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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.
	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
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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	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.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	14248220	ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	132
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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
	Keywords	action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.
	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.
	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	129
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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	28	Issue		Pages	1-7
	Keywords	Rehabilitation; RGB-D Sensor; Computer Vision; Upper limb
	Abstract	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.
	Address
	Corporate Author				Thesis
	Publisher	ESPOL	Place of Publication		Editor
	Language	English	Summary Language	English	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	39
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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
	Keywords
	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.
	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	gtsi @ user @			Serial	97
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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
	Keywords
	Abstract	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.
	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	gtsi @ user @			Serial	115
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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	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,
	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	15
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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	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
	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	16
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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
	Keywords
	Abstract	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.
	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	gtsi @ user @			Serial	110
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	Author	Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira
	Title	Incremental Texture Mapping for Autonomous Driving			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems Journal	Abbreviated Journal
	Volume	84	Issue		Pages	113-128
	Keywords	Scene reconstruction, Autonomous driving, Texture mapping
	Abstract	Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	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	50
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