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Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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2023 |
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accepted in 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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no |
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cidis @ cidis @ |
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212 |
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Gisel Bastidas-Guacho, Patricio Moreno-Vallejo, Boris Vintimilla, Angel D. Sappa |
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Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
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Conference Article |
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2023 |
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accepted in IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7 |
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cidis @ cidis @ |
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213 |
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Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe |
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UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review |
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2023 |
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accepted in IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 |
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cidis @ cidis @ |
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214 |
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Boris Vintimilla, Jorge Vulgarin, Henry Velesaca |
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Deep Learning-based Human Height Estimation from a Stereo Vision System |
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2023 |
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accepted in IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 |
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cidis @ cidis @ |
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215 |
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Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia |
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2023 |
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Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139, 109461 |
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no |
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cidis @ cidis @ |
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216 |
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Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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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. (Vol. 252, pp. 503-514) |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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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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2022 |
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In Sensors. |
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In Sensors |
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Vol. 22 |
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Issue 6 |
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Article number 2254 |
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cidis @ cidis @ |
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170 |
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Author |
Rafael E. Rivadeneira, A. D. S. and B. X. V. |
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Multi-Image Super-Resolution for Thermal Images. |
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2022 |
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17th International Conference on Computer Vision Theory and Applications (VISAPP 2022), febrero 6-8 |
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cidis @ cidis @ |
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181 |
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Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio |
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Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. |
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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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pp. 85 – 92 |
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cidis @ cidis @ |
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182 |
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Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa |
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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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cidis @ cidis @ |
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184 |
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