Abstract: A head-mounted display device (HMD) configured to be worn by a user is provided. The HMD comprises an at least partially see-through display, a front-facing camera operative to capture a first image of a real-world scene, and processing means which is operative to select a calibration object from one or more real-world objects, or parts thereof, which are visible in the first image, and derive a calibration transformation for calculating a display position based on a real-world position, such that a virtual object which is displayed on the display at the display position is aligned with a corresponding real-world object located at the real-world position, as seen by the user.

Abstract: A method performed in an entity for transmitting in a communications system. The method comprises assessing a channel to be available for transmission by establishing an on-going transmission to have a received signal strength such as to allow simultaneous transmission in view of a first clear channel assessment threshold; establishing type of transmission of the on-going transmission; and determining, based on the established type of transmission, whether to transmit or to defer transmitting. A corresponding entity, computer programs and computer program products are also provided.

Abstract: A method performed in an entity for transmitting in a communications system. The method comprises assessing a channel to be available for transmission by establishing an on-going transmission to have a received signal strength such as to allow simultaneous transmission in view of a first clear channel assessment threshold; establishing type of transmission of the on-going transmission; and determining, based on the established type of transmission, whether to transmit or to defer transmitting. A corresponding entity, computer programs and computer program products are also provided.

Abstract: A method, executed at a controller for adapting a virtual object model, and a controller for executing the suggested method, where the method comprises acquiring of a virtual object model of a relevant virtual object and acquiring of at least one real object model of a relevant real object. Matching, according to at least one matching criteria, at least a part of the virtual object model to at least a part of one of the at least one real object model, selecting the best matching real object, and modifying at least a part of the virtual object model with respect to the at least part of the selected real object model.

Abstract: Systems and methods are disclosed herein for Deep Reinforcement Learning (DRL) based packet scheduling. In one embodiment, a method performed by a network node for DRB-based scheduling comprises performing a DRL-based scheduling procedure using a preference vector for a plurality of network performance metrics correlated to one of a plurality of desired network performance behaviors, the preference vector defining weights for the plurality of network performance metrics correlated to the one of the plurality of desired network performance behaviors. In this manner, DRL-based scheduling is provided in a manner in which multiple performance metrics are jointly optimized.

Abstract: A method, executed at a controller for adapting a virtual object model, and a controller for executing the suggested method, where the method comprises acquiring of a virtual object model of a relevant virtual object and acquiring of at least one real object model of a relevant real object. Matching, according to at least one matching criteria, at least a part of the virtual object model to at least a part of one of the at least one real object model, selecting the best matching real object, and modifying at least a part of the virtual object model with respect to the at least part of the selected real object model.

Abstract: According to a first aspect, it is presented a method for dynamically selecting a learning model for a sensor device. The learning model is configured for determining output data based on sensor. The method includes the steps of: detecting a need for a new learning model for the sensor device based on performance of a currently loaded learning model in the sensor device; determining at least one feature candidate based on sensor data from the at least one sensor, wherein each one of the at least one feature candidate is associated with a different source of sensor data; selecting a new learning model, from a set of candidate learning models, based on the at least one feature candidate and input features of each one of the candidate learning models; and triggering the new learning model to be loaded on the sensor device, replacing the currently loaded learning model.

Abstract: It is presented a method performed in a vehicle action determiner for determining an undesired action of a first vehicle and a resulting action. The method comprises: detecting that a first user is focusing on the first vehicle; acquiring brain activity data of the first user; determining a negative reaction of the first user based on the brain activity data; determining when the first vehicle is performing an undesired action based on the first user focusing on the first vehicle and the negative reaction of the first user; determining the resulting action taken by an autonomous vehicle, based on the first vehicle performing the undesired action; and triggering the autonomous vehicle to perform the resulting action.

Abstract: According to a first aspect, it is presented a method for dynamically selecting a learning model for a sensor device. The learning model is configured for determining output data based on sensor. The method comprises the steps of: detecting a need for a new learning model for the sensor device based on performance of a currently loaded learning model in the sensor device; determining at least one feature candidate based on sensor data from the at least one sensor, wherein each one of the at least one feature candidate is associated with a different source of sensor data; selecting a new learning model, from a set of candidate learning models, based on the at least one feature candidate and input features of each one of the candidate learning models; and triggering the new learning model to be loaded on the sensor device, replacing the currently loaded learning model.

Abstract: A head-mounted display device (HMD) configured to be worn by a user is provided. The HMD comprises an at least partially see-through display, a front-facing camera operative to capture a first image of a real-world scene, and processing means which is operative to select a calibration object from one or more real-world objects, or parts thereof, which are visible in the first image, and derive a calibration transformation for calculating a display position based on a real-world position, such that a virtual object which is displayed on the display at the display position is aligned with a corresponding real-world object located at the real-world position, as seen by the user.

Abstract: A head-mounted display device (HMD) (100) configured to be worn by a user (121) is provided. The HMD comprises an at least partially see-through display (101), a front-facing camera (102) operative to capture a first image of a real-world scene, and processing means (104) which is operative to select a calibration object from one or more real-world objects, or parts thereof, (130-135) which are visible in the first image, and derive a calibration transformation for calculating a display position based on a real-world position, such that a virtual object which is displayed on the display at the display position is aligned with a corresponding real-world object located at the real-world position, as seen by the user.

Abstract: An input device (100) for a computing device (150) is provided, such as a data glove. The input device (100) comprises motion sensor(s) (103) configured to be worn on a fingertip or fingertips of a user, and actuator(s) (104, 114) configured to restrict motion of the fingertip(s) to be within a three-dimensional shape. The input device (100) is operative to track a position of the fingertip(s) using the motion sensor(s) (103), provide the tracked position as user input to the computing device (150), determine whether the computing device requires user input from the fingertip to be within the three-dimensional shape, and in response thereto, activate the actuator(s) (104, 114) configured to restrict motion of the fingertip(s) to be within the three-dimensional shape if the computing device (150) requires user input from the fingertip(s) to be within the three-dimensional shape.

Abstract: It is provided a method for enabling remote control of a vehicle with autonomous propulsion capability. The method is performed by a vehicle data provider and comprises: detecting a need for manual assistance of the vehicle by an operator being remote from the vehicle; obtaining a stream of vehicle data, the vehicle data relating to a time prior to when remote control starts; modifying the vehicle data, which comprises adjusting a duration of playback of the vehicle data; providing the modified vehicle data for playback to the operator; providing, once the playback of modified vehicle data has ended, vehicle data in real-time to the operator; and enabling remote control of the vehicle by the operator.

Abstract: A method, executed at a controller for adapting a virtual object model, and a controller for executing the suggested method, where the method comprises acquiring of a virtual object model of a relevant virtual object and acquiring of at least one real object model of a relevant real object. Matching, according to at least one matching criteria, at least a part of the virtual object model to at least a part of one of the at least one real object model, selecting the best matching real object, and modifying at least a part of the virtual object model with respect to the at least part of the selected real object model.

Abstract: A method, executed at a controller for adapting a virtual object model, and a controller for executing the suggested method, where the method comprises acquiring of a virtual object model of a relevant virtual object and acquiring of at least one real object model of a relevant real object. Matching, according to at least one matching criteria, at least a part of the virtual object model to at least a part of one of the at least one real object model, selecting the best matching real object, and modifying at least a part of the virtual object model with respect to the at least part of the selected real object model.

Abstract: A method performed in an entity for transmitting in a communications system. The method comprises assessing a channel to be available for transmission by establishing an on-going transmission to have a received signal strength such as to allow simultaneous transmission in view of a first clear channel assessment threshold; establishing type of transmission of the on-going transmission; and determining, based on the established type of transmission, whether to transmit or to defer transmitting. A corresponding entity, computer programs and computer program products are also provided.

Abstract: A network node for an orthogonal Frequency Division Multiplexing Access, OFDMA, based wireless local area network is provided. The network node includes processing circuitry. The processing circuitry includes a processor and a memory. The memory contains instructions that, when executed by the processor, configure the processor to reduce transmission time inequality among each of a plurality of wireless device during an OFDMA subframe by assigning a respective OFDMA subband of a plurality of OFDMA subbands to each of the plurality of wireless devices, and cause data transmission on the assigned OFDMA subbands to the plurality of wireless device.

Abstract: According to a first aspect, it is provided a method for determining control parameters for controlling flight of a formation comprising at least two physically connected UAVs. The method is performed in a controller device and comprising the steps of: determining UAVs forming part of the formation; controlling each one of the UAVs, in sequence, to perform a lift off procedure in which the UAV lifts off ground and lands on ground; acquiring, for each lift off procedure, inertial data from Inertial Measurement Units, IMUs, of each one of the UAVs of the formation; estimating UAV connection parameters for each possible pair of UAVs of the formation based on the inertial data, the UAV connection parameter comprising at least one of relative orientation, absolute orientations and distance between the UAVs of the pair; and determining control parameters for controlling flight of the formation based on the estimated connection parameters.

Abstract: A haptic garment, such as a haptic glove, is provided. The haptic glove is operative to measure haptic properties of a skin of a user wearing the haptic glove, using haptic sensors, to transmit a representation of the measured haptic properties to another haptic garment, e.g., another haptic glove worn by another user, to receive a representation of measured haptic properties of a skin of the other user from the other haptic glove, and, in response to detecting contact between the haptic glove and the other haptic glove, to render the received representation of the haptic properties of the skin of the other user within an area of contact between the haptic glove and the other haptic glove using haptic actuators. The representations of measured haptic properties are exchanged via communications modules comprised in the haptic gloves.

Abstract: A method is disclosed for generating a haptic model including at least one virtual object which can be presented using both a visual display device and a haptic device to a user. Information of at least one candidate virtual object is obtained, the candidate virtual object including both a haptic representation and a visual representation. For each of the at least one candidate virtual object, reachability data, indicating whether the candidate virtual object is reachable for the user is determined. For each of the at least one candidate virtual object, visibility data, indicating whether the candidate virtual object is visible for the user is determined. A haptic model is generated selectively including at least one candidate virtual object, the inclusion of each candidate virtual object being based on its respective reachability and visibility data. The haptic model is provided to be rendered.