يعرض 1 - 16 نتائج من 16 نتيجة بحث عن '"Barranco-Gutierrez, Alejandro Israel"', وقت الاستعلام: 0.39s تنقيح النتائج
  1. 1
    Academic Journal

    المصدر: Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol 11 No Especial2 (2023): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 16-21 ; Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; Vol. 11 Núm. Especial2 (2023): Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI; 16-21 ; 2007-6363 ; 10.29057/icbi.v11iEspecial2

    وصف الملف: application/pdf

  2. 2
    Academic Journal

    المساهمون: Mexican National Council of Science and Technology Consejo Nacional de Ciencia y Tecnología (CONACYT), Spanish, Tecnológico Nacional de México (TecNM) en Celaya, Engineering Division of the University of Guanajuato

    المصدر: IEEE Access ; volume 10, page 97348-97359 ; ISSN 2169-3536

  3. 3
    Academic Journal
  4. 4
    Academic Journal

    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

  5. 5
    Academic Journal

    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/2613/2035; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2613/1663; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2613/1664; Andreev A.A., Vozmilov A.G. Kalmakov V.A.; Simulation of lithium battery operation under severe temperature conditions. Procedia Engineering 129, 201-206 (2015).; Finegan D.P., Darcy E., Keyser M., Tjaden B., Heenan T.M.M., Jervis R., Bailey J.J., Vo N.T., Magdysyuk O.V., Drakopoulos M., Di Michiel M., Rack A., Hinds G., Brett D.J.L., Shearing P.R.; Identifying the Cause of Rupture of Li-Ion Batteries during Thermal Runaway. Advanced Science, Vol. 5, No. 1, (2018); Lienhard J.H. IV and Lienhard J.H.V; A Heat Transfer Textbook, Phlogiston Press (2020); Bergman T.L., Lavine A. S., Incropera F.P., Dewitt D.P.; Fundamentals of Heat and Mass Transfer, John Wiley and Sons (2011); Christenson M., Loiselle A., Karman D. y Graham L.A.; The Effect of Driving Conditions and Ambient Temperature on Light Duty Gasoline-Electric Hybrid Vehicles (1): Particulate Matter Emission Rates and Size Distributions. SAE Technical Paper Series, 01-2136 (2007); Christenson M., Loiselle A., Karman D. y Graham L.A.; The Effect of Driving Conditions and Ambient Temperature on Light Duty Gasoline-Electric Hybrid Vehicles (2): Fuel Consumption and Gaseous Pollutant Emission Rates. SAE Technical Paper Series, 01-2137 (2007); Environmental Protection Agency. Dynamometer Drive Schedules: https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules.; Sanguesa J.A., Torres-Sanz V., Garrido P., Martinez F.J., Marquez-Barja J.M.; A Review on Electric Vehicles: Technologies and Challenges. Smart Cities 4, 372–404. (2021); Pesaran A.,Santhanagopalan S.,Kim G.H.; Addressing the Impact of Temperature Extremes on Large Format Li-Ion Batteries for Vehicle Applications. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy13osti/58145.pdf (2013); Hartmann M., Kelly J.; Thermal Runaway Prevention of Li-ion Batteries by Novel Thermal Management System. IEEE Transportation Electrification Conference and Expo (2018); Nissan LEAF Teardown: Lithium-ion battery pack structure - MarkLines Automotive Industry Portal. https://www.marklines.com/en/report_all/rep1786_201811; http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2613

  6. 6
    Book
  7. 7
    Academic Journal

    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/2311/1856; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2311/1255; http://itcelaya.edu.mx/ojs/index.php/pistas/article/downloadSuppFile/2311/1256; Mahesh S. S., Joshitha C., Reddy M., Reddy S., Yaswanth S., Facial Detection and Recognition System on Raspberry pi with Enhanced Security, International Conference on Emerging Trends in Information Technology and Engineering, 2020.; Monroy-Sahade E. A., Lázaro-Mata D., Vázquez-Rodríguez E. A., Barranco-Gutiérrez A. I., Padilla-Medina J. A., Fuzzy color description on Raspberry PI 3, Congreso Internacional de Robótica y Computación, 2019.; Cárdenas-León A., Pérez-Pinal Francisco-Javier, Barranco-Gutierrez Alejandro-Israel, Implementación de sistema difuso en Arduino Uno, Academia Journals, Celaya, Noviembre 2016.; Gutiérrez-López M., Barranco-Gutiérrez A. I., Implementación de un sistema difuso en VHDL, AMITE 2016. Coatzacoalcos, Noviembre 2016.; K. Ogata, "Sistemas de control en tiempo discreto", Primera edición, PRENTICE HALL HISPANOAMERICANA S.A., 2002, pp. 53-213.; C. R. Dorf, "Sistemas de Control Moderno", Segunda edición, Pearson Education, 2005, pp. 158-188.; L. G. Ramirez, "Sensores y actuadores: Aplicaciones con Arduino", Primera edición, Grupo Editorial Patria, 2016, pp. 37-88.; W. Cheney, D. Kincaid, “Métodos numéricos y computación”, 6a edición, Cengage Learning, 2012, pp. 124-179.; Raspberry pi fundation, Going Straight with PID, visitado el 28 de abril del 2020. https://projects.raspberrypi.org/en/projects/robotPID; García-Martínez E., Desarrollo de un controlador PID industrial de bajo coste mediante raspberry pi para control de temperature, Tesis de la Escuela técnica superior de Ingenieros Industriales Valencia, 2016.; http://itcelaya.edu.mx/ojs/index.php/pistas/article/view/2311

  8. 8
    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

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    Academic Journal
  10. 10
    Book

    المصدر: Image and Video Technology – PSIVT 2013 Workshops ; Lecture Notes in Computer Science ; page 113-121 ; ISSN 0302-9743 1611-3349 ; ISBN 9783642539251 9783642539268

  11. 11
    Academic Journal

    المصدر: Revista Facultad de Ingeniería Universidad de Antioquia; No. 58 (2011): Revista Facultad de Ingeniería (Jan-Mar 2011); 191-198 ; Revista Facultad de Ingeniería Universidad de Antioquia; Núm. 58 (2011): Revista Facultad de Ingeniería (Ene-Mar 2011); 191-198 ; 2422-2844 ; 0120-6230

    وصف الملف: application/pdf

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    Academic Journal
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    Academic Journal
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    Academic Journal
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