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Author Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho
Title Forward and Inverse Kinematics of a Humanoid Robot Head for Social Human Robot-Interaction Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents an analysis of forward and inverse kinematics for a humanoid robotic head. The robotic head is used for the study of social human-robot interaction, such as a support tool to maintain the attention of patients with Autism Spectrum Disorder. The design of a parallel robot that emulates human head movements through a closed structure is presented. The position and orientation in this space is controlled by three servomotors. For this, the solutions made for the kinematic problem are encompassed by a geometric analysis of a mobile base. This article describes a non-systematic method,

called the geometric method, and compares some of the most popular existing methods considering reliability and computational cost. The geometric method avoids the use of changing reference systems, and instead uses geometric

relationships to directly obtain the position based on joint variables; and the other way around. Therefore, it converges in a few iterations and has a low computational cost.
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Call Number gtsi @ user @ Serial 113
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Author Stalin Francis Quinde
Title Un nuevo modelo BM3D-RNCA para mejorar la estimación de la imagen libre de ruido producida por el método BM3D. (Ph.D. Angel Sappa, Director.). M.Sc. thesis Type Book Chapter
Year 2019 Publication Ediciones FIEC-ESPOL Abbreviated Journal
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Corporate Author Ph.D. Angel Sappa, Director. Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language (up) Original Title
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Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 117
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Author Shendry Rosero Vásquez
Title Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis Type Book Chapter
Year 2019 Publication Ediciones FIEC-ESPOL Abbreviated Journal
Volume Issue Pages
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Corporate Author Ph.D. Angel Sappa, Director de tesis & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language (up) Original Title
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Notes Approved yes
Call Number gtsi @ user @ Serial 114
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Image patch similarity through a meta-learning metric based approach Type Conference Article
Year 2019 Publication 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia Abbreviated Journal
Volume Issue Pages 511-517
Keywords
Abstract Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results

have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Notes Approved no
Call Number gtsi @ user @ Serial 115
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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
Keywords
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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Notes Approved no
Call Number gtsi @ user @ Serial 116
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Author W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano
Title Open Control Architecture for the Characterization and Control of the PEM Fuel Cell Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
Volume Issue Pages 1-5
Keywords PEM fuel cell, Experimental System, Control Engineering.
Abstract Proton exchange membrane (PEM) fuel cells, are an efficient and clean source of electrical energy. The analysis of its operation requires experimental work, which allows measuring, modeling and optimizing PEM fuel cells electrical behavior under different operating conditions. Therefore, having an experimentation platform that allows to easily carry out its study and control is essential. This research presents the design and development of an open instrumental system that allows measuring, controlling and determining the operating parameters of a PEM fuel cell. As results, the polarization curves, voltage-current, obtained by the system itself in different experimental conditions are shown. These curves are a very useful tool to evaluate the electrical behavior of the PEM battery.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Notes Approved no
Call Number gtsi @ user @ Serial 118
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Author Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A.
Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
Year 2019 Publication In Robotics and Autonomous Systems Abbreviated Journal
Volume 121 Issue Pages
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Abstract
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language (up) Original Title
Series Editor Series Title Abbreviated Series Title
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Notes Approved no
Call Number gtsi @ user @ Serial 119
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Author Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca
Title Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem Type Conference Article
Year 2020 Publication The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 Abbreviated Journal
Volume 4 Issue Pages 498-505
Keywords Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters.
Abstract This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model

a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The

transfer learning consist of first training the network using pairs of images from the virtual-world scenario

considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight

of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose

estimation accuracy using the proposed model, as well as further improvements when the transfer learning

strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on

the training due to the reduced number of pairs of real-images on most of the public data sets.
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Publisher Place of Publication Editor
Language Summary Language (up) Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-989758402-2 Medium
Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 120
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Author Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla
Title Thermal Image Super-Resolution: a Novel Architecture and Dataset Type Conference Article
Year 2020 Publication The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 Abbreviated Journal
Volume 4 Issue Pages 111-119
Keywords Thermal images, Far Infrared, Dataset, Super-Resolution.
Abstract This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large

dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal

cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal

cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.

The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty

on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach

is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are

available.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language (up) Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-989758402-2 Medium
Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 121
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Author Suárez P.
Title Processing and Representation of Multispectral Images Using Deep Learning Techniques Type Magazine Article
Year 2021 Publication In Electronic Letters on Computer Vision and Image Analysis Abbreviated Journal
Volume Vol. 19 Issue Issue 2 Pages pp. 5-8
Keywords
Abstract
Address
Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language (up) Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved yes
Call Number cidis @ cidis @ Serial 122
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