Abstract: A multimodality imaging system and method for mineralogy segmentation is disclosed. Image datasets of the sample are generated for one or more modalities, including x-ray and focused ion beam scanning electron microscope (FIB-SEM) modalities. Mineral maps are then created using Energy Dispersive X-ray spectroscopy (EDX) from at least part of the sample covered by the image datasets. The EDX mineral maps are applied as a mask to the image datasets to identify and label regions of minerals within the sample. Feature vectors are then extracted from the labeled regions via feature generators such as Gabor filters. Finally, machine learning training and classification algorithms such as Random Forest are applied to the extracted feature vectors to construct a segmented image representation of the sample that classifies the minerals within the sample.

Abstract: A multimodality imaging system and method for mineralogy segmentation is disclosed. Image datasets of the sample are generated for one or more modalities, including x-ray and focused ion beam scanning electron microscope (FIB-SEM) modalities. Mineral maps are then created using Energy Dispersive X-ray spectroscopy (EDX) from at least part of the sample covered by the image datasets. The EDX mineral maps are applied as a mask to the image datasets to identify and label regions of minerals within the sample. Feature vectors are then extracted from the labeled regions via feature generators such as Gabor filters. Finally, machine learning training and classification algorithms such as Random Forest are applied to the extracted feature vectors to construct a segmented image representation of the sample that classifies the minerals within the sample.

Abstract: A processing apparatus including one or more processors and memory receives sensor measurements generated by one or more sensors of one or more devices, pre-classifies the sensor measurements as belonging to one of a plurality of pre-classifications, and selects one or more feature types to extract from the sensor measurements based at least in part on the pre-classification of the sensor measurements. The processing apparatus also extracts features of the one or more selected feature types from the sensor measurements and determines a state of a respective device of the one or more devices in accordance with a classification of the sensor measurements determined based on the one or more features extracted from the sensor measurements.

Abstract: A processing apparatus including one or more processors and memory receives sensor measurements generated by one or more sensors of one or more devices, pre-classifies the sensor measurements as belonging to one of a plurality of pre-classifications, and selects one or more feature types to extract from the sensor measurements based at least in part on the pre-classification of the sensor measurements. The processing apparatus also extracts features of the one or more selected feature types from the sensor measurements and determines a state of a respective device of the one or more devices in accordance with a classification of the sensor measurements determined based on the one or more features extracted from the sensor measurements.

Abstract: A processing apparatus including one or more processors and memory receives sensor measurements generated by one or more sensors of one or more devices, pre-classifies the sensor measurements as belonging to one of a plurality of pre-classifications, and selects one or more feature types to extract from the sensor measurements based at least in part on the pre-classification of the sensor measurements. The processing apparatus also extracts features of the one or more selected feature types from the sensor measurements and determines a state of a respective device of the one or more devices in accordance with a classification of the sensor measurements determined based on the one or more features extracted from the sensor measurements.