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Silva Steven, P. D., Verdezoto Nervo, Hernandez Juan David. (2022). TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION. In IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, (Vol. 2022-August, pp. 707–714).
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Tyrone Rodríguez, A. G., Paolo Piedrahita & Miguel Realpe. (2024). Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning. In In 9th International Congress on Information and Communication Technology ICICT 2024.
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Marjorie Chalen, & Boris X. Vintimilla. (2019). Towards Action Prediction Applying Deep Learning. Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019, , pp. 1–3.
Abstract: Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
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Emmanuel Moran Barreiro & Boris Vintimilla. (2023). Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves. In Lecture Notes in Computer Science. 26th Iberoamerican Congress on Pattern Recognition (Vol. 14469 LNCS, pp. 257–271).
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Emmanuel Moran, B. V. & M. R. (2023). Towards a Robust Solution for the Supermarket Shelf Audit Problem. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 (pp. 912–919).
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Patricia Suarez & Angel Sappa. (2023). Toward a thermal image-like representation. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 (pp. 133–140).
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Armin Mehri, Parichehr Behjati, & Angel Domingo Sappa. (2023). TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. IEEE Access, Vol. 11.
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Xavier Soria, Y. L., Mohammad Rouhani & Angel D. Sappa. (2023). Tiny and Efficient Model for the Edge Detection Generalization. In Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 (pp. 1356–1365).
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Jacome-Galarza L.-R., R. R. M. - A., Paillacho Corredores J., Benavides Maldonado J.-L. (2022). Time series in sensor data using state of the art deep learning approaches: A systematic literature review. In 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).
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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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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