Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided.

Abstract: An infusion pump can be configured to modulate a drive motor based on perceived infusion pressure requirements for quieter, more energy-efficient operation of the drive motor. The infusion pump can include an electrical motor having a variable output torque based on the electrical current input, a plunger head sensor configured to detect a force between a plunger driver and a plunger of a medicament container, and a control module configured to regulate the electrical current input to the electrical motor based on the detected force between the plunger driver and the plunger medicament container. The electrical current input for such an infusion pump can be incrementally reduced according to defined magnitudes of detected force.

Abstract: A method includes emitting a laser including a plurality of laser points onto a surface of a conveyor belt, capturing a plurality of first images of the surface of the conveyor belt during a first cycle of the conveyor belt, creating a first three-dimensional image of the surface of the conveyor belt during the first cycle, each of a plurality of locations of the surface of the conveyor belt in the first three-dimensional image being assigned first position data, capturing a plurality of second images of the surface of the conveyor belt during a second cycle of the conveyor belt; creating a second three-dimensional image of the surface during the second cycle, each of the plurality of locations of the surface in the second three-dimensional image being assigned second position data; and determining whether a difference between the first and second position data exceeds a predetermined threshold.

Abstract: Examples disclosed herein relate to an autonomous driving system in a vehicle, including a radar system with a reinforcement learning engine to control a beam steering antenna and identity targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to a radar system in an autonomous vehicle for object detection and classification. The radar system has a radar module having a dynamically controllable beam steering antenna and a perception module. The perception module includes a machine learning module trained on a first set of data and retrained on a second set of data to generate a set of object locations and classifications, and a classifier to use velocity information combined with the set of object locations and classifications to output a set of classified data.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: An infusion system and method configured to identify a break in static friction between a plunger and an infusate cartridge during an infusion of infusate for the purpose of providing a more consistent flow of infusate and promoting a more efficient use of energy. The infusion system and method can include monitoring of force between a drive mechanism and the plunger for a decrease in a rate of the force over time during actuation of the drive mechanism, thereby indicating a break in static friction between the plunger and the cartridge, and determining a low power consumption sleep duration based on an advancement of the actuator following the break in static friction.

Abstract: A building manager includes a communications interface configured to receive information from a smart energy grid. The building manager further includes an integrated control layer configured to receive inputs from and to provide outputs to a plurality of building subsystems. The integrated control layer includes a plurality of control algorithm modules configured to process the inputs and to determine the outputs. The building manager further includes a fault detection and diagnostics layer configured to use statistical analysis on the inputs received from the integrated control layer to detect and diagnose faults. The building manager yet further includes a demand response layer configured to process the information received from the smart energy grid to determine adjustments to the plurality of control algorithms of the integrated control layer.

Abstract: An infusion pump including an administration set configured to provide a fluidic pathway between a supply of infusate and an infusion set, at least one pressure sensor configured to sense a pressure of infusate within the administration set, and a control unit configured to monitor the sensed pressure, and apply a calculated tare adjustment to the monitored pressure to compensate for a decay of observable stress within the administration set as a result of stress relaxation.

Abstract: Examples disclosed herein relate to a scanning system for determining enhanced placement of an antenna within an environment. The scanning system includes a sensor system configured to emit an optical signal pulse to a surrounding environment of the scanning system and receive one or more returning optical signal pulses reflected from one or more reflective objects in the surrounding environment. The sensor system obtains a plurality of sensor data slices along a first direction from the one or more returning optical signal pulses. Each of the sensor data slices corresponds to a different position of the scanning system along a second direction orthogonal to the first direction. The scanning system also includes a perception module communicably coupled to the sensor system and configured to generate mapping information of the identified one or more reflective objects in the scene with one or more trained neural networks in the perception module.

Abstract: A method of adjusting a pressure distribution within an administration set to minimize an inadvertent delivery of a large bolus of infusate upon a sudden release of the occlusion, while ensuring that a maximum safe pressure limitation of the administration set is not exceeded.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: A building manager includes a communications interface configured to receive information from a smart energy grid. The building manager further includes an integrated control layer configured to receive inputs from and to provide outputs to a plurality of building subsystems. The integrated control layer includes a plurality of control algorithm modules configured to process the inputs and to determine the outputs. The building manager further includes a fault detection and diagnostics layer configured to use statistical analysis on the inputs received from the integrated control layer to detect and diagnose faults. The building manager yet further includes a demand response layer configured to process the information received from the smart energy grid to determine adjustments to the plurality of control algorithms of the integrated control layer.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system to detect a target in a path and a surrounding environment of the ego vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a recurrent super-resolution network having recurrent encoder layers to receive the radar data with the first resolution and produce radar data with a second resolution using first neural networks. The recurrent encoder layers perform recurrence operations prior to a max pooling operation. The radar data with the second resolution may be produced from at least an output of the recurrent encoder layers. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: A training apparatus has an input device and a wearable computing device with a bio-signal sensor and a display to provide an interactive virtual reality (“VR”) environment for a user. The bio-signal sensor receives bio-signal data from the user. The user interacts with content that is presented in the VR environment. The user interactions and bio-signal data are scored with a user state score and a performance scored. Feedback is given to the user based on the scores in furtherance of training. The feedback may update the VR environment and may trigger additional VR events to continue training.

Abstract: Examples disclosed herein relate to an autonomous driving system in a vehicle, including a radar system with a reinforcement learning engine to control a beam steering antenna and identity targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.

Abstract: A computer system or method may be provided for modulating content based on a person's brainwave data, including modifying presentation of digital content at at least one computing device. The content may also be modulated based on a set of rules maintained by or accessible to the computer system. The content may also be modulated based on user input, including through receipt of a presentation control command that may be processed by the computer system of the present invention to modify presentation of content. Content may also be shared with associated brain state information.