Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Boucheron, Laura E"'
Autor:
Landeros, Jaime A., Kirk, Michael S., Arge, C. Nick, Boucheron, Laura E., Zhang, Jie, Uritsky, Vadim M., Grajeda, Jeremy A., Dupertuis, Matthew
Coronal Holes (CHs) are large-scale, low-density regions in the solar atmosphere which may expel high-speed solar wind streams that incite hazardous, geomagnetic storms. Coronal and solar wind models can predict these high-speed streams and the perfo
Externí odkaz:
http://arxiv.org/abs/2405.04731
The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural network (CNN) to predict angle and length of a linear motion blur kernel for
Externí odkaz:
http://arxiv.org/abs/2402.07796
Autor:
Zafari, Ali, Khoshkhahtinat, Atefeh, Grajeda, Jeremy A., Mehta, Piyush M., Nasrabadi, Nasser M., Boucheron, Laura E., Thompson, Barbara J., Kirk, Michael S. F., da Silva, Daniel
Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to m
Externí odkaz:
http://arxiv.org/abs/2311.02855
Coronal Holes (CHs) are regions of open magnetic field lines, resulting in high speed solar wind. Accurate detection of CHs is vital for space weather prediction. This paper presents an intramethod ensemble for coronal hole detection based on the Act
Externí odkaz:
http://arxiv.org/abs/2308.05679
Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel. We propose a regression approach using convolutional neu
Externí odkaz:
http://arxiv.org/abs/2308.01381
In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates
Externí odkaz:
http://arxiv.org/abs/2305.09492
Publikováno v:
Opt. Express 31, 22903-22913 (2023)
Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a convolutional neural network is applied to intensity images to predict a
Externí odkaz:
http://arxiv.org/abs/2304.02576
Autor:
Winkler, Zachary, Boucheron, Laura E., Utsumi, Santiago, Nyamuryekung'e, Shelemia, McIntosh, Matthew, Estell, Richard E.
Publikováno v:
In Smart Agricultural Technology August 2024 8
Autor:
Krizmanic, John F., Shah, Neerav, Calhoun, Philip C., Harding, Alice K., Purves, Lloyd R., Webster, Cassandra M., Corcoran, Michael F., Shrader, Chris R., Stochaj, Steven J., Rankin, Kyle A., Smith, Daniel T., Park, Hyeongjun, Boucheron, Laura E., Kota, Krishna, Naseri, Asal
The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Fresnel Lenses (PFLs) in a virtual X-ray telescope with $\sim$1 km focal length and with $\sim$50 milli-arcsecond angular resolution. VTXO is formed by using precision for
Externí odkaz:
http://arxiv.org/abs/2012.15311
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.