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Author Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa pdf  doi
openurl 
  Title SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution Type Journal Article
  Year 2023 Publication (up) IEEE access Abbreviated Journal  
  Volume Vol. 11 Issue Pages 121457 - 121469  
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  ISSN 21693536 ISBN Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 227  
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. url  openurl
  Title LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
  Year 2022 Publication (up) IEEE Access journal Abbreviated Journal  
  Volume Vol. 10 Issue Pages pp. 68281-68290  
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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 183  
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Author Lukas Danev; Marten Hamann; Nicolas Fricke; Tobias Hollarek; Dennys Paillacho pdf  openurl
  Title Development of animated facial expression to express emotions in a robot: RobotIcon. Type Conference Article
  Year 2017 Publication (up) IEEE Ecuador Technical Chapter Meeting (ETCM) Abbreviated Journal  
  Volume 2017-January Issue Pages 1-6  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 71  
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Author Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero pdf  openurl
  Title Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. Type Conference Article
  Year 2018 Publication (up) IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador Abbreviated Journal  
  Volume Issue Pages 1-6  
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  Abstract The identification of shrimp organs in biology using

histological images is a complex task. Shrimp histological images

poses a big challenge due to their texture and similarity among

classes. Image classification by using feature engineering and

convolutional neural networks (CNN) are suitable methods to

assist biologists when performing organ detection. This work

evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-

Words (PBOW) models for image classification leveraging big

data techniques; and transfer learning for the same classification

task by using a pre-trained CNN. A comparative analysis

of these two different techniques is performed, highlighting

the characteristics of both approaches on the shrimp organs

identification problem.
 
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  Call Number gtsi @ user @ Serial 87  
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Author Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa pdf  openurl
  Title PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization Type Conference Article
  Year 2019 Publication (up) IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal  
  Volume Issue Pages 1-6  
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  Abstract This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different

communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic.
 
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  Call Number gtsi @ user @ Serial 112  
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Author Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho pdf  openurl
  Title Forward and Inverse Kinematics of a Humanoid Robot Head for Social Human Robot-Interaction Type Conference Article
  Year 2019 Publication (up) IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal  
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  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 W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano pdf  openurl
  Title Open Control Architecture for the Characterization and Control of the PEM Fuel Cell Type Conference Article
  Year 2019 Publication (up) 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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  Call Number gtsi @ user @ Serial 118  
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Author Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David url  openurl
  Title TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION Type Conference Article
  Year 2022 Publication (up) IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, Abbreviated Journal  
  Volume 2022-August Issue Pages 707-714  
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  Call Number cidis @ cidis @ Serial 199  
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Author Cristina L. Abad; Yi Lu; Roy H. Campbell pdf  url
openurl 
  Title DARE: Adaptive Data Replication for Efficient Cluster Scheduling Type Conference Article
  Year 2011 Publication (up) IEEE International Conference on Cluster Computing, 2011 Abbreviated Journal  
  Volume Issue Pages 159 - 168  
  Keywords MapReduce, replication, scheduling, locality  
  Abstract Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.  
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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 21  
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Author Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo pdf  openurl
  Title Learning crossspectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication (up) IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Abbreviated Journal  
  Volume Issue Pages 267-275  
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  Abstract The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 48  
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