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Author Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O.
Title Camera pose estimation in multi-view environments:from virtual scenarios to the real world Type Journal Article
Year 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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Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 147
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Author Mehri, A, Ardakani, P.B., Sappa, A.D.
Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
Year 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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Notes Approved no
Call Number cidis @ cidis @ Serial 148
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Author Mehri, A, Ardakani, P.B., Sappa, A.D.
Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
Year 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
Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
Year 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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Publisher Place of Publication Editor (up)
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.
Title Thermal Image Super-Resolution Challenge – PBVS 2021. Type Conference Article
Year 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.
Title Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. Type Conference Article
Year 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 Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza
Title Open innovation at university: a systematic literature review Type Journal Article
Year 2020 Publication Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 1159 AISC, 2020 Issue Pages 3-14
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Call Number cidis @ cidis @ Serial 189
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Author Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J.
Title Social media influence: a comprehensive review in general and in tourism domain Type Journal Article
Year 2020 Publication Smart Innovation, Systems and Technologies. Abbreviated Journal
Volume 171, 2020 Issue Pages 25-35
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Call Number cidis @ cidis @ Serial 190
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L.
Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
Year 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 Vol. 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 Rubio, G.A., Agila, W.E
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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Language English Summary Language Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 153
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