{"department":[{"_id":"78"}],"type":"conference","place":"Berlin, Heidelberg","date_created":"2021-03-31T08:58:59Z","citation":{"bibtex":"@inproceedings{Gatica_Platzner_2020, place={Berlin, Heidelberg}, title={Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures}, DOI={<a href=\"https://doi.org/10.1007/978-3-662-59084-3_9\">10.1007/978-3-662-59084-3_9</a>}, booktitle={Machine Learning for Cyber Physical Systems (ML4CPS 2017)}, author={Gatica, Carlos Paiz and Platzner, Marco}, year={2020} }","ama":"Gatica CP, Platzner M. Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures. In: <i>Machine Learning for Cyber Physical Systems (ML4CPS 2017)</i>. Berlin, Heidelberg; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-662-59084-3_9\">10.1007/978-3-662-59084-3_9</a>","mla":"Gatica, Carlos Paiz, and Marco Platzner. “Adaptable Realization of Industrial Analytics Functions on Edge-Devices Using Reconfigurable Architectures.” <i>Machine Learning for Cyber Physical Systems (ML4CPS 2017)</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-662-59084-3_9\">10.1007/978-3-662-59084-3_9</a>.","short":"C.P. Gatica, M. Platzner, in: Machine Learning for Cyber Physical Systems (ML4CPS 2017), Berlin, Heidelberg, 2020.","chicago":"Gatica, Carlos Paiz, and Marco Platzner. “Adaptable Realization of Industrial Analytics Functions on Edge-Devices Using Reconfigurable Architectures.” In <i>Machine Learning for Cyber Physical Systems (ML4CPS 2017)</i>. Berlin, Heidelberg, 2020. <a href=\"https://doi.org/10.1007/978-3-662-59084-3_9\">https://doi.org/10.1007/978-3-662-59084-3_9</a>.","ieee":"C. P. Gatica and M. Platzner, “Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures,” in <i>Machine Learning for Cyber Physical Systems (ML4CPS 2017)</i>, 2020.","apa":"Gatica, C. P., &#38; Platzner, M. (2020). Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures. In <i>Machine Learning for Cyber Physical Systems (ML4CPS 2017)</i>. Berlin, Heidelberg. <a href=\"https://doi.org/10.1007/978-3-662-59084-3_9\">https://doi.org/10.1007/978-3-662-59084-3_9</a>"},"publication":"Machine Learning for Cyber Physical Systems (ML4CPS 2017)","doi":"10.1007/978-3-662-59084-3_9","user_id":"398","language":[{"iso":"eng"}],"_id":"21584","date_updated":"2022-01-06T06:55:06Z","publication_status":"published","author":[{"first_name":"Carlos Paiz","last_name":"Gatica","full_name":"Gatica, Carlos Paiz"},{"full_name":"Platzner, Marco","first_name":"Marco","last_name":"Platzner","id":"398"}],"publication_identifier":{"isbn":["9783662590836","9783662590843"],"issn":["2522-8579","2522-8587"]},"status":"public","year":"2020","title":"Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures"}