40,438 research outputs found
Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors.
New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
Distributed Protocols at the Rescue for Trustworthy Online Voting
While online services emerge in all areas of life, the voting procedure in
many democracies remains paper-based as the security of current online voting
technology is highly disputed. We address the issue of trustworthy online
voting protocols and recall therefore their security concepts with its trust
assumptions. Inspired by the Bitcoin protocol, the prospects of distributed
online voting protocols are analysed. No trusted authority is assumed to ensure
ballot secrecy. Further, the integrity of the voting is enforced by all voters
themselves and without a weakest link, the protocol becomes more robust. We
introduce a taxonomy of notions of distribution in online voting protocols that
we apply on selected online voting protocols. Accordingly, blockchain-based
protocols seem to be promising for online voting due to their similarity with
paper-based protocols
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