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Morocho-Cayamcela, M. E. (2020). Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling. Electronic Letters on Computer Vision and Image Analysis (ELCVIA), Vol. 19(Issue 2), pp. 17–21.
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Rosero Vasquez Shendry. (2020). Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020.615–625.
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Viñán-Ludeña, M. S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. (2020). Smart university: an architecture proposal for information management using open data for research projects. Advances in Intelligent Systems and Computing, 1137 AISC, 2020, 172–178.
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Juca Aulestia M., L. J. M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza. (2020). Open innovation at university: a systematic literature review. Advances in Intelligent Systems and Computing, 1159 AISC, 2020, 3–14.
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Viñán-Ludeña M.S., D. C. L. M., Roberto Jacome Galarza, & Sinche Freire, J. (2020). Social media influence: a comprehensive review in general and in tourism domain. Smart Innovation, Systems and Technologies., 171, 2020, 25–35.
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Rubio, G. A., Agila, W.E. (2021). A fuzzy model to manage water in polymer electrolyte membrane fuel cells. In Processes Journal. (Article number 904), Vol. 9(Issue 6).
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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Rafael E. Rivadeneira, A. D. S., Vintimilla B. X. and Hammoud R. (2022). A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. Sensors, Vol. 22(Issue 6).
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Michael Teutsch, A. S. & R. H. (2021). Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches. Synthesis Lectures on Computer Vision, Vol. 10 No. 2, pp. 138.
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Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. (2021). Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends. In Journal: Robotics and Autonomous Systems. (Article number 103832), Vol. 144.
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Velesaca, H. O., Suárez, P. L., Mira, R., & Sappa, A.D. (2021). Computer Vision based Food Grain Classification: a Comprehensive Survey. In Computers and Electronics in Agriculture Journal. (Article number 106287), Vol. 187.
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