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

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Now showing 1 - 8 of 8
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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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    Monitoring der methanbildenden Mikroflora in Praxis-Biogasanlagen im ländlichen Raum : Analyse des Ist-Zustandes und Entwicklung eines quantitativen Nachweissystems
    (Hannover : Technische Informationsbibliothek, 2009) Klocke, Michael; Nettmann, Edith; Bergmann, Ingo
    Die Produktion von Biogas aus landwirtschaftlichen Primärprodukten oder Reststoffen stellt einen wesentlichen Beitrag zur Reduktion des Co2-Ausstoßes sowie zur Entwicklung einer nachhaltigen Landbewirtschaftung dar. Im Rahmen dieses Projektes soll daher die Artenzusammensetzung der methanogenen Mikroflora in ausgewählten Praxis-Biogasanlagen anhand ihrer 16S rDNA analysisert werden.
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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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    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.