doctoral thesis
Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things

University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture

Cite this document

Čulić Gambiroža, J. (2023). Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things (Doctoral thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:106445

Čulić Gambiroža, Jelena. "Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:179:106445

Čulić Gambiroža, Jelena. "Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:179:106445

Čulić Gambiroža, J. (2023). 'Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things', Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 15 November 2024, https://urn.nsk.hr/urn:nbn:hr:179:106445

Čulić Gambiroža J. Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things [Doctoral thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2023 [cited 2024 November 15] Available at: https://urn.nsk.hr/urn:nbn:hr:179:106445

J. Čulić Gambiroža, "Machine Learning Methods for Efficient Data Reduction and Reconstruction in the concept of Internet of Things", Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:179:106445

Please login to the repository to save this object to your list.