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Ambient RF/microwave energy recycle system with deep convolutional neural networks
The Taiwanese-German collaboration is based on highly complementary research history of the two groups. The Chair of High Frequency Electronics, RWTH, Germany, and the PI focus on RF and microwave circuits, such as wireless power transfer and harvesting, power amplifiers, and system research on various technologies, e.g. CMOS, graphene, and PCB. We have long and successful record about this research topic. The topic in this cooperation is to study and design an ambient RF/microwave energy recycle system with the help of deep learning, which is the strength of our partner in Taiwan. It aims to recycle ambient RF/microwave energy. The bottleneck is in this energy recycle system is that a broadband matching network with extremely low loss is required. Thus, we would like to investigate the feasibilities, advantage, disadvantage, and trade-off using the deep learning or other methods proposed by our Taiwanese partner.
Field of action:
CMOS and more
- Faculty 6 – Electrical Engineering and Information Technology
Address:
Lehrstuhl für Höchstfrequenzelektronik (HFE)
Contact:
Univ.-Prof. Dr. Renato Negra
Renato.negra@hfe.rwth-aachen.de
Homepage:
http://www.hfe.rwth-aachen.de
Status:
in preparation