Martinelli, A.;                     Meocci, M.;                     Dolfi, M.;                     Branzi, V.;                     Morosi, S.;                     Argenti, F.;                     Berzi, L.;                     Consumi, T.    
        Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach. Sensors 2022, 22, 3788.
    https://doi.org/10.3390/s22103788
    AMA Style
    
                                Martinelli A,                                 Meocci M,                                 Dolfi M,                                 Branzi V,                                 Morosi S,                                 Argenti F,                                 Berzi L,                                 Consumi T.        
                Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach. Sensors. 2022; 22(10):3788.
        https://doi.org/10.3390/s22103788
    
    Chicago/Turabian Style
    
                                Martinelli, Alessio,                                 Monica Meocci,                                 Marco Dolfi,                                 Valentina Branzi,                                 Simone Morosi,                                 Fabrizio Argenti,                                 Lorenzo Berzi,                                 and Tommaso Consumi.        
                2022. "Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach" Sensors 22, no. 10: 3788.
        https://doi.org/10.3390/s22103788
    
    APA Style
    
                                Martinelli, A.,                                 Meocci, M.,                                 Dolfi, M.,                                 Branzi, V.,                                 Morosi, S.,                                 Argenti, F.,                                 Berzi, L.,                                 & Consumi, T.        
        
        (2022). Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach. Sensors, 22(10), 3788.
        https://doi.org/10.3390/s22103788