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| Author | Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa | ||||
| Title | Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy | Type | Conference Article | ||
| Year | 2021 | Publication | 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 13017 | Issue | Pages | 131-143 | |
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| Call Number | cidis @ cidis @ | Serial  |
163 | ||
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| Author | Patricia L. Suárez, Dario Carpio, and Angel Sappa | ||||
| Title | Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. | Type | Conference Article | ||
| Year | 2021 | Publication | 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 13018 | Issue | Pages | 178-190 | |
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| Call Number | cidis @ cidis @ | Serial |
162 | ||
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| Author | Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. | ||||
| Title | Computer Vision based Food Grain Classification: a Comprehensive Survey | Type | Journal Article | ||
| Year | 2021 | Publication | In Computers and Electronics in Agriculture Journal. (Article number 106287) | Abbreviated Journal | |
| Volume | Vol. 187 | Issue | Pages | ||
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| Call Number | cidis @ cidis @ | Serial |
159 | ||
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| Author | Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. | ||||
| Title | Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends | Type | Journal Article | ||
| Year | 2021 | Publication | In Journal: Robotics and Autonomous Systems. (Article number 103832) | Abbreviated Journal | |
| Volume | Vol. 144 | Issue | Pages | ||
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| Call Number | cidis @ cidis @ | Serial |
158 | ||
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| Author | Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado | ||||
| Title | Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. | Type | Conference Article | ||
| Year | 2021 | Publication | 19th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | processing, software engineering, deep learning, intelligent vision systems, cloud computing. | ||||
| Abstract | Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems. Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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| Language | English | Summary Language | Original Title | ||
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| Call Number | cidis @ cidis @ | Serial |
154 | ||
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| Author | Rubio, G.A., Agila, W.E | ||||
| Title | A fuzzy model to manage water in polymer electrolyte membrane fuel cells | Type | Journal Article | ||
| Year | 2021 | Publication | In Processes Journal. (Article number 904) | Abbreviated Journal | |
| Volume | Vol. 9 | Issue | Issue 6 | Pages | |
| Keywords | PEM fuel cell, fuzzy, neural network, electrical response, flooding, drying. | ||||
| Abstract | In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
153 | ||
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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 | 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 | Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. | ||||
| Title | Thermal Image Super-Resolution Challenge – PBVS 2021. | Type | Conference Article | ||
| Year | 2021 | Publication | In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 | Abbreviated Journal | |
| Volume | Issue | Pages | 4354-4362 | ||
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| Call Number | cidis @ cidis @ | Serial |
151 | ||
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| Author | 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 | |
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| 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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| Call Number | cidis @ cidis @ | Serial |
150 | ||
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| Author | Mehri, A, Ardakani, P.B., Sappa, A.D. | ||||
| Title | LiNet: A Lightweight Network for Image Super Resolution | Type | Conference Article | ||
| Year | 2021 | Publication | 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 | Abbreviated Journal | |
| Volume | Issue | Pages | 7196-7202 | ||
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| Call Number | cidis @ cidis @ | Serial |
149 | ||
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