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Pabelco Zambrano, F. C., Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe. (2023). UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review. In IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, 4-7 julio 2023.
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Sara Nieto, E. M., Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe. (2024). A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning. In Lecture Notes in Geoinformation and Cartography: 3rd International Conference on Geospatial Information Sciences, iGISc 2023 Ciudad de México 14-17 November 2023 (pp. 147–158).
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Tyrone Rodríguez, A. G., Paolo Piedrahita & Miguel Realpe. (2025). Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning. In Lecture Notes in Networks and Systems: 9th International Congress on Information and Communication Technology ICICT 2024 London 19 – 22 Febrero 2024 (Vol. Vol. 1054 LNNS, pp. 45–57).
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Henry O. Velesaca, M. R., Angel D. Sappa & Alice Gomez. (2024). Analysis of Hidden Patterns in Road Accident Dataset using Clustering Techniques. In SmartTech-IC 2024 4th International Conference on Smart Technologies, Systems and Applications.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2015). Sensor Fault Detection and Diagnosis for autonomous vehicles. In 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 (Vol. 30, pp. 1–6). EDP Sciences.
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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