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End date: 2019 × Start date: 2011 × Type: Report × Type: Text ×

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Now showing 1 - 10 of 1540
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    Direct transfer of magnetic sensor devices to elastomeric supports for stretchable electronics
    (Hoboken, NJ : Wiley, 2015) Melzer, Michael; Karnaushenko, Daniil; Lin, Gungun; Baunack, Stefan; Makarov, Denys; Schmidt, Oliver G.
    A novel fabrication method for stretchable magnetoresistive sensors is introduced, which allows the transfer of a complex microsensor systems prepared on common rigid donor substrates to prestretched elastomeric membranes in a single step. This direct transfer printing method boosts the fabrication potential of stretchable magnetoelectronics in terms of miniaturization and level of complexity, and provides strain‐invariant sensors up to 30% tensile deformation.
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    Biomimetic microelectronics for regenerative neuronal cuff implants
    (Hoboken, NJ : Wiley, 2015) Karnaushenko, Daniil; Münzenrieder, Niko; Karnaushenko, Dmitriy D.; Koch, Britta; Meyer, Anne K.; Baunack, Stefan; Petti, Luisa; Tröster, Gerhard; Makarov, Denys; Schmidt, Oliver G.
    Smart biomimetics, a unique class of devices combining the mechanical adaptivity of soft actuators with the imperceptibility of microelectronics, is introduced. Due to their inherent ability to self‐assemble, biomimetic microelectronics can firmly yet gently attach to an inorganic or biological tissue enabling enclosure of, for example, nervous fibers, or guide the growth of neuronal cells during regeneration.
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    Efficient retrieval of 3D building models using embeddings of attributed subgraphs
    (Institut für Informatik II, Universität Bonn, 2011) Wessel, R.; Ochmann, S.; Vock, R.; Blümel, Ina; Klein, R.
    We present a novel method for retrieval and classification of 3D building models that is tailored to the specific requirements of architects. In contrast to common approaches our algorithm relies on the interior spatial arrangement of rooms instead of exterior geometric shape. We first represent the internal topological building structure by a Room Connectivity Graph (RCG). Each room is characterized by a node. Connections between rooms like e.g. doors are represented by edges. Nodes and edges are additionally assigned attributes reflecting room and edge properties like e.g area or window size. To enable fast and efficient retrieval and classification with RCGs, we transform the structured graph representation into a vector-based one. We first decompose the RCG into a set of subgraphs. For each subgraph, we compute the similarity to a set of codebook graphs. Aggregating all similarity values finally provides us with a single vector for each RCG which enables fast retrieval and classification. For evaluation, we introduce a classification scheme that was carefully developed following common guidelines in architecture.We finally provide comprehensive experiments showing that the introduced subgraph embeddings yield superior performance compared to state-of-the-art graph retrieval approaches.
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    Self‐assembled on‐chip‐integrated giant magneto‐impedance sensorics
    (Hoboken, NJ : Wiley, 2015) Karnaushenko, Daniil; Karnaushenko, Dmitriy D.; Makarov, Denys; Baunack, Stefan; Schäfer, Rudolf; Schmidt, Oliver G.
    A novel method relying on strain engineering to realize arrays of on‐chip‐integrated giant magneto‐impedance (GMI) sensors equipped with pick‐up coils is put forth. The geometrical transformation of an initially planar layout into a tubular 3D architecture stabilizes favorable azimuthal magnetic domain patterns. This work creates a solid foundation for further development of CMOS compatible GMI sensorics for magnetoencephalography.
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    High-performance magnetic sensorics for printable and flexible electronics
    (Hoboken, NJ : Wiley, 2014) Karnaushenko, Daniil; Makarov, Denys; Stöber, Max; Karnaushenko, Dmitriy D.; Baunack, Stefan; Schmidt, Oliver G.
    High‐performance giant magnetoresistive (GMR) sensorics are realized, which are printed at predefined locations on flexible circuitry. Remarkably, the printed magnetosensors remain fully operational over the complete consumer temperature range and reveal a giant magnetoresistance up to 37% and a sensitivity of 0.93 T−1 at 130 mT. With these specifications, printed magnetoelectronics can be controlled using flexible active electronics for the realization of smart packaging and energy‐efficient switches.
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    Mathematical modeling and numerical simulations of diode lasers with micro-integrated external resonators
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2016) Radziunas, Mindaugas
    This report summarizes our scientific activities within the project MANUMIEL (BMBF Program “Förderung der Wissenschaftlich-Technologischen Zusammenarbeit (WTZ) mit der Republik Moldau”, FKZ 01DK13020A). Namely, we discuss modeling of external cavity diode lasers, numerical simulations and analysis of these devices using the software package LDSL-tool, as well as the development of this software.
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    TetGen: A quality tetrahedral mesh generator and a 3D Delaunay triangulator (Version 1.5 — User’s Manual)
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Si, Hang
    TetGen is a software for tetrahedral mesh generation. Its goal is to generate good quality tetrahedral meshes suitable for numerical methods and scientific computing. It can be used as either a standalone program or a library component integrated in other software. The purpose of this document is to give a brief explanation of the kind of tetrahedralizations and meshing problems handled by TetGen and to give a fairly detailed documentation about the usage of the program. Readers will learn how to create tetrahedral meshes using input files from the command line. Furthermore, the programming interface for calling TetGen from other programs is explained.
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    Calibration methods for gas turbine performance models
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2016) Borchardt, Jürgen; Mathé, Peter; Printsypar, Galina
    The WIAS software package BOP is used to simulate gas turbine models. In order to make accurate predictions the underlying models need to be calibrated. This study compares different strategies of model calibration. These are the deterministic optimization tools as nonlinear least squares (MSO) and the sparsity promoting variant LASSO, but also the probabilistic (Bayesian) calibration. The latter allows for the quantification of the inherent uncertainty, and it gives rise to a surrogate uncertainty measure in the MSO tool. The implementation details are accompanied with a numerical case study, which highlights the advantages and drawbacks of each of the proposed calibration methods.