Acta Marisiensis.
|
|
||||||
Year 2024
Volume 21 (XXXVIII), no 1 Year 2023 Volume 20 (XXXVII), no 1 Volume 20 (XXXVII), no 2 Year 2022 Volume 19 (XXXVI), no 1 Volume 19 (XXXVI), no 2 Year 2021 Volume 18 (XXXV), no 1 Volume 18 (XXXV), no 2 Year 2020 Volume 17 (XXXIV), no 1 Volume 17 (XXXIV), no 2 Year 2019 Volume 16 (XXXIII), no 1 Volume 16 (XXXIII), no 2 Year 2018 Volume 15 (XXXII), no 1 Volume 15 (XXXII), no 2 Year 2017 Volume 14 (XXXI), no 1 Volume 14 (XXXI), no 2 Year 2016 Volume 13 (XXX), no 1 Volume 13 (XXX), no 2 Year 2015 Volume 12 (XXIX), no 1 Volume 12 (XXIX), no 2 Year 2014 Volume 11 (XXVIII), no 1 Volume 11 (XXVIII), no 2 Year 2013 Volume 10 (XXVII), no 1 Volume 10 (XXVII), no 2 Year 2012 Volume 9 (XXVI), no 1 Volume 9 (XXVI), no 2 Year 2011 Volume 8 (XXV), no 1 Volume 8 (XXV), no 2 Year 2010 Volume 7 (XXIV), no 1 Volume 7 (XXIV), no 2 Year 2009 Volume 6 (XXIII) |
2020, Volume 17 (XXXIV), no 2
Zoltán GERMÁN-SALLÓ, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Tirgu Mures, Romania Zoltán GERMÁN-SALLÓ jr, Technical University of Cluj-Napoca Abstract: Discrete time signals carry information about systems and their internal functional mechanisms which characterize their complexity. Complexity measures are strongly related to information content and evaluations have been made on various signals in many ways in last few years. This paper uses information theory estimates of complexity as different types of entropies in order to estimate the complexity of various time discrete synthesized signals. Results show that this kind of indices can be a useful tool in diagnostic, fault detection and further development. DOI: https://doi.org/10.2478/amset-2020-0020 Pages: 54-57 View full article |
||||||
Update: 19-Jun-2024 | © Published by University Press |