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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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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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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 |
Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero |
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Title |
Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. |
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Conference Article |
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2018 |
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IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador |
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1-6 |
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The identification of shrimp organs in biology using
histological images is a complex task. Shrimp histological images
poses a big challenge due to their texture and similarity among
classes. Image classification by using feature engineering and
convolutional neural networks (CNN) are suitable methods to
assist biologists when performing organ detection. This work
evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-
Words (PBOW) models for image classification leveraging big
data techniques; and transfer learning for the same classification
task by using a pre-trained CNN. A comparative analysis
of these two different techniques is performed, highlighting
the characteristics of both approaches on the shrimp organs
identification problem. |
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gtsi @ user @ |
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87 |
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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 |
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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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cidis @ cidis @ |
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139 |
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Author |
W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano |
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Title |
Open Control Architecture for the Characterization and Control of the PEM Fuel Cell |
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Conference Article |
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2019 |
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IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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1-5 |
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PEM fuel cell, Experimental System, Control Engineering. |
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Proton exchange membrane (PEM) fuel cells, are an efficient and clean source of electrical energy. The analysis of its operation requires experimental work, which allows measuring, modeling and optimizing PEM fuel cells electrical behavior under different operating conditions. Therefore, having an experimentation platform that allows to easily carry out its study and control is essential. This research presents the design and development of an open instrumental system that allows measuring, controlling and determining the operating parameters of a PEM fuel cell. As results, the polarization curves, voltage-current, obtained by the system itself in different experimental conditions are shown. These curves are a very useful tool to evaluate the electrical behavior of the PEM battery. |
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gtsi @ user @ |
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118 |
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Author |
Cristina L. Abad; Yi Lu; Roy H. Campbell |
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Title |
DARE: Adaptive Data Replication for Efficient Cluster Scheduling |
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Conference Article |
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2011 |
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IEEE International Conference on Cluster Computing, 2011 |
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159 - 168 |
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MapReduce, replication, scheduling, locality |
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Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively. |
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yes |
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cidis @ cidis @ |
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21 |
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Author |
Xavier Soria; Angel D. Sappa; Riad Hammoud |
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Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. |
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2018 |
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Vol. 18 |
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Issue 7 |
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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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gtsi @ user @ |
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96 |
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Author |
Del Pino, J.; Salazar, G.; Cedeño, V. Msc. |
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Adaptación de un Recomendador de Filtro Colaborativo Basado en el Usuario para la Creación de un Recomendador de Materias de Pregrado Basado en el Historial Académico de los Estudiantes |
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Journal Article |
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2011 |
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Revista Tecnológica ESPOL |
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Vol. 24 |
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pp. 29 - 34 |
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Los sistemas de recomendación son ampliamente utilizados hoy en día gracias a su capacidad de analizar las preferencias de usuarios y sugerir ítems. No obstante, el uso de los recomendadores está limitado a un modelo basado en el usuario y no en su historial de preferencias, discriminando así el campo de aplicación, por ejemplo, a sistemas académicos donde sea primordial el estudio de las decisiones del estudiante a lo largo de su carrera. El presente
trabajo presenta un esfuerzo por adaptar filtros colaborativos basados en el usuario a filtros colaborativos basados en el historial del usuario. Con un conjunto de pruebas mediremos su efectividad utilizando dos algoritmos distintos de similaridad para recomendar materias a un estudiante en el sexto semestre de la carrera de Ingeniería en Electrónica y Telecomunicaciones ofertada por la FIEC – ESPOL. Los resultados muestran que es factible adaptar un recomendador a un modelo basado en el historial del usuario |
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Campus “Gustavo Galindo Velasco” La prosperina Km 30,5 vía perimetral, Guayaquil, Ecuador |
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Español |
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Español |
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cidis @ cidis @ |
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13 |
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Author |
José Reyes; Axel Godoy; Miguel Realpe. |
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Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. |
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Conference Article |
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2019 |
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International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica |
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2019-July |
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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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102 |
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Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon |
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Open Source system for identification of corn leaf chlorophyll contents based on multispectral images |
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Conference Article |
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2019 |
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International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador |
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572-581 |
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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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gtsi @ user @ |
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116 |
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Author |
Carlos Monsalve; Alain April and Alain Abran |
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Measuring software functional size from business process models |
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2011 |
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International Journal of Software Engineering and Knowledge Engineering |
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Vol. 21 |
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pp. 311–338 |
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ISO 14143-1 specifies that a functional size measurement (FSM) method must provide measurement procedures to quantify the functional user requirements (FURs) of software. Such quantitative information, functional size, is typically used, for instance, in software estimation. One of the international standards for FSM is the COSMIC FSM method — ISO 19761 — which was designed to be applied both to the business application (BA) software domain and to the real-time software domain. A recurrent problem in FSM is the availability and quality of the inputs required for measurement purposes; that is, well documented FURs. Business process (BP) models, as they are commonly used to gather requirements from the early stages of a project, could be a valuable source of information for FSM. In a previous article, the feasibility of such an approach for the BA domain was analyzed using the Qualigram BP modeling notation. This paper complements that work by: (1) analyzing the use of BPMN for FSM in the BA domain; (2) presenting notation-independent guidelines for the BA domain; and (3) analyzing the possibility of using BP models to perform FSM in the real-time domain. The measurement results obtained from BP models are compared with those of previous FSM case studies. |
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cidis @ cidis @ |
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