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