Abstract: A system comprising a work machine and an attachment removably coupled with the work machine. The attachment includes an identification module comprising a transceiver configured to communicate with a control system of the work machines so as to improve the functionality and efficiency of the attachment and of the work machines.

Abstract: Star tracker systems and methods are provided. The star tracker incorporates deep learning processes in combination with relatively low cost hardware components to provide moderate (e.g. ˜1 arc second attitude uncertainty) accuracy. The neural network implementing the deep learning processes can include a Hinton's capsule network or a coordinate convolution layer to maintain spatial relationships between features in images encompassing a plurality of features. The hardware components can be configured to collect a blurred or defocused image in which point sources of light appear as blurs, and in which the blurs create points of intersection. Alternatively or in addition, a blurred or defocused image can be created using processes implemented as part of application programming. The processing of collected images by a neural network to provide an attitude determination can include analyzing a plurality of blurs and blur intersections across an entire frame of image data.

Abstract: Imaging systems and methods that implement a deep learning network are disclosed. The deep learning network utilizes pose information associated with at least some identified objects. The network is pruned, to reduce the amount of information processed and to optimize runtime processing when the network is deployed. In operation, the network identifies objects, and propagates pose information for at least some of the objects or components of identified objects. The network can be deployed as part of a processing system of an imaging system included as part of a remote platform.