Chair

Browse all posts in the Chair category.

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02MAY 2023
Chair
Best Paper Award in Collaboration with the IT Security Infrastructures Lab
We congratulate Sandra Bergmann, Denise Moussa, Fabian Brand, André Kaup and Christian Riess to their Best Paper Award at the International Workshop on Biometrics and Forensics (IWBF). This paper was the first to systematically examine the forensic traces of neural-network-based image compression. With deep image coders gaining more and more popularity and first standardization being […]
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28APR 2023
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Research Visit of Alexandre Mercat
From May 29th until June 2nd, Dr. Alexandre Mercat from the Ultra Video Group, University of Tampere, Finland, will visit the LMS for a research collaboration. During his stay, he will give a presentation entitled “Overview of the Multifaceted Research Activities at the Ultra Video Group, Tampere University ” on Wednesday, May 31st, 10am, in […]
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19APR 2023
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Results of the “Signals and Systems II” exam
The results of the exam “Signals and Systems II” from March 31, 2023 are now available on Campo. Examination records can be inspected on May 3, 2023 at 9:00 am in room 06.025 (Cauerstr. 7).
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14APR 2023
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Final of ICPHM Data Challenge reached
We congratulate our colleague Matthias Kreuzer on qualifying for the final three of the IEEE Data Challenge on Industrial Systems’ Health Monitoring using Vibration Signal Analysis as part of the IEEE International Conference on Prognostics and Health Management (ICPHM) . The task of the challenge is to reliably classify damages in gearboxes based on the analysis […]
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13APR 2023
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New Colleague in April 2023
Our research group working on “Machine Learning in Signal Processing” has a new team member: We welcome Rohan Asthana, who will conduct research on the architecture of neuronal networks.
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13APR 2023
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Particle Filtering for Data Augmentation in Image Classification
We congratulate Prof. Belagiannis and Alexander Tsaregorodtsev for their journal publication entitled “ParticleAugment: Sampling-based data augmentation“. Data Augmentation (DA) is employed during the training process of neural networks in order to prevent overfitting on the training data as well as ensure better generalization capabilities of the resulting network. While data augmentation policies can be hand-chosen […]We congratulate Prof. Belagiannis and Alexander Tsaregorodtsev for their journal publication entitled “ParticleAugment: Sampling-based data augmentation“. Data Augmentation (DA) is employed during the training process of neural networks in order to prevent overfitting on the training data as well as ensure better generalization capabilities of the resulting network. While data augmentation policies can be hand-chosen […]
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