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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 (up) 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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Call Number cidis @ cidis @ Serial 154
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Author Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla
Title An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System Type Conference Article
Year 2017 Publication (up) 2017 International Conference on Deep Learning Technologies (ICDLT 2017) Abbreviated Journal
Volume Part F128535 Issue Pages 60-65
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Call Number cidis @ cidis @ Serial 56
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title Sensor Fault Detection and Diagnosis for autonomous vehicles Type Conference Article
Year 2015 Publication (up) 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 Abbreviated Journal
Volume 30 Issue MATEC Web of Conferences Pages 1-6
Keywords
Abstract In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.
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Publisher EDP Sciences Place of Publication Editor
Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 42
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Author Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title A Fault Tolerant Perception system for autonomous vehicles Type Conference Article
Year 2016 Publication (up) 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu Abbreviated Journal
Volume Issue Pages 1-6
Keywords Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture
Abstract Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Call Number cidis @ cidis @ Serial 52
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Author Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe
Title A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning Type Conference Article
Year 2024 Publication (up) 3rd International Conference on Geospatial Information Sciences, iGISc 2023 Ciudad de México 14-17 November 2023 Abbreviated Journal
Volume Issue Pages 147 - 158
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Call Number cidis @ cidis @ Serial 228
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Author Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa
Title Detection of utility poles from noisy Point Cloud Data in Urban environments. Type Conference Article
Year 2018 Publication (up) Artificial Intelligence and Cloud Computing Conference (AICCC 2018) Abbreviated Journal
Volume Issue Pages 53-57
Keywords
Abstract In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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Call Number gtsi @ user @ Serial 94
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Author Pabelco Zambrano, Fernanda Calderon, Héctor Villegas, Jonathan Paillacho, Doménica Pazmiño, Miguel Realpe
Title UAV Remote Sensing applications and current trends in crop monitoring and diagnostics: A Systematic Literature Review Type Conference Article
Year 2023 Publication (up) IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, 4-7 julio 2023 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 214
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Author Miguel Realpe; Boris X. Vintimilla; L. Vlacic
Title Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. Type Conference Article
Year 2015 Publication (up) IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. Abbreviated Journal
Volume Issue Pages 253-258
Keywords
Abstract Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.
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Call Number cidis @ cidis @ Serial 37
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Author Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe
Title Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning Type Conference Article
Year 2024 Publication (up) In 9th International Congress on Information and Communication Technology ICICT 2024 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 239
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Author Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon
Title Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
Year 2019 Publication (up) International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 572-581
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Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
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Call Number gtsi @ user @ Serial 116
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