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:
- 2019
2874.
Haßler, Marc; Burgdorf, Andreas; Pomp, André; Kohlschein, Christian; Busing, Christina; Jonas, Stephan
A Holistic System for Pre-clinical Diagnosis of Sleep Disorders in the Home Environment
2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Page 1—4
Publisher: IEEE
2019ISBN: 978-1-7281-0402-7
2873.
Maus, Gerrit; Kuxdorf-Alkirata, Nizam; Brückmann, Dieter; Gemci, Mustafa
A low-cost {BLE} based sensor platform for efficient Received Signal Strength monitoring
2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Page 1199--1202
20192872.
Kolonko, L.; Velten, J.; Kummert, A.
A Raspberry Pi Based Video Pipeline for 2-D Wave Digital Filters on Low-Cost FPGA Hardware
2019 IEEE International Symposium on Circuits and Systems (ISCAS), Page 1-5
20192871.
Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34 :789—797
2019
ISSN: 2351-97892870.
Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34 :789—797
2019
ISSN: 2351-97892869.
Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34 :789—797
2019
ISSN: 2351-97892868.
Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34 :789—797
2019
ISSN: 2351-97892867.
Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34 :789—797
2019
ISSN: 2351-97892866.
Barta, Max; Farooghi, Dena; Tutsch, Dietmar
A System for User Centered Classification and Ranking of Points of Interest using Data Mining in Geographical Data Sets
10th International Conference on Applied Human Factors and Ergonomics (AHFE 2019) -- Advances in Artificial Intelligence, Software and Systems Engineering, Washington D.C., USA, Page 571--579
Publisher: Springer-Verlag
20192865.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644
20192864.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644
20192863.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644
20192862.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644
20192861.
Meyes, Richard; Lu, Melanie; Puiseau, Constantin; Meisen, Tobias
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644
20192860.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI)
20192859.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI)
20192858.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI)
20192857.
Meyes, Richard; Lu, Melanie; Puiseau, Constantin; Meisen, Tobias
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI)
20192856.
Meyes, Richard; Lu, Melanie; Waubert-de-Puiseau, Constantin; Meisen, Tobias
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI)
20192855.
Ferreira, João; Callou, Gustavo; Josua, Albert; Tutsch, Dietmar; Maciel, Paulo
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers
Information :10(3), 113; doi: 10.3390/info10030113
2019
Publisher: MDPI2854.
Ferreira, João; Callou, Gustavo; Josua, Albert; Tutsch, Dietmar; Maciel, Paulo
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers
Information, 10 (3) :113ff
2019
Publisher: MDPI2853.
Callou, Gustavo; Josua, Albert; Tutsch, Dietmar; Maciel, Paulo
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers
Information, Page 10(3), 113; doi: 10.3390/info10030113
Publisher: MDPI
20192852.
Callou, Gustavo; Josua, Albert; Tutsch, Dietmar; Maciel, Paulo
An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers
Information, Page 10(3), 113; doi: 10.3390/info10030113
Publisher: MDPI
20192851.
Kasolis, F.; Clemens, M.
An Entropy-Based Sampling Framework for Reducing Large-Scale Nonlinear Field Problems in the Electro-Quasistatic Limit
Nineteenth Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2020), 18.-22.04.2020, Pisa, Italy. One page digest, accepted
20192850.
Meyes, Richard; Tercan, Hasan; Meisen, Tobias
Artificial Intelligence in Automotive Production
Mobility in a Globalised World 2018, 22 :308—324
2019
