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Tim Tiedemann

Professor of Intelligent Sensors

University of Applied Sciences Hamburg

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

Furhter Interests

  • FPGA-basierte Implementierung von Algorithmen
  • Anwendung maschineller Lernverfahren (ML) allgemein, Deep-Learning speziell
  • Data Mining mit ML-Methoden

Betreute Bachelor-, Master- und Diplomarbeitsthemen

  • Deep Learning for Time Series Classification and Prediction on Big Crowd Sensed Automotive Data (Master-Arbeit, extern)
  • Bachelor-Arbeit zu FPGA-basierter Implementierung spezifischer Algorithmen (Industrie-Kooperation)
  • Bachelor-Arbeit zu datengetriebener Sensordatenfusion
  • Master-Arbeit/Master-Projekte im Bereich Kooperation im autonomen Fahren

Weitere Interessen

  • FPGA-basierte Implementierung von Algorithmen
  • Anwendung maschineller Lernverfahren (ML) allgemein, Deep-Learning speziell
  • Data Mining mit ML-Methoden

Ämter und Gremien

  • Mitglied im Hochschulsenat

Lehrgebiete, Lehrfächer

  • Algorithmen und Datenstrukturen
  • Betriebssysteme
  • Rechnerstrukturen und Maschinennahes Programmieren
  • Intelligente Sensorik (iSen)
  • Intelligente Sensorsysteme (in Vorbereitung)
  • Master-Grundseminar, Master-Hauptseminar
  • Bachelor-Seminar
  • Projekte: Lehr-CPU-/Lehr-BS-Entwicklung, Deep Learning, Autonome Systeme

Publikationen

[Publikationsliste (vor 2016) noch im Aufbau]

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)

Interests

  • Intelligent Sensors
  • Sensor Data Processing
  • Applications in Autonomous Driving
  • Machine Learning Methods
  • Applications of Deep Learning

2019 · Powered by the Academic theme for Hugo.

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