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Auflistung nach Autor:in "Bogdan, Martin"

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  • Konferenzbeitrag
    Canola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProduction
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Kukushkin, Maksim; Enders, Matthias; Kaschuba, Reinhard; Bogdan, Martin; Schmid, Thomas
    Analysing harvested seeds is a time-consuming task in the seed-producing industry. Automating this process has the potential to enhance and expedite agricultural seed production. In our study, we focus on differentiating Canola seeds from visually similar non-Canola seeds using computer vision techniques. Our approach utilises both RGB and hyperspectral images, captured by a specialised camera, to train separate autoencoder neural networks. By leveraging the high spatial resolution of RGB data and the high spectral resolution of hyperspectral data, we develop distinct models for Canola seed analysis, ensuring a comprehensive and robust assessment. The autoencoder networks are trained on a dataset of Canola seeds, allowing for the extraction of latent representations from both RGB and hyperspectral data. This enables efficient compression of input data and effective discrimination between Canola and non-Canola seeds. Our proposed approach demonstrates promising results in detecting non-Canola seeds in unseen test data.
  • Textdokument
    Separated Random Number Generators for Virtual Machines
    (INFORMATIK 2017, 2017) Fritzsch, Clemens; Hoffmann, Jörn; Bogdan, Martin
    High quality random numbers are essential for secure servers, alas virtual machines suffer from low entropy. The generation of hard to predict random numbers is a crucial problem. Moreover, existing solutions can pose severe security risks, e.g. by providing covert channels. This work illustrates basic ways to provide high entropy random numbers to virtual machines. Furthermore the security risks and effectiveness of the random number distributions are compared. Finally a new approach is presented. It improves the security of virtualized server environments by assigning each virtual machine a separate TRNG.