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Author |
Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan |
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Title |
A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells |
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2023 |
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12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 |
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273 - 278 |
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979-835033793-8 |
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cidis @ cidis @ |
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230 |
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Author |
Suarez Patricia; Carpio Dario; Sappa Angel D. |
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Title |
A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
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Conference Article |
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2023 |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 |
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14234 LNCS |
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369 - 380 |
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03029743 |
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978-303143152-4 |
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cidis @ cidis @ |
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231 |
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Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Title |
Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
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2022 |
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VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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Author |
Rafael E. Rivadeneira, Angel Domingo Sappa, Vintimilla B. X. and Hammoud R. |
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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. |
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Journal Article |
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Year |
2022 |
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Sensors |
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Sensors |
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Vol. 22 |
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Issue 6 |
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cidis @ cidis @ |
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170 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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no |
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cidis @ cidis @ |
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181 |
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Author |
Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio |
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Title |
Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. |
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Conference Article |
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2022 |
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16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 |
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85-92 |
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cidis @ cidis @ |
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182 |
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Author |
Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa |
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Title |
A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. |
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2022 |
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Journal of Manufacturing Systems |
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Vol. 64 |
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pp. 497-507 |
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yes |
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cidis @ cidis @ |
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184 |
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Author |
Velesaca, Henry O.; Suárez, Patricia L.; Sappa, Angel D.; Carpio, Dario; Rivadeneira, Rafael E.; Sanchez, Angel |
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Title |
Review on Common Techniques for Urban Environment Video Analytics. |
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Conference Article |
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2022 |
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WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) |
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Porto Alegre: Sociedade Brasileira de Computação |
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107-118 |
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yes |
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cidis @ cidis @ |
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192 |
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Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla |
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Title |
Human Pose Estimation through A Novel Multi-View Scheme |
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Conference Article |
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Year |
2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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5 |
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855-862 |
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Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach |
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This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations. |
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cidis @ cidis @ |
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169 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
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2022 |
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IEEE Access journal |
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Vol. 10 |
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pp. 68281-68290 |
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cidis @ cidis @ |
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183 |
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