@article{NoureddineKraffLaddetal.2019, author = {Noureddine, Yacine and Kraff, Oliver and Ladd, Mark E. and Wrede, Karsten and Chen, Bixia and Quick, Harald H. and Schaefers, Georg and Bitz, Andreas}, title = {Radiofrequency induced heating around aneurysm clips using a generic birdcage head coil at 7 Tesla under consideration of the minimum distance to decouple multiple aneurysm clips}, series = {Magnetic Resonance in Medicine}, journal = {Magnetic Resonance in Medicine}, number = {Early view}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2594}, doi = {10.1002/mrm.27835}, pages = {1 -- 17}, year = {2019}, language = {en} } @article{NoureddineBitzLaddetal.2015, author = {Noureddine, Yacine and Bitz, Andreas and Ladd, Mark E. and Th{\"u}rling, Markus and Ladd, Susanne C. and Schaefers, Gregor and Kraff, Oliver}, title = {Experience with magnetic resonance imaging of human subjects with passive implants and tattoos at 7 T: a retrospective study}, series = {Magnetic Resonance Materials in Physics, Biology and Medicine}, volume = {28}, journal = {Magnetic Resonance Materials in Physics, Biology and Medicine}, number = {6}, publisher = {Springer}, address = {Berlin}, issn = {1352-8661}, doi = {10.1007/s10334-015-0499-y}, pages = {577 -- 590}, year = {2015}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, language = {en} } @inproceedings{NikolovskiLimpertNessauetal.2023, author = {Nikolovski, Gjorgji and Limpert, Nicolas and Nessau, Hendrik and Reke, Michael and Ferrein, Alexander}, title = {Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {979-8-3503-4691-6 (Online)}, doi = {10.1109/IV55152.2023.10186806}, pages = {6 Seiten}, year = {2023}, abstract = {The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle's drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.}, language = {en} } @article{NiemuellerLakemeyerFerreinetal.2013, author = {Niem{\"u}ller, Tim and Lakemeyer, Gerhard and Ferrein, Alexander and Reuter, S. and Ewert, D. and Jeschke, S. and Pensky, D. and Karras, Ulrich}, title = {Proposal for advancements to the LLSF in 2014 and beyond}, pages = {Publ. online}, year = {2013}, language = {en} } @inproceedings{NiemuellerLakemeyerFerrein2013, author = {Niem{\"u}ller, Tim and Lakemeyer, Gerhard and Ferrein, Alexander}, title = {Aspects of integrating diverse software into robotic systems extended abstract}, series = {ICRA 2013 - 8th Workshop on Software Development and Integration in Robotics (SDIR), Karlsruhe, Germany}, booktitle = {ICRA 2013 - 8th Workshop on Software Development and Integration in Robotics (SDIR), Karlsruhe, Germany}, pages = {1 -- 2}, year = {2013}, language = {en} } @inproceedings{NiemuellerLakemeyerFerrein2013, author = {Niem{\"u}ller, Tim and Lakemeyer, Gerhard and Ferrein, Alexander}, title = {Incremental task-level reasoning in a competitive factory automation scenario}, series = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)}, booktitle = {Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4)}, editor = {Boots, Byron}, organization = {American Association for Artificial Intelligence}, isbn = {9781577356011}, pages = {43 -- 48}, year = {2013}, language = {en} } @article{NiemuellerFerreinLakemeyer2010, author = {Niem{\"u}ller, Tim and Ferrein, Alexander and Lakemeyer, Gerhard}, title = {A Lua-based Behavior Engine for Controlling the Humanoid Robot Nao}, series = {RoboCup 2009: Robot Soccer World Cup XIII}, journal = {RoboCup 2009: Robot Soccer World Cup XIII}, pages = {240 -- 251}, year = {2010}, language = {en} } @article{NiemuellerFerreinEckeletal.2011, author = {Niem{\"u}ller, Tim and Ferrein, Alexander and Eckel, Gerhard and Pirro, David and Podbregar, Patrick and Kellner, Tobias and Rath, Christoph and Steinbauer, Gerald}, title = {Providing Ground-truth Data for the Nao Robot Platform}, series = {RoboCup 2010: Robot Soccer World Cup XIV}, journal = {RoboCup 2010: Robot Soccer World Cup XIV}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-642-20217-9}, pages = {133 -- 144}, year = {2011}, language = {en} } @article{NiemuellerFerreinBecketal.2010, author = {Niem{\"u}ller, Tim and Ferrein, Alexander and Beck, Daniel and Lakemeyer, Gerhard}, title = {Design Principles of the Component-Based Robot Software Framework Fawkes}, series = {Simulation, Modeling, and Programming for Autonomous Robots}, journal = {Simulation, Modeling, and Programming for Autonomous Robots}, pages = {300 -- 311}, year = {2010}, language = {en} }