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