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| Author | Miguel Realpe; Boris X. Vintimilla; L. Vlacic | ||||
| 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 | Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla | ||||
| 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 | ||||
| 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 94 | ||
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| Author | José Reyes; Axel Godoy; Miguel Realpe. | ||||
| 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 102 | ||
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| Author | Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon | ||||
| 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 | ||||
| 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 | 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 | ||||
| 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 | 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 | 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 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 42 | ||
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| Author | Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic | ||||
| Title | Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Automation and Control Engineering (JOACE) | Abbreviated Journal | |
| Volume | Vol. 4 | Issue | Pages | pp. 430-436 | |
| Keywords | Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System | ||||
| Abstract | Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 51 | ||
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