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Author |
Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. |

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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. |
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Journal Article |
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Year |
2022 |
Publication |
In Sensors. |
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In Sensors |
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Vol. 22 |
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Issue 6 |
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Article number 2254 |
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Call Number |
cidis @ cidis @ |
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170 |
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Author |
Morocho-Cayamcela, M.E. & W. Lim |

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Title |
Lateral confinement of high-impedance surface-waves through reinforcement learning |
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Journal Article |
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Year |
2020 |
Publication |
Electronics Letters |
Abbreviated Journal |
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56 |
Issue  |
23, 12 November 2020 |
Pages |
1262-1264 |
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The authors present a model-free policy-based reinforcement learning
model that introduces perturbations on the pattern of a metasurface.
The objective is to learn a policy that changes the size of the
patches, and therefore the impedance in the sides of an artificially structured
material. The proposed iterative model assigns the highest reward
when the patch sizes allow the transmission along a constrained path
and penalties when the patch sizes make the surface wave radiate to
the sides of the metamaterial. After convergence, the proposed
model learns an optimal patch pattern that achieves lateral confinement
along the metasurface. Simulation results show that the proposed
learned-pattern can effectively guide the electromagnetic wave
through a metasurface, maintaining its instantaneous eigenstate when
the homogeneity is perturbed. Moreover, the pattern learned to
prevent reflections by changing the patch sizes adiabatically. The
reflection coefficient S1, 2 shows that most of the power gets transferred
from the source to the destination with the proposed design. |
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cidis @ cidis @ |
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139 |
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Author |
Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García |

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Title |
APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE |
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Journal Article |
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Year |
2022 |
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Granja |
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36 |
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2 |
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cidis @ cidis @ |
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200 |
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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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Year |
2020 |
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In Sensors |
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Volume |
Vol. 2020-August |
Issue  |
16 |
Pages |
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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no |
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Call Number |
cidis @ cidis @ |
Serial |
133 |
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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |

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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
Type |
Journal Article |
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Year |
2020 |
Publication |
Sensors 2020 |
Abbreviated Journal |
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Volume |
Vol. 2020-June |
Issue  |
11 |
Pages |
pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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English |
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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
132 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |

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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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Year |
2018 |
Publication |
In Sensors 2018 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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no |
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gtsi @ user @ |
Serial |
89 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |

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Title |
Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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Journal Article |
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Year |
2017 |
Publication |
Lecture Notes in Computer Science |
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no |
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gtsi @ user @ |
Serial |
63 |
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Author |
Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |

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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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Journal Article |
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Year |
2017 |
Publication |
In Sensors Journal |
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17 |
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Pages |
873 |
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gtsi @ user @ |
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64 |
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Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |

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Title |
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 |
Del Pino, J.; Salazar, G.; Cedeño, V. Msc. |

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Title |
Adaptación de un Recomendador de Filtro Colaborativo Basado en el Usuario para la Creación de un Recomendador de Materias de Pregrado Basado en el Historial Académico de los Estudiantes |
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Journal Article |
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2011 |
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Revista Tecnológica ESPOL |
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24 |
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29 - 34 |
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Abstract |
Los sistemas de recomendación son ampliamente utilizados hoy en día gracias a su capacidad de analizar las preferencias de usuarios y sugerir ítems. No obstante, el uso de los recomendadores está limitado a un modelo basado en el usuario y no en su historial de preferencias, discriminando así el campo de aplicación, por ejemplo, a sistemas académicos donde sea primordial el estudio de las decisiones del estudiante a lo largo de su carrera. El presente
trabajo presenta un esfuerzo por adaptar filtros colaborativos basados en el usuario a filtros colaborativos basados en el historial del usuario. Con un conjunto de pruebas mediremos su efectividad utilizando dos algoritmos distintos de similaridad para recomendar materias a un estudiante en el sexto semestre de la carrera de Ingeniería en Electrónica y Telecomunicaciones ofertada por la FIEC – ESPOL. Los resultados muestran que es factible adaptar un recomendador a un modelo basado en el historial del usuario |
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Campus “Gustavo Galindo Velasco” La prosperina Km 30,5 vía perimetral, Guayaquil, Ecuador |
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Español |
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Español |
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cidis @ cidis @ |
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13 |
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