Abstract: The present disclosure relates to an apparatus, system and method for selectively capturing a carbon-containing gas from an input gas mixture.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on for performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.

Abstract: Embodiments of the present disclosure relate to an apparatus, system and method for making an admixture of a polymer and carbon nanomaterials (CNM). The admixture of such embodiments comprise about 10% or less by weight (wt %) of CNMs. The CNM content of such admixture may impart new or enhanced properties to the admix and to materials and products made therefrom. Such new or enhanced products may include enhanced tensile strength, new or enhanced electronic medical, structural thermal, catalytic properties or any combination thereof.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a universal sensor model based on a combined dataset provided by sensor devices of a common device class. Once trained, the universal sensor model may be deployed for providing recommendations based on performing object detection on datasets received from different types of sensor devices of the common device class. Embodiments include determining whether to generate the combined dataset from different datasets from sensor devices of the common device class and determining when the sensor model will perform better using the combined dataset from sensor device rather than a single dataset from a single sensor device. In some embodiments, the datasets are image datasets comprising image data provided by the sensor devices.