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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |

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Title  |
Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles |
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2016 |
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Journal of Automation and Control Engineering (JOACE) |
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4 |
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430-436 |
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Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System |
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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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cidis @ cidis @ |
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51 |
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Author |
Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza |

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Open innovation at university: a systematic literature review |
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Journal Article |
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2020 |
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Advances in Intelligent Systems and Computing |
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1159 AISC, 2020 |
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3-14 |
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cidis @ cidis @ |
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189 |
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Author |
Pereira J., Mora M. & W. Agila |

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Title  |
Qualitative Model to Maximize Shrimp Growth at Low Cost |
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2021 |
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5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
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cidis @ cidis @ |
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167 |
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Author |
Dennis G. Romero; Anselmo Frizera N.; Teodiano Freire B. |

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Title  |
Reconocimiento en-l?nea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes |
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2014 |
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Revista Iberoamericana de Automática e Informática industrial 00 (2014) |
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11 |
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202-211 |
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Vision por ordenador, Mapas de profundidad, Reconocimiento de acciones humanas, Relative Wavelet Energy, Distancia de ´ Mahalanobis |
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Durante los últimos años ha existido un fuerte incremento en el acceso a internet, causando que los centros de datos (DC) deban adaptar dinámicamente su infraestructura de red de cara a enfrentar posibles problemas de congestión, la cual no siempre se da de forma oportuna. Ante esto, nuevas topologías de red se han propuesto en los últimos años, como una forma de brindar mejores condiciones para el manejo de tráfico interno, sin embargo es común que para el estudio de estas mejoras, se necesite recrear el comportamiento de un verdadero DC en modelos de simulación/emulación. Por lo tanto se vuelve esencial validar dichos modelos, de cara a obtener resultados coherentes con la realidad. Esta validación es posible por medio de la identificación de ciertas propiedades que se deducen a partir de las variables y los parámetros que describen la red, y que se mantienen en las topologías de los DC para diversos escenarios y/o configuraciones. Estas propiedades, conocidas como invariantes, son una expresión del funcionamiento de la red en ambientes reales, como por ejemplo la ruta más larga entre dos nodos o el número de enlaces mínimo que deben fallar antes de una pérdida de conectividad en alguno de los nodos de la red. En el presente trabajo se realiza la identificación, formulación y comprobación de dos invariantes para la topología Fat-Tree, utilizando como software emulador a mininet. Las conclusiones muestran resultados concordantes entre lo analítico y lo práctico. |
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Español |
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cidis @ cidis @ |
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30 |
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Author |
Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. |

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Smart university: an architecture proposal for information management using open data for research projects. |
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2020 |
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Advances in Intelligent Systems and Computing |
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1137 AISC, 2020 |
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172-178 |
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cidis @ cidis @ |
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188 |
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Author |
Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. |

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Title  |
Social media influence: a comprehensive review in general and in tourism domain |
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2020 |
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Smart Innovation, Systems and Technologies. |
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171, 2020 |
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25-35 |
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cidis @ cidis @ |
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190 |
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Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |

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Spcial Issue on Autonomous Driving an Driver Assistance Systems |
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2017 |
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In Robotics and Autonomous Systems Journal |
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gtsi @ user @ |
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65 |
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Author |
Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. |

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Special Issue on Autonomous Driving and Driver Assistance Systems |
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2019 |
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In Robotics and Autonomous Systems |
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Vol. 121 |
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gtsi @ user @ |
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119 |
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Author |
Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |

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Title  |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
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Journal Article |
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2020 |
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In Sensors |
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Vol. 2020-August |
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16 |
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pp. 1-23 |
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Keywords |
object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities |
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Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images oers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with dierent types of semantic segmentation networks and using the same evaluation metrics is
also included. |
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14248220 |
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cidis @ cidis @ |
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133 |
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Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |

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Title  |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
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Journal Article |
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2023 |
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IEEE Access |
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Vol. 11 |
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pp. 11529 - 11540 |
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Call Number |
cidis @ cidis @ |
Serial |
207 |
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