Tim Tiedemann is a professor of intelligent sensors in computer science department of HAW Hamburg (University of Applied Sciences Hamburg).

Further Interests

• Multi-Spectrum Imaging (MSI), Hyper-Spectrum Imaging (HSI)
• FPGA-based implementation of machine learning (ML) methods
• Data Mining

Supervised Bachelor’s Thesis, Master’s Thesis and PhD Topics

• Many theses dealt and deal with sensor data processing, fusing, classification, ML-based detection
• Using image data (RGB to HSI), Lidar data, Radar data, and other sensors
• Applications in robotics
• Deep Learning for Time Series Classification and Prediction on Big Crowd Sensed Automotive Data (Master’ Thesis, external)
• Bachelor’s Thesis studying FPGA-based implementations of application-specific algorithms (industry cooperation)
• Master projects in the field of cooperation im autonomous driving

Ämter und Gremien (Academic Functions and Committees)

• Mitglied im Hochschulsenat (Academic Senate)
• Mitglied in der Ethikkommission (Ethic’s Committee)

Lehrgebiete, Lehrfächer (Teaching)

• Intelligente Sensorsysteme (Bachelor ITS)
• Einführung in die Robotik (Bachelor AI + ITS)
• Autonomes Fahren und Robotik (AFR, Master)
• Rechnerstrukturen und Maschinennahes Programmieren (Bachelor AI)
• Betriebssysteme (Bachelor AI)
• Algorithmen und Datenstrukturen (Bachelor AI/ITS)
• Master-Grundseminar, Master-Hauptseminar, Bachelor-Seminar
• Projekte: Lehr-CPU-/Lehr-BS-Entwicklung, Deep Learning, Autonome Systeme

Publications

2025:

• Blum, F., Meyer, P., Lange, T., Trost, M., and Tiedemann, T.: “Bulky waste classification from a distance: Challenges and first insights”. OCM 2025 - 7th International Conference on Optical Characterization of Materials, March 26th – 27th, 2025, Karlsruhe, Germany : Conference Proceedings, pp. 295 – 304, DOI: http://10.5445/KSP/1000178356/v2, 2025
• Philipp Meyer, Timo Lange, Matthis Trost, Tim Tiedemann: “First results of combining RGB segments and multi-spectral pixel classes for strawberry ripeness detection”. Lecture Notes in Informatics (LNI) – Proceedings, Series of the Gesellschaft für Informatik (GI), Volume P-358, ISSN 1617-5468 (Online), “Informatik in der Land-, Forst- und Ernährungswirtschaft Fokus: Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft, Referate der 45. GIL-Jahrestagung, 25. - 26. Februar 2025”, pp. 333-338, Wieselburg, Austria, Springer, URL https://gil-net.de/Publikationen/GIL25_Proceedings_final_01.pdf, 2025

2024:

• Tiedemann, T., Schmidt, A., Stricker, J., Fuhrmann, J.: “First results of a comparison of machine learning hardware acceleration approaches using field programmable gate arrays in an agricultural mobile robotic application case”. Intelligent Distributed Computing XVI : 16th International Symposium on Intelligent Distributed Computing, IDC 2023, Studies in computational intelligence, Vol. 1138, pp. 27 – 39, DOI:https://doi.org/10.1007%2F978-3-031-60023-4_8 Springer, 2024
• Lange, T., Babu, A., Meyer, P., Keppner, M., Tiedemann, T., Wittmaier, M., Wolff, S., Vögele, T.: „First lessons learned of an artificial intelligence robotic system for autonomous coarse waste recycling using multispectral imaging-based method“. Proceedings SARDINIA 2023, International Symposium on Waste Management, Resource Recovery and Sustainable Landfilling 2023, URI http://hdl.handle.net/20.500.12738/15338, ISBN 9788862650335, ISSN 2282-0027, CISA Publisher, 2024

2023:

• Schurwanz, Mietzner, De Muirier, Tiedemann, and Hoeher: “Compressed sensing based obstacle detection for future urban air mobility scenarios”. IEEE Sensors Lett., vol. 7, no. 11, pp. 1 - 4, Nov. 2023.
• Tiedemann, Lange, Meyer, Keppner: “Intermediate Results of a Multi- Spectral-Imaging-Based Ripeness and Malformed Classification on an Object- and Pixel-Basis Using Field Programmable Gate Arrays for an Autonomous Strawberry-Harvesting Robot”. IEEE IROS 2023 Workshop on Agricultural Robotics for a Sustainable Future (WARS), DOI https://doi.org/10.48441%2F4427.1504, 2023, (winner 3rd. prize “Advancement in Robotic Farm Technology”) (IROS workshop poster presentation, peer-reviewed, but without proceedings article)
• Zach, Tiedemann: “First Results of a Low-Cost Visual Odometry Approach for Autonomous Underwater Localization and Fish Habitat Mapping”. IEEE IROS 2023 2nd Advanced Marine Robotics TC Workshop, DOI https://doi.org/10.48441%2F4427.1503, 2023, (IROS workshop poster presentation, peer-reviewed, but without proceedings article)

2022:

• Zach, J., Busse, C., Funk, S., Möllmann, C., Renner, B., Tiedemann, T.: „Towards non-invasive fish monitoring in hard-to-access habitats using autonomous underwater vehicles and machine learning“. OCEANS 2021: San Diego-Porto, ISBN: 978-0-692-93559-0, DOI: https://doi.org/10.23919%2FOCEANS44145.2021.9705867, IEEE, 2022
• Tiedemann, T., Schwalb, L., Kasten, M., Grotkasten, R., & Pareigis, S.: „Miniature autonomy as means to find new approaches in reliable autonomous driving AI method design“. Frontiers in Neurorobotics, Vol. 16, ISSN 1662-5218, doi:https://doi.org/10.3389/fnbot.2022.846355 (Open Access), 2022

2018:

• Tiedemann, T. (2018): Communication Hardware, in: Bosse, S., Lehmhus, D., Lang, W. and Busse, M. (2018) Material-Integrated Intelligent Systems - Technology and Applications: Technology and Applications, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527679249.ch15

2017:

• Schenck, Horst, Tiedemann, Gaulik, Möller (2017): Comparing parallel hardware architectures for visually guided robot navigation. Concurrency Computat.: Pract. Exper., 29: pe3833, doi: 10.1002/cpe.3833
• Tiedemann, Bauer, Kirchner: Concept of Cognitively Inspired Automotive Sensor Data Fusion. Talk at IEEE Intelligent Vehicles 2017, WS on Cognitively Inspired Vehicles.
• Tiedemann, Backe, Vögele, Conradi: Automotive Ad Hoc Sensor Networks in the Project SADA: Concept and Current State. Poster presentation at the “Fachgespräche Sensornetze 2017”.
• Tiedemann: Dynamic and Automatic Sensor Data Fusion in the Automotive Research Project SADA. Talk at the Int. Conf “Vehicle Intelligence”, Dec. 2017, Munich.

2016:

• Tim Tiedemann, Christian Backe, Thomas Vögele, Peter Conradi (2016): An Automotive Distributed Mobile Sensor Data Collection with Machine Learning Based Data Fusion and Analysis on a Central Backend System. Procedia Technology, Volume 26, 2016, Pages 570-579, ISSN 2212-0173, dx.doi.org/10.1016/j.protcy.2016.08.071.
• Wendelin Feiten, Susana Alcalde Baguees, Michael Fiegert, Feihu Zhang, Dhiraj Gulati, Tim Tiedemann: A New Concept for a Cooperative Fusion Platform. Proceedings of 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.
• Susana Alcalde Bagüés, Wendelin Feiten, Tim Tiedemann, Christian Backe, Dhiraj Gulati, Steffen Lorenz and Peter Conradi: Towards Dynamic and Flexible Sensor Fusion for Automotive Applications. Proceedings of the 20th International Forum on Advanced Microsystems for Automotive Applications (AMAA 2016).

2015:

• T. Tiedemann, T. Vögele, Mario M. Krell, Jan H. Metzen, F. Kirchner: Concept of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot Region. Proceedings of the AAAI Workshop on AI for Transportation (WAIT), 2015.
• T. Köhler: Bio-Inspired Motion Detection Based on an FPGA Platform. In G. Cristobal et al. (Herausgeber): Biologically-Inspired Computer Vision: Fundamentals and Applications, Wiley-VCH, Weinheim, Kapitel 17, Okt/2015. ISBN: 978-3-527-41264-8. (Buchkapitel)
• T. Tiedemann, T. Vögele: Wissen, wann ein Parkplatz frei wird. In Internationales Verkehrswesen, DVV Media Group GmbH, volume 67, pages 84-85, 2015. (nicht peer-reviewed)