Stefan Angerbauer;Nunzio Tuccitto;Giuseppe Trusso Sfrazzetto;Rossella Santonocito;Werner Haselmayr
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引用次数: 0
Abstract
Intelligent nano-machines are a promising candidate technology for the next generation of health care. The realization of such units relies on novel, unconventional approaches, to navigate the challenges of this particular domain. In this work, we present three chemical processes, that can be used to realize a recently proposed molecular matrix multiplication unit on the lab-scale. The matrix multiplication is the fundamental operation for the realization of neural networks and, therefore, artificial intelligence. Hence, this work presents an important step towards practical realization of intelligent nano-machines for the next generation of health care.
期刊介绍:
As a result of recent advances in MEMS/NEMS and systems biology, as well as the emergence of synthetic bacteria and lab/process-on-a-chip techniques, it is now possible to design chemical “circuits”, custom organisms, micro/nanoscale swarms of devices, and a host of other new systems. This success opens up a new frontier for interdisciplinary communications techniques using chemistry, biology, and other principles that have not been considered in the communications literature. The IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMSC) is devoted to the principles, design, and analysis of communication systems that use physics beyond classical electromagnetism. This includes molecular, quantum, and other physical, chemical and biological techniques; as well as new communication techniques at small scales or across multiple scales (e.g., nano to micro to macro; note that strictly nanoscale systems, 1-100 nm, are outside the scope of this journal). Original research articles on one or more of the following topics are within scope: mathematical modeling, information/communication and network theoretic analysis, standardization and industrial applications, and analytical or experimental studies on communication processes or networks in biology. Contributions on related topics may also be considered for publication. Contributions from researchers outside the IEEE’s typical audience are encouraged.