Recurrent Neural Networks: An Embedded Computing Perspective
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2982416
Abstract: Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties have… read more here.
Keywords: rnn models; embedded computing; neural networks; networks embedded ... See more keywords
Microfluidic networks embedded in a printed circuit board
Sign Up to like & getrecommendations! Published in 2017 at "Modern Physics Letters B"
DOI: 10.1142/s0217984917400176
Abstract: In order to improve the robustness of microfluidic networks in printed circuit board (PCB)-based microfluidic platforms, a new method was presented. A pattern in a PCB was formed using hollowed-out technology. Polydimethylsiloxane was partly filled… read more here.
Keywords: printed circuit; circuit board; networks embedded; microfluidic networks ... See more keywords
The Emergence of Sequential Buckling in Reconfigurable Hexagonal Networks Embedded into Soft Matrix
Sign Up to like & getrecommendations! Published in 2021 at "Materials"
DOI: 10.3390/ma14082038
Abstract: The extreme and unconventional properties of mechanical metamaterials originate in their sophisticated internal architectures. Traditionally, the architecture of mechanical metamaterials is decided on in the design stage and cannot be altered after fabrication. However, the… read more here.
Keywords: networks embedded; embedded soft; buckling; sequential buckling ... See more keywords