Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo
El Alzheimer es una enfermedad degenerativa la cual se presenta principalmente en personas de avanzada edad y se esta presentando cada vez mas en población de mediana edad. Esta enfermedad se presenta en todo el mundo, sin distinguir clima o estrato por lo que muchas personas que lo padecen sufren d...
- Autores:
-
Guio Rodriguez, Carlos Fernando
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/35675
- Acceso en línea:
- http://hdl.handle.net/11634/35675
- Palabra clave:
- Deep Learning
Convolutional Network
LSTM Network
Yolo
Alzheimer
Peces Cebra
Trayectoria
Aprendizaje profundo
Redes Convolucionales
Redes LSTM
Yolo
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| title |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| spellingShingle |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo Deep Learning Convolutional Network LSTM Network Yolo Alzheimer Peces Cebra Trayectoria Aprendizaje profundo Redes Convolucionales Redes LSTM Yolo |
| title_short |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| title_full |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| title_fullStr |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| title_full_unstemmed |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| title_sort |
Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo |
| dc.creator.fl_str_mv |
Guio Rodriguez, Carlos Fernando |
| dc.contributor.advisor.none.fl_str_mv |
Calderón Chavez, Juan Manuel |
| dc.contributor.author.none.fl_str_mv |
Guio Rodriguez, Carlos Fernando |
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https://orcid.org/0000-0002-4471-3980 |
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Universidad Santo Tomás |
| dc.subject.keyword.spa.fl_str_mv |
Deep Learning Convolutional Network LSTM Network Yolo |
| topic |
Deep Learning Convolutional Network LSTM Network Yolo Alzheimer Peces Cebra Trayectoria Aprendizaje profundo Redes Convolucionales Redes LSTM Yolo |
| dc.subject.lemb.spa.fl_str_mv |
Alzheimer Peces Cebra Trayectoria |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje profundo Redes Convolucionales Redes LSTM Yolo |
| description |
El Alzheimer es una enfermedad degenerativa la cual se presenta principalmente en personas de avanzada edad y se esta presentando cada vez mas en población de mediana edad. Esta enfermedad se presenta en todo el mundo, sin distinguir clima o estrato por lo que muchas personas que lo padecen sufren de condiciones de vida lamentable o abandono. Actualmente no se cuenta con un tratamiento definitivo para curar esta enfermedad. En la investigación para la búsqueda de un tratamiento o una cura, los investigadores están usando a los peces cebra como modelo animal ya que cuentan con una gran similitud neurobiológica con los seres humanos al momento de padecer el Alzheimer. Por ello, se propone un sistema de reconocimiento visual basado en aprendizaje profundo para entregar la trayectoria recorrida por los peces cebra la cual ayudara a los investigadores en sus diferentes estudios. |
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2021 |
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2021-09-23T12:25:01Z |
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2021-09-23T12:25:01Z |
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2021-09-22 |
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Trabajo de grado |
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Guio Rodriguez. (2021). Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo [Tesis de pregrado], Universidad Santo Tomas sede Bogotá. |
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http://hdl.handle.net/11634/35675 |
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Guio Rodriguez. (2021). Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo [Tesis de pregrado], Universidad Santo Tomas sede Bogotá. reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co |
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G. Audira, B. P. Sampurna, S. Juniardi, S.-T. Liang, Y.-H. Lai, and C.-D. Hsiao, “A simple setup to perform 3d locomotion tracking in zebrafish by using a single camera,” Inventions, Jan 2018 S. Macrì, D. Neri, T. Ruberto, V. Mwaffo, S. Butail, and M. Porfiri, “Three-dimensional scoring of zebrafish behavior unveils biological phenomena hidden by two-dimensional analyses,” Scientific Reports, May 2017 A. L. Ribinstein, “Zebrafish: from disease modeling to drug discovery,” Drug Discovery & Development, pp. 218–223, Mar 2003, pMID:12669457 “Intro a las redes neuronales convolucionales.” [Online]. Available: https://bootcampai.medium.com/ redes-neuronales-convolucionales-5e0ce960caf8 “Redes neuronales convolucionales.” [Online]. Available: https://developer.ibm.com/es/technologies/artificial-intelligence/ articles/cc-convolutional-neural-network-vision-recognition/ “SKRWT: herramienta para la corrección trapezoidal.” [Online]. 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Available: https://www.alzint.org/u/ WorldAlzheimerReport2019-Spanish-Summary.pdf M. C. Fishman, “Zebrafish–the canonical vertebrate,” Science, vol. 294, no. 5545, pp. 1290–1291, 2001. M. de Jong and T. Maina, “Of mice and humans: Are they the same?—implications in cancer translational research,” Journal of Nuclear Medicine, vol. 51, no. 4, pp. 501–504, 2010. E. M. Caramillo and D. J. Echevarria, “Alzheimer’s disease in the zebrafish: where can we take it?” Behavioural pharmacology, 2017. H. Li, Y. Li, and F. Porikli, “Deeptrack: Learning discriminative feature representations online for robust visual tracking,” IEEE Transactions on Image Processing, vol. 25, no. 4, p. 1834–1848, Apr 2016. [Online]. Available: http://dx.doi.org/10.1109/TIP.2015.2510583 N. Wang and D.-Y. Yeung, “Learning a deep compact image representation for visual tracking,” in Advances in Neural Information Processing Systems, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds., vol. 26. Curran Associates, Inc., 2013. [Online]. Available: https://proceedings.neurips. cc/paper/2013/file/dc6a6489640ca02b0d42dabeb8e46bb7-Paper.pdf A. Graves, “Generating sequences with recurrent neural networks,” 2014. A. F. Reyes, E. C. Camacho, M. Armando, and J. M. Calderón, “Lstm based brain-machine interface tool for text generation through eyes blinking detection,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2021, pp. 1–6. Y. Zhang, M. Pezeshki, P. Brakel, S. Zhang, C. L. Y. Bengio, and A. Courville, “Towards end-to-end speech recognition with deep convolutional neural networks,” 2017. F. Altché and A. de La Fortelle, “An lstm network for highway trajectory prediction,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 353–359. A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2016/html/ Alahi_Social_LSTM_Human_CVPR_2016_paper.html Z. XU and X. E. Cheng, “Zebrafish tracking using convolutional neural networks,” Scientific Reports, vol. 7, 2017. P. R. Martineau and P. Mourrain, “Tracking zebrafish larvae in group – status and perspectives,” Methods, vol. 62, no. 3, pp. 292–303, 2013, zebrafish Methods. C. Quintero, S. Rodríguez, K. Pérez, J. López, E. Rojas, and J. Calderón, “Learning soccer drills for the small size league of robocup,” in Robot Soccer World Cup. Springer, 2014, pp. 395–406. S. Rodríguez, E. Rojas, K. Pérez, J. López, C. Quintero, and J. Calderón, “Fast path planning algorithm for the robocup small size league,” in Robot Soccer World Cup. Springer, 2014, pp. 407–418. G. A. Cardona, W. Moreno, A. Weitzenfeld, and J. M. Calderon, “Reduction of impact force in falling robots using variable stiffness,” in SoutheastCon 2016. IEEE, 2016, pp. 1–6. E. Elibol, J. Calderon, M. Llofriu, C. Quintero, W. Moreno, and A. Weitzenfeld, “Power usage reduction of humanoid standing process using q-learning,” in Robot Soccer World Cup. Springer, 2015, pp. 251–263. E. Elibol, J. Calderon, M. Llofriu, W. Moreno, and A. Weitzenfeld, “Analyzing and reducing energy usage in a humanoid robot during standing up and sitting down tasks,” International Journal of Humanoid Robotics, vol. 13, no. 04, p. 1650014, 2016. J. Calderon, G. A. Cardona, M. Llofriu, M. Shamsi, F. Williams, W. Moreno, and A. Weitzenfeld, “Impact force reduction using variable stiffness with an optimal approach for falling robots,” in Robot World Cup. Springer, 2016, pp. 404–415. J. M. Calderon, E. R. Rojas, S. Rodriguez, H. R. Baez, and J. A. Lopez, “A robot soccer team as a strategy to develop educational iniciatives,” in Latin American and Caribbean Conference for Engineering and Technology, Panama City, Panama, 2012. J.G.G.Marin,G.C.O.Díaz,A.F.T.Rodríguez,andÉ.C.C.Poveda, “Entorno pedagógico para la enseñanza en básica primaria mediante el uso de sistema robótico comercial,” Ingeniería, vol. 26, no. 1, 2021. H. Báez, K. Perez, E. Rojas, S. Rodriguez, J. López, C. Quintero, and J. M. Calderón, “Application of an educational strategy based on a soccer robotic platform,” in 2013 16th International Conference on Advanced Robotics (ICAR). IEEE, 2013, pp. 1–6. C. Higuera, F. Lozano, E. C. Camacho, and C. H. Higuera, “Multiagent reinforcement learning applied to traffic light signal control,” in International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 2019, pp. 115–126. G. A. Cardona and J. M. Calderon, “Robot swarm navigation and victim detection using rendezvous consensus in search and rescue operations,” Applied Sciences, vol. 9, no. 8, p. 1702, 2019. G. A. Cardona, J. Ramirez-Rugeles, E. Mojica-Nava, and J. M. Calderon, “Visual victim detection and quadrotor-swarm coordination control in search and rescue environment.” International Journal of Electrical & Computer Engineering (2088-8708), vol. 11, no. 3, 2021. J. León, G. A. Cardona, A. Botello, and J. M. Calderón, “Robot swarms theory applicable to seek and rescue operation,” in International Conference on Intelligent Systems Design and Applications. Springer, 2016, pp. 1061–1070. W. O. Quesada, J. I. Rodriguez, J. C. Murillo, G. A. Cardona, D. Yanguas-Rojas, L. G. Jaimes, and J. M. Calderón, “Leader-follower formation for uav robot swarm based on fuzzy logic theory,” in International Conference on Artificial Intelligence and Soft Computing. Springer, 2018, pp. 740–751. J. Calderon, A. Obando, and D. Jaimes, “Road detection algorithm for an autonomous ugv based on monocular vision,” in Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007). IEEE, 2007, pp. 253–259. G. Cardona, C. Bravo, W. Quesada, D. Ruiz, M. Obeng, X. Wu, and J. Calderon, “Autonomous navigation for exploration of unknown environments and collision avoidance in mobile robots using reinforcement learning,” in 2019 SoutheastCon. IEEE, 2019, pp. 1–7. S. Amaya and A. Mateus, “Tasks allocation for rescue robotics: a replicator dynamics approach,” in International Conference on Artificial Intelligence and Soft Computing. Springer, 2019, pp. 609– 621. Y. Suarez, C. Higuera, and E. C. Camacho, “Inverse reinforcement learning application for discrete and continuous environments,” in International Conference on Advanced Engineering Theory and Applications. Springer, 2019, pp. 345–355. M. Newman, E. Ebrahimie, and M. Lardelli, “Using the zebrafish model for alzheimer’s disease research,” Frontiers in Genetics, vol. 5, p. 189, 2014. L. Bleiler and T. William, “2012 alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 8, no. 2, pp. 131–168, 2012. “ALZHEIMER UN PROBLEMA DE SALUD PÚBLICA EN COLOMBIA.” [Online]. Available: https://www.icesi.edu.co/unicesi/todas-las-noticias/ 2241-alzheimer-un-problema-de-salud-publica-en-colombia. A. Daisy and W. Marc, “The Global Economic Impact of Dementia,” Alzheimer’s Association, Jun 2010. [Online]. Available: https://www.alzint.org/resource/world-alzheimer-report-2010/ J. McCarthy, C. Twomey, and P. Wujek, “Presenilin-dependent regulated intramembrane proteolysis and γ-secretase activity,” Cellular and Molecular Life Sciences, vol. 66, pp. 1534–1555, 2009. K. Blennow, M. J. de Leon, and H. Zettenberg, “Alzheimer’s disease,” The Lancet, vol. 368, pp. 387–403, Jul 2006. U. Leimer, K. Lun, H. Romig, J. Walter, J. Grünberg, M. Brand, and C. Haass, “Zebrafish (danio rerio) presenilin promotes aberrant amyloid β-peptide production and requires a critical aspartate residue for its function in amyloidogenesis,” Biochemistry, vol. 38, no. 41, pp. 13 602– 13 609, 1999, pMID: 10521267. C. Groth, S. Nornes, R. McCarty, R. Tamme, and M. Lardelli, “Identification of a second presenilin gene in zebrafish with similarity to the human alzheimer’s disease gene presenilin2,” Dev. Genes Evol, pp. 486–490, Nov 2002. W. Y. Hwang, Y. Fu, D. Reyon, M. L. Maeder, S. Q. Tsai, J. D. Sander, R. T. Peterson, J.-R. J. Yeh, and J. K. Joung, “Efficient genome editing in zebrafish using a crispr-cas system,” nature biotechnology, vol. 31, pp. 227—-229, Jan 2013. “Frame rate.” [Online]. Available: https://developer.mozilla.org/es/ docs/Glossary/FPS “Pixel.” [Online]. Available: http://aulainformatica.eu/datos/ dise~no_grafico/gimp/capitulo2/Teoria2.pdf Medlineplus, “Conducción nerviosa.” [Online]. Available: https: //medlineplus.gov/spanish/ency/anatomyvideos/000089.htm Conceptos Tecnológica [Online]. Available: https://www.frro.utn.edu.ar/repositorio/catedras/quimica/ 5_anio/orientadora1/monograias/matich-redesneuronales.pdf E. C. A. Tepán, “Estudio de los principales tipos de redes neuronales y las herramientas para su aplicaciÓn,” UNIVERSIDAD POLITÉCNICA SALESIANA SEDE CUENCA, 2013. [Online]. Available: https://dspace.ups.edu.ec/bitstream/123456789/4098/1/ UPS-CT002584.pdf E. G. Sánchez, “Introducción a las redes neuronales de convolución. aplicación a la visión por ordenador,” Universidad de Zaragoza, 2019. [Online]. Available: https://core.ac.uk/download/pdf/290002463.pdf “Convolutional visualization.” [Online]. Available: https://www.cs. ryerson.ca/~aharley/vis/conv/flat.html |
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Calderón Chavez, Juan ManuelGuio Rodriguez, Carlos Fernandohttps://orcid.org/0000-0002-4471-3980https://scholar.google.com/citations?user=095RddUAAAAJ&hl=enhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000380938Universidad Santo Tomás2021-09-23T12:25:01Z2021-09-23T12:25:01Z2021-09-22Guio Rodriguez. (2021). Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundo [Tesis de pregrado], Universidad Santo Tomas sede Bogotá.http://hdl.handle.net/11634/35675reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEl Alzheimer es una enfermedad degenerativa la cual se presenta principalmente en personas de avanzada edad y se esta presentando cada vez mas en población de mediana edad. Esta enfermedad se presenta en todo el mundo, sin distinguir clima o estrato por lo que muchas personas que lo padecen sufren de condiciones de vida lamentable o abandono. Actualmente no se cuenta con un tratamiento definitivo para curar esta enfermedad. En la investigación para la búsqueda de un tratamiento o una cura, los investigadores están usando a los peces cebra como modelo animal ya que cuentan con una gran similitud neurobiológica con los seres humanos al momento de padecer el Alzheimer. Por ello, se propone un sistema de reconocimiento visual basado en aprendizaje profundo para entregar la trayectoria recorrida por los peces cebra la cual ayudara a los investigadores en sus diferentes estudios.Alzheimer is a degenerative disease which occurs mainly in elderly people and is increasingly occurring in the middle-aged population. This disease occurs all over the world, without distinguishing climate or stratum, so many people who suffer it suffer from unfortunate living conditions or abandonment. Currently there is no definitive treatment to cure this disease or a cure. Researchers are using zebrafish as an animal model as they have a strong neurobiological similarity to humans when suffering Alzheimer. Therefore, a visual recognition system based on deep learning is proposed to deliver the trajectory traveled by zebrafish, which will help researchers in their different studies.Ingeniero Electronicohttp://unidadinvestigacion.usta.edu.coPregradoapplication/pdfspaUniversidad Santo TomásPregrado Ingeniería ElectrónicaFacultad de Ingeniería ElectrónicaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Desarrollo de un sistema de seguimiento de la navegación de peces cebra en 3 dimensiones usando aprendizaje profundoDeep LearningConvolutional NetworkLSTM NetworkYoloAlzheimerPeces CebraTrayectoriaAprendizaje profundoRedes ConvolucionalesRedes LSTMYoloTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionFormación de Recurso Humano para la Ctel: Trabajo de grado de Pregradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA BogotáG. Audira, B. P. Sampurna, S. Juniardi, S.-T. Liang, Y.-H. Lai, and C.-D. Hsiao, “A simple setup to perform 3d locomotion tracking in zebrafish by using a single camera,” Inventions, Jan 2018S. Macrì, D. Neri, T. Ruberto, V. Mwaffo, S. Butail, and M. Porfiri, “Three-dimensional scoring of zebrafish behavior unveils biological phenomena hidden by two-dimensional analyses,” Scientific Reports, May 2017A. L. Ribinstein, “Zebrafish: from disease modeling to drug discovery,” Drug Discovery & Development, pp. 218–223, Mar 2003, pMID:12669457“Intro a las redes neuronales convolucionales.” [Online]. Available: https://bootcampai.medium.com/ redes-neuronales-convolucionales-5e0ce960caf8“Redes neuronales convolucionales.” [Online]. Available: https://developer.ibm.com/es/technologies/artificial-intelligence/ articles/cc-convolutional-neural-network-vision-recognition/“SKRWT: herramienta para la corrección trapezoidal.” [Online]. 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