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dblp: Max Ghiglione https://dblp.org/pid/319/6990.html dblp person page RSS feed Fri, 04 Oct 2024 21:03:27 +0200 en-US daily 1 released under the CC0 1.0 license dblp@dagstuhl.de (dblp team) dblp@dagstuhl.de (dblp team) Computers/Computer_Science/Publications/Bibliographies http://www.rssboard.org/rss-specification https://dblp.org/img/logo.144x51.pngdblp: Max Ghiglionehttps://dblp.org/pid/319/6990.html14451 Advancements in Onboard Processing of Synthetic Aperture Radar (SAR) Data: Enhancing Efficiency and Real-Time Capabilities.https://doi.org/10.1109/JSTARS.2024.3406155, , , , , , , :
Advancements in Onboard Processing of Synthetic Aperture Radar (SAR) Data: Enhancing Efficiency and Real-Time Capabilities. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17: 16625-16645 ()]]>https://dblp.org/rec/journals/staeors/GarciaFGZIICT24Mon, 01 Jan 2024 00:00:00 +0100 Raw Data Compression Exploiting Model-Based Approaches and Artificial Intelligence For Present And Next-Generation SAR Systems.https://doi.org/10.1109/IGARSS53475.2024.10640933, , , , , :
Raw Data Compression Exploiting Model-Based Approaches and Artificial Intelligence For Present And Next-Generation SAR Systems. IGARSS : 2550-2554]]>https://dblp.org/rec/conf/igarss/MartoneGRKGI24Mon, 01 Jan 2024 00:00:00 +0100 Accelerated Deep-Learning inference on FPGAs in the Space Domain.https://doi.org/10.1145/3587135.3592763, , , , :
Accelerated Deep-Learning inference on FPGAs in the Space Domain. CF : 222-228]]>https://dblp.org/rec/conf/cf/PetryGKG023Sun, 01 Jan 2023 00:00:00 +0100 Machine Learning Application Benchmark.https://doi.org/10.1145/3587135.3592769, , , , , , , , :
Machine Learning Application Benchmark. CF : 229-235]]>https://dblp.org/rec/conf/cf/KochPGRDF0TL23Sun, 01 Jan 2023 00:00:00 +0100 Opportunities and challenges of AI on satellite processing units.https://doi.org/10.1145/3528416.3530985, :
Opportunities and challenges of AI on satellite processing units. CF : 221-224]]>https://dblp.org/rec/conf/cf/GhiglioneS22Sat, 01 Jan 2022 00:00:00 +0100 Benchmarking and feasibility aspects of machine learning in space systems.https://doi.org/10.1145/3528416.3530986, , , , , :
Benchmarking and feasibility aspects of machine learning in space systems. CF : 225-226]]>https://dblp.org/rec/conf/cf/RaoofyDSG0T22Sat, 01 Jan 2022 00:00:00 +0100