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Author Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García pdf  openurl
  Title APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE Type Journal Article
  Year 2022 Publication (up) Granja Abbreviated Journal  
  Volume 36 Issue 2 Pages  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 200  
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Author Armin Mehri; Parichehr Behjati; Angel Domingo Sappa url  openurl
  Title TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. Type Journal Article
  Year 2023 Publication (up) IEEE Access Abbreviated Journal  
  Volume Vol. 11 Issue Pages pp. 11529 - 11540  
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  Call Number cidis @ cidis @ Serial 207  
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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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  Call Number cidis @ cidis @ Serial 183  
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Author Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. pdf  openurl
  Title Computer Vision based Food Grain Classification: a Comprehensive Survey Type Journal Article
  Year 2021 Publication (up) In Computers and Electronics in Agriculture Journal. (Vol. 187, Article number 106287) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 159  
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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 2021 Publication (up) In Image and Vision Computing Journal. (Vol. 110. Article number 104182) Abbreviated Journal  
  Volume 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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  Call Number cidis @ cidis @ Serial 147  
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Author Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. pdf  openurl
  Title Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends Type Journal Article
  Year 2021 Publication (up) In Journal: Robotics and Autonomous Systems. (Vol. 144, Article number 103832) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 158  
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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 2021 Publication (up) In Processes Journal. (Vol. 9, Issue 6, Article number 904) Abbreviated Journal  
  Volume Issue 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 Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. pdf  openurl
  Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
  Year 2019 Publication (up) In Robotics and Autonomous Systems Abbreviated Journal  
  Volume Vol. 121 Issue Pages  
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  Call Number gtsi @ user @ Serial 119  
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Author Victor Santos; Angel D. Sappa; Miguel Oliveira pdf  openurl
  Title Spcial Issue on Autonomous Driving an Driver Assistance Systems Type Journal Article
  Year 2017 Publication (up) In Robotics and Autonomous Systems Journal Abbreviated Journal  
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  Call Number gtsi @ user @ Serial 65  
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Author Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa pdf  openurl
  Title Melamine faced panels defect classification beyond the visible spectrum. Type Journal Article
  Year 2018 Publication (up) In Sensors 2018 Abbreviated Journal  
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  Abstract In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond

the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
 
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 89  
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