TY - JOUR A1 - Schulte-Tigges, Joschua A1 - Förster, Marco A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Kaszner, Daniel A1 - Matheis, Dominik A1 - Walter, Thomas T1 - Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments JF - Sensors N2 - Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars. KW - Lidar KW - Benchmark KW - Self-driving Y1 - 2022 U6 - https://doi.org/10.3390/s22197146 SN - 1424-8220 N1 - This article belongs to the Special Issue "Sensor Fusion for Vehicles Navigation and Robotic Systems" VL - 22 IS - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Abulnaga, El-Hussiny A1 - Pinkenburg, Olaf A1 - Schiffels, Johannes A1 - E-Refai, Ahmed A1 - Buckel, Wolfgang A1 - Selmer, Thorsten T1 - Effect of an Oxygen-Tolerant Bifurcating Butyryl Coenzyme A Dehydrogenase/Electron-Transferring Flavoprotein Complex from Clostridium difficile on Butyrate Production in Escherichia coli JF - Journal of bacteriology Y1 - 2013 SN - 1098-5530 [E-Journal] SN - 0021-9193 [Print] VL - 195 IS - 16 SP - 3704 EP - 3713 ER - TY - CHAP A1 - Ritz, Thomas A1 - Izquierdo Tello, César A1 - Damm, Sebastian T1 - Connecting a pedelec to the cloud as basis for gamification in multi modal mobility planning T2 - MobileCloud 2014 : 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering Oxford, United Kingdom 7-10 April 2014 Y1 - 2014 SN - 978-1-4799-2504-9 U6 - https://doi.org/10.1109/MobileCloud.2014.25 SP - 101 EP - 108 PB - IEEE Service Center CY - Piscataway, NJ ER - TY - JOUR A1 - Meyer-Stork, L. Sebastian A1 - Höcker, Hartwig A1 - Berndt, Heinz T1 - Syntheses and reactions of urethanes of cellobiose and cellulose-containing uretdione groups JF - Journal of applied polymer science Y1 - 1992 SN - 1097-4628 VL - 44 IS - 6 SP - 1043 EP - 1049 ER - TY - CHAP A1 - Peeken, Heinz A1 - Troeder, Christoph A1 - Schmidt, J. A1 - Rosenkranz, Josef T1 - Principles of machine noise reduction T2 - Inter-noise 85 : proceedings ; 1985 international conference on noise control engineering ; Munich, Sept. 18 - 20, 1985. - (Schriftenreihe der Bundesanstalt für Arbeitsschutz : Tagungsbericht ; 39) Y1 - 1985 SN - 3-88314-417-7 SP - 23 EP - 36 PB - Bundesanstalt für Arbeitsschutz [u.a.] CY - Dortmund [u.a.] ER - TY - JOUR A1 - Kuperjans, Isabel A1 - Starke, M. A1 - Esser, J. A1 - [u.a.], T1 - Analyse und Konzeption von Energieanlagen unter ökologischen, wirtschaftlichen und technischen Gesichtspunkten JF - WLB : Umwelttechnik für Industrie und Kommune Y1 - 2000 SN - 0341-2679 VL - 44 IS - 11/12 SP - 26 EP - 29 ER - TY - JOUR A1 - Pauksztat, Anja A1 - Kuperjans, Isabel A1 - Meyer, Jörg T1 - Formeln statt Zahlen : Referenzwerte Formeln zur energetischen Bewertung von Produktionsanlagen JF - BWK : das Energie-Fachmagazin Y1 - 2005 SN - 0006-9612 SN - 1618-193X VL - 57 IS - 12 SP - 52 EP - 55 ER - TY - JOUR A1 - Pauksztat, Anja A1 - Kuperjans, Isabel A1 - Meyer, Jörg T1 - Produktbezogene Referenzwerte für Energieeffizienz und CO2-Emissionen JF - Energiewirtschaftliche Tagesfragen : et ; Zeitschrift für Energiewirtschaft, Recht, Technik und Umwelt Y1 - 2005 SN - 0013-743X SN - 0720-6240 VL - 55 IS - 6 SP - 374 EP - 376 ER - TY - JOUR A1 - Frauenrath, Tobias A1 - Fuchs, Katharina A1 - Dieringer, Matthias A. A1 - Özerdem, Celal A1 - Patel, Nishan A1 - Renz, Wolfgang A1 - Greiser, Andreas A1 - Elgeti, Thomas A1 - Niendorf, Thoralf T1 - Detailing the use of magnetohydrodynamic effects for synchronization of MRI with the cardiac cycle: A feasibility study JF - Journal of Magnetic Resonance Imaging N2 - Purpose: To investigate the feasibility of using magnetohydrodynamic (MHD) effects for synchronization of magnetic resonance imaging (MRI) with the cardiac cycle. Materials and Methods: The MHD effect was scrutinized using a pulsatile flow phantom at B0 = 7.0 T. MHD effects were examined in vivo in healthy volunteers (n = 10) for B0 ranging from 0.05–7.0 T. Noncontrast-enhanced MR angiography (MRA) of the carotids was performed using a gated steady-state free-precession (SSFP) imaging technique in conjunction with electrocardiogram (ECG) and MHD synchronization. Results: The MHD potential correlates with flow velocities derived from phase contrast MRI. MHD voltages depend on the orientation between B0 and the flow of a conductive fluid. An increase in the interelectrode spacing along the flow increases the MHD potential. In vivo measurement of the MHD effect provides peak voltages of 1.5 mV for surface areas close to the common carotid artery at B0 = 7.0 T. Synchronization of MRI with the cardiac cycle using MHD triggering is feasible. MHD triggered MRA of the carotids at 3.0 T showed an overall image quality and richness of anatomic detail, which is comparable to ECG-triggered MRAs. Conclusion: This feasibility study demonstrates the use of MHD effects for synchronization of MR acquisitions with the cardiac cycle. J. Magn. Reson. Imaging 2012;36:364–372. © 2012 Wiley Periodicals, Inc. Y1 - 2012 U6 - https://doi.org/10.1002/jmri.23634 SN - 1522-2586 VL - 36 IS - 2 SP - 364 EP - 372 PB - Wiley-Liss CY - New York ER - TY - INPR A1 - Grieger, Niklas A1 - Mehrkanoon, Siamak A1 - Bialonski, Stephan T1 - Preprint: Data-efficient sleep staging with synthetic time series pretraining T2 - arXiv N2 - Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Y1 - 2024 ER -