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Author G.A. Rubio; Wilton Agila
Title Sustainable Energy: A Strategic View of Fuel Cells Type Conference Article
Year 2019 Publication 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania Abbreviated Journal
Volume Issue Pages 239-243
Keywords
Abstract (up) Based on the model of the proton exchange fuel cell in a strategic context,

this document develops the issue of energy as one of the pillars to achieve the

sustainability of our planet, considering the future scenarios up to the year 2060 of the

situation energy, hydrogen as a strategic vector and the contribution of the fuel cell in

solving the serious problems of environmental pollution and economic inequity that

humanity faces; for its application in the energy generation, telecommunications and

vehicle manufacturing industries.
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Call Number gtsi @ user @ Serial 110
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Author Carlos Monsalve; Alain April; Alain Abran
Title Requirements Elicitation Using BPM Notations: Focusing on the Strategic Level Representation Type Conference Article
Year 2011 Publication 10th WSEAS international conference on Applied computer and applied computational science Abbreviated Journal
Volume Issue Pages 235-241
Keywords Business process modeling, levels of abstraction, requirements elicitation, case study, action research
Abstract (up) Business process models (BPM) can be useful for requirements elicitation, among other uses. Since the active participation of all stakeholders is a key factor for successful requirements engineering, it is important that BPM be shared by all stakeholders. Unfortunately, organizations may end up with inconsistent BPM not covering all stakeholders’ needs and constraints. The use of multiple levels of abstraction (MLA), such as at the strategic, tactical and operational levels, is often used in various process-oriented initiatives to facilitate the consolidation of various stakeholders’ needs and constraints. This article surveys the use of MLA in recent BPM research publications and reports on a BPM action-research case study conducted in a Canadian organization, with the aim of exploring the usefulness of the strategic level.
Address CIDIS – Electrical and Computer Engineering Department Escuela Superior Politécnica del Litoral Km. 30.5 vía Perimetral
Corporate Author Thesis
Publisher Place of Publication 1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3 CANADA Editor
Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 16
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Author Carlos Monsalve; Alain April; Alain Abran
Title BPM and requirements elicitation at multiple levels of abstraction: A review Type Conference Article
Year 2011 Publication IADIS International Conference on Information Systems 2011 Abbreviated Journal
Volume Issue Pages 237-242
Keywords Business process modeling, levels of abstraction, requirements elicitation, requirements modeling, review
Abstract (up) Business process models can be useful for requirements elicitation, among other things. Software development depends on the quality of the requirements elicitation activities, and so adequately modeling business processes (BPs) is critical. A key factor in achieving this is the active participation of all the stakeholders in the development of a shared vision of BPs.

Unfortunately, organizations often find themselves left with inconsistent BPs that do not cover all the stakeholders’ needs

and constraints. However, consolidation of the various stakeholder requirements may be facilitated through the use of multiple levels of abstraction (MLA). This article contributes to the research into MLA use in business process modeling (BPM) for software requirements by reviewing the theoretical foundations of MLA and their use in various BP-oriented approaches.
Address CIDIS-FIEC, Escuela Superior Politécnica del Litoral (ESPOL) Km. 30.5 vía Perimetral,
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Notes Approved no
Call Number cidis @ cidis @ Serial 15
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Image patch similarity through a meta-learning metric based approach Type Conference Article
Year 2019 Publication 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia Abbreviated Journal
Volume Issue Pages 511-517
Keywords
Abstract (up) Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results

have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Notes Approved no
Call Number gtsi @ user @ Serial 115
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Author Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco
Title Computer vision for image understanding. A comprehensive review Type Conference Article
Year 2019 Publication International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 248-259
Keywords
Abstract (up) 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 Ma. Paz Velarde; Erika Perugachi; Dennis G. Romero; Ángel D. Sappa; Boris X. Vintimilla
Title Análisis del movimiento de las extremidades superiores aplicado a la rehabilitación física de una persona usando técnicas de visión artificial. Type Journal Article
Year 2015 Publication Revista Tecnológica ESPOL-RTE Abbreviated Journal
Volume Vol. 28 Issue Pages pp. 1-7
Keywords Rehabilitation; RGB-D Sensor; Computer Vision; Upper limb
Abstract (up) Comúnmente durante la rehabilitación física, el diagnóstico dado por el especialista se basa en observaciones cualitativas que sugieren, en algunos casos, conclusiones subjetivas. El presente trabajo propone un enfoque cuantitativo, orientado a servir de ayuda a fisioterapeutas, a través de una herramienta interactiva y de bajo costo que permite medir los movimientos de miembros superiores. Estos movimientos son capturados por un sensor RGB-D y procesados mediante la metodología propuesta, dando como resultado una eficiente representación de movimientos, permitiendo la evaluación cuantitativa de movimientos de los miembros superiores.
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Publisher ESPOL Place of Publication Editor
Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 39
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Author Marjorie Chalen; Boris X. Vintimilla
Title Towards Action Prediction Applying Deep Learning Type Journal Article
Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal
Volume Issue Pages pp. 1-3
Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.
Abstract (up) 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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Notes Approved no
Call Number cidis @ cidis @ Serial 129
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Author Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa
Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
Year 2020 Publication Sensors 2020 Abbreviated Journal
Volume Vol. 2020-June Issue 11 Pages pp. 1-13
Keywords stereo matching; deep learning; embedded GPU
Abstract (up) Current CNN-based stereo depth estimation models can barely run under real-time

constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art

evaluations usually do not consider model optimization techniques, being that it is unknown what is

the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models

on three different embedded GPU devices, with and without optimization methods, presenting

performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth

estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture

for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically

augmenting the runtime speed of current models. In our experiments, we achieve real-time inference

speed, in the range of 5–32 ms, for 1216  368 input stereo images on the Jetson TX2, Jetson Xavier,

and Jetson Nano embedded devices.
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Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 14248220 ISBN Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 132
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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 (up) 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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Notes Approved no
Call Number cidis @ cidis @ Serial 154
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles Type Journal Article
Year 2016 Publication Journal of Automation and Control Engineering (JOACE) Abbreviated Journal
Volume Vol. 4 Issue Pages pp. 430-436
Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System
Abstract (up) Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Notes Approved no
Call Number cidis @ cidis @ Serial 51
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