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Author Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe
Title UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review Type Conference Article
Year 2023 Publication IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 Abbreviated Journal
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ISSN ISBN 979-835033337-4 Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 214
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Author Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe
Title A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning Type Conference Article
Year 2023 Publication International Conference on Geospatial Information Sciences, iGISc 2023 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 228
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Author Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe
Title Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning Type Conference Article
Year 2024 Publication In 9th International Congress on Information and Communication Technology ICICT 2024 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 239
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title Sensor Fault Detection and Diagnosis for autonomous vehicles Type Conference Article
Year 2015 Publication 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 Abbreviated Journal
Volume 30 Issue (up) MATEC Web of Conferences Pages 1-6
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Abstract In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.
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Publisher EDP Sciences Place of Publication Editor
Language English Summary Language English Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 42
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