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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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Year |
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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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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English |
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Call Number |
cidis @ cidis @ |
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139 |
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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 |
Type |
Journal Article |
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Year |
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 |
Patricia L. Suarez |
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Title |
Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. |
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Year |
2020 |
Publication |
Ediciones FIEC-ESPOL. |
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Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. |
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Español |
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Call Number |
cidis @ cidis @ |
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144 |
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Author |
Rosero Vasquez Shendry |
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Title |
Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. |
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Journal Article |
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Year |
2020 |
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Pages |
615-625 |
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Call Number |
cidis @ cidis @ |
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145 |
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Author |
Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. |
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Title |
Smart university: an architecture proposal for information management using open data for research projects. |
Type |
Journal Article |
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Year |
2020 |
Publication |
Advances in Intelligent Systems and Computing |
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1137 AISC, 2020 |
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Pages |
172-178 |
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no |
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Call Number |
cidis @ cidis @ |
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188 |
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Author |
Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza |
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Title |
Open innovation at university: a systematic literature review |
Type |
Journal Article |
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Year |
2020 |
Publication |
Advances in Intelligent Systems and Computing |
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1159 AISC, 2020 |
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3-14 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
189 |
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Author |
Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. |
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Title |
Social media influence: a comprehensive review in general and in tourism domain |
Type |
Journal Article |
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Year |
2020 |
Publication |
Smart Innovation, Systems and Technologies. |
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171, 2020 |
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Pages |
25-35 |
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Call Number |
cidis @ cidis @ |
Serial |
190 |
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Author |
Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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Title |
Computer vision for image understanding. A comprehensive review |
Type |
Conference Article |
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Year |
2019 |
Publication |
International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
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248-259 |
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Abstract |
Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. |
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Call Number |
gtsi @ user @ |
Serial |
97 |
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Author |
Marjorie Chalen; Boris X. Vintimilla |
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Title |
Towards Action Prediction Applying Deep Learning |
Type |
Journal Article |
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Year |
2019 |
Publication |
Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
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pp. 1-3 |
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Keywords |
action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
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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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cidis @ cidis @ |
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129 |
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Author |
Armin Mehri; Angel D. Sappa |
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Title |
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples |
Type |
Conference Article |
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Year |
2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
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971-979 |
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This paper presents a novel approach for colorizing
near infrared (NIR) images. The approach is based on
image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored
networks that require less computation times, converge
faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation
metrics—and qualitatively evaluated showing considerable
improvements with respect to the state of the art |
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gtsi @ user @ |
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105 |
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