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Author Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa url  doi
openurl 
  Title Corn Kernel Classification From Few Training Samples Type Journal Article
  Year (down) 2023 Publication In journal Artificial Intelligence in Agriculture Abbreviated Journal  
  Volume Vol. 9 Issue Pages pp. 89-99  
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  ISSN 25897217 ISBN Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 223  
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Author Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa openurl 
  Title Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach Type Conference Article
  Year (down) 2023 Publication 17th International Conference On Signal Image Technology & Internet Based System Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 224  
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Author Patricia L. Suarez, Dario Carpio, Angel Sappa openurl 
  Title Boosting Guided Super-Resolution Performance with Synthesized Images Type Conference Article
  Year (down) 2023 Publication 17th International Conference On Signal Image Technology & Internet Based Systems Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 225  
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Author Patricia L. Suarez, Dario Carpio, Angel Sappa openurl 
  Title Depth Map Estimation from a Single 2D Image Type Conference Article
  Year (down) 2023 Publication 17th International Conference On Signal Image Technology & Internet Based Systems Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 226  
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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 (down) 2023 Publication 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 Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe openurl 
  Title A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning Type Conference Article
  Year (down) 2023 Publication International Conference on Geospatial Information Sciences, iGISc 2023 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 228  
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Author Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa pdf  openurl
  Title Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
  Year (down) 2023 Publication Workshop on Resource Efficient Deep Learning for Computer Vision – International Conference on Computer Vision ICCV 2023 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 229  
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Author Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan doi  isbn
openurl 
  Title A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells Type Conference Article
  Year (down) 2023 Publication 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 Abbreviated Journal  
  Volume Issue Pages 273 - 278  
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  ISSN ISBN 979-835033793-8 Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 230  
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. url  openurl
  Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
  Year (down) 2022 Publication VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. Abbreviated Journal  
  Volume 252 Issue Pages 503-514  
  Keywords time series, deep learning, recurrent networks, sensor data, IoT.  
  Abstract IoT (Internet of Things) and AI (Artificial Intelligence) are becoming

support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity

recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results.
 
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  Call Number cidis @ cidis @ Serial 152  
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Author 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 (down) 2022 Publication Sensors Abbreviated Journal Sensors  
  Volume Vol. 22 Issue Issue 6 Pages  
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  Call Number cidis @ cidis @ Serial 170  
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