Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Magnetodielectric effect and optic soft mode behaviour in quantum paraelectric EuTiO3 ceramics

Abstract: Infrared reflectivity and time-domain terahertz transmission spectra of EuTiO3 ceramics revealed a polar optic phonon at 6–300 K whose softening is fully responsible for the recently observed quantum paraelectric behaviour. Even if our EuTiO3 ceramics show lower permittivity than the single crystal due to a reduced density and/or small amount of secondary pyrochlore Eu2Ti2O7 phase, we confirmed a magnetic field dependence of the permittivity, also slightly smaller than in single crystal. An attempt to reveal the soft phonon dependence at 1.8 K on the magnetic field up to 13 T remained below the accuracy of our infrared reflectivity experiment.

Insight Into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures

Abstract: Insider threats are one of today’s most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. In this work, we propose structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research while using an existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include incidents and datasets, analysis of incidents, simulations, and defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents that is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers’ efforts in the domain of insider threat because it provides (1) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, (2) an overview on publicly available datasets that can be used to test new detection solutions against other works, (3) references of existing case studies and frameworks modeling insiders’ behaviors for the purpose of reviewing defense solutions or extending their coverage, and (4) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.

Extrusions and intrusions in fatigued metals. Part 2. AFM and EBSD study of the early growth of extrusions and intrusions in 316L steel fatigued at room temperature

Abstract: Early stages of surface relief evolution of persistent slip markings (PSMs), formed in areas where persistent slip bands (PSBs) intersect the free surface, in polycrystalline 316L stainless steel cycled with constant plastic strain amplitude were studied using atomic force microscopy (AFM) and electron backscattering diffraction (EBSD). Focused ion beam (FIB) technique was employed to obtain additional, more detailed information on the shape of PSMs. To reveal true qualitative and quantitative information on the simultaneous growth of intrusions and extrusions within individual PSMs, identical areas both on the specimen surface and on its inverse copy obtained via plastic replica were studied using AFM. Intrusions are preceded by extrusions regardless of orientation of individual grains of the polycrystal. The first intrusions appear largely around 1% of fatigue life at the moment when ‘static’ extrusions are developed. They appear predominantly but not exclusively at the side of extrusions where the emer...

Collar Line Segments for fast odometry estimation from Velodyne point clouds

Abstract: We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an efficient way, enabling more precise registration. Alignment of the point clouds which yields the final odometry is based on random sampling of the clouds using Collar Line Segments (CLS). The closest line segment pairs are identified in two sets of line segments obtained from two consequent Velodyne scans. From each pair of correspondences, a transformation aligning the matched line segments into a 3D plane is estimated. By this, significant planes (ground, walls, …) are preserved among aligned point clouds. Evaluation using the KITTI dataset shows that our method outperforms publicly available and commonly used state-of-the-art method GICP for point cloud registration in both accuracy and speed, especially in cases where the scene lacks significant landmarks or in typical urban elements. For such environments, the registration error of our method is reduced by 75% compared to the original GICP error.