Abstract: In some aspects, the techniques described herein relate to a method including calling, by a first event driven function associated with a spoke account, an inventory interface of the spoke account; writing, by the first event driven function, inventory data received from the inventory interface to a messaging queue, wherein the messaging queue is associated with a hub account; receiving, by a second event driven function, the inventory data from the messaging queue, wherein the second event driven function is associated with the hub account; writing, by the second event driven function, the inventory data to a database, wherein the database is associated with the hub account; executing, by a third event driven function, a query of the database, wherein the query retrieves the inventory data, and wherein the third event driven function is associated with an administrative account; and displaying the inventory data via a front-end application.

Abstract: A computer can execute instructions to: receive power-receiving vehicle data identifying a power-receiving vehicle; identify one or more power-supplying vehicles for providing charging to the power-receiving vehicle; determine a rank for each of the identified one or more power-supplying vehicles based on the received power-receiving vehicle data and data received from the one or more power-supplying vehicles; upon selecting one of the one or more power-supplying vehicles, provide a navigation instruction to navigate at least one of the power-receiving vehicle and the selected power-supplying vehicle to a charging location based on a power-receiving vehicle location and a selected power-supplying vehicle location; and send access data to the power-receiving vehicle to access a charge port of the selected power-supplying vehicle; send a first light actuation instruction to the power-supplying vehicle based on a charging status; and send a second light actuation instruction to the power-receiving vehicle based

Abstract: The present disclosure relates to recombinant RNA molecules encoding an oncolytic virus genome. The present disclosure further relates to the encapsulation of the recombinant RNA molecules and the use of the recombinant RNA molecules and/or particles for the treatment and prevention of cancer.

Abstract: The disclosure generally pertains to battery charging management of battery electric vehicles (BEVs). In an example method, a processor generates an operational profile for a first BEV among a number of BEVs. The profile may be based on a mileage accumulation pattern, a parking pattern, and/or a battery charging pattern, detected over an information collection period. The mileage accumulation pattern may be based on the use of the first BEV for commuting from a residence to a workplace. The processor determines an availability of a battery charging outlet in a section of a garage of the residence, and based on the operational profile of the first BEV, identifies a period of time for charging a battery of the first BEV in the garage. The processor then issues a directive to park the first BEV in the section of the garage over the identified period of time for charging the battery.

Abstract: Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may include one or more processors configured to capture, with one or more image sensors, and while the UAV is in flight, a plurality of images of a target. The one or more processors may compare a first image of the plurality of images with a second image of the plurality of images to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. In addition, the one or more processors may determine, based at least on the difference, and while the UAV is in flight, an update to a three-dimensional model of the target.

Abstract: An item repositioning method includes, in response to a request for an item, moving a vehicle along a first route to a location of the item, loading the item in the vehicle using a lift assist device mounted to the vehicle, moving the vehicle along a second route to transport the item to a desired location, and at the desired location, unloading the item from the vehicle using the lift assist device.

Abstract: The disclosure is generally directed to systems and methods for battery life cycle prediction for a preowned electrified vehicle including receiving state of charge (SOC) and mileage data associated with the preowned electrified vehicle, providing one or more driving maneuvers to be performed by a driver, providing one or more instructions to the driver to operate power-driven accessories of the preowned electrified vehicle, collecting data representing battery usage by the driver by monitoring the driving maneuvers and the operation of power-driven accessories as performed by the driver, and responsive to the collected data representing battery usage and the SOC and mileage, providing a battery life prediction for the preowned electrified vehicle.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may access a scan plan that includes a sequence of poses for the UAV to assume to capture images of a scan target using one or more image sensors. The UAV may check a next pose of the scan plan for obstructions. Responsive to detection of an obstruction, the UAV may determine a backup pose based at least on a field of view of the next pose. The UAV may control a propulsion mechanism to cause the UAV to fly to assume the backup pose. The UAV may capture, based on the backup pose and using the one or more image sensors, one or more images of the scan target.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may identify a scan target. The UAV may navigate to two or more positions in relation to the scan target. The UAV may capture, using one or more image sensors of the UAV, two or more images of the scan target from different respective positions in relation to the scan target. For instance, the two or more respective positions may be selected by controlling a spacing between the two or more respective positions to enable determination of parallax disparity between a first image captured at a first position and a second image captured at a second position of the two or more positions. The UAV may determine a three-dimensional model corresponding to the scan target based in part on the determined parallax disparity of the two or more images including the first image and the second image.

Abstract: A holster for carrying a handgun of the type having a front section including a muzzle, a middle section including a trigger guard, and a back section including a grip, includes a holster upper section with walls configured to substantially enclose the middle section of a handgun when the handgun is carried in the holster. The holster also includes a generally open holster web portion that extends forward from the holster upper section and that defines a window through which is visible a substantial portion of the front section of a handgun when the handgun is carried in the holster.

Abstract: In some examples, an image of a scan target is presented in a user interface on a display associated with a computing device. The user interface receives at least one user input indicating at least one point in a perimeter or edge of a volume for encompassing the scan target presented in the image of the scan target. A graphical representation of the volume in relation to the image of the scan target is generated in the user interface. Information for defining a location of at least a portion of the volume in three-dimensional space is sent to an unmanned aerial vehicle (UAV) to cause, at least in part, the UAV to scan at least a portion of the scan target corresponding to the volume.

Abstract: This disclosure describes systems, methods, and devices related to multi-modal trip planning and event coordination. A method may include detecting, by a first device, based on first location data of a vehicle, that the vehicle has arrived at a destination location for a passenger of the vehicle; generating directions from the vehicle at the destination location to a physical structure at the destination location; presenting the directions; detecting an image of the passenger exterior to the vehicle at the destination location; identifying, by the at least one processor, based on the image, user information associated with the passenger at the destination location; detecting, based on the user information and second location data associated with a second device of the passenger, a disorientation event associated with the passenger; generating, based on the detecting of the disorientation event, instructions to be presented to the passenger; and presenting the instructions.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may include one or more processors configured to capture, with one or more image sensors, and while the UAV is in flight, a plurality of images of a target. The one or more processors may compare a first image of the plurality of images with a second image of the plurality of images to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. In addition, the one or more processors may determine, based at least on the difference, and while the UAV is in flight, an update to a three-dimensional model of the target.

Abstract: A computer accesses an input element storage and an output element storage. The computer accesses a symbolic expression for output element storage as a function of the input element storage. The computer computes, using a symbolic computation engine of the computer, a symbolic expression for the tangent space Jacobian of the output element storage with respect to an input tangent space. The computer outputs the computed expression.

Abstract: The disclosure is generally directed to systems and methods associated with a vehicle that is configured to provide photography assistance. An example method executed by processor of a vehicle may include generating a guidance associated with a photo capture operation and conveying the guidance to a vehicle controller. The processor may assist the vehicle controller to execute a vehicle maneuvering operation to capture a photograph of an object of photographical interest located outside the vehicle. The vehicle maneuvering operation can include parking the vehicle at a location that provides a view of the object or slowing down the vehicle while driving past the location. The photo capture operation can include the processor determining that the object is in a field of view of a camera mounted on the vehicle, configuring the camera to capture a photograph of the object, and operating the camera to capture the photograph of the object.