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Author Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. pdf  url
openurl 
  Title Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. Type Conference Article
  Year 2022 Publication Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. Abbreviated Journal CONFERENCE  
  Volume 2022-June Issue Pages 417-425  
  Keywords  
  Abstract This paper details the results and main findings of the

second iteration of the Multi-modal Aerial View Object

Classification (MAVOC) challenge. This year’s MAVOC

challenge is the second iteration. The primary goal of

both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input

modalities. Teams are encouraged/challenged to develop

multi-modal approaches that incorporate complementary

information from both domains. While the 2021 challenge

showed a proof of concept that both modalities could be

used together, the 2022 challenge focuses on the detailed

multi-modal models. Using the same UNIfied COincident

Optical and Radar for recognitioN (UNICORN) dataset and

competition format that was used in 2021. Specifically, the

challenge focuses on two techniques, (1) SAR classification

and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods

and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For

SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement

from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average

improvement over 2021.
 
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  Call Number cidis @ cidis @ Serial 177  
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Author Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo pdf  url
doi  openurl
  Title Monocular visual odometry: a cross-spectral image fusion based approach Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume Vol. 86 Issue Pages pp. 26-36  
  Keywords Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion  
  Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.  
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  Call Number cidis @ cidis @ Serial 54  
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Author Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho url  openurl
  Title Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework Type Conference Article
  Year 2022 Publication Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 Abbreviated Journal  
  Volume 256 Issue Pages 119-132  
  Keywords  
  Abstract In gamification, the development of “visual metrics for educational

video games linked to social assistance robots in the framework of inclusive education” seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games

through established metrics, which allow monitoring the user's facial expressions

during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly

adaptable for inclusive early childhood education.
 
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  Call Number cidis @ cidis @ Serial 180  
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. url  openurl
  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 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.
 
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  Call Number cidis @ cidis @ Serial 152  
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Author Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. url  openurl
  Title Social media influence: a comprehensive review in general and in tourism domain Type Journal Article
  Year 2020 Publication Smart Innovation, Systems and Technologies. Abbreviated Journal  
  Volume 171, 2020 Issue Pages 25-35  
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  Call Number cidis @ cidis @ Serial 190  
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Author Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos url  openurl
  Title Deep Learning based Shrimp Classification Type Conference Article
  Year 2022 Publication 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) Abbreviated Journal  
  Volume 13598 LNCS Issue Pages 36-45  
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  Call Number cidis @ cidis @ Serial 194  
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Author Roberto Jacome Galarza. url  openurl
  Title Multimodal deep learning for crop yield prediction. Type Conference Article
  Year 2022 Publication Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. Abbreviated Journal  
  Volume 1647 Issue Communicationsin Computer and Infor Pages 106-117  
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  Call Number cidis @ cidis @ Serial 193  
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Author Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. url  openurl
  Title Video Analytics in Urban Environments: Challenges and Approaches. Type Book Chapter
  Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK  
  Volume 224 Issue Pages 101-122  
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  Call Number cidis @ cidis @ Serial 196  
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. url  openurl
  Title Human Body Pose Estimation in Multi-view Environments. Type Book Chapter
  Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK  
  Volume 224 Issue Pages 79-99  
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  Call Number cidis @ cidis @ Serial 197  
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Author Velez R., Paredes A., Silva S., Paillacho D., and Paillacho J. url  openurl
  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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