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Author Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi
Title Thermal Image Super-Resolution Challenge – PBVS 2020 Type Conference Article
Year 2020 Publication The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) Abbreviated Journal
Volume 2020-June Issue 9151059 Pages (up) 432-439
Keywords
Abstract This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework.

The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution

results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
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 21607508 ISBN 978-172819360-1 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 123
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Author Wilton Agila; Gomer Rubio; Francisco Vidal; B. Lima
Title Real time Qualitative Model for estimate Water content in PEM Fuel Cell Type Conference Article
Year 2019 Publication 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania Abbreviated Journal
Volume Issue Pages (up) 455-459
Keywords
Abstract To maintain optimum performance of the electrical

response of a fuel cell, a real time identification of the

malfunction situations is required. Critical fuel cell states depend,

among others, on the variable demand of electric load and are

directly related to the membrane hydration level. The real time

perception of relevant states in the PEM fuel cell states space, is

still a challenge for the PEM fuel cell control systems. Current

work presents the design and implementation of a methodology

based upon fuzzy decision techniques that allows real time

characterization of the dehydration and flooding states of a PEM

fuel cell. Real time state estimation is accomplished through a

perturbation-perception process on the PEM fuel cell and further

on voltage oscillation analysis. The real time implementation of

the perturbation-perception algorithm to detect PEM fuel cell

critical states is a novelty and a step forwards the control of the

PEM fuel cell to reach and maintain optimal performance.
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 109
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Author Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa
Title Off-the-Shelf Based System for Urban Environment Video Analytics. Type Conference Article
Year 2020 Publication The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) Abbreviated Journal
Volume 2020-July Issue 9145121 Pages (up) 459-464
Keywords Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics.
Abstract This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary

information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to

evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
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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 21578672 ISBN 978-172817539-3 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 125
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Author Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa
Title Fine-tuning deep convolutional networks for lepidopterous genus recognition Type Journal Article
Year 2017 Publication Lecture Notes in Computer Science Abbreviated Journal
Volume Vol. 10125 LNCS Issue Pages (up) pp. 467-475
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 63
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Author Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye
Title Thermal Image Super-Resolution Challenge Results – PBVS 2023 Type Conference Article
Year 2023 Publication 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 Abbreviated Journal
Volume 2023-June Issue Pages (up) 470 - 478
Keywords
Abstract
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 21607508 ISBN 979-835030249-3 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 210
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Author Rivadeneira, Rafael E.; Sappa, Angel D. and Vintimilla Boris X.
Title Thermal Image Super-Resolution: A Novel Unsupervised Approach. Type Book Chapter
Year 2022 Publication Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal BOOK
Volume 1474 Issue Pages (up) 495-506
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 179
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Author Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa
Title A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. Type Journal Article
Year 2022 Publication Journal of Manufacturing Systems Abbreviated Journal
Volume Vol. 64 Issue Pages (up) pp. 497-507
Keywords
Abstract
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 yes
Call Number cidis @ cidis @ Serial 184
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Author Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca
Title Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem Type Conference Article
Year 2020 Publication The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 Abbreviated Journal
Volume 4 Issue Pages (up) 498-505
Keywords Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters.
Abstract This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model

a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The

transfer learning consist of first training the network using pairs of images from the virtual-world scenario

considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight

of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose

estimation accuracy using the proposed model, as well as further improvements when the transfer learning

strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on

the training due to the reduced number of pairs of real-images on most of the public data sets.
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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 978-989758402-2 Medium
Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 120
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Author Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias
Title Scene representations for autonomous driving: an approach based on polygonal primitives Type Conference Article
Year 2015 Publication Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 Abbreviated Journal
Volume 417 Issue Pages (up) 503-515
Keywords Scene reconstruction, Point cloud, Autonomous vehicles
Abstract In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. 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. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Address
Corporate Author Thesis
Publisher Springer International Publishing Switzerland 2016 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 Second Iberian Robotics Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 45
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L.
Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
Year 2022 Publication VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. Abbreviated Journal
Volume 252 Issue Pages (up) 503-514
Keywords time series, deep learning, recurrent networks, sensor data, IoT.
Abstract IoT (Internet of Things) and AI (Artificial Intelligence) are becoming

support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity

recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising 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 cidis @ cidis @ Serial 152
Permanent link to this record