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Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
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Conference Article |
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2022 |
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VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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Vol. 252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho |
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Title |
Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework |
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Conference Article |
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2022 |
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Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 |
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256 |
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119-132 |
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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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cidis @ cidis @ |
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180 |
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Mildred Cruz; Cristhian A. Aguilera; Boris X. Vintimilla; Ricardo Toledo; Ángel D. Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
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2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
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331 |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Computer Vision Center |
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Barcelona, Spain |
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English |
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English |
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cidis @ cidis @ |
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35 |
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Author |
Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa |
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Title |
A visible-Thermal Fusion based Monocular Visual Odometry |
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Conference Article |
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Year |
2015 |
Publication |
Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 |
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417 |
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517-528 |
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Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion |
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The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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English |
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no |
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cidis @ cidis @ |
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44 |
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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
Type |
Conference Article |
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Year |
2015 |
Publication |
Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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Volume |
417 |
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503-515 |
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Keywords |
Scene reconstruction, Point cloud, Autonomous vehicles |
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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. |
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Springer International Publishing Switzerland 2016 |
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English |
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Second Iberian Robotics Conference |
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cidis @ cidis @ |
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45 |
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Author |
Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa |
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Title |
Fruit Grading based on Deep Learning and Active Vision System |
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Conference Article |
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2024 |
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2nd International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development, CIIA 2024 Guayaquil 28 – 30 May 2024 |
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Vol. 532 |
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25550403 |
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cidis @ cidis @ |
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241 |
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Henry Velesaca Lara, Juan Antonio Holgado & José Miguel Gutiérrez |
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Title |
Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA |
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Conference Article |
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2024 |
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Second International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development (CIIA 2024) Guayaquil 28 – 30 May 2024 |
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Vol. 532 |
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25550403 |
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cidis @ cidis @ |
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242 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel D. Sappa |
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Title |
Enhancement of Guided Thermal Image Super-Resolution Approaches |
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2024 |
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Neurocomputing |
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573 |
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Neurocomputing |
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cidis @ cidis @ |
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247 |
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Author |
Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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619 |
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155-163 |
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Call Number |
cidis @ cidis @ |
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59 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
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Conference Article |
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2018 |
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International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
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721 |
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This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation. |
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gtsi @ user @ |
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