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:
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Platte, Laura; Schönefeld, Kathrin; Hees, Frank
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Haberstroh, Max C.; Sauer, Julia; Pomp, André; Kirmse, Andreas; Meisen, Tobias; Hees, Frank; Jeschke, Sabina
Method to Create a VR Event by Evaluating Third Party Information and Re-Providing the Processed Information in Real-Time
(United States Patent Application 20190213423)
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Haberstroh, Max C.; Sauer, Julia; Pomp, André; Kirmse, Andreas; Meisen, Tobias; Hees, Frank; Jeschke, Sabina
Method to Create a VR Event by Evaluating Third Party Information and Re-Providing the Processed Information in Real-Time
(United States Patent Application 20190213423)
20192744.
Haberstroh, Max C.; Sauer, Julia; Pomp, André; Kirmse, Andreas; Meisen, Tobias; Hees, Frank; Jeschke, Sabina
Method to Create a VR Event by Evaluating Third Party Information and Re-Providing the Processed Information in Real-Time
(United States Patent Application 20190213423)
20192743.
Haberstroh, Max C.; Sauer, Julia; Pomp, André; Kirmse, Andreas; Meisen, Tobias; Hees, Frank; Jeschke, Sabina
Method to Create a VR Event by Evaluating Third Party Information and Re-Providing the Processed Information in Real-Time
(United States Patent Application 20190213423)
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Alsayegh, M.; Schmuelling, B.; Clemens, M.
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Krause, Michael; Michalik, David; Kummert, Anton; Kohl, Per; Treichel, Heinz-Reiner
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Samsonov, Vladimir; Lipp, Johannes; Noodt, Philipp; Fenollar Solvay, Alexia; Meisen, Tobias
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Samsonov, Vladimir; Lipp, Johannes; Noodt, Philipp; Fenollar Solvay, Alexia; Meisen, Tobias
More Machine Learning for Less: Comparing Data Generation Strategies in Mechanical Engineering and Manufacturing
Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
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Samsonov, Vladimir; Lipp, Johannes; Noodt, Philipp; Fenollar Solvay, Alexia; Meisen, Tobias
More Machine Learning for Less: Comparing Data Generation Strategies in Mechanical Engineering and Manufacturing
Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
Publisher: IEEE
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Samsonov, Vladimir; Lipp, Johannes; Noodt, Philipp; Fenollar Solvay, Alexia; Meisen, Tobias
More Machine Learning for Less: Comparing Data Generation Strategies in Mechanical Engineering and Manufacturing
Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
Publisher: IEEE
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Samsonov, Vladimir; Lipp, Johannes; Noodt, Philipp; Fenollar Solvay, Alexia; Meisen, Tobias
More Machine Learning for Less: Comparing Data Generation Strategies in Mechanical Engineering and Manufacturing
Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
Publisher: IEEE
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Baer, Schirin; Bakakeu, Jupiter; Meyes, Richard; Meisen, Tobias
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
2019 Second IEEE International Conference on Artificial Intelligence for Industries, Page 22—25
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2019ISBN: 978-1-7281-4087-2
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Baer, Schirin; Bakakeu, Jupiter; Meyes, Richard; Meisen, Tobias
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
2019 Second IEEE International Conference on Artificial Intelligence for Industries, Page 22—25
Publisher: IEEE-Computer-Society, Los Alamitos, CA
2019ISBN: 978-1-7281-4087-2
2733.
Baer, Schirin; Bakakeu, Jupiter; Meyes, Richard; Meisen, Tobias
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
2019 Second IEEE International Conference on Artificial Intelligence for Industries, Page 22—25
Publisher: IEEE-Computer-Society, Los Alamitos, CA
2019ISBN: 978-1-7281-4087-2
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Baer, Schirin; Bakakeu, Jupiter; Meyes, Richard; Meisen, Tobias
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
2019 Second IEEE International Conference on Artificial Intelligence for Industries, Page 22—25
Publisher: IEEE-Computer-Society, Los Alamitos, CA
2019ISBN: 978-1-7281-4087-2
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Baer, Schirin; Bakakeu, Jupiter; Meyes, Richard; Meisen, Tobias
Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
2019 Second IEEE International Conference on Artificial Intelligence for Industries, Page 22—25
Publisher: IEEE-Computer-Society, Los Alamitos, CA
2019ISBN: 978-1-7281-4087-2
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Tilgner, S.; Wagner, D.; Kalischewski, K.; Velten, J.; Kummert, A.
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Mounaix, Patrick; Quentin, Cassar; MacGrogan, Gaëtan; Pfeiffer, Ullrich; Zimmer, Thomas; Guillet, Jean-Paul
Near field and far field investigations on breast cancer tissus
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Schwerdtfeger, T.; Kummert, A.
Nonlinear Circuit Simulation by Means of Alfred Fettweis' Wave Digital Principles
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Heredia Conde, M.; Bhandari, A.; Loffeld, O.
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Kratz, Sascha; Schmidt, Aljoscha; Krüger, Benjamin; Wegener, Ralf; Soter, Stefan
Power Supply of a Short-Range Public Transportation System Based on Photovoltaics - Potential Analysis and Implementation
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2019ISBN: 0160-8371
