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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Cross-spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
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1-5 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 57 | ||
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| Author | Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
| Year | 2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
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1-6 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 60 | ||
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| Author | Dennis G. Romero; Roberto Yoncon; Angel Guale; Bonny Bayot; Fanny Panchana | ||||
| Title | Evaluación de técnicas de clasificación orientadas a la identificación automática de órganos del camarón a partir de imágenes histológicas | Type | Conference Article | ||
| Year | 2017 | Publication | 15th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
| Volume | 2017-July | Issue | Pages |
1-6 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 61 | ||
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| Author | Marjorie Chalen; Boris X. Vintimilla | ||||
| Title | Towards Action Prediction Applying Deep Learning | Type | Journal Article | ||
| Year | 2019 | Publication | Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 | Abbreviated Journal | |
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pp. 1-3 | ||
| Keywords | action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. | ||||
| Abstract | Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 129 | ||
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| Author | Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa | ||||
| Title | PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador | Abbreviated Journal | |
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1-6 | ||
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| Abstract | This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 112 | ||
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| Author | W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano | ||||
| Title | Open Control Architecture for the Characterization and Control of the PEM Fuel Cell | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador | Abbreviated Journal | |
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1-5 | ||
| Keywords | PEM fuel cell, Experimental System, Control Engineering. | ||||
| Abstract | Proton exchange membrane (PEM) fuel cells, are an efficient and clean source of electrical energy. The analysis of its operation requires experimental work, which allows measuring, modeling and optimizing PEM fuel cells electrical behavior under different operating conditions. Therefore, having an experimentation platform that allows to easily carry out its study and control is essential. This research presents the design and development of an open instrumental system that allows measuring, controlling and determining the operating parameters of a PEM fuel cell. As results, the polarization curves, voltage-current, obtained by the system itself in different experimental conditions are shown. These curves are a very useful tool to evaluate the electrical behavior of the PEM battery. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 118 | ||
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| Author | Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa | ||||
| Title | Fast CNN Stereo Depth Estimation through Embedded GPU Devices | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors 2020 | Abbreviated Journal | |
| Volume | Vol. 2020-June | Issue | 11 | Pages |
pp. 1-13 |
| Keywords | stereo matching; deep learning; embedded GPU | ||||
| Abstract | Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. |
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| Language | English | Summary Language | English | Original Title | |
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| ISSN | 14248220 | ISBN | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 132 | ||
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| Author | Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez | ||||
| Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. | Type | Journal Article | ||
| Year | 2020 | Publication | Abbreviated Journal | In Sensors | |
| Volume | Vol. 2020-August | Issue | 16 | Pages |
pp. 1-23 |
| Keywords | object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities | ||||
| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images o ers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the diculty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with di erent types of semantic segmentation networks and using the same evaluation metrics is also included. |
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| Language | English | Summary Language | English | Original Title | |
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| ISSN | ISBN | 14248220 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 133 | ||
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| Author | Rafael Rivadeneira, Henry Velesaca & Angel Sappa | ||||
| Title | Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset | Type | Conference Article | ||
| Year | 2024 | Publication | Lecture Notes in Networks and Systems: 4th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) | Abbreviated Journal | |
| Volume | Vol. 1117 LNNS | Issue | Pages |
1-12 | |
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| ISSN | 23673370 | ISBN | 978-981976991-9 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 237 | ||
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| Author | Luis Chuquimarca, Boris Vintimilla & Sergio Velastin | ||||
| Title | A Review of External Quality Inspection for Fruit Grading using CNN Models | Type | Journal | ||
| Year | 2024 | Publication | Artificial Intelligence in Agriculture | Abbreviated Journal | |
| Volume | Vol. 14 | Issue | Pages |
1-20 | |
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| ISSN | 25897217 | ISBN | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 254 | ||
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