Abstract: The present disclosure describes systems and methods to select fovea containing optical coherence tomography (OCT) images. The systems and methods described herein receive a plurality of OCT images. The portion of the OCT images are selected for further processing, where a line tracing the border between the retina and non-retina tissue is generated. A difference of the line is generated. Candidate OCT images are then generated responsive to the generated difference line. The lowest point among each difference lines generated for each of the OCT images is identified, and the OCT image to which the lowest point corresponds is identified as the fovea containing OCT image.

Abstract: The present disclosure describes a system and method to segment optical coherence tomography (OCT) images. The present system uses a hybrid method that employs both Bayesian level sets (BLS) and graph-based segmentation algorithms. The system first identifies retinal tissue within an OCT image using the BLS algorithms. The identified retinal tissue is then further segmented using the graph-based segmentation algorithms.

Abstract: In an embodiment, a localization module can provide coordinates of the vehicle relative to the Earth and relative to the drivable surface, both of which are precise enough to allow for self-driving, and further can compensate for a temporary lapse in reliable GPS service by continuing to track the car's position by tracking its movement with inertial sensors (e.g., accelerometers and gyroscopes) and RADAR data. The localization module bases its output on a geolocation relative to the Earth and sensor measurements of the drivable surface and its surroundings to determine where the car is in relation to the Earth and the drivable surface.

Abstract: The present disclosure describes a system and method to segment optical coherence tomography (OCT) images. The present system uses a hybrid method that employs both Bayesian level sets (BLS) and graph-based segmentation algorithms. The system first identifies retinal tissue within an OCT image using the BLS algorithms. The identified retinal tissue is then further segmented using the graph-based segmentation algorithms.

Abstract: The present disclosure describes systems and methods to select fovea containing optical coherence tomography (OCT) images. The systems and methods described herein receive a plurality of OCT images. The portion of the OCT images are selected for further processing, where a line tracing the border between the retina and non-retina tissue is generated. A difference of the line is generated. Candidate OCT images are then generated responsive to the generated difference line. The lowest point among each difference lines generated for each of the OCT images is identified, and the OCT image to which the lowest point corresponds is identified as the fovea containing OCT image.

Abstract: A method for collaborative navigation between two or more platforms is provided. The method comprises establishing a communication link between a first platform and a second platform, making a sensor measurement from the first platform, updating state and covariance elements of the first platform, and transmitting the updated state and covariance elements from the first platform to the second platform. A conditional update is performed on the second platform to compute a new estimate of state and covariance elements on the second platform, which takes into account the measurement from the first platform. The method further comprises making a sensor measurement from the second platform, updating state and covariance elements of the second platform, and transmitting the updated state and covariance elements from the second platform to the first platform.

Abstract: A motion classification system comprises an inertial measurement unit configured to sense motion of a user and to output one or more channels of inertial motion data corresponding to the sensed motion; and a processing unit configured to calculate a coefficient vector for each of the one or more channels based on a wavelet transformation of the respective inertial motion data, and to select one of a plurality of gaits as the user's gait based on the calculated coefficient vector of at least one of the one or more channels and on a plurality of templates, each template corresponding to one of the plurality of gaits.

Abstract: A method for collaborative navigation between two or more platforms is provided. The method comprises establishing a communication link between a first platform and a second platform, making a sensor measurement from the first platform, updating state and covariance elements of the first platform, and transmitting the updated state and covariance elements from the first platform to the second platform. A conditional update is performed on the second platform to compute a new estimate of state and covariance elements on the second platform, which takes into account the measurement from the first platform. The method further comprises making a sensor measurement from the second platform, updating state and covariance elements of the second platform, and transmitting the updated state and covariance elements from the second platform to the first platform.

Abstract: A motion classification system comprises an inertial measurement unit configured to sense motion of a user and to output one or more channels of inertial motion data corresponding to the sensed motion; and a processing unit configured to calculate a coefficient vector for each of the one or more channels based on a wavelet transformation of the respective inertial motion data, and to select one of a plurality of gaits as the user's gait based on the calculated coefficient vector of at least one of the one or more channels and on a plurality of templates, each template corresponding to one of the plurality of gaits.

Abstract: This invention provides a system and method for autonomously tracking a moving target from unmanned aerial vehicles (UAVs) with a variety of airframe and sensor payload capabilities so that the target remains within the vehicle's sensor field of view regardless of the specific target motion patterns. The invention uses information about target location, UAV platform type and states, sensor payload capability, and ratio of target-to-UAV speeds to select from a suite of sub-algorithms, each of which generates desired platform positions (in form of waypoints) and/or sensor orientation commands to keep the target in view.

Abstract: Methods and apparatus are provided for determining a navigational state of a vehicle, the vehicle having at least one pivotable wheel and a plurality of front wheels. The apparatus comprises a steering angle sensor coupled to the at least one pivotable wheel for determining a steering angle, a plurality of wheel speed sensors each coupled to a different one of the plurality of pivotable wheels for determining an angular velocity of each of the plurality of pivotable wheels, a GPS receiver coupled to the vehicle for receiving GPS positioning data, and a processor coupled to the steering angle sensor, the plurality of wheel speed sensors, and the GPS receiver. The processor is configured to determine a yaw rate for the vehicle based on the positioning data, the steering angle, and the longitudinal angular velocity of each of the plurality of front wheels, and integrate the yaw rate to determine a heading for the vehicle.

Abstract: This invention provides a system and method for autonomously tracking a moving target from unmanned aerial vehicles (UAVs) with a variety of airframe and sensor payload capabilities so that the target remains within the vehicle's sensor field of view regardless of the specific target motion patterns. The invention uses information about target location, UAV platform type and states, sensor payload capability, and ratio of target-to-UAV speeds to select from a suite of sub-algorithms, each of which generates desired platform positions (in form of waypoints) and/or sensor orientation commands to keep the target in view.

Abstract: Collision avoidance systems and methods are implemented on unmanned mobile vehicles to supplement map-based trajectories generated by the vehicles' navigation systems. These systems include radar, which detect obstacles in the path of the unmanned mobile vehicles, and collision avoidance modules, which enable the vehicles to avoid unexpected obstacles by adjusting their trajectories and velocities based on feedback received from the radar. In general, when an obstacle is detected by the radar, the collision avoidance module modifies the commanded velocity of an unmanned mobile vehicle by subtracting from the nominal commanded velocity the component that is in the direction of the obstacle. The magnitude of the velocity modification typically increases as the distance between the mobile vehicle and the obstacle decreases.