Inkjet printers eject drops from microscopic nozzles and deposit them on substrates. For a number of years after its initial development, inkjet printing remained a method for visualizing computer output and printing documents. Beginning in the late 1990s, a number of researchers realized that inkjet printers could be employed as robotic pipettes to create microarrays, manufacture three-dimensional parts and spherical particles, print electrical devices, and facilitate combinatorial chemistry. Although most inks are low-viscosity Newtonian fluids, liquids in new applications are complex fluids. At the same time that these new applications were emerging, the replacement of traditional photography by digital imaging and the quest for ever-faster printing speeds resulted in the development of novel printing methods. Whereas most previous reviews of the field have focused on evaluations of well-known printing methods, this review instead presents a critical analysis from a fluid mechanics perspective of the recent developments in nonstandard printing techniques and the increasingly widespread use of nonstandard inks of complex fluids.
Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks which can enable secure and privacypreserving average consensus in a decentralized architecture in the absence of an aggregator or third-party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor which follows the consensus protocol faithfully without attempts to infer other nodes' states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection based privacy-preserving mechanisms which have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacypreserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based micro-controller network are also presented to verify the effectiveness and efficiency of the approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.