10 research outputs found
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Single atom imaging with time-resolved electron microscopy
Developments in scanning transmission electron microscopy (STEM) have opened
up new possibilities for time-resolved imaging at the atomic scale. However, rapid
imaging of single atom dynamics brings with it a new set of challenges, particularly
regarding noise and the interaction between the electron beam and the specimen. This
thesis develops a set of analytical tools for capturing atomic motion and analyzing the
dynamic behaviour of materials at the atomic scale.
Machine learning is increasingly playing an important role in the analysis of electron
microscopy data. In this light, new unsupervised learning tools are developed here for
noise removal under low-dose imaging conditions and for identifying the motion of
surface atoms. The scope for real-time processing and analysis is also explored, which is
of rising importance as electron microscopy datasets grow in size and complexity.
These advances in image processing and analysis are combined with computational
modelling to uncover new chemical and physical insights into the motion of atoms
adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis,
where the catalytic activity can depend intimately on the atomic environment. The
study of Cu atoms on a graphene oxide support reveals that the atoms undergo
anomalous diffusion as a result of spatial and energetic disorder present in the substrate.
The investigation is extended to examine the structure and stability of small Cu clusters
on graphene oxide, with atomistic modelling used to understand the significant role
played by the substrate. Finally, the analytical methods are used to study the surface
reconstruction of silicon alongside the electron beam-induced motion of adatoms on
the surface.
Taken together, these studies demonstrate the materials insights that can be obtained
with time-resolved STEM imaging, and highlight the importance of combining state-ofthe-
art imaging with computational analysis and atomistic modelling to quantitatively
characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered
Calibration and data analysis strategies for enhanced 21-cm EoR limits with the MWA
This thesis targets observations of the 21 cm signal emitted by neural hydrogen during the early stages of the Universe using data from the Murchison Widefield Array radio telescope. The detection of this elusive signal is challenging, and this thesis tackles the obstacles involved, investigating current methods and proposing novel ones. The work presented by this research is therefore another step towards a complete understanding of the history of our Universe
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
MC 2019 Berlin Microscopy Conference - Abstracts
Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2019", die vom 01. bis 05.09.2019, in Berlin stattfand