Abstract: Provided is a method for predicting an internal state of a machine. The method includes obtaining real time machine test data from a plurality of sensors installed in a test bench of the machine and identifying differences between the obtained real time machine test data and simulation data of a machine model. The method further includes updating the machine model by eliminating the identified differences and generating synthetic data corresponding to each of a normal condition and at least one deteriorated condition of the machine using the updated machine model. The method further includes detecting one or more deterioration levels of the machine over a period of time along with timestamp data based on a plurality of machine parameters using an Artificial Intelligence (AI) model and predicting a deterioration time period of the machine indicating an RUL of the machine based on the detected deterioration levels.

Abstract: A set of calibration procedures that can be run to assist in calibrating a camera module, such as may be intended for installation into a mobile consumer device. The procedures include lens shading calibration, white balance calibration, light source color temperature calibration, auto focus macro calibration, static defect pixel calibration, and mechanical shutter delay calibration. The light source color temperature calibration may be performed to assist in the other calibrations, each of which may generate data that can be potentially be stored in non-volatile memory on board the camera module for use during operation.