Telefonaktiebolaget Lm Ericsson (publ)

José Araújo

Bengt Lindoff

Volodya Grancharov

Harald Gustafsson

Robert Baldemair

Paul Schliwa-Bertling

Yufei W Blankenship

Yi-Pin Eric Wang

John Walter Diachina

Stefano Sorrentino

Lars M Andersson

Soma Tayamon

AmirHossein Taher Kouhestani

Zeid Al-Husseiny

Guoqiang Zhang

Aitor Hernandez Herranz

Leonard Bruns

Sorour Falahati

Pradeepa Ramachandra

Miguel Lopez

Kumar Balachandran

Joachim Sachs

Andreas Höglund

Torsten Dudda

Osman Nuri Can Yilmaz

Jonas Fröberg Olsson

Jose Luis Pradas

Niklas Andgart

Mattias Andersson

Sándor Rácz

János Farkas

Ying Sun

Dinand Roeland

György Miklós

Henrik Enbuske

Stefano Ruffini

Patrik Salmela

Bernard Smeets

Nianshan Shi

Zhenhua Zou

Sara Sandberg

Dennis Sundman

Anna Larmo

Balázs Varga

Magnus Sandgren

Kittipong Kittichokechai

Geza Szabo

Kimmo Juhani Hiltunen

Alexey Shapin

Till Burkert

Per Persson

Norbert Reider

András Kern

Abdulrahman Alabbasi

Tommy Falk

Joakim Persson

Alexandros Palaios

Gabor Nemeth

Zsolt Kenesi

Ola Angelsmark

Kun Wang

Johan Lundsjö

Bikramjit Singh

Ananya Muddukrishna

Diego Gonzalez Morin

Ioannis Karagiannis

Lars Andersson

Majid Gerami

Hubertus Andreas Munz

Dhruvin Patel

Mårten Wahlström

Alvin Jude Hari Haran

Johan Kristofer Olsson

Manish Sonal

Alfredo Fanghella

Fredrik Svensson

Johannes Nygren

Roberto Morabito

Gunilla Berndtsson

Muhammad Ikram Ashraf

Anders Höglund

Per Skarin

Torgny Holmberg

Malgorzata Svensson

Fedor Chernogorov

Paula Carbó Cubero

Simona Gugliermo

Rodrigo Berg

Wolfgang Tonutti

Sandra Hernandez

Elisa Baldassari

Zed Al-Husseiny

Paula Cubero

Amir Hossein Taher Kouhestani

A mobile computing device (MCD,) for controlling concurrent execution of perception algorithms is provided, comprising network interface circuitry and processing circuitry. The MCD is operative to acquire () sensor data from one or more sensors, representing a physical environment in which the MCD is located, execute () an application which is dependent on one or more perception algorithms processing the sensor data, the perception algorithm(s) being subject to respective perception-performance constraints and a power-consumption constraint, determine () operating conditions of the MCD which have an impact on perception performance and power consumption of the perception algorithm(s), estimate () a level of contention for a local computing resource on which at least one of the perception algorithms and the application, or at least two of the perception algorithms, can be executed concurrently, estimate () a probability of failure for at least one of the perception algorithm(s) based on the estimated level of contention, respective current configurations of the perception algorithm(s), a current configuration of the local computing resource, and the determined operating conditions, and if () the estimated probability of failure exceeds a failure threshold, offload () execution of the at least one perception algorithm to an external computing resource (), else () execute () the at least one perception algorithm on the local computing resource.

Controlling concurrent execution of perception algorithms

Techniques for enhancing performance in Industrial Internet-of-Things (IIoT) scenarios, including techniques for time-sensitive networking (TSN) and 5G wireless network integration. An example method, performed by a wireless device, comprises receiving system information (SI) from a radio base station (RBS) of a radio access network (RAN), the SI being indicative of support for TSN through the RBS, and establishing at least one TSN stream with an external data network, through the RBS. The example method further includes receiving a first timing signal from the wireless communications network, via the RBS, receiving a second timing signal from the external TSN data network to which the wireless device is connected, comparing the first timing signal to the second timing signal to determine an offset, and transmitting the offset to the wireless communications network.

Industrial automation with 5G and beyond

A device is disclosed that is configured to perform localization using one or both of a monocular camera and a depth sensor that are transportable with the device. The device includes at least one processor operationally connected to the monocular camera and the depth sensor. The device also includes at least one memory storing program code that is executed by the at least one processor to perform operations to receive image data from the monocular camera. The operations determine a benefit level of activating the depth sensor for localization, based on the image data, and activate the depth sensor for localization based on a determination that the benefit level of activating the depth sensor satisfies an activation rule. Related methods and computer program products are also disclosed.

Depth sensor activation for localization based on data from monocular camera

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.

Head-mounted display device and method thereof

A mobile communications device for performing localization and mapping is disclosed, comprising a sensor operative to capture sensor data of its local environment, and operative to derive point-cloud data from the sensor data, which represents structural features of the local environment, determine a pose of the mobile communications device relative to a point-cloud map representing the local environment, derive a concealed representation of the point-cloud data, by applying a current linear transformation to the point-cloud data, and transmit the concealed representation of the point-cloud data to a map server. The current linear transformation is a shared secret, or is derivable from a shared secret, which shared secret is available to a group of mobile communications devices comprising the mobile communications device. Also disclosed are the map sever and a trusted server.

Localization and mapping by a group of mobile communications devices

A method and a communication device for changing the shape of a flexible communication device is suggested. The suggested method is initiated by recognizing a request for actuating a haptic effect on the communication device. At least one property associated with the requested haptic effect, and at least one property of the communication device, where the latter is being caused by the shape of the communication device, are determined. Based on the mentioned properties, a determination is made on whether conditions, specifying requirements for reproducing the haptic effect on the communication device, are met or not. In case the mentioned conditions are not met, a modification of the shape of the device is determined, such that after such a modification the conditions are met with respect to the mentioned properties.

Method for and a communication device adapted to modify the shape of the device

A method performed by a device for occlusion handling in augmented reality is provided. The device can generate at least one pixel classification image in a frame including an occluding object and having foreground, background, and unknown pixels. Generation of the at least one pixel classification image can include (1) calculating an initial foreground pixel probability image, and an initial background pixel probability image, and (2) calculating a normalized depth image based on depth information of the occluding object. The device can obtain an alpha mask to blend a virtual object and the foreground of the at least one pixel classification image based on determining a color of the unknown pixels. The device can render a final composition of an augmented reality image containing the virtual object occluded by the occluding object based on applying the alpha mask to pixels in the at least one pixel classification image.

Methods for handling occlusion in augmented reality applications using memory and device tracking and related apparatus

A mobile electronic device is provided for use with a headset. A camera outputs digital pictures of a portion of the headset. A display device displays information for viewing by a user wearing the headset. A processor retrieves calibration parameters that characterize at least a pose of the camera relative to the display device, and processes a digital picture from the camera to identify a pose of an optically identifiable feature within the digital picture. A pose of the mobile electronic device is identified relative to the holder based on the identified pose of the optically identifiable feature within the digital picture and based on at least the pose of the camera relative to the display device as characterized by the calibration parameters. The processor controls where graphical objects are rendered on the display device based on the identified pose of the mobile electronic device relative to the holder.

Calibration of mobile electronic devices connected to headsets wearable by users

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.

Method and arrangement for handling haptic feedback

A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

Coordinating alignment of coordinate systems used for a computer generated reality device and a haptic device

A device is disclosed that is configured for performing localization using one or both of a depth sensor and a monocular camera that are transportable with the device. The device is adapted to receive depth data from the depth sensor, determine a benefit level of activating the monocular camera for localization, based on the depth data, and activate the monocular camera for localization based on a determination that the benefit level of activating the monocular camera satisfies an activation rule. Related methods and computer program products are also disclosed.

Monocular camera activation for localization based on data from depth sensor

A method of controlling a portable device including a camera, the method including: performing localization of the portable device using a localization algorithm having as an input image data representing images captured by the camera; identifying a current focus area of the camera; determining a desired focus area for the camera; determining that a change of focus is required when the desired focus area is different to the current focus area; and generating a control signal configured to cause the focus of the camera to change in respect of a new focus area.

Method of controlling a camera

A method for detecting a change in an area of interest. The method includes: 1) forming a pointcloud that comprises: i) a first set of points derived from a first set of one or more images and ii) a second set of points derived from a second set of one or more images; 2) for each point included in the pointcloud, assigning a similarity score to the point; 3) for at least each point from the pointcloud that is included in a particular set of points (e.g., for each point in the pointcloud that is assigned a similarity score that exceeds a threshold (e.g. 0)), determining a value Nt and a value Nnew and adjusting the similarity score assigned to the point based on Nt and Nnew; and 4) using the adjusted similarity scores to determine whether the area of interest has changed.

Methods and apparatuses for detecting a change in an area of interest

It is presented a method for controlling display of content, the method being performed in a wearable device, the wearable device comprising a head mounted display allowing a user to see both real-world objects and computer-rendered content. The method comprises the steps of: selecting an external display device in a vicinity of the wearable device; determining first visual content to be suitable for being displayed on the external display device; and sending a request for the external display device to display the first visual content and refraining from displaying the first visual content using the head mounted display.

Controlling display of content using an external display device

Growing community of inventors

Stockholm, Sweden

José Araújo