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Author | Velez R., Paredes A., Silva S., Paillacho D., and Paillacho J. | ||||
Title | Implementation of a UVC lights disinfection system for a diferential robot applying security methods in indoor. | Type | Conference Article | ||
Year | 2022 | Publication | Communications in Computer and Information Science, International Conference on Applied Technologies (ICAT 2021), octubre 27-29 | Abbreviated Journal | CONFERENCE |
Volume | 1535 | Issue | Pages | 319-331 | |
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Call Number | cidis @ cidis @ | Serial | 178 | ||
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Author | Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira | ||||
Title | Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems Journal | Abbreviated Journal | |
Volume | Vol. 83 | Issue | Pages | pp. 312-325 | |
Keywords | Incremental scene reconstructionPoint cloudsAutonomous vehiclesPolygonal primitives | ||||
Abstract | When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. | ||||
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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 49 | ||
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Author | Carlos Monsalve; Alain April and Alain Abran | ||||
Title | Measuring software functional size from business process models | Type | Journal Article | ||
Year | 2011 | Publication | International Journal of Software Engineering and Knowledge Engineering | Abbreviated Journal | |
Volume | Vol. 21 | Issue | Pages | pp. 311–338 | |
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Abstract | 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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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 19 | ||
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Author | Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. | ||||
Title | Deep Learning based Corn Kernel Classification. | Type | Conference Article | ||
Year | 2020 | Publication | The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
Volume | 2020-June | Issue | 9150684 | Pages | 294-302 |
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Abstract | This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline. |
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Language | English | Summary Language | Original Title | ||
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ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
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Call Number | cidis @ cidis @ | Serial | 124 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture | Type | Conference Article | ||
Year | 2017 | Publication | 19th International Conference on Image Analysis and Processing. | Abbreviated Journal | |
Volume | Issue | Pages | 287-297 | ||
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Call Number | gtsi @ user @ | Serial | 66 | ||
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Author | Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan | ||||
Title | A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells | Type | Conference Article | ||
Year | 2023 | Publication | 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 | Abbreviated Journal | |
Volume | Issue | Pages | 273 - 278 | ||
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ISSN | ISBN | 979-835033793-8 | Medium | ||
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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 230 | ||
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Author | Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
Title | Learning crossspectral similarity measures with deep convolutional neural networks | Type | Conference Article | ||
Year | 2016 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 267-275 | ||
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Abstract | The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. | ||||
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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 48 | ||
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Author | Xavier Soria; Angel D. Sappa | ||||
Title | Improving Edge Detection in RGB Images by Adding NIR Channel. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 266-273 | ||
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Call Number | gtsi @ user @ | Serial | 95 | ||
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Author | Miguel Realpe; Boris X. Vintimilla; L. Vlacic | ||||
Title | Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. | Type | Conference Article | ||
Year | 2015 | Publication | IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. | Abbreviated Journal | |
Volume | Issue | Pages | 253-258 | ||
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Abstract | Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system. | ||||
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Call Number | cidis @ cidis @ | Serial | 37 | ||
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Author | G.A. Rubio; Wilton Agila | ||||
Title | Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review | Type | Conference Article | ||
Year | 2019 | Publication | 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania | Abbreviated Journal | |
Volume | Issue | Pages | 249-252 | ||
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Abstract | When a proton exchange fuel cell operates it produces in addition to electrical energy, heat and water as sub products, which impact on the performance of the cell. This paper analyzes the issue of transients and proposes a model that describes the dynamic operation of the fuel cell. The model considers the transients produced by electrochemical reactions, by flow water and by heat transfer. Two-phase flow transients result in increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable and predictable performance from the cell. |
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Call Number | gtsi @ user @ | Serial | 111 | ||
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