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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 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 (down) 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 Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. url  openurl
  Title Video Analytics in Urban Environments: Challenges and Approaches. Type Book Chapter
  Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK  
  Volume (down) 224 Issue Pages 101-122  
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  Call Number cidis @ cidis @ Serial 196  
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. url  openurl
  Title Human Body Pose Estimation in Multi-view Environments. Type Book Chapter
  Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK  
  Volume (down) 224 Issue Pages 79-99  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 197  
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Author Angel D. Sappa. url  openurl
  Title ICT Applications for Smart Cities Type Book Chapter
  Year 2022 Publication Intelligent Systems Reference Library Abbreviated Journal BOOK  
  Volume (down) 224 Issue Pages  
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  Call Number cidis @ cidis @ Serial 198  
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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 In Computers and Electronics in Agriculture Journal. (Article number 106287) Abbreviated Journal  
  Volume (down) Vol. 187 Issue Pages  
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  Call Number cidis @ cidis @ Serial 159  
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Author Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. url  openurl
  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 (down) 171, 2020 Issue Pages 25-35  
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  Call Number cidis @ cidis @ Serial 190  
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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 In Journal: Robotics and Autonomous Systems. (Article number 103832) Abbreviated Journal  
  Volume (down) Vol. 144 Issue Pages  
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  Call Number cidis @ cidis @ Serial 158  
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Author Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia url  doi
openurl 
  Title Dense extreme inception network for edge detection. Type Journal Article
  Year 2023 Publication Pattern Recognition Abbreviated Journal  
  Volume (down) Vol. 139 Issue Pages  
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  ISSN 00313203 ISBN Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 216  
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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 In Robotics and Autonomous Systems Abbreviated Journal  
  Volume (down) 121 Issue Pages  
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  Call Number gtsi @ user @ Serial 119  
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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 In Image and Vision Computing Journal. (Article number 104182) Abbreviated Journal  
  Volume (down) 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 147  
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