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Velez R., P. A., Silva S., Paillacho D., and Paillacho J. (2022). Implementation of a UVC lights disinfection system for a diferential robot applying security methods in indoor. In Communications in Computer and Information Science, International Conference on Applied Technologies (ICAT 2021), octubre 27-29 (Vol. 1535, pp. 319–331).
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Rivadeneira, R. E., & Sappa, A. D. and V. B. X. (2022). Thermal Image Super-Resolution: A Novel Unsupervised Approach. In Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 1474, pp. 495–506).
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Alex Ferrin, Julio Larrea, Miguel Realpe, & Daniel Ochoa. (2018). Detection of utility poles from noisy Point Cloud Data in Urban environments. In Artificial Intelligence and Cloud Computing Conference (AICCC 2018) (pp. 53–57).
Abstract: In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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Viñán-Ludeña, M. S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. (2020). Smart university: an architecture proposal for information management using open data for research projects. Advances in Intelligent Systems and Computing, 1137 AISC, 2020, 172–178.
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Juca Aulestia M., L. J. M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza. (2020). Open innovation at university: a systematic literature review. Advances in Intelligent Systems and Computing, 1159 AISC, 2020, 3–14.
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M. Diaz, Dennys Paillacho, C. Angulo, O. Torres, J. Gonzálalez, & J. Albo Canals. (2014). A Week-long Study on Robot-Visitors Spatial Relationships during Guidance in a Sciences Museum. In ACM/IEEE International Conference on Human-Robot Interaction (pp. 152–153).
Abstract: In order to observe spatial relationships in social human- robot interactions, a field trial was carried out within the CosmoCaixa Science Museum in Barcelona. The follow me episodes studied showed that the space configurations formed by guide and visitors walking together did not always fit the robot social affordances and navigation requirements to perform the guidance successfully, thus additional commu- nication prompts are considered to regulate effectively the walking together and follow me behaviors.
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Dennis G. Romero, A. Frizera, Angel D. Sappa, Boris X. Vintimilla, & T.F. Bastos. (2015). A predictive model for human activity recognition by observing actions and context. In ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 (pp. 323–333).
Abstract: This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
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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 Accepted in CIIA – II International Conference of Applied Industrial Engineering.
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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 Accepted in CIIA – II International Conference of Applied Industrial Engineering.
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Rafael E. Rivadeneira, A. D. S., Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang. (2024). Thermal Image Super Resolution Challenge Results – PBVS 2024. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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