Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos

ilustraciones, graficas, tablas

Autores:: Agudelo Villalobos, Leandro Esneyder

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
dc.title.translated.eng.fl_str_mv	Automatic characterization of echolocation signals of fishing bats in Villavicencio - Meta for the analysis and support of biodiversity research at the Universidad de los Llanos
title	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
spellingShingle	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos 620 - Ingeniería y operaciones afines Ecolocación Ecolocalización Echolocation Animal echolocation Agrupamiento Análisis de Señales Aprendizaje Automático Ecolocalización de murciélagos Forrajeo Procesamiento Digital de Señales Señales de Ultrasonido Clustering Signal Analysis Machine Learning Bat Echolocation Foraging Digital Signal Processing Ultrasound Signals
title_short	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
title_full	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
title_fullStr	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
title_full_unstemmed	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
title_sort	Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos
dc.creator.fl_str_mv	Agudelo Villalobos, Leandro Esneyder
dc.contributor.advisor.none.fl_str_mv	Cruz Roa, Ángel Alfonso González Osorio, Fabio Augusto
dc.contributor.author.none.fl_str_mv	Agudelo Villalobos, Leandro Esneyder
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines
topic	620 - Ingeniería y operaciones afines Ecolocación Ecolocalización Echolocation Animal echolocation Agrupamiento Análisis de Señales Aprendizaje Automático Ecolocalización de murciélagos Forrajeo Procesamiento Digital de Señales Señales de Ultrasonido Clustering Signal Analysis Machine Learning Bat Echolocation Foraging Digital Signal Processing Ultrasound Signals
dc.subject.other.spa.fl_str_mv	Ecolocación Ecolocalización
dc.subject.other.eng.fl_str_mv	Echolocation Animal echolocation
dc.subject.proposal.spa.fl_str_mv	Agrupamiento Análisis de Señales Aprendizaje Automático Ecolocalización de murciélagos Forrajeo Procesamiento Digital de Señales Señales de Ultrasonido
dc.subject.proposal.eng.fl_str_mv	Clustering Signal Analysis Machine Learning Bat Echolocation Foraging Digital Signal Processing Ultrasound Signals
description	ilustraciones, graficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-08T20:00:46Z
dc.date.available.none.fl_str_mv	2022-06-08T20:00:46Z
dc.date.issued.none.fl_str_mv	2022-06-07
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81540
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81540 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abhay Padda. (2016). Introduction to Random Forest. Retrieved from https://analyticsdefined.com/introduction-random-forests/ Agranat, I. (2013). Bat species identification from zero crossing and full spectrum echolocation calls using Hidden Markov Models, Fisher scores, unsupervised clustering and balanced winnow pairwise classifiers. Acoustical Society of America, 19(1), 00–16. https://doi.org/10.1121/1.4799403 Ahlén, I., & Baagoe, H. J. (1999). Use of ultrasound detectors for bat studies in Europe: experiences from field identification, surveys, and monitoring. Acta Chiropterologica, 1(2), 137–150. Altringham, J. (1996). Echolocation. In J. D. Altringham (Ed.), Bats - Biology and Behaviour (pp. 79–113). New York: Oxford University Press. Angélica Yantén. (2020). Efecto Del Ruido Urbano Sobre El Forrajeo Del Murciélago Pescador Menor, Noctilio Albiventris (Chiroptera: Noctilionidae). 1–74. Armitage, D. W., \& Ober, H. K. (2010). A comparison of supervised learning techniques in the classification of bat echolocation calls. Ecological Informatics. 5(6), 465–473. https://doi.org/https://doi.org/10.1016/j.ecoinf.2010.08.001 Armitage, David W, & Ober, H. K. (2010). Ecological Informatics A comparison of supervised learning techniques in the classi fi cation of bat echolocation calls. Ecological Informatics, 5(6), 465–473. https://doi.org/10.1016/j.ecoinf.2010.08.001 Bao, L., & Cui, Y. (2005). Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics, 21(10), 2185–2190. https://doi.org/10.1093/bioinformatics/bti365 Behrend, O. & Schuller, G. (2000). The central acoustic tract and audio-vocal coupling in the horseshoe bat, Rhinolophus rouxi. European Journal of Neuroscience, 12, 4268-4280. Biscardi, S., Oprecio, J., Fenton, M.B., Tsoar, A., Ratcliffe, J. M. (2004). 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Bats. https://doi.org/10.1007/978-3-319-66538-2 Existence., E. of. (2018). EDGE of Existence : Evolutionarily Distinct & Globally Endangered. Fenton. (1974). The role echolocation in the evolution of bats. Am Nat, 108, 386–388. Fenton, M. B. (1994). Echolocation: its impact on the behaviour and ecology of bats. Ecoscience, 1(1), 21–30. https://doi.org/10.1080/11956860.1994.11682224 Fonseca Guerrero, J. M. (2016). Murciélagos: los mamíferos voladores. https://doi.org/ISSN 2529-895X Fraser, E. E. (2018). Manual analysis of recorded bat echolocation calls: summary, synthesis, and proposal for increased standardization in training practices. Canadian Journal of Zoology, 96(6), 505–512. https://doi.org/10.1139/cjz-2017-0175 Galindo González. (1998). Dispersión de semillas por murciélagos: Su importancia en la conservación y regeneración del bosque tropical. Acta Zoológica Mexicana (Nueva Serie), 73. Gaston, K. J., & O’Neill, M. A. (2004). Automated species identification: why not? 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Expert Systems with Applications, 41(11), 5451–5465. https://doi.org/10.1016/j.eswa.2014.02.021 I. Zualkernan, J. Judas, T. Mahbub, A. Bhagwagar, P. C. (2020). A Timy CNN Architecture form Identifying Bat Species from Echolocation Calls. 1–23. J. G. Proakis, D. G. Manolakis. (2007). Digital Signal Processing. Prentice Hall, New Jersy,. Jung, K., Kalko, E. K. V., & von Helversen, O. (2007). Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation. Journal of Zoology, 272(2), 125–137. https://doi.org/10.1111/j.1469-7998.2006.00250.x Kalko, E. K. V., & Schnitzler, H. U. (1998). How echolocating bats approach and acquire food. In T. H. Kunz & P. A. Racey (Eds.), Bat biology and conservation (pp. 197–204). Washington, DC: Smithsonian Institution Press. Kalko, E. K. V., Schnitzler, H.-U., Kaipf, I., & Grinnell, A. D. (1998). Echolocation and foraging behavior of the lesser bulldog bat, Noctilio albiventris: preadaptations for piscivory? Behavioral Ecology and Sociobiology, 42(5), 305–319. https://doi.org/10.1007/s002650050443 Kalko, K. V. (1995). Insect pursuit, prey capture and echolocation in pipestirelle bats (Microchiroptera). Anim. Behav., 50(4), 861–880. https://doi.org/10.1016/0003-3472(95)80090-5 Kobayashi, K., Masuda, K., Haga, C., Matsui, T., Fukui, D., & Machimura, T. (2021). Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks. Ecological Informatics, 62, 101253. https://doi.org/10.1016/j.ecoinf.2021.101253 Larsson, Johan;Isak, Å. (2020). Numerical methods for spectral theory. 101–151. https://doi.org/10.1090/conm/720/14523 Lucas, T. (2010). Bat identification with gaussian process learning. 16–20. https://doi.org/10.6084/m9.figshare.92334.v1 Luciano, L., & Brian, H. (2019). Un Enfoque para Evaluar y Diseñar Nuevas Técnicas de Refactoring de Aplicaciones SOA Índice. Mallat, S. (2008). A Wavelet Tour of Signal Processing. San Diego California: Elsevier. Mirzaei, G., Majid, M. W., Ross, J., Jamali, M. M., Gorsevski, P. V., Frizado, J. P., & Bingman, V. P. (2012a). The BIO-acoustic feature extraction and classification of bat echolocation calls. In 2012 IEEE International Conference on Electro/Information Technology. https://doi.org/10.1109/EIT.2012.6220700 Moss, C. F., Redish, D., Gounden, C., & Kunz, T. H. (1997). Ontogeny of vocal signals in the little brown bat, Myotis lucifugus. Animal Behaviour, 54(1), 131–141. https://doi.org/10.1006/anbe.1996.0410 Neuweiler, G. (2003). Evolutionary aspects of bat echolocation. J. Comp. Physiol. A, 189(4), 245–256. https://doi.org/10.1007/s00359-003-0406-2 Neuweiler, G., Metzner, W., Heilmann, U., Rübsamen, R., Eckrich, M., & Costa, H. H. (1987). Foraging behaviour and echolocation in the rufous horseshoe bat (Rhinolophus rouxi) of Sri Lanka. Behavioral Ecology and Sociobiology, 20(1), 53–67. https://doi.org/10.1007/BF00292166 O’Farrell, M. J., C. Corben, and W. L. G. (2000). Geographic variation in the echolocation calls of the hoary bat (Lasiurus cinereus). Acta Chiropterologica, 2, 185–196. Orozco-Lugo, L., Guillén-Servent, A., Valenzuela-Galván, D., & Arita, H. T. (2013). Descripción de los pulsos de ecolocalización de once especies de murciélagos insectívoros aéreos de una selva baja caducifolia en Morelos, México. Therya, 4(1), 33–46. https://doi.org/10.12933/therya-13-103 Preatoni, D.G; Nodari, M; Chirichella, R; Tosi, G; Wauters, A. . (2005). Identifying Bats from Time-Expanded Recordings of search Calls: Comparing Classification Methods. The Journal of Wildlife Management., 64(2), 1601–1614. R. Hasan, M.Jamil, G. R. & S. R. (2004). Speaker Identification using Mel Frequency Ceptral Coefficients. 3th International Conference on Electrical & Computer Engineering, 565–568. Redgwell, R.D., Szewczak, J.M., Jones, G. & Parsons, S. (2009). Classification of echolocation calls from 14 species of bat by support vector machines and ensembles of neural networks. Algorithms, 2, 907– 924. Rosenberg, A., & Hirschberg, J. (2007). V-Measure: A conditional entropy-based external cluster evaluation measure. EMNLP-CoNLL 2007 - Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, (June), 410–420. Roverud, R. C., & Grinnell, A. D. (1985a). Discrimination performance and echolocation signal integration requirements for target detection and distance determination in the CF / FM bat , Noctilio Mbiventris. Roverud, R. C., & Grinnell, A. D. (1985b). Echolocation sound features processed to provide distance information in the CF/FM bat,Noctilio albiventris: evidence for a gated time window utilizing both CF and FM components. Journal of Comparative Physiology A, 156(4), 457–469. https://doi.org/10.1007/BF00613970 Schnitzler, H.-U., & Kalko, E. K. V. (2001). Echolocation by Insect-Eating Bats. BioScience, 51(7), 557. https://doi.org/10.1641/0006-3568(2001)051[0557:EBIEB]2.0.CO;2 Schnitzler, H., Moss, C. F., & Denzinger, A. (2003). From spatial orientation to food acquisition in echolocating bats. 18(8), 386–394. https://doi.org/10.1016/S0169-5347(03)00185-X Schnitzler, H. U., & Kalko, E. K. V. (1998). How echolocating bats search and find food. In T. H. Kunz & P. A. Racey (Eds.), Bat biology and conservation (pp. 183–196). Washington, DC: Smithsonian Institution Press. Sejdić, E., Djurović, I., & Jiang, J. (2009). Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing: A Review Journal, 19(1), 153–183. https://doi.org/10.1016/j.dsp.2007.12.004 Skowronski, M., & Harris, J. (2006). Acoustic detection and classification of microchiroptera using machine learning: Lessons learned from automatic speech recognition. 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L., Freeman, R., Collen, A., Dietz, C., Brock Fenton, M., Jones, G., … Jones, K. E. (2012). A continental-scale tool for acoustic identification of European bats. Journal of Applied Ecology, 49(5), 1064–1074. https://doi.org/10.1111/j.1365-2664.2012.02182.x Waters, D., & Barlow, K. (2013). Automatic recognition systems for bat call identification. Bulletin of the Institute of Ecology and Environmental Management. In Practice, 79, 19–23. Y. Paumen, M. Mälzer, S. Alipek, J. Moll, B. L. & H. S.-W. (2021). Development and test of a bat calls detection and classification method based on convolutional neural networks. Retrieved from https://doi.org/10.1080/09524622.2021.1978863 Zamora-Gutierrez, V., Lopez-Gonzalez, C., MacSwiney Gonzalez, M. C., Fenton, B., Jones, G., Kalko, E. K. V., … Jones, K. E. (2016). Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design. Methods in Ecology and Evolution, 7(9), 1082–1091. https://doi.org/10.1111/2041-210X.12556 Zhu, X., Wang, J., Sun, K. P., Jiang, T. L., Jiang, Y. L., & Feng, J. (2008). The echolocation calls of Rhinolophus ferrumequinum in relation to habitat type and environmental factors. Shengtai Xuebao/ Acta Ecologica Sinica, 28(11), 5248–5258. https://doi.org/10.1016/S1872-2032(09)60007-X Zurc, D & Guillén-Servent, A & Solari, S. (2017). Chillidos de ecolocación de murciélagos emballonuridae en una sabana xerófila-semiseca del caribe colombiano. Mastozoología Neotropical. Mastozoología Neotropical, 24(1), 201–218.
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	xxiii, 151 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cruz Roa, Ángel Alfonso46998d223286d3d4d34f7436c6934037González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Agudelo Villalobos, Leandro Esneyder3830ed0662b8316c5dfd5bdcc98796342022-06-08T20:00:46Z2022-06-08T20:00:46Z2022-06-07https://repositorio.unal.edu.co/handle/unal/81540Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficas, tablasLos murciélagos cuentan con la capacidad de la generación de llamados de ecolocalización para los procesos de desplazamiento y captura del alimento. Estos llamados presentan una serie de características temporales y espectrales que permiten adelantar la identificación de especies, géneros o familias, partiendo de los comportamientos asociados a las variaciones de frecuencias conocidos como tipos de pulsos (frecuencia modulada - FM, frecuencia constante - CF y frecuencia cuasi-constante - QCF) las cuales están enmarcadas en las fases del proceso de forrajeo (búsqueda, aproximación y terminal). Debido a la dependencia directa de los expertos y la falta de bases de datos anotadas existentes, se adelantó el presente trabajo el cual consiste en la caracterización automática de señales de ecolocalización de murciélagos pescadores por medio de técnicas de procesamiento digital de señales y aprendizaje computacional no supervisado, aplicadas a un conjunto de 4.426 señales anotadas y validadas por biólogos de la Universidad de los Llanos. A cada audio se le adelantó un preprocesamiento que permitió la extracción e identificación de cada señal de ecolocalización, a la cual se le aplicó un filtro Butterworth pasa banda, previo a la extracción de características espectrales y temporales (Fast Fourier Transform FFT, spectral rolloff, chroma, melspectrogram, Mel Frequency Cepstral Coefficients, spectral centroid, zero crossing rate, entre otras), logrando construir un conjunto de datos de 600 características. Al cual, se le aplicaron los algoritmos Random Forest y Principal Component Analysis para adelantar la reducción de la dimensionalidad; A estos resultados se aplicaron los algoritmos de agrupamiento K-means y Spectral Clustering. De la evaluación realizada se encontró como factor predominante que para la etiqueta de tipos de pulsos la cantidad de clústeres con mejores resultados es de tres (3), tanto para K-means y Spectral Clustering, con un valor máximo de 0,610 para la métrica de coeficiente de silueta. Mientras que para la etiqueta de fases de forrajeo la cantidad de clústeres con mejores resultados es de dos (2), se encontró una mejora en los resultados al implementar PCA a las características identificadas como relevantes mediante Random Forest antes de implementar el proceso de agrupamiento. (Texto tomado de la fuente)Bats have the ability to generate echolocation calls for the processes of movement and capture of food. These calls present a series of temporal and spectral characteristics that allow to identification of species, genera or families, starting from the behaviors associated with frequency variations known as pulses types (modulated frequency - FM, constant frequency -CF and quasi-constant frequency - QCF), which are in the phases of the foraging process (search, approach and terminal phases). By the direct dependence of the experts and the lack of existing annotated databases, the present work was carried out, which consists of the automatic characterization of echolocation signals of fishing bats by means of digital signal processing techniques and unsupervised computational learning, applied to a set of 4,426 signals noted and validated by Biologists from the Universidad de los Llanos. Each audio was preprocessed to extraction and identification of each echolocation signal, to which a Butterworth band-pass filter was applied, prior to the extraction of spectral and temporal characteristics (chroma, melspectrogram, cepstral coefficients of Mel frequency, spectral centroid, zero crossing rate, among others), to build a data set of 600 characteristics. Which the Random Forest and Principal Component Analysis algorithms were applied to advance the reduction of dimensionality; The K-means and Spectral Clustering algorithms were applied to these results. From the evaluation carried out, it was found as a predominant factor that for the label of pulse types, the number of clusters with the best results is three (3), both for K-means and Spectral Clustering, with a maximum value of 0,610 for the silhouette coefficient metric.; While for the label of foraging phases, the number of clusters with the best results is two (2), an improvement in the results was found when implementing PCA to the characteristics identified as relevant by Random Forest before implementing the clustering process.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónComputación Aplicadaxxiii, 151 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afinesEcolocaciónEcolocalizaciónEcholocationAnimal echolocationAgrupamientoAnálisis de SeñalesAprendizaje AutomáticoEcolocalización de murciélagosForrajeoProcesamiento Digital de SeñalesSeñales de UltrasonidoClusteringSignal AnalysisMachine LearningBat EcholocationForagingDigital Signal ProcessingUltrasound SignalsCaracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los LlanosAutomatic characterization of echolocation signals of fishing bats in Villavicencio - Meta for the analysis and support of biodiversity research at the Universidad de los LlanosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbhay Padda. 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Mastozoología Neotropical, 24(1), 201–218.EstudiantesORIGINAL86073636.2022.pdf86073636.2022.pdfTesis de Maestría en Ingeniería de Sistemas y Computaciónapplication/pdf6446189https://repositorio.unal.edu.co/bitstream/unal/81540/1/86073636.2022.pdf64aa8faf16f03b87998ec2a889393ad0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81540/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL86073636.2022.pdf.jpg86073636.2022.pdf.jpgGenerated Thumbnailimage/jpeg6111https://repositorio.unal.edu.co/bitstream/unal/81540/3/86073636.2022.pdf.jpga8c933f7eb95b5b567dac3e2ab52e8bbMD53unal/81540oai:repositorio.unal.edu.co:unal/815402024-08-06 23:10:07.54Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

Caracterización automática de señales de ecolocalización de murciélagos pescadores en Villavicencio - Meta para el análisis y apoyo a la investigación en biodiversidad en la Universidad de los Llanos

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