The present application provides an unmanned aerial vehicle (UAV)-assisted federated learning resource allocation method for an UAV-assisted federated learning wireless network scenario, which takes into account the effect of altitude of the UAV on the coverage range in order to achieve an equilibrium between the total energy consumption of the user and federated learning performance. The method simultaneously considers the total energy consumption of the user and the federated learning performance, defines the total cost function of the system. The total cost function consists of weighting of the total energy consumption of the user and the inverse of the number of users participating in federated learning, and forms the optimization problem with a minimization of the total cost function.

The present application provides an unmanned aerial vehicle (UAV)-assisted federated learning resource allocation method for an UAV-assisted federated learning wireless network scenario, which takes into account the effect of altitude of the UAV on the coverage range in order to achieve an equilibrium between the total energy consumption of the user and federated learning performance. The method simultaneously considers the total energy consumption of the user and the federated learning performance, defines the total cost function of the system. The total cost function consists of weighting of the total energy consumption of the user and the inverse of the number of users participating in federated learning, and forms the optimization problem with a minimization of the total cost function.

A return method or device for an unmanned aerial vehicle (UAV), a UAV and a storage medium are provided. The method includes: detecting whether a sensor for obstacle avoidance fails; if the sensor fails, determining a return path of the UAV based on a first return strategy; if the sensor operates normally, determining the return path of the UAV based on a second return strategy; the first return strategy includes controlling the UAV to fly to a return altitude; the second return strategy includes determining the return path of the UAV based on detection data from the sensor. The combination of these two return strategies can achieve a balance between the return efficiency and safety of the UAV.

A return method or device for an unmanned aerial vehicle (UAV), a UAV and a storage medium are provided. The method includes: detecting whether a sensor for obstacle avoidance fails; if the sensor fails, determining a return path of the UAV based on a first return strategy; if the sensor operates normally, determining the return path of the UAV based on a second return strategy; the first return strategy includes controlling the UAV to fly to a return altitude; the second return strategy includes determining the return path of the UAV based on detection data from the sensor. The combination of these two return strategies can achieve a balance between the return efficiency and safety of the UAV.

Systems, devices, and methods for an aircraft autopilot guidance control system for guiding an aircraft having a body, the system comprising: a processor configured to determine if a yaw angle difference and a pitch angle difference meet corresponding angle thresholds; a skid-to-turn module configured to generate a skid-to-turn signal if the corresponding angle thresholds are met; a bank-to-turn module configured to generate a bank-to-turn signal having a lower bandwidth than the generated skid-to-turn signal; a rudder integrator module configured to add a rudder integrator feedback signal to the bank-to-turn signal, where the rudder integrator feedback signal is proportional to a rudder integrator; and a filter module configured to filter the generated bank-to-turn signal, wherein the filter module comprises a low-pass filter configured by a set of gains to pass the bank-to-turn signal if a side force on the body meets a side force threshold.

An assembly and a method for maintaining and inspecting a wellbore are described. The assembly includes a body. Multiple magnetic wheel sub-assemblies are adjustably coupled to the body. The magnetic wheel sub-assemblies position the body within the wellbore and reposition relative to the body in response to a wellbore narrowing or a wellbore widening. The assembly includes a tool connection coupled to the body. The tool connection couples a well tool to the body. The assembly includes a control sub-assembly to control the magnetic wheel sub-assemblies and operate the well tool within the wellbore. The assembly includes a power sub-assembly to supply power to the control sub-assembly and the well tool. The assembly includes a backup retrieval mechanism coupled to the body. The backup retrieval mechanism couples to a retrieval tool.

Apparatuses, components and methods are provided herein useful to provide assistance to customers and/or workers in a shopping facility. In some embodiments, a shopping facility personal assistance system comprises: a plurality of motorized transport units located in and configured to move through a shopping facility space; a plurality of user interface units, each corresponding to a respective motorized transport unit during use of the respective motorized transport unit; and a central computer system having a network interface such that the central computer system wirelessly communicates with one or both of the plurality of motorized transport units and the plurality of user interface units, wherein the central computer system is configured to control movement of the plurality of motorized transport units through the shopping facility space based at least on inputs from the plurality of user interface units.

The present application provides an unmanned aerial vehicle (UAV)-assisted federated learning resource allocation method for an UAV-assisted federated learning wireless network scenario, which takes into account the effect of altitude of the UAV on the coverage range in order to achieve an equilibrium between the total energy consumption of the user and federated learning performance. The method simultaneously considers the total energy consumption of the user and the federated learning performance, defines the total cost function of the system. The total cost function consists of weighting of the total energy consumption of the user and the inverse of the number of users participating in federated learning, and forms the optimization problem with a minimization of the total cost function.

The present application provides an unmanned aerial vehicle (UAV)-assisted federated learning resource allocation method for an UAV-assisted federated learning wireless network scenario, which takes into account the effect of altitude of the UAV on the coverage range in order to achieve an equilibrium between the total energy consumption of the user and federated learning performance. The method simultaneously considers the total energy consumption of the user and the federated learning performance, defines the total cost function of the system. The total cost function consists of weighting of the total energy consumption of the user and the inverse of the number of users participating in federated learning, and forms the optimization problem with a minimization of the total cost function.

Methods and apparatuses are provided for use in monitoring product placement within a shopping facility. Some embodiments provide an apparatus configured to determine product placement conditions within a shopping facility, comprising: a transceiver configured to wirelessly receive communications; a product monitoring control circuit coupled with the transceiver; a memory coupled with the control circuit and storing computer instructions that when executed by the control circuit cause the control circuit to: obtain a composite three-dimensional (3D) scan mapping corresponding to at least a select area of the shopping facility and based on a series of 3D scan data; evaluate the 3D scan mapping to identify multiple product depth distances; and identify, from the evaluation of the 3D scan mapping, when one or more of the multiple product depth distances is greater than a predefined depth distance threshold from the reference offset distance of the product support structure.