Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Andrew Pomerance"'
Publikováno v:
IEEE Access, Vol 9, Pp 44855-44867 (2021)
We introduce a Physically Unclonable Function (PUF) based on an ultra-fast chaotic network known as a Hybrid Boolean Network (HBN) implemented on a field programmable gate array. The network, consisting of $N$ coupled asynchronous logic gates display
Externí odkaz:
https://doaj.org/article/63346cb911644d138becac654a607eba
Publikováno v:
IEEE Access, Vol 9, Pp 146203-146213 (2021)
We introduce the waveform capture device (WCD), a flexible measurement system capable of recording complex digital signals on trillionth-of-a-second (ps) time scales. The WCD is implemented via modular code on an off-the-shelf field-programmable gate
Externí odkaz:
https://doaj.org/article/08a31150e08846ccabe15a6b249f7a81
Publikováno v:
2022 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0510136d96bc1e29c43c74daaf96cbcd
https://doi.org/10.1109/host54066.2022.9839670
https://doi.org/10.1109/host54066.2022.9839670
We introduce a mathematical framework for simulating Hybrid Boolean Network (HBN) Physically Unclonable Functions (PUFs, HBN-PUFs). We verify that the model is able to reproduce the experimentally observed PUF statistics for uniqueness $\mu_{inter}$
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ed93e04dac4e00dc5515e1ddbeecce4
Publikováno v:
2021 IEEE Conference on Communications and Network Security (CNS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bdc484580655da80df16406291b3cd6b
https://doi.org/10.1109/cns53000.2021.9705035
https://doi.org/10.1109/cns53000.2021.9705035
Publikováno v:
Chaos (Woodbury, N.Y.). 31(3)
We develop and test machine learning techniques for successfully using past state time series data and knowledge of a time-dependent system parameter to predict the evolution of the “climate” associated with the long-term behavior of a non-statio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bc71eda035a257d0aa471c393ee84f6
https://pubmed.ncbi.nlm.nih.gov/33810745
https://pubmed.ncbi.nlm.nih.gov/33810745
We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::861a9a8ca926b6021f884f7abb12dafa
http://arxiv.org/abs/2010.02285
http://arxiv.org/abs/2010.02285
Autor:
Alexander Wikner, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Troy Arcomano, Edward Ott, Andrew Pomerance, Jaideep Pathak
We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22f145297e128c16f970f65e1ed77048
Publikováno v:
IEEE Access, Vol 9, Pp 44855-44867 (2021)
We introduce a Physically Unclonable Function (PUF) based on an ultra-fast chaotic network known as a Hybrid Boolean Network (HBN) implemented on a field programmable gate array. The network, consisting of $N$ coupled asynchronous logic gates display
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebae2a0e9764b449c9375a28710061eb
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6748ec0617cadbdafc8939b026ac12b1