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Author (up) Jácome Galarza, Luis Roberto
Title Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors Type Conference Article
Year 2024 Publication 19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 246
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Author (up) Jacome-Galarza L.-R
Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
Year 2021 Publication 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 Abbreviated Journal
Volume Issue Pages
Keywords Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.
Abstract La agricultura de precisión es una práctica vital para

mejorar la producción de cosechas. El presente trabajo tiene

como objetivo desarrollar un modelo multimodal de aprendizaje

profundo que es capaz de producir un mapa de salud de

cosechas. El modelo recibe como entradas imágenes multiespectrales

y datos de sensores de campo (humedad,

temperatura, estado del suelo, etc.) y crea un mapa de

rendimiento de la cosecha. La utilización de datos multimodales

tiene como finalidad extraer patrones ocultos del estado de salud

de las cosechas y de esta manera obtener mejores resultados que

los obtenidos mediante los índices de vegetación.
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Language Español Summary Language Original Title
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Notes Approved no
Call Number cidis @ cidis @ Serial 150
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Author (up) 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 Vol. 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 (up) Jorge Alvarez Tello; Mireya Zapata; Dennys Paillacho
Title Kinematic optimization of a robot head movements for the evaluation of human-robot interaction in social robotics. Type Conference Article
Year 2019 Publication 10th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2019), Washington D.C.; United States. Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 975 Issue Pages 108-118
Keywords
Abstract This paper presents the simplification of the head movements from

the analysis of the biomechanical parameters of the head and neck at the

mechanical and structural level through CAD modeling and construction with

additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the

denominated MASHI (Multipurpose Assistant robot for Social Human-robot

Interaction) experimental robotic telepresence platform, implemented by a

display with a fish-eye camera along with the mechanical mechanism, which

permits 4 degrees of freedom (DoF). In the development of mathematicalmechanical modeling for the kinematics codification that governs the robot and

the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and

the combination of all of them to establish an active communication through

telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement.
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Call Number gtsi @ user @ Serial 108
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Author (up) Jorge Alvarez; Mireya Zapata; Dennys Paillacho
Title Mechanical Design of a spatial mechanism for the robot head movements in social robotics for the evaluation of Human-Robot Interaction. Type Conference Article
Year 2019 Publication 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2019); Munich, Alemania Abbreviated Journal
Volume 1026 Issue Pages 160-165
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Notes Approved no
Call Number gtsi @ user @ Serial 104
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Author (up) Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla
Title Human Pose Estimation through A Novel Multi-View Scheme Type Conference Article
Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal
Volume 5 Issue Pages 855-862
Keywords Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach
Abstract This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human

pose estimation problem. The proposed approach first obtains the human body joints of a set of images,

which are captured from different views at the same time. Then, it enhances the obtained joints by using a

multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from

another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed

for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and

comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements

in the accuracy of body joints estimations.
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Notes Approved yes
Call Number cidis @ cidis @ Serial 169
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Author (up) Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca.
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 (up) 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 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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ISSN ISBN 978-989758402-2 Medium
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Notes Approved no
Call Number gtsi @ user @ Serial 120
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Author (up) Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa
Title Deep learning based camera pose estimation in multi-view environment. Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal
Volume Issue Pages 224-228
Keywords
Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
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Call Number gtsi @ user @ Serial 93
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Author (up) José Reyes; Axel Godoy; Miguel Realpe.
Title Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. Type Conference Article
Year 2019 Publication International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica Abbreviated Journal
Volume 2019-July Issue Pages
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Abstract Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.
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Notes Approved no
Call Number gtsi @ user @ Serial 102
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