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Author  |
Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa |
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Title |
Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy |
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Conference Article |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13017 |
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131-143 |
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cidis @ cidis @ |
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163 |
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Author |
Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. |
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Title |
Video Analytics in Urban Environments: Challenges and Approaches. |
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Year |
2022 |
Publication |
ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series |
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BOOK |
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224 |
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101-122 |
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cidis @ cidis @ |
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196 |
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Author |
Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa |
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Title |
Off-the-Shelf Based System for Urban Environment Video Analytics. |
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Conference Article |
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Year |
2020 |
Publication |
The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) |
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2020-July |
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9145121 |
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459-464 |
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Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. |
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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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21578672 |
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978-172817539-3 |
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cidis @ cidis @ |
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125 |
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Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. |
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Title |
Deep Learning based Corn Kernel Classification. |
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Conference Article |
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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) |
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2020-June |
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9150684 |
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294-302 |
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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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21607508 |
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978-172819360-1 |
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no |
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cidis @ cidis @ |
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124 |
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Author |
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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Year |
2024 |
Publication |
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 |
Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa |
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Title |
Fruit Grading based on Deep Learning and Active Vision System |
Type |
Conference Article |
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Year |
2024 |
Publication |
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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no |
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cidis @ cidis @ |
Serial |
241 |
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Author |
Henry Velesaca, Boris Vintimilla, Jorge Vulgarin, Coen Antens & Alberto Rubio Pérez |
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Title |
Deep Learning-based Multimodal Sensing Framework for AntiSpoofing Systems |
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Journal Article |
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Year |
2024 |
Publication |
Lecture Notes in Networks and Systems: 4th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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Vol. 1116 LNNS |
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39-54 |
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ISSN |
23673370 |
ISBN |
978-981976994-0 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
238 |
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Author |
Jácome Galarza, Luis Roberto |
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Title |
Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors |
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Conference Article |
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2024 |
Publication |
19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 |
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Call Number |
cidis @ cidis @ |
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246 |
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Author |
Jacome-Galarza L.-R |
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Title |
Crop yield prediction utilizing multimodal deep learning |
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Conference Article |
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2021 |
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16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
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Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
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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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Español |
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Call Number |
cidis @ cidis @ |
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150 |
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Author |
Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Title |
Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
Type |
Conference Article |
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Year |
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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Keywords |
time series, deep learning, recurrent networks, sensor data, IoT. |
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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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cidis @ cidis @ |
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152 |
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