Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.

Abstract: In one embodiment, a distributed oscillator computes a first error value based on a first state value for the distributed oscillator and first state values for other distributed oscillators, and computes a second error value based on a second state value for the distributed oscillator and second state values for the other distributed oscillators. The distributed oscillator computes a new first state value for the distributed oscillator based on the first error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator, and computes a new second state value for the distributed oscillator based on the second error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator. The distributed oscillator transmits the new first state value and the new second state value to the other distributed oscillators.

Abstract: Systems and methods include receiving, values of one or more first external time variables from a first external node and values of one or more second external time variables from a second external node. The values of one or more local time variables of the local node are adjusted based at least upon the values of the one or more first external time variables and the values of the one or more second external time variables.

Abstract: An example unmanned aerial vehicle traffic signal and related methods are disclosed. The example unmanned aerial vehicle includes a housing, a rotor, a motor, a sensor, a traffic signal, and a processor. The rotor is to lift the housing off ground. The motor is to drive the rotor. The sensor is to monitor traffic. The traffic signal is carried by the housing. The processor is to control the traffic signal based on the traffic monitored by the sensor.

Abstract: Unmanned aerial vehicles include a processor to determine whether a first location of an unmanned vehicle and a second location of a virtual event is within a threshold distance; and a game experience controller to: control the unmanned vehicle based on a first command associated with a non-augmented state of the unmanned vehicle in response to the first location of the unmanned vehicle and the second location of the virtual event being outside of the threshold distance; and in response to the first location of the unmanned vehicle and the second location of the virtual event being within the threshold distance, control the unmanned vehicle based on a second command associated with an augmented state of the unmanned vehicle to simulate the unmanned vehicle being affected by the virtual event.

Abstract: In one embodiment, a distributed oscillator computes a first error value based on a first state value for the distributed oscillator and first state values for other distributed oscillators, and computes a second error value based on a second state value for the distributed oscillator and second state values for the other distributed oscillators. The distributed oscillator computes a new first state value for the distributed oscillator based on the first error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator, and computes a new second state value for the distributed oscillator based on the second error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator. The distributed oscillator transmits the new first state value and the new second state value to the other distributed oscillators.

Abstract: Methods and apparatus to implement nonlinear control of vehicles moved using multiple motors, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a logic circuit configured to calculate virtual position control variables for a vehicle moved using multiple motors. The virtual position control variables calculated based on an application of a control law to position information of the vehicle. The control law is derived from a nonlinear model of movement of the vehicle. The logic circuit is configured further to calculate control inputs based on the virtual position control variables. The control inputs are to control the motors to navigate the vehicle along a designated path of movement.

Abstract: Systems and methods include receiving, values of one or more first external time variables from a first external node and values of one or more second external time variables from a second external node. The values of one or more local time variables of the local node are adjusted based at least upon the values of the one or more first external time variables and the values of the one or more second external time variables.

Abstract: Methods and apparatus to implement nonlinear control of vehicles moved using multiple motors, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a logic circuit configured to calculate virtual position control variables for a vehicle moved using multiple motors. The virtual position control variables calculated based on an application of a control law to position information of the vehicle. The control law is derived from a nonlinear model of movement of the vehicle. The logic circuit is configured further to calculate control inputs based on the virtual position control variables. The control inputs are to control the motors to navigate the vehicle along a designated path of movement.

Abstract: Unmanned aerial vehicles and related methods and systems are disclosed. An example apparatus includes a processor to determine whether a first location of an unmanned vehicle and a second location of a virtual event is within a threshold distance; and a game experience controller to: control the unmanned vehicle based on a first command associated with a non-augmented state of the unmanned vehicle in response to the first location of the unmanned vehicle and the second location of the virtual event being outside of the threshold distance; and in response to the first location of the unmanned vehicle and the second location of the virtual event being within the threshold distance, control the unmanned vehicle based on a second command associated with an augmented state of the unmanned vehicle to simulate the unmanned vehicle being affected by the virtual event.

Abstract: An example unmanned aerial vehicle traffic signal and related methods are disclosed. The example unmanned aerial vehicle includes a housing, a rotor, a motor, a sensor, a traffic signal, and a processor. The rotor is to lift the housing off ground. The motor is to drive the rotor. The sensor is to monitor traffic. The traffic signal is carried by the housing. The processor is to control the traffic signal based on the traffic monitored by the sensor.