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Arias Alexandra. Ing., Peña Roxanna. Ing., Chávez Patricia. MSEE., & Basurto Juan. Ing. (2011). Análisis Comparativo de la Implementación de una PBX de Código Abierto instalada en un Servidor Tradicional y en un Enrutador Inalámbrico en términos de Calidad de Servicio en Redes Inalámbricas Amalladas. Revista Tecnologica ESPOL RTE, Vol. 24, pp. 1–6.
Abstract: El presente trabajo compara dos Implementaciones de Centrales Telefónicas VoIP de Código Abierto implementados sobre una Red Inalámbrica Amallada. El primero comprende la instalación de la PBX en un servidor tradicional y el segundo la instalación de una PBX en un enrutador inalámbrico. Nuestro objetivo es
determinar cuál de estos dos sistemas es superior en cuanto a calidad de servicio se refiere. Para determinar la mejor solución, realizamos un estudio técnico de los paquetes capturados durante diferentes pruebas, considerando parámetros como el ancho de banda, retardo y jitter. Nuestros métodos de análisis pueden ser utilizados para futuros trabajos con una mayor complejidad y número de enrutadores inalámbrico, así como establecer el grado de afectación y el comportamiento de las dos PBX cuando haya congestión en la red.
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Xavier Soria, Angel D. Sappa, & Riad Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059.Vol. 18(Issue 7).
Abstract: Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Dennis G. Romero, A. F. N., & Teodiano Freire B. (2014). Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes. Revista Iberoamericana de Automática e Informática industrial 00 (2014), Vol. 11, pp. 202–211.
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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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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, 121.
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Morocho-Cayamcela, M. E. & W. L. (2020). Lateral confinement of high-impedance surface-waves through reinforcement learning. Electronics Letters, Vol. 56(23, 12 November 2020), pp. 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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Morocho-Cayamcela, M. E. (2020). Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling. Electronic Letters on Computer Vision and Image Analysis (ELCVIA), Vol. 19(Issue 2), pp. 17–21.
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Rosero Vasquez Shendry. (2020). Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020.615–625.
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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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