Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.

Abstract: A system, apparatus and method are provided for securing a neural network (or other artificial intelligence model) against malicious activity, such as piracy, theft of intellectual property, sabotage, etc. One or more security elements or features (e.g., digital watermarks, encryption, obfuscation) are applied to the neural network model during training and/or optimization. Therefore, the model is enhanced with robust security before it is linked or merged with application software for performing inference processing using the model.

Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.

Abstract: A method, apparatus and system for human skeleton pose estimation includes synchronously capturing images of a human moving through an area from a plurality of different points of view, for each of the plurality of captured images, determining a bounding box that bounds the human in the captured image and identifying pixel locations of the bounding box in the image, for each of the plurality of captured images, determining 2D and single-view 3D skeletons from the pixel locations of the bounding box, determining a first, multi-view 3D skeleton using a combination of the 2D and single-view 3D skeletons, and optimizing the first, multi-view 3D skeleton to determine a final 3D skeleton pose for the human. The method, apparatus and system can further include illuminating the area with structured light during the capturing of the images of the human moving through the area.

Abstract: A method, apparatus and system for human skeleton pose estimation includes synchronously capturing images of a human moving through an area from a plurality of different points of view, for each of the plurality of captured images, determining a bounding box that bounds the human in the captured image and identifying pixel locations of the bounding box in the image, for each of the plurality of captured images, determining 2D and single-view 3D skeletons from the pixel locations of the bounding box, determining a first, multi-view 3D skeleton using a combination of the 2D and single-view 3D skeletons, and optimizing the first, multi-view 3D skeleton to determine a final 3D skeleton pose for the human. The method, apparatus and system can further include illuminating the area with structured light during the capturing of the images of the human moving through the area.

Abstract: A method for generating an electronic component representation for use in a printed circuit board design tool includes providing a plurality of training datasheets, learning, during off-line symbol processing, to identify component symbols based on the training datasheets, and storing in memory the learned identified symbol characteristics. Also included is learning, during off-line footprint processing, to identify component footprints based on the training datasheets, and storing the learned identified footprint characteristics in memory. Once off-line training has been performed, a user provides a selected component datasheet containing a component to use in the printed circuit board design tool, and on-line processing extracts a component symbol and footprint of the selected component based on the learned symbol and learned footprint characteristics.

Abstract: A method for generating an electronic component representation for use in a printed circuit board design tool includes providing a plurality of training datasheets, learning, during off-line symbol processing, to identify component symbols based on the training datasheets, and storing in memory the learned identified symbol characteristics. Also included is learning, during off-line footprint processing, to identify component footprints based on the training datasheets, and storing the learned identified footprint characteristics in memory. Once off-line training has been performed, a user provides a selected component datasheet containing a component to use in the printed circuit board design tool, and on-line processing extracts a component symbol and footprint of the selected component based on the learned symbol and learned footprint characteristics.