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Author (up) Morocho-Cayamcela, M.E. & W. Lim pdf  openurl
  Title Lateral confinement of high-impedance surface-waves through reinforcement learning Type Journal Article
  Year 2020 Publication Electronics Letters Abbreviated Journal  
  Volume Vol. 56 Issue 23, 12 November 2020 Pages pp. 1262-1264  
  Keywords  
  Abstract 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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  Call Number cidis @ cidis @ Serial 139  
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Author (up) Ortiz J.; Londono J.; Novillo F.; Ampuno A.; Chávez M. pdf  url
openurl 
  Title Determinación de Invariantes en Grandes Centros de Datos basados en Topología Fat-Tree Type Journal Article
  Year 2015 Publication Revista Politécnica Abbreviated Journal  
  Volume Vol. 35 Issue Pages pp. 91-96  
  Keywords Invariantes de red, topologías, Fat-tree, simulación, emulación  
  Abstract Durante los últimos años ha existido un fuerte incremento en el acceso a internet, causando que los centros de datos ( DC) deban adaptar dinámicamente su infraestructura de red de cara a enfrentar posibles problemas de congestión, la cual no siempre se da de forma oportuna. Ante esto, nuevas topologías de red se han propuesto en los últimos años, como una forma de brindar mejores condiciones para el manejo de tráfico interno, sin embargo es común que para el estudio de estas mejoras, se necesite recrear el comportamiento de un verdadero DC en modelos de simulación/emulación. Por lo tanto se vuelve esencial validar dichos modelos, de cara a obtener resultados coherentes con la realidad. Esta validación es posible por medio de la identificación de ciertas propiedades que se deducen a partir de las variables y los parámetros que describen la red, y que se mantienen en las topologías de los DC para diversos escenarios y/o configuraciones. Estas propiedades, conocidas como invariantes, son una expresión del funcionamiento de la red en ambientes reales, como por ejemplo la ruta más larga entre dos nodos o el número de enlaces mínimo que deben fallar antes de una pérdida de conectividad en alguno de los nodos de la red. En el presente trabajo se realiza la identificación, formulación y comprobación de dos invariantes para la topología Fat-Tree, utilizando como software emulador a mininet. Las conclusiones muestran resultados concordantes entre lo analítico y lo práctico.  
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  Publisher Escuela Politécnica Nacional Place of Publication Editor  
  Language Español Summary Language Español Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 32  
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Author (up) Patricia L. Suarez, Dario Carpio, Angel D. Sappa pdf  openurl
  Title Enhancement of Guided Thermal Image Super-Resolution Approaches Type Journal
  Year 2024 Publication Neurocomputing Abbreviated Journal  
  Volume 573 Issue Pages  
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  Area Expedition Conference Neurocomputing  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 247  
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Author (up) Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa pdf  url
doi  openurl
  Title Corn Kernel Classification From Few Training Samples Type Journal Article
  Year 2023 Publication In journal Artificial Intelligence in Agriculture Abbreviated Journal  
  Volume Vol. 9 Issue Pages pp. 89-99  
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  Series Volume Series Issue Edition  
  ISSN 25897217 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 223  
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Author (up) Pereira J., Mora M. & W. Agila url  openurl
  Title Qualitative Model to Maximize Shrimp Growth at Low Cost Type Journal Article
  Year 2021 Publication 5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 167  
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Author (up) Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. pdf  openurl
  Title A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. Type Journal Article
  Year 2022 Publication Sensors Abbreviated Journal Sensors  
  Volume Vol. 22 Issue Issue 6 Pages  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 170  
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Author (up) Rato D., Oliveira M., Santos V., Sappa A. & Raducanu B. openurl 
  Title Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction Type Journal Article
  Year 2024 Publication IEEE Int. Conf. on Intelligent Robots and Systems (IROS), Abu Dhabi, October 14-18, 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 255  
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Author (up) Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Journal Abbreviated Journal  
  Volume Vol. 14 Issue Pages pp. 3690-3701  
  Keywords cross-spectral imaging; feature point descriptors  
  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 28  
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Author (up) Rosero Vasquez Shendry url  openurl
  Title Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. Type Journal Article
  Year 2020 Publication Abbreviated Journal  
  Volume Issue Pages 615-625  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 145  
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Author (up) 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 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 153  
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