High Precision Orientation Mapping from 4D-STEM Precession Electron Diffraction data through Quantitative Analysis of Diffracted Intensities

The association of scanning transmission electron microscopy (STEM) and the detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of materials with nanometric resolution and low irradiation levels. This is widely used for texture analysis of material using automated crystal orientation mapping (ACOM). Herein, we perform orientation mapping in InP nanowires exploiting precession electron diffraction (PED) patterns acquired by an axial CMOS camera. Crystal orientation is determined at each probe position by the quantitative analysis of diffracted intensities minimizing a residue comparing experiments and simulations in analogy to structural refinement. Our simulations are based on the two-beam dynamical diffraction approximation and yield a high angular precision (~0.03 degrees), much lower than the traditional ACOM based on pattern matching algorithms (~1 degrees). We anticipate that simultaneous exploration of both spot positions and high precision crystal misorientation will allow the exploration of the whole potentiality provided by PED-based 4D-STEM for the characterization of deformation fields in nanomaterials.Comment: 31 pages, 6 figures, 1 table and supplementary informatio

Crosscutting, what is and what is not? A Formal definition based on a Crosscutting Pattern

Crosscutting is usually described in terms of scattering and tangling. However, the distinction between these concepts is vague, which could lead to ambiguous statements. Sometimes, precise definitions are required, e.g. for the formal identification of crosscutting concerns. We propose a conceptual framework for formalizing these concepts based on a crosscutting pattern that shows the mapping between elements at two levels, e.g. concerns and representations of concerns. The definitions of the concepts are formalized in terms of linear algebra, and visualized with matrices and matrix operations. In this way, crosscutting can be clearly distinguished from scattering and tangling. Using linear algebra, we demonstrate that our definition generalizes other definitions of crosscutting as described by Masuhara & Kiczales [21] and Tonella and Ceccato [28]. The framework can be applied across several refinement levels assuring traceability of crosscutting concerns. Usability of the framework is illustrated by means of applying it to several areas such as change impact analysis, identification of crosscutting at early phases of software development and in the area of model driven software development

SCJ-Circus: specification and refinement of Safety-Critical Java programs

Safety-Critical Java (SCJ) is a version of Java for real-time, embedded, safety-critical applications. It supports certification via abstractions that enforce a particular program architecture, with controlled concurrency and memory models. SCJ is an Open Group standard, with a reference implementation, but little support for reasoning. Here, we present SCJ-Circus, a refinement notation for specification and verification of low-level models of SCJ programs. SCJ-Circus is part of the Circus family of state-rich process algebras: it includes the Circus constructs for modelling of sequential and concurrent behaviour based on Z and CSP, and the real-time and object-oriented extensions of Circus, in addition to the SCJ abstractions. We present the syntax of SCJ-Circus and its semantics, defined by mapping SCJ-Circus constructs to those of Circus. We also detail a refinement strategy that takes a Circus design that adheres to a multiprocessor cyclic executive pattern and produces an SCJ program design, described in SCJ-Circus. Finally, we show how this refinement strategy can be extended for more complex program architectures

Mesh-based 3D Textured Urban Mapping

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201