number and the intensity of the images allows quantifying the distribution of Bi atoms in this material. A bidimensional map of Bi atoms is extracted showing areas where nanoclustering is pos...

In this paper a technique to design three dimensional (3D) devices to focus acoustic waves composed of scattering elements is proposed. The devices are designed and optimized in two dimensions (2D) with the help of a genetic algorithm and the 2D multiple scattering formalism. The transition from 2D to 3D is made by applying a rotation operation to...

High-Angle Annular Dark-Field Scanning Transmission Electron Microscopy (HAADF-STEM) in combination with strain mapping techniques provides a powerful tool for quantitative analysis of crystalline semiconductor materials. Due to the complex interaction of a focused probe and a sample in HAADF, the calculation of each pixel in a simulation process r...

Geometric phase and peak pairs strain mapping techniques have been applied to high angular annular dark field scanning transmission electron microscopy (HAADF-STEM) simulated images of an InAs/InP strained nanowire at different sample thicknesses. Strain values determined from HAADF-STEM images have been compared to theoretical values obtained by s...

While high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) has been successfully used for the analysis of heavy atoms in a lighter matrix, the detection of light atoms in a heavy matrix remains challenging. In this paper, we show that the combination of first-principles total-energy calculations with aberration-corre...

Stress fields at the surface of the capping layer of self-assembled InAsP quantum wires grown on an InP (001) substrate have been determined from atomistic models, which were built by Molecular Dynamics using Stillinger-Weber potentials. The compositional distribution representing the quantum wires has been extracted by electron energy loss spectr...

High angle annular dark field scanning transmission electron microscopy (HAADF-STEM) is a powerful tool to quantify size, shape, position, and composition of nano-objects with the assessment of image simulation. Due to the high computational requirements needed, nowadays it can only be applied to a few unit cells in standard computers. To overpass...

In this work, we analyze the influence of atomic displacements due to elastic strain in HAADF-STEM simulated images. This methodology is demonstrated on an InP/InAsxP(1−x) nanowire heterostructure theoretical model under strained/unstrained conditions. Since the HRTEM image simulation requires an enormous amount of computing power, all simulations...

The combination of strain measurements1 and the analysis of High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) images constitutes a powerful approach to investigate strained heterostructures on the nanometer scale and has proved to be extremely powerful for characterizing nanomaterials2,3.

Strain mapping is defined as a numerical image-processing technique that measures the local shifts of image details around a crystal defect with respect to the ideal, defect-free, positions in the bulk. Algorithms to map elastic strains from high-resolution transmission electron microscopy (HRTEM) images may be classified into two categories: those...

In this article a method for determining errors of the strain values when applying strain mapping techniques has been devised. This methodology starts with the generation of a thickness/defocus series of simulated high-resolution transmission electron microscopy images of InAsxP1-x/InP heterostructures and the application of geometric phase. To obt...

The goal of combining the outputs of multiple models is to form an improved meta-model with higher generalization capability than the best single model used in isolation. Most popular ensemble methods do specify neither the number of component models nor their complexity. However, these parameters strongly influence the generalization capability o...

In this paper we describe a pattern recognition system implemented to determine thickness and defocus from HRTEM simulated images. A specific task has been designed to quantify the influence of certain operation parameters of a transmission electron microscope in the global recognition error rate. This influence allows us to estimate human recognit...

Strain mapping is defined as a numerical image processing technique that measures the local shifts of image details around a crystal defect with respect to the ideal, defect-free, positions in the bulk. The most common algorithms for strain mapping are based on peak finding (real space) and geometric phase (Fourier space) methods. In this paper, we...

In this paper we describe a new penalty-based model selection criterion for nonlinear models which is based on the influence of the noise in the fitting. According to Occam’s razor we should seek simpler models over complex ones and optimize the trade-off between model complexity and the accuracy of a model’s description to the training data. An em...

Model combination provides an alternative to model selection. With a little additional effort we can obtain MML models that improve the generalization capabilities of their individual members. However, it has been recognized that the individual members must be as accurate and diverse as possible. In this paper we present a novel method for building...

Estimating Prediction Risk is important for providing a way of computing the expected error for predictions made by a model, but it is also an important tool for model selection. This paper addresses an empirical comparison of model selection techniques based on the Prediction Risk estimation, with particular reference to the structure of nonlinear...

This paper proposes a new complexity-penalization model selection strategy derived from the minimum risk principle and the behavior of candidate models under noisy conditions. This strategy seems to be robust in small sample size conditions and tends to AIC criterion as sample size grows up. The simulation study at the end of the paper will show th...

One of the most important difficulties in using neural networks for a real-world problem is the issue of model complexity, and how affects the generalization performance. We present a new algorithm based on multiple comparison methods for finding low complexity neural networks with high generalization capability.

In this paper we show how several fields of Advanced Computing (Pattern Recognition, Neural Networks and Machine Learning) are powerful tools for the analysis and manipulation of High Resolution Transmission Electron Microscopy images. A specific task has been designed for the determination of thickness and defocus from High Resolution Transmission...

One of the main research concern in neural networks is to find the appropriate network size in order to minimize the trade-off between overfitting and poor approximation. In this paper the choice among different competing models that fit to the same data set is faced when statistical methods for model comparison are applied. The study has been cond...

Complexity-penalization strategies are one way to decide on the most appropriate network size in order to address the trade-off between overfitted and underfitted models. In this paper we propose a new penalty term derived from the behaviour of candidate models under noisy conditions that seems to be much more robust against catastrophic overfittin...