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Jorge L. Charco, A. D. S., Boris X. Vintimilla, Henry O. Velesaca. (2022). Human Body Pose Estimation in Multi-view Environments. In ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series (Vol. 224, pp 79 – 99).
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Angel D. Sappa. (2022). ICT Applications for Smart Cities. In Intelligent Systems Reference Library (Vol. 224).
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Viñán-Ludeña M.S., D. C. L. M., Roberto Jacome Galarza, & Sinche Freire, J. (2020). Social media influence: a comprehensive review in general and in tourism domain. Smart Innovation, Systems and Technologies., 171, 2020, 25–35.
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Santos V., Angel D. Sappa., & Oliveira M. & de la Escalera A. (2019). Special Issue on Autonomous Driving and Driver Assistance Systems. In Robotics and Autonomous Systems, Vol. 121.
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Angel D. Sappa, Cristhian A. Aguilera, Juan A. Carvajal Ayala, Miguel Oliveira, Dennis Romero, Boris X. Vintimilla, et al. (2016). Monocular visual odometry: a cross-spectral image fusion based approach. Robotics and Autonomous Systems Journal, vol 86, 26–36.
Abstract: This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Texture Mapping for Autonomous Driving. Robotics and Autonomous Systems Journal, 84, 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives. Robotics and Autonomous Systems Journal, 83, 312–325.
Abstract: When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
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Daniela Rato, M. O., Victor Santos, Manuel Gomes & Angel Sappa. (2022). A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. Journal of Manufacturing Systems, Vol. 64, pp 497 – 507.
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Morocho-Cayamcela, M. E. & W. L. (2020). Lateral confinement of high-impedance surface-waves through reinforcement learning. Electronics Letters, 56(23, 12 November 2020), 1262–1264.
Abstract: The authors present a model-free policy-based reinforcement learning
model that introduces perturbations on the pattern of a metasurface.
The objective is to learn a policy that changes the size of the
patches, and therefore the impedance in the sides of an artificially structured
material. The proposed iterative model assigns the highest reward
when the patch sizes allow the transmission along a constrained path
and penalties when the patch sizes make the surface wave radiate to
the sides of the metamaterial. After convergence, the proposed
model learns an optimal patch pattern that achieves lateral confinement
along the metasurface. Simulation results show that the proposed
learned-pattern can effectively guide the electromagnetic wave
through a metasurface, maintaining its instantaneous eigenstate when
the homogeneity is perturbed. Moreover, the pattern learned to
prevent reflections by changing the patch sizes adiabatically. The
reflection coefficient S1, 2 shows that most of the power gets transferred
from the source to the destination with the proposed design.
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Ulises Gildardo Quiroz Antúnez, A. I. M. R., María Fernanda Calderón Vega, Adán Guillermo Ramírez García. (2022). APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE. Granja, 36(2).
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