Digital tracking cloud distributed architecture for detection of faint NEAs

Sichitiu, R.E., Frincu, M.E. ORCID: 0000-0003-1034-8409 and Vaduvescu, O., 2019. Digital tracking cloud distributed architecture for detection of faint NEAs. In: H. Hong, V. Negru, D. Petcu and D. Zaharie, eds., Proceedings of the 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2019). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), pp. 121-128. ISBN 9781728157245

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Abstract

There is an exponential volume of captured images, millions of captures taken every night being processed and scrutinized. Big Data analysis has become essential for the study of the solar system, discovery and orbital knowledge of the asteroids. This analysis often requires more advanced algorithms capable of processing the available data and solve the essential problems in almost real time. One such problem that needs very rapid investigation involves the detection of Near Earth Asteroids (NEAs) and their orbit refinement which should answer the question "will the Earth collide in the future with any hazardous asteroid?". This paper proposes a cloud distributed architecture meant to render near real-time results, focusing on the image stacking techniques aimed to detect very faint moving objects, and pairing of unknown objects with known orbits for asteroid discovery and identification.

Item Type: Chapter in book
Description: Paper presented at the 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2019), Timisoara, Romania, 4-7 September 2019.
Creators: Sichitiu, R.E., Frincu, M.E. and Vaduvescu, O.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: Piscataway, NJ
Date: September 2019
ISBN: 9781728157245
Identifiers:
NumberType
10.1109/synasc49474.2019.00025DOI
1392581Other
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 07 Dec 2020 14:54
Last Modified: 07 Dec 2020 14:54
URI: http://irep.ntu.ac.uk/id/eprint/41798

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