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Author |
Morocho-Cayamcela, M.E. & W. Lim |
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Title |
Lateral confinement of high-impedance surface-waves through reinforcement learning |
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Journal Article |
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2020 |
Publication |
Electronics Letters |
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Vol. 56 |
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23, 12 November 2020 |
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pp. 1262-1264 |
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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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Call Number |
cidis @ cidis @ |
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139 |
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Author |
Miguel A. Murillo, Julio E. Alvia, & Miguel Realpe |
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Title |
Beyond visual and radio line of sight UAVs monitoring system through open software in a simulated environment. |
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Conference Article |
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Year |
2021 |
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The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science |
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1388 |
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629-642 |
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Drone, Open Source, Internet, Web Application, Web Server, SITL, Line of sight, UAV. |
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Abstract |
The problem of loss of line of sight when operating drones has be-come a reality with adverse effects for professional and amateur drone opera-tors, since it brings technical problems such as loss of data collected by the de-vice in one or more instants of time during the flight and even misunderstand-ings of legal nature when the drone flies over prohibited or private places. This paper describes the implementation of a drone monitoring system using the In-ternet as a long-range communication network in order to avoid the problem of loss of communication between the ground station and the device. For this, a simulated environment is used through an appropriate open software tool. The operation of the system is based on a client that makes requests to a server, the latter in turn communicates with several servers, each of which has a drone connected to it. In the proposed system when a drone is ready to start a flight, its server informs the main server of the system, which in turn gives feedback to the client informing it that the device is ready to carry out the flight; this way customers can send a mission to the device and keep track of its progress in real time on the screen of their web application. |
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cidis @ cidis @ |
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186 |
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Author |
Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Deep learning-based vegetation index estimation |
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2021 |
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Generative Adversarial Networks for Image-to-Image Translation Book. |
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Chapter 9 |
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Issue 2 |
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205-232 |
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cidis @ cidis @ |
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137 |
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Author |
Morocho-Cayamcela, M.E. |
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Title |
Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling |
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Journal Article |
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2020 |
Publication |
Electronic Letters on Computer Vision and Image Analysis (ELCVIA) |
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Vol. 19 |
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Issue 2 |
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pp. 17-21 |
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cidis @ cidis @ |
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140 |
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Author |
Steven Silva, Dennys Paillacho., David Soque, María Guerra & Jonathan Paillacho |
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Title |
Autonomous Intelligent Navigation For Mobile Robots In Closed Environments. |
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Conference Article |
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2021 |
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The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science |
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1388 |
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391-402 |
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cidis @ cidis @ |
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187 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
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Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
Type |
Conference Article |
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Year |
2021 |
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In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
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2703-2712 |
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cidis @ cidis @ |
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148 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
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Title |
LiNet: A Lightweight Network for Image Super Resolution |
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Conference Article |
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2021 |
Publication |
25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 |
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7196-7202 |
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cidis @ cidis @ |
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149 |
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Author |
Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. |
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Title |
Thermal Image Super-Resolution Challenge – PBVS 2021. |
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Conference Article |
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Year |
2021 |
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In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 |
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4354-4362 |
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cidis @ cidis @ |
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151 |
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Author |
Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. |
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Title |
Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. |
Type |
Conference Article |
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Year |
2021 |
Publication |
The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science |
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1388 |
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164-178 |
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cidis @ cidis @ |
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191 |
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Author |
Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Title |
Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
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Conference Article |
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2022 |
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VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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