Abstract: A computer-implemented method of machine learning classification for Computer Assisted Design (CAD) is provided. The method comprises receiving a number of CAD volumes comprising a dataset, wherein each CAD volume is characterized by a number of scalar values corresponding to features of the CAD volume, wherein the features rely on geometry queries from a CAD kernel. Each CAD volume is labeled with part names according to a set of categories defined by a user, and a machine learning model is trained with the features of the labeled CAD volumes.

Abstract: A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An “a posteriori” distribution is derived for an uncertainty of the specific time at which the onset occurs.

Abstract: A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An “a posteriori” distribution is derived for an uncertainty of the specific time at which the onset occurs.