Zobrazeno 1 - 10
of 909
pro vyhledávání: '"E. Chandrasekhar"'
Autor:
Narender Chinthamu, Adapa Gopi, A. Radhika, E. Chandrasekhar, Kamred Udham Singh, Dinesh Mavaluru
Publikováno v:
Measurement: Sensors, Vol 33, Iss , Pp 101210- (2024)
Through the integration of AI and IoT, the digital twin transforms industrial sectors by virtually portraying physical systems. Simulation and the application of lifecycle management improve decision-making. In this paper, a virtual prototype system
Externí odkaz:
https://doaj.org/article/f5351447875943c5a3e6b61b344d6808
Autor:
E. Chandrasekhar
Publikováno v:
International Journal for Research in Applied Science and Engineering Technology. 9:1093-1097
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8954dbe4f1d8c494235e2fe97d944652
https://doi.org/10.22214/ijraset.2021.34435
https://doi.org/10.22214/ijraset.2021.34435
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 164:184-199
In this paper, we investigate the correction of ionospheric amplitude scintillations appearing as image artifacts in low frequency synthetic aperture radar (SAR) data. A method has been proposed to separate amplitude and phase components of fully pol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e6e9b25b6231261e5eab502c5a81e80
https://doi.org/10.1016/j.isprsjprs.2020.04.005
https://doi.org/10.1016/j.isprsjprs.2020.04.005
Autor:
E. Chandrasekhar, Rizwan Ahmed Ansari
Publikováno v:
Encyclopedia of Mathematical Geosciences ISBN: 9783030260507
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8881c26e0206eef31c5d5ac44017463e
https://doi.org/10.1007/978-3-030-26050-7_29-1
https://doi.org/10.1007/978-3-030-26050-7_29-1
Publikováno v:
Arabian Journal of Geosciences. 14
Frequency attributes embedded in nonlinear geophysical signals such as well-log data or seismic data can provide vital clues in the effective characterization of the subsurface reservoir. Therefore, it is always important to recognize suitable mathem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ce66718e2c948454ddc88e8f512df24
https://doi.org/10.1007/s12517-021-06606-5
https://doi.org/10.1007/s12517-021-06606-5
Akademický článek
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Publikováno v:
Journal of Atmospheric and Solar-Terrestrial Physics. 149:31-39
The spatio-temporal variations in ionospheric vertical total electron content (TEC) data, which often reflect their scale invariant properties, can well be studied with multifractal analysis. We discuss the multifractal behaviour of TEC recorded at a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::560d828a21c79b5f87d3a522e7fba189
https://doi.org/10.1016/j.jastp.2016.09.007
https://doi.org/10.1016/j.jastp.2016.09.007
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 547:124404
Fractional Gaussian noise (fGn) provides an important parametric representation for the data recorded from long-memory processes. Also it has been well established in literature that the orthogonal wavelet transforms prove to be the optimal bases to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::770fa5e12ff65bbdee5b8a3072df3161
https://doi.org/10.1016/j.physa.2020.124404
https://doi.org/10.1016/j.physa.2020.124404
Publikováno v:
Chaos, Solitons & Fractals. 134:109653
Understanding of the spatio-temporal behaviour of nonlinear geophysical signals, such as ionospheric total electron content (TEC) by multifractal analysis brings out the chaotic and intermittent nature of the signal under consideration. Wavelet-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::475baa69943b542675f88f6eb07dd353
https://doi.org/10.1016/j.chaos.2020.109653
https://doi.org/10.1016/j.chaos.2020.109653
Publikováno v:
Computers & Geosciences. 138:104461
Time-series modeling forms an important area of research in geophysics. Time-series models can be linear, like linear state-space models or non-linear, like artificial neural networks. One way to judge the goodness of different models associated with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4a9694479fc84b7fca40efcd549a1d7
https://doi.org/10.1016/j.cageo.2020.104461
https://doi.org/10.1016/j.cageo.2020.104461