TY - JOUR A1 - Sommer, Angela M. A1 - Bitz, Andreas A1 - Streckert, Joachim A1 - Hansen, Volkert W. A1 - Lerchl, Alexander T1 - Lymphoma development in mice chronically exposed to UMTS-modulated radiofrequency electromagnetic fields JF - Radiation Research Y1 - 2007 U6 - https://doi.org/10.1667/RR0857.1 SN - 1938-5404 VL - 168 IS - 1 SP - 72 EP - 80 ER - TY - JOUR A1 - Heuermann, Holger T1 - LZY: A Self-Calibration Approach in Competition to the LRM Method for On-Wafer Measurements Y1 - 1995 N1 - 45th ARFTG Conference digest, Spring 1995 : [conference topic: Testing and design of RFIC'S], May 19, 1995, Orange County Convention Center, Orlando, Florida / Automatic RF Techniques Group. Publications chairman: Ed Godshalk; ARFTG Conference digest ; 4 SP - 129 EP - 136 ER - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Abel, Dirk T1 - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead T2 - Operations Research Proceedings 2018 N2 - In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead. KW - Product family optimization KW - Mixed-integer nonlinear black-box optimization KW - Engineering optimization KW - Machine learning Y1 - 2019 SN - 978-3-030-18499-5 (Print) SN - 978-3-030-18500-8 (Online) U6 - https://doi.org/10.1007/978-3-030-18500-8_47 SP - 379 EP - 385 PB - Springer CY - Cham ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - 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. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Giresini, Linda A1 - Butenweg, Christoph A1 - Andreini, M. A1 - De Falco, A. A1 - Sassu, M. T1 - Macro-elements identification in historic chapels: the case of St. Venerio Chapel in Reggiolo - Emilia Romagna T2 - 9th International Conference on Structural Analyses of Historical Conctruction, 14 - 17 October, 2014, Mexico City Y1 - 2014 SP - 1 EP - 12 ER - TY - JOUR A1 - Eijck, Lambert van A1 - Demmel, Franz A1 - Artmann, Gerhard A1 - Stadtler, Andreas Maximilian T1 - Macromolecular dynamics in red blood cells investigated using neutron spectroscopy JF - Journal of the Royal Society Interface Y1 - 2011 SN - 1742-5689 VL - 8 IS - 57 SP - 590 EP - 600 PB - The Royal Society CY - London ER - TY - CHAP A1 - Simsek, Beril A1 - Krause, Hans-Joachim A1 - Engelmann, Ulrich M. ED - Digel, Ilya ED - Staat, Manfred ED - Trzewik, Jürgen ED - Sielemann, Stefanie ED - Erni, Daniel ED - Zylka, Waldemar T1 - Magnetic biosensing with magnetic nanoparticles: Simulative approach to predict signal intensity in frequency mixing magnetic detection T2 - YRA MedTech Symposium (2024) N2 - Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation. Y1 - 2024 SN - 978-3-940402-65-3 U6 - https://doi.org/10.17185/duepublico/81475 N1 - 4th YRA MedTech Symposium, February 1, 2024. FH Aachen, Campus Jülich SP - 27 EP - 28 PB - Universität Duisburg-Essen CY - Duisburg ER - TY - JOUR A1 - Rabehi, Amine A1 - Garlan, Benjamin A1 - Achtsnicht, Stefan A1 - Krause, Hans-Joachim A1 - Offenhäusser, Andreas A1 - Ngo, Kieu A1 - Neveu, Sophie A1 - Graff-Dubois, Stephanie A1 - Kokabi, Hamid T1 - Magnetic detection structure for Lab-on-Chip applications based on the frequency mixing technique JF - Sensors N2 - A magnetic frequency mixing technique with a set of miniaturized planar coils was investigated for use with a completely integrated Lab-on-Chip (LoC) pathogen sensing system. The system allows the detection and quantification of superparamagnetic beads. Additionally, in terms of magnetic nanoparticle characterization ability, the system can be used for immunoassays using the beads as markers. Analytical calculations and simulations for both excitation and pick-up coils are presented; the goal was to investigate the miniaturization of simple and cost-effective planar spiral coils. Following these calculations, a Printed Circuit Board (PCB) prototype was designed, manufactured, and tested for limit of detection, linear response, and validation of theoretical concepts. Using the magnetic frequency mixing technique, a limit of detection of 15 µg/mL of 20 nm core-sized nanoparticles was achieved without any shielding. KW - Lab-on-Chip KW - magnetic sensing KW - frequency mixing KW - superparamagnetic nanoparticles KW - magnetic beads Y1 - 2018 U6 - https://doi.org/10.3390/s18061747 SN - 1424-8220 VL - 18 IS - 6 PB - MDPI CY - Basel ER - TY - CHAP A1 - Engelmann, Ulrich M. A1 - Shasha, Carolyn A1 - Slabu, Ioana T1 - Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating T2 - Magnetic nanoparticles in human health and medicine Y1 - 2021 SN - 978-1-119-75467-1 SP - 327 EP - 354 PB - Wiley-Blackwell CY - Hoboken, New Jeersey ER - TY - JOUR A1 - Engelmann, Ulrich M. A1 - Buhl, Eva Miriam A1 - Draack, Sebastian A1 - Viereck, Thilo A1 - Frank, A1 - Schmitz-Rode, Thomas A1 - Slabu, Ioana T1 - Magnetic relaxation of agglomerated and immobilized iron oxide nanoparticles for hyperthermia and imaging applications JF - IEEE Magnetic Letters N2 - Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy. Y1 - 2018 U6 - https://doi.org/10.1109/LMAG.2018.2879034 SN - 1949-307X VL - 9 IS - Article number 8519617 PB - IEEE CY - New York, NY ER -