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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 (up)
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 D. Sappa and Boris X. Vintimilla
Title Multi-Image Super-Resolution for Thermal Images. Type Conference Article
Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal (up)
Volume 4 Issue Pages 635 - 642
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Call Number cidis @ cidis @ Serial 181
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Author Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio
Title Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. Type Conference Article
Year 2022 Publication 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 Abbreviated Journal (up)
Volume Issue Pages 85-92
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Call Number cidis @ cidis @ Serial 182
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Author Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa
Title A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. Type Journal Article
Year 2022 Publication Journal of Manufacturing Systems Abbreviated Journal (up)
Volume Vol. 64 Issue Pages pp. 497-507
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Call Number cidis @ cidis @ Serial 184
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Author Velesaca, Henry O.; Suárez, Patricia L.; Sappa, Angel D.; Carpio, Dario; Rivadeneira, Rafael E.; Sanchez, Angel
Title Review on Common Techniques for Urban Environment Video Analytics. Type Conference Article
Year 2022 Publication WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) Abbreviated Journal (up)
Volume Issue Porto Alegre: Sociedade Brasileira de Computação Pages 107-118
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Call Number cidis @ cidis @ Serial 192
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla
Title Human Pose Estimation through A Novel Multi-View Scheme Type Conference Article
Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal (up)
Volume 5 Issue Pages 855-862
Keywords Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach
Abstract 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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Call Number cidis @ cidis @ Serial 169
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa.
Title LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
Year 2022 Publication IEEE Access journal Abbreviated Journal (up)
Volume Vol. 10 Issue Pages pp. 68281-68290
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Call Number cidis @ cidis @ Serial 183
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Author Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho
Title Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework Type Conference Article
Year 2022 Publication Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 Abbreviated Journal (up)
Volume 256 Issue Pages 119-132
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Abstract In gamification, the development of “visual metrics for educational

video games linked to social assistance robots in the framework of inclusive education” seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games

through established metrics, which allow monitoring the user's facial expressions

during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly

adaptable for inclusive early childhood education.
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Call Number cidis @ cidis @ Serial 180
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Author Roberto Jacome Galarza.
Title Multimodal deep learning for crop yield prediction. Type Conference Article
Year 2022 Publication Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. Abbreviated Journal (up)
Volume 1647 Issue Communicationsin Computer and Infor Pages 106-117
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Call Number cidis @ cidis @ Serial 193
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Author Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos
Title Deep Learning based Shrimp Classification Type Conference Article
Year 2022 Publication 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) Abbreviated Journal (up)
Volume 13598 LNCS Issue Pages 36-45
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Call Number cidis @ cidis @ Serial 194
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