|
Records |
Links |
|
Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
|
|
Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
Type |
Conference Article |
|
Year |
2021 |
Publication |
In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2703-2712 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
148 |
|
Permanent link to this record |
|
|
|
|
Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
|
|
Title |
LiNet: A Lightweight Network for Image Super Resolution |
Type |
Conference Article |
|
Year |
2021 |
Publication |
25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
7196-7202 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
149 |
|
Permanent link to this record |
|
|
|
|
Author |
Jacome-Galarza L.-R |
|
|
Title |
Crop yield prediction utilizing multimodal deep learning |
Type |
Conference Article |
|
Year |
2021 |
Publication |
16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
|
|
Abstract |
La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
Español |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
150 |
|
Permanent link to this record |
|
|
|
|
Author |
Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. |
|
|
Title |
Thermal Image Super-Resolution Challenge – PBVS 2021. |
Type |
Conference Article |
|
Year |
2021 |
Publication |
In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
4354-4362 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
151 |
|
Permanent link to this record |
|
|
|
|
Author |
Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
|
|
Title |
Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
Type |
Conference Article |
|
Year |
2021 |
Publication |
19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
|
|
Abstract |
Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
154 |
|
Permanent link to this record |
|
|
|
|
Author |
Pereira J., Mora M. & W. Agila |
|
|
Title |
Qualitative Model to Maximize Shrimp Growth at Low Cost |
Type |
Journal Article |
|
Year |
2021 |
Publication |
5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
167 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla |
|
|
Title |
Deep learning-based vegetation index estimation |
Type |
Book Chapter |
|
Year |
2021 |
Publication |
Generative Adversarial Networks for Image-to-Image Translation Book. |
Abbreviated Journal |
|
|
|
Volume |
Chapter 9 |
Issue |
Issue 2 |
Pages |
205-232 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
137 |
|
Permanent link to this record |
|
|
|
|
Author |
Rubio, G.A., Agila, W.E |
|
|
Title |
A fuzzy model to manage water in polymer electrolyte membrane fuel cells |
Type |
Journal Article |
|
Year |
2021 |
Publication |
In Processes Journal. (Article number 904) |
Abbreviated Journal |
|
|
|
Volume |
Vol. 9 |
Issue |
Issue 6 |
Pages |
|
|
|
Keywords |
PEM fuel cell, fuzzy, neural network, electrical response, flooding, drying. |
|
|
Abstract |
In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
153 |
|
Permanent link to this record |
|
|
|
|
Author |
Michael Teutsch, Angel Sappa & Riad Hammoud |
|
|
Title |
Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Synthesis Lectures on Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
Vol. 10 No. 2 |
Issue |
|
Pages |
pp. 138 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
166 |
|
Permanent link to this record |
|
|
|
|
Author |
Suárez P. |
|
|
Title |
Processing and Representation of Multispectral Images Using Deep Learning Techniques |
Type |
Magazine Article |
|
Year |
2021 |
Publication |
In Electronic Letters on Computer Vision and Image Analysis |
Abbreviated Journal |
|
|
|
Volume |
Vol. 19 |
Issue |
Issue 2 |
Pages |
pp. 5-8 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector |
Thesis |
Master's thesis |
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
Español |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
yes |
|
|
Call Number |
cidis @ cidis @ |
Serial |
122 |
|
Permanent link to this record |