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Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla url  openurl
  Title Deep learning-based vegetation index estimation Type Book Chapter
  Year (down) 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal  
  Volume Chapter 9 Issue Issue 2 Pages 205-232  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 137  
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Author Steven Silva, Dennys Paillacho., David Soque, María Guerra & Jonathan Paillacho url  openurl
  Title Autonomous Intelligent Navigation For Mobile Robots In Closed Environments. Type Conference Article
  Year (down) 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science Abbreviated Journal  
  Volume 1388 Issue Pages 391-402  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 187  
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Author Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. pdf  openurl
  Title Camera pose estimation in multi-view environments:from virtual scenarios to the real world Type Journal Article
  Year (down) 2021 Publication In Image and Vision Computing Journal. (Article number 104182) Abbreviated Journal  
  Volume Vol. 110 Issue Pages  
  Keywords Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments  
  Abstract This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired

images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of

overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly

trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the

relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity

on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the

scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,

highlighting the importance on the similarity between virtual-real scenarios.
 
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 147  
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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
  Year (down) 2021 Publication In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 Abbreviated Journal  
  Volume Issue Pages 2703-2712  
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  Call Number cidis @ cidis @ Serial 148  
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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
  Year (down) 2021 Publication 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 Abbreviated Journal  
  Volume Issue Pages 7196-7202  
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  Call Number cidis @ cidis @ Serial 149  
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Author Jacome-Galarza L.-R pdf  openurl
  Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
  Year (down) 2021 Publication 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 Abbreviated Journal  
  Volume Issue Pages  
  Keywords Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.  
  Abstract La agricultura de precisión es una práctica vital para

mejorar la producción de cosechas. El presente trabajo tiene

como objetivo desarrollar un modelo multimodal de aprendizaje

profundo que es capaz de producir un mapa de salud de

cosechas. El modelo recibe como entradas imágenes multiespectrales

y datos de sensores de campo (humedad,

temperatura, estado del suelo, etc.) y crea un mapa de

rendimiento de la cosecha. La utilización de datos multimodales

tiene como finalidad extraer patrones ocultos del estado de salud

de las cosechas y de esta manera obtener mejores resultados que

los obtenidos mediante los índices de vegetación.
 
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  Language Español Summary Language Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 150  
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Author Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. pdf  openurl
  Title Thermal Image Super-Resolution Challenge – PBVS 2021. Type Conference Article
  Year (down) 2021 Publication In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 Abbreviated Journal  
  Volume Issue Pages 4354-4362  
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  Call Number cidis @ cidis @ Serial 151  
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Author Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. url  openurl
  Title Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. Type Conference Article
  Year (down) 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science Abbreviated Journal  
  Volume 1388 Issue Pages 164-178  
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  Call Number cidis @ cidis @ Serial 191  
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Author Rubio, G.A., Agila, W.E pdf  openurl
  Title A fuzzy model to manage water in polymer electrolyte membrane fuel cells Type Journal Article
  Year (down) 2021 Publication In Processes Journal. (Article number 904) Abbreviated Journal  
  Volume Vol. 9 Issue Issue 6 Pages  
  Keywords PEM fuel cell, fuzzy, neural network, electrical response, flooding, drying.  
  Abstract In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits.  
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  Call Number cidis @ cidis @ Serial 153  
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Author Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado pdf  openurl
  Title Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. Type Conference Article
  Year (down) 2021 Publication 19th LACCEI International Multi-Conference for Engineering, Education, and Technology Abbreviated Journal  
  Volume Issue Pages  
  Keywords processing, software engineering, deep learning, intelligent vision systems, cloud computing.  
  Abstract Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field

of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most

utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.

Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud.
 
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 154  
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