Thermally induced magnetic switching in bit-patterned media

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Journal of Applied Physics 122, 043907 (2017) and may be found at https://doi.org/10.1063/1.4992808.We have studied the thermal variation of the switching field of magnetic islands at room temperature. A model bit-pattern media composed of an assembly of islands with 80 nm width was fabricated by sputter deposition onto a pre-patterned substrate. Using direct magnetic-contrast imaging of the islands under applied field, we extract the switching probabilities of individual islands. Based on an analytical model for the thermally activated switching of the islands, we are able to determine the intrinsic magnetic anisotropy of each island and, consequentially, a distribution of anisotropies for the island ensemble investigated. In the distribution, we identify a separated group of islands with a particularly small anisotropy. We attribute this group to islands containing misaligned grains triggering the magnetic reversal. At room temperature and slow field sweep rates, the observed thermal broadening of the switching-field distribution is small compared to the intrinsic broadening. However, we illustrate that thermal fluctuations play a crucial role at high sweep rates by extrapolating our results to technological relevant regimes

Model Reduction for Multiscale Lithium-Ion Battery Simulation

In this contribution we are concerned with efficient model reduction for multiscale problems arising in lithium-ion battery modeling with spatially resolved porous electrodes. We present new results on the application of the reduced basis method to the resulting instationary 3D battery model that involves strong non-linearities due to Buttler-Volmer kinetics. Empirical operator interpolation is used to efficiently deal with this issue. Furthermore, we present the localized reduced basis multiscale method for parabolic problems applied to a thermal model of batteries with resolved porous electrodes. Numerical experiments are given that demonstrate the reduction capabilities of the presented approaches for these real world applications

Infrastructure for the Coupling of Dune Grids

We describe an abstract interface for the geometric coupling of finite element grids. The scope of the interface encompasses a wide range of domain decomposition techniques in use today, including nonconforming grids and grids of different dimensions. The couplings are described as sets of remote intersections, which encapsulate the relationships between pairs of elements on the coupling interface. The abstract interface is realized in a module dune-grid-glue for the software framework dune. Several implementations of this interface exist, including one for general nonconforming couplings and a special efficient implementation for conforming interfaces. We present two numerical examples to show the flexibility of the approach

Numerical solution of steady-state groundwater flow and solute transport problems: Discontinuous Galerkin based methods compared to the Streamline Diffusion approach

In this study, we consider the simulation of subsurface flow and solute transport processes in the stationary limit. In the convection-dominant case, the numerical solution of the transport problem may exhibit non-physical diffusion and under- and overshoots. For an interior penalty discontinuous Galerkin (DG) discretization, we present a $h$-adaptive refinement strategy and, alternatively, a new efficient approach for reducing numerical under- and overshoots using a diffusive $L^2$-projection. Furthermore, we illustrate an efficient way of solving the linear system arising from the DG discretization. In $2$-D and $3$-D examples, we compare the DG-based methods to the streamline diffusion approach with respect to computing time and their ability to resolve steep fronts

Statistical comparison of clouds and star clusters

The extent to which the projected distribution of stars in a cluster is due to a large-scale radial gradient, and the extent to which it is due to fractal sub-structure, can be quantified -- statistically -- using the measure ${\cal Q} = \bar{m}/\bar{s}$. Here $\bar{m}$ is the normalized mean edge length of its minimum spanning tree (i.e. the shortest network of edges connecting all stars in the cluster) and $\bar{s}$ is the correlation length (i.e. the normalized mean separation between all pairs of stars). We show how ${\cal Q}$ can be indirectly applied to grey-scale images by decomposing the image into a distribution of points from which $\bar{m}$ and $\bar{s}$ can be calculated. This provides a powerful technique for comparing the distribution of dense gas in a molecular cloud with the distribution of the stars that condense out of it. We illustrate the application of this technique by comparing ${\cal Q}$ values from simulated clouds and star clusters.Comment: Accepted 2010 October 27. Received 2010 October 25; in original form 2010 September 13 The paper contains 7 figures and 2 table

Metric-Scale Truncation-Robust Heatmaps for 3D Human Pose Estimation

Heatmap representations have formed the basis of 2D human pose estimation systems for many years, but their generalizations for 3D pose have only recently been considered. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and the Z axis to metric depth around the subject. To obtain metric-scale predictions, these methods must include a separate, explicit post-processing step to resolve scale ambiguity. Further, they cannot encode body joint positions outside of the image boundaries, leading to incomplete pose estimates in case of image truncation. We address these limitations by proposing metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are defined in metric 3D space near the subject, instead of being aligned with image space. We train a fully-convolutional network to estimate such heatmaps from monocular RGB in an end-to-end manner. This reinterpretation of the heatmap dimensions allows us to estimate complete metric-scale poses without test-time knowledge of the focal length or person distance and without relying on anthropometric heuristics in post-processing. Furthermore, as the image space is decoupled from the heatmap space, the network can learn to reason about joints beyond the image boundary. Using ResNet-50 without any additional learned layers, we obtain state-of-the-art results on the Human3.6M and MPI-INF-3DHP benchmarks. As our method is simple and fast, it can become a useful component for real-time top-down multi-person pose estimation systems. We make our code publicly available to facilitate further research (see https://vision.rwth-aachen.de/metro-pose3d).Comment: Accepted for publication at the 2020 IEEE Conference on Automatic Face and Gesture Recognition (FG

MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation

Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code will be made publicly available to facilitate further research.Comment: See project page at https://vision.rwth-aachen.de/metrabs . Accepted for publication in the IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM), Special Issue "Selected Best Works From Automated Face and Gesture Recognition 2020". Extended version of FG paper arXiv:2003.0295