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نمایش تعداد 1-6 از 6

Extracting multiple cropping index based on NDVI time series: A method integrating temporal and spatial information

نوع: Conference Paper

ناشر: IEEE

سال: 2014

A dual-band, dual-polarized array element for Ka band satcom on the move terminals

نوع: Conference Paper

ناشر: IEEE

سال: 2014

Nonlinear reflection of elastic waves from the boundary of solids

نوع: Conference Paper

نویسنده : Shirgina, N.V.; Kokshaiski, A.I.; Korobov, A.I.

ناشر: IEEE

سال: 2014

Quantitative estimation of drought risk in Ukraine using satellite data

نوع: Conference Paper

نویسنده : Skakun, S.; Kussul, N.; Kussul, O.; Shelestov, A.

ناشر: IEEE

سال: 2014

Resource-aware architecture design and implementation of hough transform for a real-time iris boundary detection system

نوع: Journal Paper

نویسنده : Ngo, Hau; Rakvic, Ryan; Broussard, Randy; Ives, Robert

ناشر: IEEE

سال: 2014

Model-based multi-objective reinforcement learning

نوع: Conference Paper

نویسنده : Wiering, Marco A.; Withagen, Maikel; Drugan, Madalina M

ناشر: IEEE

سال: 2014

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Extracting multiple cropping index based on NDVI time series: A method integrating temporal and spatial information

A dual-band, dual-polarized array element for Ka band satcom on the move terminals

Nonlinear reflection of elastic waves from the boundary of solids

Quantitative estimation of drought risk in Ukraine using satellite data

Resource-aware architecture design and implementation of hough transform for a real-time iris boundary detection system

Model-based multi-objective reinforcement learning

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Extracting multiple cropping index based on NDVI time series: A method integrating temporal and spatial information

A dual-band, dual-polarized array element for Ka band satcom on the move terminals

Nonlinear reflection of elastic waves from the boundary of solids

Quantitative estimation of drought risk in Ukraine using satellite data

Resource-aware architecture design and implementation of hough transform for a real-time iris boundary detection system

Model-based multi-objective reinforcement learning