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Author Miguel Realpe; Boris X. Vintimilla; L. Vlacic pdf  openurl
  Title Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. Type Conference Article
  Year 2015 Publication IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. Abbreviated Journal  
  Volume Issue Pages 253-258  
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  Abstract Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.  
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  Call Number cidis @ cidis @ Serial 37  
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic pdf  url
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  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 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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  Language English Summary Language English Original Title  
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  Call Number cidis @ cidis @ Serial 42  
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Author Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla pdf  openurl
  Title An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System Type Conference Article
  Year 2017 Publication 2017 International Conference on Deep Learning Technologies (ICDLT 2017) Abbreviated Journal  
  Volume Part F128535 Issue Pages 60-65  
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  Call Number cidis @ cidis @ Serial 56  
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Author Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa pdf  openurl
  Title Detection of utility poles from noisy Point Cloud Data in Urban environments. Type Conference Article
  Year 2018 Publication Artificial Intelligence and Cloud Computing Conference (AICCC 2018) Abbreviated Journal  
  Volume Issue Pages 53-57  
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  Abstract In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.  
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  Call Number gtsi @ user @ Serial 94  
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Author José Reyes; Axel Godoy; Miguel Realpe. pdf  openurl
  Title Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. Type Conference Article
  Year 2019 Publication International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica Abbreviated Journal  
  Volume 2019-July Issue Pages  
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  Abstract Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.  
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  Call Number gtsi @ user @ Serial 102  
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Author Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon pdf  openurl
  Title Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
  Year 2019 Publication International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal  
  Volume Issue Pages 572-581  
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  Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
 
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  Call Number gtsi @ user @ Serial 116  
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Author Emmanuel Moran, Boris Vintimilla & Miguel Realpe openurl 
  Title Towards a Robust Solution for the Supermarket Shelf Audit Problem. Type Conference Article
  Year 2023 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) Lisbon, 19-21 Febrero 2023 Abbreviated Journal  
  Volume Issue Pages 912 - 919  
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  Call Number cidis @ cidis @ Serial 204  
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Author Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe pdf  isbn
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  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, 4-7 julio 2023 Abbreviated Journal  
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  ISSN ISBN 979-835033337-4 Medium  
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  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 pdf  openurl
  Title A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning Type Conference Article
  Year 2024 Publication 3rd International Conference on Geospatial Information Sciences, iGISc 2023 Ciudad de México 14-17 November 2023 Abbreviated Journal  
  Volume Issue Pages 147 - 158  
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  Call Number cidis @ cidis @ Serial 228  
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Author Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe openurl 
  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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 239  
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