首页|Investigators from University of California Berkeley Target Machine Learning (A Machine-learning Enabled Digital-twin Framework for the Rapid Design of Satellit e Constellations for 'planet-x.')
Investigators from University of California Berkeley Target Machine Learning (A Machine-learning Enabled Digital-twin Framework for the Rapid Design of Satellit e Constellations for 'planet-x.')
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Berkeley, California, by NewsRx edit ors, the research stated, “Worldwide communication bandwidth growth has largely been driven by (1) multimedia demands, (2) multicommunication-point demands and (3) multicommunication-rate demands, and has increased dramatically over the las t two decades due to e-commerce, internet communication and the explosion of cel l-phone use, in particular for in-flight services, all of which necessitate broa dband use and low latency. In order to accommodate this huge surge in demand, ne xt generation ‘mega-constellations’ of satellites are being proposed combining a mix of heterogeneous unit types in LEO, MEO and GEO orbital shells, in order to provide continuous lowerlatency and high-bandwidth service which exploits a wi de-range of frequencies for fast internet connections (broadband, which is not p ossible with single satellite-type orbital shell systems).” Financial support for this research came from UC Berkeley College of Engineering .
Berkeley, California, United States, Nor th and Central America, Broadband, Cyborgs, Electronics, Emerging Technologies, Machine Learning, University of California Berkeley
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