TY  - JOUR
A1  - Sommer, Angela M.
A1  - Streckert, Joachim
A1  - Bitz, Andreas
A1  - Hansen, Volkert W.
A1  - Lerchl, Alexander
T1  - No effects of GSM-modulated 900 MHz electromagnetic fields on survival rate and spontaneous development of lymphoma in female AKR/J mice
JF  - BMC Cancer
Y1  - 2004
U6  - https://doi.org/10.1186/1471-2407-4-77
VL  - 77
IS  - 4
ER  - 
TY  - JOUR
A1  - Hansen, Volkert W.
A1  - Bitz, Andreas
A1  - Streckert, Joachim R.
T1  - RF Exposure of Biological Systems in Radial Waveguides
JF  - IEEE Transactions on Electromagnetic Compatibility
Y1  - 1999
U6  - https://doi.org/10.1109/15.809852
SN  - 1558-187X
VL  - 41
IS  - 4
SP  - 487
EP  - 493
ER  - 
TY  - CHAP
A1  - Bitz, Andreas
A1  - Kobus, Thiele
A1  - Scheenen, Tom W. J.
A1  - Ladd, Mark E.
T1  - RF Safety of the Combination of a 31P Tx/Rx Endorectal Coil & a 1H Tx/Rx Body Array for 31P MRSI of the Prostate at 7T (311.)
T2  - 20th Annual ISMRM scientific meeting and exhibition 2012 : Melbourne, Australia, 5 - 11 May 2012
Y1  - 2013
SN  - 978-1-62276-943-8
SN  - 1545-4428
IS  - Volume 1
SP  - 311
PB  - Curran
CY  - Red Hook, NY
ER  - 
TY  - CHAP
A1  - Bitz, Andreas
A1  - Kraff, O.
A1  - Orzada, S.
A1  - Maderwald, S.
A1  - Brote, I.
A1  - Johst, S.
A1  - Ladd, E.
T1  - Assessment of RF Safety of Transmit Coils at 7 Tesla by Experimental and Numerical Procedures (490.)
T2  - 19th annual ISMRM scientific meeting and exhibition 2011 : Montreal, Quebec, Canada, 7 - 13 May 2011
Y1  - 2012
SN  - 978-1-61839-284-8
IS  - Volume 1
SP  - 475
PB  - Curran
CY  - Red Hook, NY
ER  - 
TY  - CHAP
A1  - Bitz, Andreas
A1  - Kraff, O.
A1  - Orzada, S.
A1  - Maderwald, S.
A1  - Brote, I.
A1  - Ladd, M.
T1  - Experimental and Numerical Assessment of RF Safety of Transmit Coils at 7 Tesla
T2  - ISMRM workshop on MR safety 2010 : RF heating of the human in MRI : workshop series. The Washington County Historic Courthouse, Stillwater, Minnesota, USA, 15 - 17 October 2010
Y1  - 2010
SN  - 978-1-62276-088-6
SP  - 195
ER  - 
TY  - CHAP
A1  - Scholl, Ingrid
A1  - Bartella, Alex
A1  - Moluluo, Cem
A1  - Ertural, Berat
A1  - Laing, Frederic
A1  - Suder, Sebastian
T1  - MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality
T2  - Bildverarbeitung für die Medizin 2019 : Algorithmen – Systeme – Anwendungen
Y1  - 2019
SN  - 978-3-658-25326-4
U6  - https://doi.org/10.1007/978-3-658-25326-4_32
SP  - 152
EP  - 157
PB  - Springer Vieweg
CY  - Wiesbaden
ER  - 
TY  - CHAP
A1  - Serror, Martin
A1  - Henze, Martin
A1  - Hack, Sacha
A1  - Schuba, Marko
A1  - Wehrle, Klaus
T1  - Towards in-network security for smart homes
T2  - 13th International Conference on Availability, Reliability and Security, ARES 2018; Hamburg; Germany; 27 August 2018 through 30 August 2018
Y1  - 2018
SN  - 978-145036448-5
U6  - https://doi.org/10.1145/3230833.3232802
SP  - Article numer 3232802
ER  - 
TY  - CHAP
A1  - Rao, Deepak
A1  - Pathrose, Plato
A1  - Hüning, Felix
A1  - Sid, Jithin
T1  - An Approach for Validating Safety of Perception Software in Autonomous Driving Systems
T2  - Model-Based Safety and Assessment: 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings
N2  - The increasing complexity of Advanced Driver Assistance Systems (ADAS) presents a challenging task to validate safe and reliable performance of these systems under varied conditions. The test and validation of ADAS/AD with real test drives, although important, involves huge costs and time. Simulation tools provide an alternative with the added advantage of reproducibility but often use ideal sensors, which do not reflect real sensor output accurately. This paper presents a new validation methodology using fault injection, as recommended by the ISO 26262 standard, to test software and system robustness. In our work, we investigated and developed a tool capable of inserting faults at different software and system levels to verify its robustness. The scope of this paper is to cover the fault injection test for the Visteon’s DriveCore™ system, a centralized domain controller for Autonomous driving which is sensor agnostic and SoC agnostic. With this new approach, the validation of safety monitoring functionality and its behavior can be tested using real-world data instead of synthetic data from simulation tools resulting in having better confidence in system performance before proceeding with in-vehicle testing.
KW  - Advanced driver assistance systems (ADAS/AD)
KW  - ISO 26262
KW  - Safety-critical systems validation
KW  - Safety of the intended functionality (SOTIF)
Y1  - 2019
SN  - 978-3-030-32872-6
U6  - https://doi.org/10.1007/978-3-030-32872-6_20
SP  - 303
EP  - 316
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Orzada, Stephan
A1  - Solbach, Klaus
A1  - Gratz, Marcel
A1  - Brunheim, Sascha
A1  - Fiedler, Thomas M.
A1  - Johst, Sören
A1  - Bitz, Andreas
A1  - Shooshtary, Samaneh
A1  - Abuelhaija, Asjraf
A1  - Voelker, Maximilian N.
A1  - Rietsch, Stefan H. G.
A1  - Kraff, Oliver
A1  - Maderwald, Stefan
A1  - Flöser, Martina
A1  - Oehmingen, Mark
A1  - Quick, Harald H.
A1  - Ladd, Mark E.
T1  - A 32-channel parallel transmit system add-on for 7T MRI
JF  - Plos one
Y1  - 2019
U6  - https://doi.org/10.1371/journal.pone.0222452
ER  - 
TY  - CHAP
A1  - Dinghofer, Kai
A1  - Hartung, Frank
T1  - Analysis of Criteria for the Selection of Machine Learning Frameworks
T2  - 2020 International Conference on Computing, Networking and Communications (ICNC)
N2  - With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Y1  - 2020
U6  - https://doi.org/10.1109/ICNC47757.2020.9049650
N1  - 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA
SP  - 373
EP  - 377
PB  - IEEE
CY  - New York, NY
ER  -