Simplified model for the energy levels of quantum rings in single layer and bilayer graphene

Within a minimal model, we present analytical expressions for the eigenstates and eigenvalues of carriers confined in quantum rings in monolayer and bilayer graphene. The calculations were performed in the context of the continuum model, by solving the Dirac equation for a zero width ring geometry, i.e. by freezing out the carrier radial motion. We include the effect of an external magnetic field and show the appearance of Aharonov-Bohm oscillations and of a non-zero gap in the spectrum. Our minimal model gives insight in the energy spectrum of graphene-based quantum rings and models different aspects of finite width rings.Comment: To appear in Phys. Rev.

Topological confinement in graphene bilayer quantum rings

We demonstrate the existence of localized electron and hole states in a ring-shaped potential kink in biased bilayer graphene. Within the continuum description, we show that for sharp potential steps the Dirac equation describing carrier states close to the K (or K') point of the first Brillouin zone can be solved analytically for a circular kink/anti-kink dot. The solutions exhibit interfacial states which exhibit Aharonov-Bohm oscillations as functions of the height of the potential step and/or the radius of the ring

Conditions for non-monotonic vortex interaction in two-band superconductors

We describe a semi-analytic approach to the two-band Ginzburg-Landau theory, which predicts the behavior of vortices in two-band superconductors. We show that the character of the short-range vortex-vortex interaction is determined by the sign of the normal domain - superconductor interface energy, in analogy with the conventional differentiation between type-I and type-II superconductors. However, we also show that the long-range interaction is determined by a modified Ginzburg-Landau parameter $\kappa^*$, different from the standard $\kappa$ of a bulk superconductor. This opens the possibility for non-monotonic vortex-vortex interaction, which is temperature-dependent, and can be further tuned by alterations of the material on the microscopic scale

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

Risk Management Practices in the Aviation Industry: Lessons Learned and Effective Tools… A Mixed Method Approach

This paper provides a data-driven approach to apply lessons learned from Project Risk Management in the aviation industry. This is accomplished by performing a qualitative analysis to inform a second-pass quantitative analysis using tools like surveys, word clods and analysis through NVivo and Singular Value Decomposition (SVD). The results of this analysis show that the most important lessons are elements of early identification, rigorous risk identification and analysis and a focus on reducing risks inherent within the aviation history. Furthermore, the analysis yielded the following top tools for risks management within their phases of the project life-cycle: document review, checklists or taxonomies, retrospection, assessment of risk parameter, data quality assessment, data analytics and status meetings