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DENISE

Research

Doctoral School for Dependable Electronic-Based Systems
Electronic Engineering

Research Focus

Electronic-based systems (EBS) make our world “smart” by combining advanced electronics and software, often in networked systems that interact with the physical world through sensors and actuators. Most EBS applications in production or transportation are safety-critical: EBS failures may cost human lives. A system is only as dependable as its weakest part. As EBS combine many heterogeneous aspects – software, signals, electronics, networks, sensors, actuators – our hypothesis is that a concerted effort of all relevant research disciplines is required to achieve EBS dependability.

Our objective is thus to establish an integrated research framework across the disciplinary boundaries to link up dependability concepts along the data processing chain into a holistic toolbox of methods. We aim to devise fundamental concepts and methods, but also application-oriented tools to make EBS dependable, where dependability summarizes attributes of a system allowing humans to trust EBS.

PhD Projects

Infrastructure and Facilities

  • aiMotionLab: A collaborative, multidisciplinary research infrastructure for the development, implementation and evaluation of AI systems (AI = artificial intelligence) for connected, mobile cyber-physical systems
  • ATE Lab: On-chip testing lab for R&D testing and measurement
  • Shielding anechoic chamber with frequency range from 30 megahertz to 18 gigahertz
  • JOANNEUM Power Electronic Center: Minimising energy losses and increasing the efficiency of electricity transmission
  • Benchmarking infrastructure for evaluation of performance of low-power wireless systems

Publications

Conferences:

  1. A. A. Dawara, A. Muetze, „Overview of Post-Fault Operation Strategies for Open-End Winding Machines Considering ZSC”, 2023 IEEE Energy Conversion Congress and Exposition (ECCE), Naschville, TN, USA, 2023, doi: 10.1109/ECCE53617.2023.10361964
  2. F. Mayer and C. Vogel, “An Optimization-Based Approach to One-Bit Quantization,” 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, Singapore, 2024, pp. 1-5, doi: 10.1109/ISCAS58744.2024.10558238
  3. F. Corti, B. Maag, J. Schauer, U. Pferschy, O, Saukh, „REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints”, Annual International Conference on Mobile Computing and Networking, Washington, D.C., USA, 2024, https://arxiv.org/pdf/2311.13349
  4. A. A. Dawara, R. Seebacher, A. Muetze, „Frequency-Adaptive Repetitive Control Based Zero-Sequence Current Elimination in Leg Sharing Post-Fault Operation of OW Machine Drives”, 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024 – ECCE Asia), Chengdu, China, 2024, pp. 2906-2911,  doi: 10.1109/IPEMC-ECCEAsia60879.2024.10567974

 

Poster:

  1. H. Hydher, M. Schuß, O. Saukh, C. A. Boano, K. Roemer, „Automatic Parameter Exploration for Low-Power Wireless Protocols”, EWSN ’23: Proceedings of the 2023 International Conference on embedded Wireless Systems and Networks, Rende, Italy, 2023, https://dl.acm.org/doi/proceedings/10.5555/3639940
  2. F. Corti, C. Hinterer, J. Rudolf, B. Maag, J. Schauer, O. Saukh, „Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints on IoT Devices”, 2023 International Conference on Embedded Wireless Systems and Networks (EWSN), Italy, http://olgasaukh.com/paper/corti23reds_poster.pdf
  3. S. Nabavi, J. Schauer, “Anchor Placement Optimization for Area-Based Localization Using Tabu Search Algorithm”, EWSN ’23: Proceedings of the 2023 International Conference on embedded Wireless Systems and Networks, Rende, Italy, 2023, https://doi.org/10.1049/wss2.12092

 

Journal:

  1. A. A. Dawara, R. Seebacher, A. Muetze, „Zero-Sequence Current Elimination Strategy Based on Frequency-Adaptive Repetitive Control of OW Machines During Leg-Sharing Post-Fault Operation, IEEE Transactions on Industry Applications, 2024 (link not available, submitted 21.8.24)
  2. S. Nabavi, J. Schauer, C.A. Boano, K. Roemer, “APOTSA:Anchor Placement Optimisation Using Discrete TabuSearch Algorithm for Area‐Based Localisation”, . IETWirel. Sens. Syst. 1–14 (2024), https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12092
  3. Javadi S., Rezaee B, Nabavi S. Gadringer M. E., Bösch W., Machine Learning-Driven Approaches for Advanced Microwave Filter Design, Electronics 202514(2), 367; https://doi.org/10.3390/electronics14020367
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