يعرض 1 - 20 نتائج من 168 نتيجة بحث عن '"Padilla-Medina, José A."', وقت الاستعلام: 0.61s تنقيح النتائج
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    مصطلحات موضوعية: RD Cirugía

    وصف الملف: text

    Relation: http://eprints.uanl.mx/28023/1/643.pdf; Leos Leija, Ana Karen y Padilla Medina, José Ramón y Reyes Fernández, Pedro Martin y Peña Martínez, Víctor M. y Montes Tapia, Fernando Félix y Castillo Bejarano, José I. (2022) Vertebral destruction in an 11-month-old child with spinal tuberculosis: a case report and review of literature. Annals of Pediatric Surgery, 18 (1). pp. 1-7. ISSN 2090-5394

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    المصدر: Pistas Educativas; Vol. 43, Núm. 140 (2021): Número Especial: Difusión del conocimiento 2021 ; 2448-847X ; 1405-1249

    وصف الملف: application/pdf

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    المصدر: Pistas Educativas; Vol. 43, Núm. 140 (2021): Número Especial: Difusión del conocimiento 2021 ; 2448-847X ; 1405-1249

    وصف الملف: application/pdf

    Relation: http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2599/2032; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2599/1657; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2599/1658; Kafy, Abdulla - Al. (2018). Importance of Surface Water Bodies for Sustainable Cities: A Case Study of Rajshahi City Corporation.; Zhang, Fangfang & Li, Junsheng & Shen, Qian & Ye, Huping & Wang, Shenglei & lu, Zoe. (2018). A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images. International Journal of Remote Sensing. 39. 3429-3451. https://doi.org/10.1080/01431161.2018.1444292.; Ko, Byoungchul & Kim, Hyeong & Nam, Jae. (2015). Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers. Sensors. 15. 13763-13777. https://doi.org/10.3390/s150613763.; Litjens, G. & Kooi, T. & Bejnordi, B.E. & Setio, A. & Ciompi, F. & Ghafoorian, M. & Laak, J.V. & Ginneken, B. & Sánchez, C. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.; Xie, Qiaoyun & Dash, Jadu & Huete, Alfredo & Jiang, Aihui & Yin, Gaofei & Ding, Yanling & Peng, Dailiang & Hall, Christopher & Brown, Luke & Shi, Yue & Ye, Huichun & Dong, Yingying & Huang, Wenjiang. (2019). Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation. 80. 187-195. https://doi.org/10.1016/j.jag.2019.04.019.; Utton, Albert. (2019). Water in a Developing World: The Management of a Critical Resource. Routledge.; Chawla, Ila & Karthikeyan, L. & Mishra, Ashok. (2020). A Review of Remote Sensing Applications for Water Security: Quantity, Quality, and Extremes. Journal of Hydrology. 585. 124826. https://doi.org/10.1016/j.jhydrol.2020.124826.; Yuan, Qiangqiang & Shen, Huanfeng & Li, Tongwen & Li, Zhiwei & Li, Shuwen & Jiang, Yun & Xu, Hongzhang & Weiwei, Tan & Yang, Qianqian & Wang, Jiwen & Gao, Jianhao & Zhang, Liangpei. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment. 241. 111716. https://doi.org/10.1016/j.rse.2020.111716.; Cheng, Gong & Xie, Xingxing & Han, Junwei & Li, Kaiming & Xia, Gui-Song. (2020). Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13. 3735-3756. https://doi.org/10.1109/JSTARS.2020.3005403.; Hong, Danfeng & Gao, Lianru & Yokoya, Naoto & Yao, Jing & Chanussot, Jocelyn & Du, Qian & Zhang, Bing. (2020). More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-15. https://doi.org/10.1109/TGRS.2020.3016820.; Nima Pahlevan & Sandeep K. Chittimalli & Sundarabalan V. Balasubramanian & Vincenzo Vellucci. (2019). Sentinel-2/Landsat-8 product consistency and implications for monitoring aquatic systems. Remote Sensing of Environment. 20. 19-29. https://doi.org/10.1016/j.rse.2018.10.027.; Randles, Bernadette & Pasquetto, Irene & Golshan, Milena & Borgman, Christine. (2017). Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study. 1-2. https://doi.org/10.1109/JCDL.2017.7991618.; Bisong E. (2019) Google Colaboratory. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4470-8_7; Pessoa, Tiago & Medeiros, Raul & Nepomuceno, Thiago & Bian, Gui-Bin & Albuquerque, V.H.C. & Filho, Pedro Pedrosa. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access. PP. 1-1. https://doi.org/10.1109/ACCESS.2018.2874767.; MATLAB. (2019). version 9.6.0.1062519 (R2019a). The MathWorks Inc. Natick, Massachusetts; Manaswi, Navin Kumar & John, Suresh. (2018). Deep learning with applications using python. Springer.; Maragoudakis, Manolis & Kontos, Konstantinos. (2018). Machine learning for water bodies identification from satellite images. International Journal of Data Mining, Modelling and Management. 10. 209. 10.1504/IJDMMM.2018.10015048.; Isikdogan, Furkan & Bovik, Alan & Passalacqua, Paola. (2017). Surface Water Mapping by Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. PP. 1-10. 10.1109/JSTARS.2017.2735443.; Chen, Yang & Fan, Rongshuang & Yang, Xiucheng & Wang, Jingxue & Latif, Aamir. (2018). Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning. Water. 10. 585. 10.3390/w10050585.; http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2599

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    المصدر: Pistas Educativas; Vol. 43, Núm. 140 (2021): Número Especial: Difusión del conocimiento 2021 ; 2448-847X ; 1405-1249

    وصف الملف: application/pdf

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    المصدر: Pistas Educativas; Vol. 43, Núm. 140 (2021): Número Especial: Difusión del conocimiento 2021 ; 2448-847X ; 1405-1249

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    المصدر: Pistas Educativas; Vol. 42, Núm. 137 (2020): Número Especial: Difusión del conocimiento 2020 ; 2448-847X ; 1405-1249

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    المصدر: Pistas Educativas; Vol. 42, Núm. 137 (2020): Número Especial: Difusión del conocimiento 2020 ; 2448-847X ; 1405-1249

    وصف الملف: application/pdf

    Relation: http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2302/1848; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2302/1301; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2302/1302; Durán, J. M., & Nava, L. M., “Los cultivos sin suelo: de la hidroponía a la aeroponía”, Cultivos intensivos, 40-43, 2000.; Douglas A. Skoog, F. J., “Principios de análisis instrumental. México”, Cengage Learning, 2008.; Górriz Sáez, H., Álvarez Martínez, A. J., & Oliva Molina, R. M. “Procesamiento digital de imagen para la caracterización morfométrica de la especie Frankliniella Occidentalis. Almeria España: Universidad de Almeria”, 2018.; Grau, P., “Técnicas de análisis de imagen Aplicaciones en biología. Valencia: Universidad de Valencia”, 2003.; Varela, S. A., “Aspectos básicos de la fisiología en respuesta a estrés y el clima”, INTA. 2010.; Martins, L. M., Clarete, E., & Carlos, Lanamar De Almeida, L. de C. L. F. G. M. M. J. L. C. ,”Physical and Chemical Characteristics of Lettuce Cultivars Grown Under Three Production Systems Características Físicas E Químicas De Cultivares De Alface”, Bioscience Journal, 33, 621–630, 2017.; Morgunov, A. P., & Kirgizova, I. V., “Control unit for the dosed feeding of the nutrient solution into the industrial aeroponic installation system”, Journal of Physics: Conference Series”, 2019.; Faget, M., Nagel, K. A., Walter, A., Herrera, J. M., Jahnke, S., Schurr, U., & Temperton, V. M., “Root–root interactions: extending our perspective to be more inclusive of the range of theories in ecology and agriculture using in-vivo analyses”, ANNALS OF BOTANY, pp. 253-266, 2013.; Urrestarazu, M., Gallegos, V., & Álvaro, J. E., “The Use of Thermography Images in the Humidification Bulb in Soilless Culture”, Communications in soil science and plant analysis, pp. 1595–1602, 2017.; Morales, I., & Urrestarazu, M., “Thermography study of moderate electrical conductivity and nutrient solution distribution system effects on grafted tomato soilless culture”, pp. 1508–1512, Hortscience, 2013.; Urrestarazu, M., “Infrared thermography used to diagnose the effects of salinity in a soilless culture”, Quantitative InfraRed Thermography Journal, pp. 1-8, 2013.; Morales, I., Alvaro, J. E., & Urrestarazu, M., “Contribution of thermal imaging to fertigation in soilless culture”, Thermal Analysis and calorimetry, pp. 1033-1039, 2014.; http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2302

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    Academic Journal

    المساهمون: Tecnológico Nacional de México, Instituto Tecnológico de Celaya, Consejo Nacional de Ciencia y Tecnología, CONACYT through the Ph.D. Grant

    المصدر: IEEE Access ; volume 8, page 116733-116743 ; ISSN 2169-3536