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Gomer Rubio, & Wilton Agila. (2018). Dynamic Modeling of Fuel Cells in a Strategic Context. In 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia..
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Henry O. Velesaca, D. C., Angel D. Sappa, Juan A. Holgado-Terriza & Wilton Agila. (2024). Anomaly Detection in Industrial Systems: Classical vs. Deep Learning Approaches with OPC-UA integration. In Accepted in SmartTech-IC 2024 4th International Conference on Smart Technologies, Systems and Applications.
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Henry O. Velesaca, G. B., Mohammad Rouhani, Angel D. Sappa. (2024). Multimodal image registration techniques: a comprehensive survey. Multimedia Tools and Applications, Vol. 83, 63919–63947.
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Henry O. Velesaca, J. A. H. - T., Doménica Carrasco, José Miguel Gutiérrez Guerrero, Tonny Toscano, Darío Carpio & Angel Sappa. (2024). Anomaly Detection in Industrial Production Products using OPC-UA and Deep Learning. In 13th International Conference on Data Science, Technology and Applications, DATA 2024 Dijon 9-11 July 2024 (pp. 505–512).
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Henry O. Velesaca, P. L. S., Dario Carpio, and Angel D. Sappa. (2021). Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy. In 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science (Vol. 13017, pp. 131–143).
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Henry O. Velesaca, P. L. S., Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. (2022). Video Analytics in Urban Environments: Challenges and Approaches. In ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series (Vol. 224, pp. 101–122).
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Henry O. Velesaca, S. A., Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa. (2020). Off-the-Shelf Based System for Urban Environment Video Analytics. In The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) (Vol. 2020-July, pp. 459–464).
Abstract: This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary
information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to
evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
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Henry O. Velesaca, Raul A. Mira, Patricia L. Suarez, Christian X. Larrea, & Angel D. Sappa. (2020). Deep Learning based Corn Kernel Classification. In The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) (Vol. 2020-June, pp. 294–302).
Abstract: This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based
approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.
As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and
the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
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Henry Velesaca Lara, J. A. H. & J. M. G. (2024). Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA. In Second International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development (CIIA 2024) Guayaquil 28 – 30 May 2024 (Vol. Vol. 532).
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Henry Velesaca Lara, P. S., Darío Carpio & Angel Sappa. (2024). Fruit Grading based on Deep Learning and Active Vision System. In 2nd International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development, CIIA 2024 Guayaquil 28 – 30 May 2024 (Vol. Vol. 532).
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