Abstract: An electronic device, having a primary function different from detecting a fire, is configured for detecting a fire. The device includes processor circuitry, and a temperature sensor configured to generate temperature data, wherein the processor circuitry is configured to determine whether a first generated temperature data meets a first temperature criteria at a first time, and determine whether a second generated temperature data meets a second temperature criteria at a second time after the first time.

Abstract: A server obtains a first model for a first area and first operational data associated with the first area. The server determines a second model, based on the first operational data and the first model, and obtains a first performance parameter indicative of a performance of the first model. The server obtains a second performance parameter indicative of a performance of the second model and determines a model performance parameter based on the first and second performance parameters. The server determines whether the model performance parameter satisfies a first criterion. The server, when the model performance parameter does not satisfy the first criterion, determines whether the second performance parameter satisfies a second criterion. The server, when the second performance parameter does not satisfy the second criterion, determines a second area that is smaller than the first area; and obtains a third model for the second area.

Abstract: An electronic device includes memory circuitry, a wireless interface, and processor circuitry. The processor circuitry is configured to obtain positioning data and movement data from a wireless device. The processor circuitry is configured to determine, based on the positioning data, one or more stops including a first stop of a transportation journey of the wireless device. The processor circuitry is configured to determine, based on movement data associated with the first stop, a transition parameter associated with the first stop. The processor circuitry is configured to determine whether the transition parameter satisfies a first criterion indicative of a change of transportation mode at the first stop. The processor circuitry is configured to output, based on the transition parameter, a transportation parameter.

Abstract: A wireless device includes a positioning sensor, memory circuitry, a wireless interface, and processor circuitry. The processor circuitry is configured to obtain a channel quality parameter indicative of a channel quality of a wireless communication channel. The processor circuitry is configured to determine whether the channel quality parameter satisfies a first criterion. The processor circuitry is configured to, when the channel quality parameter satisfies the first criterion, determine, by using an activation model, a control parameter based on the channel quality parameter. The processor circuitry is configured to, when the channel quality parameter satisfies the first criterion, control the positioning sensor based on the control parameter.

Abstract: A server device is configured to provide a combined environment and includes processor circuitry configured to determine first parameters indicative of a first location, generate first environment data indicative of the first location, determine second parameters indicative of a second location, and associate the second parameters with the first environment data for providing combined environment data, and output the combined environment data.

Abstract: A control device operates a drone with an onboard camera. The control device obtains a current performance metric to be computed for an activity performed by an individual, determines, based on a positioning rule associated with the current performance metric, a selected relative position, SRP, between the individual and the onboard camera, identifies a reference plane of the individual, operates the drone to move the onboard camera from an initial relative position to attain the SRP in relation to the reference plane; operates the onboard camera, when in the SRP, to capture image(s) of the individual, and provides the image(s) for computation of the current performance metric for the activity performed by the individual. The SRP may be defined, by the positioning rule, to ensure that the orientation of the individual in the image(s) is relevant or optimal for the current performance metric.

Abstract: A method for monitoring a primary variable is carried out in a device having access to a set of sensors. The method includes the steps of receiving, from a network service, a series of forecasted values for the primary variable, each forecasted value being associated with one of a series of future time points; for at least one of the future time points, predicting a value for the primary variable using data of at least one secondary variable captured by a subset of the set of sensors, comparing the predicted value to the forecasted value associated with the future time point, and switching to a different subset of the set of sensors, if the predicted value deviates from the forecasted value with more than a specified threshold value.

Abstract: An electronic device configured to monitor an object. The electronic device is configured to generate a first area indicative of a person, generate a second area indicative of an object, determine, based on the first area and the second area, an interaction parameter indicative of an interaction between the person and the object, determine, based on the interaction parameter, a status indicator of the object, generate a maintenance indicator based on the status indicator, and output the maintenance indicator.

Abstract: An animal monitoring device includes memory circuitry, interface circuitry configured to communicate via short-range wireless communication to a second animal monitoring device and via cellular communication, and processor circuitry configured to obtain, from a first sensor, sensor data having a first transmission deadline and provide the sensor data into a sensor data set.

Abstract: Drones are controlled by one or more control devices and comprise a respective camera for image capture. The one or more control devices perform a method including obtaining projected flight paths of the drones, obtaining a projected camera setting of the respective camera, computing, as a function of the projected camera setting, a projected viewing frustum of the respective camera, defining, for the drones, projected time-space trajectories of no-fly zones based on the projected viewing frustum of the respective camera, analyzing the projected flight paths of the drones in relation to the projected time-space trajectories for detection of a violation of one or more of the no-fly zones, and setting an operative flight path and/or an operative camera setting for at least one selected drone to prevent the violation.

Abstract: A portable electronic device is controlled, for example to selectively activate a flight mode, based on first event data and/or second event data. The first event data is generated by operating a first model for detection of airplane flight events, AFEs, on first sensing data representing ambient pressure. The second event data is generated by operating a second model for detection of AFEs on second sensing data representing own motion or ambient sound. A control method in the portable electronic device generates training data comprising groups of time-aligned data samples from the first event data and the second sensing data, generates an updated second model by use of the training data, and replaces the second model by the updated second model. The control method facilitates adaptation of the portable electronic device to detect airplane flight events in new environments and enables improved robustness when detecting AFEs.

Abstract: A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

Abstract: An electronic device that includes a power source and a wireless transmitter is controlled by a method. The method predicts a magnitude of surplus energy in the power source under the assumption that the electronic device is operated in accordance with a default schedule over a time period. The default schedule defines sampling of sensor data from at least one sensor and transmission of the sensor data by use of the wireless transmitter. The method further determines, based on the magnitude of surplus energy, a modified schedule that results in sampling of an increased amount of sensor data per unit time compared to the default schedule, and configures the electronic device to operate in accordance with the modified schedule for at least part of the time period. The method increases availability of sensor data without compromising the mission of the electronic device.

Abstract: A method (100) is disclosed, the method (100) being performed by a server device, where the server device (400) is configured to communicate with a first electronic device (300) of a plurality of electronic devices (300, 300A-300I), the first electronic device being configured to operate using a processing scheme based on a machine-learning model. The method (100) comprises receiving (S102), from the first electronic device (300), an update request for updating the machine-learning model, the update request comprising sensor data indicative of a context surrounding the first electronic device (300); generating (S107) an updated machine-learning model based on the sensor data of the update request; and transmitting (S110) an update response comprising the updated machine-learning model to the first electronic device (300).

Abstract: A method for updating a neural network model on a computation device. The method includes estimating a bandwidth for data download from a server device to the computation device; estimating a time point of available computation capacity of the computation device; computing a maximum partition size as a function of the bandwidth and the time point; and causing download of a selected partition of the neural network model from the server device to the computation device, the selected partition being determined based on the maximum partition size. The method enables the selected partition to be executed by the computation device upon downloading and thereby provides for seamless updating of the neural network model while it is being executed on the computation device.

Abstract: A method for controlling upload of data, performed in an electronic device (100), wherein said electronic device is configured to intermittently upload data (520) to a wireless network to operate as a tracker, said method comprising: obtaining (500) a current position of the electronic device, determining, dependent on network coverage associated with said position, to either upload data (530) to the network or postpone (430) upload of data.

Abstract: The present disclosure provides a method, performed at an electronic device, for determining a geofence parameter of a geofence area related to a point of interest, POI. The method comprises obtaining a location of the POI, obtaining first POI data based on the location of the POI. The method may comprise determining, based on the location of the POI, one or more entities in proximity of the POI. The method comprises obtaining second POI data related to at least one entity of the one or more entities. The method comprises generating a set of enclosing features related to the POI based on the second POI data, wherein generating the set of enclosing features comprises applying a processing scheme to the second POI data; and determining a geofence parameter based on the first POI data and the set of enclosing features.

Abstract: A system performs a control method to launch an unmanned aerial vehicle, UAV. The control method includes calculating a selected altitude, relative to a reference level, for launching the UAV, operating a lifting arrangement to vertically elevate the UAV to the selected altitude, and causing the UAV to be launched from the selected altitude. Elevating the UAV may improve the performance of the UAV in terms of range, power consumption, ability to carry payload, transit time to destination, etc. The selected altitude may be calculated as a function of parameter data representing any of the UAV, the lifting arrangement, or surrounding conditions. An apparatus is further provided to determine an optimal location of the system in relation to a plurality of destinations of UAVs to be elevated by the system.

Abstract: A scheduling device performs a method of scheduling transports by a fleet of unmanned aerial vehicles, UAVs, to enable improved utilization of the fleet. The method includes computing, for the fleet, aggregated cost data, ACM, that associates utilization cost with distributed sub-regions within a geographic region, the utilization cost of a respective sub-region representing an estimated cost for directing at least one of the UAVs to the respective sub-region. The method further includes receiving a query indicative of one or more potential locations for pick-up or delivery of a payload, determining, based on the ACM, a transportation price for at least one potential location, and presenting the transportation price for the at least one potential location. The scheduling device thereby provides an interactive ordering system that enables utilization of the fleet to be automatically optimized by swarm intelligence.

Abstract: A control system in a delivery system performs a method to deliver a payload by an unmanned aerial vehicle, UAV, without requiring presence of the recipient even if the payload comprises sensitive or vulnerable goods. The method comprises obtaining a designated location for delivery of the payload, obtaining at least one payload vulnerability index for the payload, and obtaining a pick-up time indicative of a time point when the payload is expected to be retrieved by the recipient. The method further comprises selecting a final delivery area at or near the designated location as a function of the at least one payload vulnerability index and the pick-up time, and operating the UAV to deliver the payload to the final delivery area.