75 research outputs found
Current Tomography -- Localization of void fractions in conducting liquids by measuring the induced magnetic flux density
A novel concept of a measurement technology for the localization and
determination of the size of gas bubbles is presented, which is intended to
contribute to a further understanding of the dynamics of efficiency-reducing
gas bubbles in electrolyzers. A simplified proof-of-concept (POC) model is used
to numerically simulate the electric current flow through materials with
significant differences in electrical conductivity. Through an automated
approach, an extensive data set of electric current density and conductivity
distributions is generated, complemented with determined magnetic flux
densities in the surroundings of the POC cell at virtual sensor positions. The
generated data set serves as testing data for various reconstruction
approaches. Based on the measurable magnetic flux density, solving Biot-Savarts
law inversely is demonstrated and discussed with a model-based solution of an
optimization problem, of which the gas bubble locations are derived
scenery: Flexible Virtual Reality Visualization on the Java VM
Life science today involves computational analysis of a large amount and
variety of data, such as volumetric data acquired by state-of-the-art
microscopes, or mesh data from analysis of such data or simulations.
Visualization is often the first step in making sense of data, and a crucial
part of building and debugging analysis pipelines. It is therefore important
that visualizations can be quickly prototyped, as well as developed or embedded
into full applications. In order to better judge spatiotemporal relationships,
immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and
associated controllers are becoming invaluable tools. In this work we introduce
scenery, a flexible VR/AR visualization framework for the Java VM that can
handle mesh and large volumetric data, containing multiple views, timepoints,
and color channels. scenery is free and open-source software, works on all
major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce
scenery's main features and example applications, such as its use in VR for
microscopy, in the biomedical image analysis software Fiji, or for visualizing
agent-based simulations.Comment: Added IEEE DOI, version published at VIS 201
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Streaming Compression of Tetrahedral Volume Meshes
Geometry processing algorithms have traditionally assumed that the input data is entirely in main memory and available for random access. This assumption does not scale to large data sets, as exhausting the physical memory typically leads to IO-inefficient thrashing. Recent works advocate processing geometry in a 'streaming' manner, where computation and output begin as soon as possible. Streaming is suitable for tasks that require only local neighbor information and batch process an entire data set. We describe a streaming compression scheme for tetrahedral volume meshes that encodes vertices and tetrahedra in the order they are written. To keep the memory footprint low, the compressor is informed when vertices are referenced for the last time (i.e. are finalized). The compression achieved depends on how coherent the input order is and how many tetrahedra are buffered for local reordering. For reasonably coherent orderings and a buffer of 10,000 tetrahedra, we achieve compression rates that are only 25 to 40 percent above the state-of-the-art, while requiring drastically less memory resources and less than half the processing time
Hierarchical Shape-Adaptive Quantization for Geometry Compression
The compression of polygonal mesh geometry is still an active field of research as in 3d no theoretical bounds are known. This work proposes a geometry coding method based on predictive coding. Instead of using the vertex to vertex distance as distortion measurement, an approximation to the Hausdorffdistance is used resulting in additional degrees of freedom. These are exploited by a new adaptive quantization approach, which is independent of the encoding order. The achieved compression rates are similar to those of entropy based optimization but with a significantly faster compression performance
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