Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Thilo Strauss"'
Publikováno v:
IEEE Access, Vol 12, Pp 23798-23807 (2024)
Connected and Autonomous Vehicles (CAV) which interact with Roadside Units (RSU) as part of a smart city infrastructure are currently seeing first real-world deployments. Not only can CAVs benefit from access to a cities’ infrastructure by obtainin
Externí odkaz:
https://doaj.org/article/2bd098bd9e2f4d21acc6c2101933a91c
Publikováno v:
Algorithms, Vol 16, Iss 10, p 461 (2023)
This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to investigate the effect of regularization to estimate the network parame
Externí odkaz:
https://doaj.org/article/ddd88ab721f848d5acec54b7fbcc71ca
Publikováno v:
IEEE Access, Vol 8, Pp 58194-58205 (2020)
We propose a neural network architecture for detecting intrusions on the controller area network (CAN). The latter is the standard communication method between the electronic control units (ECUs) of automobiles. However, CAN lacks security mechanisms
Externí odkaz:
https://doaj.org/article/de4cabd3ef354a1b850898404b8becf7
Autor:
Thilo Strauss, Taufiquar Khan
Publikováno v:
Entropy, Vol 25, Iss 3, p 493 (2023)
Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical
Externí odkaz:
https://doaj.org/article/e87b5321f64c4757b58344e76c08c3ab
Publikováno v:
SIAM Journal on Imaging Sciences. 15:797-821
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::556e79efe7df126396ee9d7336a20aad
https://doi.org/10.1137/21m1462842
https://doi.org/10.1137/21m1462842
Publikováno v:
Applied Mathematics and Computation. 358:436-448
In this paper, we investigate image reconstruction from the Electrical Impedance Tomography (EIT) problem using a statistical inversion method based on Bayes’ theorem and an Iteratively Regularized Gauss Newton (IRGN) method. We compare the traditi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a210a05121052ac7ac5c5370ff24367
https://doi.org/10.1016/j.amc.2019.03.063
https://doi.org/10.1016/j.amc.2019.03.063
Autor:
Thilo Strauss, Markus Hanselmann, Andrej Junginger, Jens S. Buchner, Sebastian Boblest, Holger Ulmer
Publikováno v:
Proceedings ISBN: 9783658347512
In recent years, neural networks have shown astonishing results as data generators, especially in settings of generative adversarial networks (GANs). One special application is the field of style transfer, where the goal is to transform a data sample
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd1b11f9270efb5ac16a7d24b2f63332
https://doi.org/10.1007/978-3-658-34752-9_13
https://doi.org/10.1007/978-3-658-34752-9_13
Autor:
Andreas Geiger, Michael Niemeyer, Christian Reiser, Thilo Strauss, Lars Mescheder, Michael Oechsle
Publikováno v:
3DV
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reason
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5be85aef1b4d11165ed1894286314843
https://doi.org/10.1109/3dv50981.2020.00055
https://doi.org/10.1109/3dv50981.2020.00055
Publikováno v:
Interaktive Datenvisualisierung in Wissenschaft und Unternehmenspraxis ISBN: 9783658295615
In diesem Artikel wird eine Python-basierte Bibliothek fur Visualisierungs- und Analysetechniken vorgestellt, die bei ETAS intern Anwendung finden wird. Diese soll Anwender bei der Bearbeitung von Machine-Learning-Fragestellungen unterstutzen. Der Fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::644003387a63eec86c98464e1529cc2c
https://doi.org/10.1007/978-3-658-29562-2_9
https://doi.org/10.1007/978-3-658-29562-2_9
Publikováno v:
ICCV
In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely relate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4681ef92f3cda745866aae233d2b2d9a
http://arxiv.org/abs/1905.07259
http://arxiv.org/abs/1905.07259