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Author Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe
Title Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning Type Conference Article
Year 2024 Publication In 9th International Congress on Information and Communication Technology ICICT 2024 Abbreviated Journal
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Call Number cidis @ cidis @ Serial (down) 239
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Author Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe
Title A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning Type Conference Article
Year 2024 Publication 3rd International Conference on Geospatial Information Sciences, iGISc 2023 Ciudad de México 14-17 November 2023 Abbreviated Journal
Volume Issue Pages 147 - 158
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Call Number cidis @ cidis @ Serial (down) 228
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Author Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe
Title UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review Type Conference Article
Year 2023 Publication IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, 4-7 julio 2023 Abbreviated Journal
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ISSN ISBN 979-835033337-4 Medium
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Call Number cidis @ cidis @ Serial (down) 214
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Author Emmanuel Moran, Boris Vintimilla & Miguel Realpe
Title Towards a Robust Solution for the Supermarket Shelf Audit Problem. Type Conference Article
Year 2023 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) Lisbon, 19-21 Febrero 2023 Abbreviated Journal
Volume Issue Pages 912 - 919
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Call Number cidis @ cidis @ Serial (down) 204
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Author Miguel A. Murillo, Julio E. Alvia, & Miguel Realpe
Title Beyond visual and radio line of sight UAVs monitoring system through open software in a simulated environment. Type Conference Article
Year 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science Abbreviated Journal
Volume 1388 Issue Pages 629-642
Keywords Drone, Open Source, Internet, Web Application, Web Server, SITL, Line of sight, UAV.
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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Call Number cidis @ cidis @ Serial (down) 186
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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.
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Call Number cidis @ cidis @ Serial (down) 154
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Author Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon
Title Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
Year 2019 Publication International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 572-581
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Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
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Call Number gtsi @ user @ Serial (down) 116
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Author José Reyes; Axel Godoy; Miguel Realpe.
Title Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. Type Conference Article
Year 2019 Publication International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica Abbreviated Journal
Volume 2019-July Issue Pages
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Abstract Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.
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Call Number gtsi @ user @ Serial (down) 102
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Author Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa
Title Detection of utility poles from noisy Point Cloud Data in Urban environments. Type Conference Article
Year 2018 Publication Artificial Intelligence and Cloud Computing Conference (AICCC 2018) Abbreviated Journal
Volume Issue Pages 53-57
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Abstract In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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Call Number gtsi @ user @ Serial (down) 94
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Author Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla
Title An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System Type Conference Article
Year 2017 Publication 2017 International Conference on Deep Learning Technologies (ICDLT 2017) Abbreviated Journal
Volume Part F128535 Issue Pages 60-65
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Call Number cidis @ cidis @ Serial (down) 56
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