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- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (199) (remove)
In dieser Arbeit wurde ein Getriebesteuergerät für ein Zweigang-Schaltgetriebe entwickelt. Dieses soll sowohl im manuellen als auch im autonomen Fahrbetriebe des Schluckspecht 5 einen Gangwechsel mittels eines Elektromotors ermöglichen. Da für den autonomen Fahrbetrieb eine rechnergestützte Ansteuerung der Drosselklappe erforderlich ist, wurde auch der Anschluss eines dafür gedachten Servomotors vorgesehen und die Auswertung des Motordrehzahlsensors realisiert. Für die eigentliche Getriebesteuerung wurden ein Raddrehzahlsensor, ein Getriebeeingangsdrehzahlsensor, Endlagensensoren für die beweglichen Teile sowie ein Stromsensor zum Schutz des Elektromotors und des Motortreibers vor thermischer Überlastung an den ausgewählten Mikrocontroller angeschlossen und der notwendige Source-Code zur Auswertung bzw. Ansteuerung geschrieben. Darüber hinaus wurden Zustandsautomaten für die Elektromotor- und die Getriebesteuerung vorbereitet.
In dieser Arbeit wird ein historischer Fallbericht des bis heute weit über seine Landesgrenzen
bekannten italienischen Kriminalanthropologen Cesare Lombroso (1835–1909)
vorgestellt. In diesem Fallbericht wird der berüchtigte und psychisch auffällige Dieb Pietro
Bersone mit Hilfe eines sog. Hydrosphygmographen überführt, einem zur damaligen Zeit
neuartigen technischen Gerät, das den Puls nicht-invasiv aufzeichnen konnte. Lombroso ist
vermutlich einer der ersten, wenn nicht sogar der erste, der durch den Einsatz eines solchen
Geräts die Idee zum „Lügendetektor“ vorweggenommen hat. Die vorgestellte Textstelle aus
Lombrosos Buch „Neue Fortschritte in den Verbrecherstudien“ ist daher ein besonderes
Fundstück auch für die Geschichte der Polygraphie.
eLetter zum Artikel "The Hannes hand prosthesis replicates the key biological properties of the human hand" von Matteo Laffranchi et al., veröffentlicht in Science Robotics, Vol. 5, Issue 46, eabb0467 (doi.org/10.1126/scirobotics.abb0467)
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
Investigation of the Angle Dependency of Self-Calibration in Multiple-Input-Multiple-Output Radars
(2021)
Multiple-Input-Multiple-Output (MIMO) is a key technology in improving the angular resolution (spatial resolution) of radars. In MIMO radars the amplitude and phase errors in antenna elements lead to increase in the sidelobe level and a misalignment of the mainlobe. As the result the performance of the antenna channels will be affected. Firstly, this paper presents analysis of effect of the amplitude and phase errors on angular spectrum using Monte-Carlo simulations. Then, the results are compared with performed measurements. Finally, the error correction with a self-calibration method is proposed and its angle dependency is evaluated. It is shown that the values of the errors change with an incident angle, which leads to a required angle-dependent calibration.
Estimation of Scattering and Transfer Parameters in Stratified Dispersive Tissues of the Human Torso
(2021)
The aim of this study is to understand the effect of the various layers of biological tissues on electromagnetic radiation in a certain frequency range. Understanding these effects could prove crucial in the development of dynamic imaging systems under operating environments during catheter ablation in the heart. As the catheter passes through some arterial paths in the region of interest inside the heart through the aorta, a three-dimensional localization of the catheter is required. In this paper, a study is given on the detection of the catheter by using electromagnetic waves. Therefor, an appropriate model for the layers of the human torso is defined and simulated without and with an inserted electrode.
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator.
In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
Eine kontinuierliche Überwachung von Ethernet-Leitungne beugt Maschinenausfällen in der Industrie vor. Aktuell fehlen jedoch geiegnete Methoden, um diese Überwachung flächendeckend durchzuführen. Im Projekt Ko²SiBus wurde deshalb ein kostengünstiges Verfahren zur kontinuierlichen Überwachung von Ethernet-Leitungen entwickelt.