Terahertz-Light Field Camera Prototyp
Light-field cameras record both the brightness and direction of the incident light rays. This spatiodirectional information can be post-processed for a dynamic focal point adjustment and 3-D imaging. Light-field has traditionally been a domain of visible light computational imaging. Our chair is leading the development of light-field methodologies for the THz spectrum, bringing new foundational understanding and hardware capabilities for bridging the terahertz gap.
Currently, we are building the first ever THz light-field camera prototype - which consists of a 3x3 super-array of lens coupled 1k-pixel THz CMOS cameras - in a single package. The CMOS camera chip is presented in the International Solid-State Circuits Conference (ISSCC) 2021. The figure shows the prototype of the light field array. The prototype serves as the hardware platform for the development of light-field based real-time THz 3-D imaging techniques.
Preliminary work:
- 2022
3546.
Vietz, Hannes; Maschler, Benjamin; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Industrielles Transfer-Lernen: Von der Wissenschaft in die Praxis
atp magazin, 63 (9) :86—93
20223545.
Vietz, Hannes; Maschler, Benjamin; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Industrielles Transfer-Lernen: Von der Wissenschaft in die Praxis
atp magazin, 63 (9) :86—93
20223544.
Vietz, Hannes; Maschler, Benjamin; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Industrielles Transfer-Lernen: Von der Wissenschaft in die Praxis
atp magazin, 63 (9) :86—93
20223543.
Vietz, Hannes; Maschler, Benjamin; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Industrielles Transfer-Lernen: Von der Wissenschaft in die Praxis
atp magazin, 63 (9) :86—93
20223542.
Maschler, Benjamin; Vietz, Hannes; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Insights and Example Use Cases on Industrial Transfer Learning
Procedia CIRP, 107 :511—516
2022
ISSN: 221282713541.
Maschler, Benjamin; Vietz, Hannes; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Insights and Example Use Cases on Industrial Transfer Learning
Procedia CIRP, 107 :511—516
2022
ISSN: 221282713540.
Maschler, Benjamin; Vietz, Hannes; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Insights and Example Use Cases on Industrial Transfer Learning
Procedia CIRP, 107 :511—516
2022
ISSN: 221282713539.
Maschler, Benjamin; Vietz, Hannes; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Insights and Example Use Cases on Industrial Transfer Learning
Procedia CIRP, 107 :511—516
2022
ISSN: 221282713538.
Maschler, Benjamin; Vietz, Hannes; Tercan, Hasan; Bitter, Christian; Meisen, Tobias; Weyrich, Michael
Insights and Example Use Cases on Industrial Transfer Learning
Procedia CIRP, 107 :511—516
2022
ISSN: 221282713537.
Steiniger, Yannik; Stoppe, Jannis; Kraus, Dieter; Meisen, Tobias
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
OCEANS 2022, Hampton Roads, Page 1—6
Publisher: IEEE
2022ISBN: 978-1-6654-6809-1
3536.
Steiniger, Yannik; Stoppe, Jannis; Kraus, Dieter; Meisen, Tobias
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
OCEANS 2022, Hampton Roads, Page 1—6
Publisher: IEEE
2022ISBN: 978-1-6654-6809-1
3535.
Steiniger, Yannik; Stoppe, Jannis; Kraus, Dieter; Meisen, Tobias
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
OCEANS 2022, Hampton Roads, Page 1—6
Publisher: IEEE
2022ISBN: 978-1-6654-6809-1
3534.
Steiniger, Yannik; Stoppe, Jannis; Kraus, Dieter; Meisen, Tobias
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
OCEANS 2022, Hampton Roads, Page 1—6
Publisher: IEEE
2022ISBN: 978-1-6654-6809-1
3533.
Steiniger, Yannik; Stoppe, Jannis; Kraus, Dieter; Meisen, Tobias
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
OCEANS 2022, Hampton Roads, Page 1—6
Publisher: IEEE
2022ISBN: 978-1-6654-6809-1
3532.
Bitter, Christian; Thun, Timo; Meisen, Tobias
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
arXiv arXiv:2212.00906
20223531.
Bitter, Christian; Thun, Timo; Meisen, Tobias
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
arXiv arXiv:2212.00906
20223530.
Bitter, Christian; Thun, Timo; Meisen, Tobias
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
arXiv arXiv:2212.00906
20223529.
Bitter, Christian; Thun, Timo; Meisen, Tobias
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
arXiv arXiv:2212.00906
20223528.
Bitter, Christian; Thun, Timo; Meisen, Tobias
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
arXiv arXiv:2212.00906
20223527.
KuVS Fachgespräch - Würzburg Workshop on Next-Generation Communication Networks (WüWoWAS'22), Würzburg
20223526.
Kasolis, F.; Henkel, M.-L.; Clemens, M.
Low-Frequency Stable Electro-Quasistatic Field Formulations Based on Penalty Approximations of Continuous Extensions
11th Conference on the Computation of Electromagnetic Fields (CEM 2023)
Cannes, France
11.-14.04.2023
Publisher: Abstract accepted
20223525.
Tercan, Hasan; Meisen, Tobias
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Journal of Intelligent Manufacturing
2022
ISSN: 0956-55153524.
Tercan, Hasan; Meisen, Tobias
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Journal of Intelligent Manufacturing
2022
ISSN: 0956-55153523.
Tercan, Hasan; Meisen, Tobias
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Journal of Intelligent Manufacturing
2022
ISSN: 0956-55153522.
Tercan, Hasan; Meisen, Tobias
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Journal of Intelligent Manufacturing
2022
ISSN: 0956-5515