Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Arvind Prasadan"'
Autor:
Jeremy D. Wendt, Richard Field, Cynthia Phillips, Arvind Prasadan, Tegan Wilson, Sucheta Soundarajan, Sanjukta Bhowmick
Publikováno v:
IEEE Transactions on Network Science and Engineering. 10:809-826
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54d9e8e048a9a9bfc89d91c48ddcfbf0
https://doi.org/10.1109/tnse.2022.3223614
https://doi.org/10.1109/tnse.2022.3223614
Autor:
Dan Krofcheck, Esther John, Hugh Galloway, Asael Sorensen, Carter Jameson, Connor Aubrey, Arvind Prasadan, Robert Forrest
Publikováno v:
Anomaly Detection and Imaging with X-Rays (ADIX) VII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32b6cac235efc899d08bcb334a36b9fb
https://doi.org/10.1117/12.2618972
https://doi.org/10.1117/12.2618972
Publikováno v:
Proposed for presentation at the Sandia MLDL Conference (virtual) held July 19-22, 2021 in , ..
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dafe01b4d70852d3880c1410c8b33673
https://doi.org/10.2172/1884145
https://doi.org/10.2172/1884145
Publikováno v:
CAMSAP
We analyze the Dynamic Mode Decomposition (DMD) algorithm in the noisy data setting. Previous work has shown that DMD is a source separation algorithm in disguise, i.e., that it is capable of unmixing linearly mixed time series. In this work, we anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f24856f6e9244b22223ae683e538e55
https://doi.org/10.1109/camsap45676.2019.9022604
https://doi.org/10.1109/camsap45676.2019.9022604
Autor:
Arvind Prasadan, Raj Rao Nadakuditi
Publikováno v:
GlobalSIP
We analyze the Dynamic Mode Decomposition (DMD) algorithm as applied to multivariate time-series data. Our analysis reveals the critical role played by the lag-one cross-correlation, or cross-covariance, terms. We show that when the rows of the multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d8c9e0e462c0ec34860fe686306bfae
https://doi.org/10.1109/globalsip.2018.8646587
https://doi.org/10.1109/globalsip.2018.8646587