TRANSLATING SENSORY COMMUNICATIONS
    1.
    发明申请

    公开(公告)号:WO2023007213A1

    公开(公告)日:2023-02-02

    申请号:PCT/IB2021/056806

    申请日:2021-07-27

    Abstract: A computer implemented method (100) is disclosed for translating a sensory input to a sensory output for communication between first and second entities, wherein the first entity comprises a user and the second entity comprises a user or a computing system. The method comprises obtaining a first profile specifying a communication capability of the first entity, and a second profile specifying a communication capability of the second entity (110) and obtaining a trained Machine Learning (ML) model operable to map an input sensory communication in accordance with one of the first or second profiles to an output sensory communication in accordance with the other of the first or second profiles (120). The method further comprises receiving an input sensory communication from one of the first or second entities (130), using the ML model to map the input sensory communication to an output sensory communication (140), and providing the output sensory communication to the other of the first or second entities (150).

    GRAPH-BASED SYSTEMS AND METHODS FOR CONTROLLING POWER SWITCHING OF COMPONENTS

    公开(公告)号:WO2022063391A1

    公开(公告)日:2022-03-31

    申请号:PCT/EP2020/076411

    申请日:2020-09-22

    Abstract: A computer implemented method of managing power control in a communication system includes generating a graph representation of interdependencies of components of the communication system, wherein the graph representation includes graph nodes corresponding to the components of the communication system and edges between pairs of graph nodes representing dependency relationships between the pairs of nodes. The method generates edge weights for the edges of the graph representation that correspond to the relative importance of the dependency relationship represented by the edge weight, and generates a policy for managing power control by determining an order for switching the components of the communication system on or off based on the edge weights.

    OBJECT TRACKING
    5.
    发明申请
    OBJECT TRACKING 审中-公开

    公开(公告)号:WO2022012744A1

    公开(公告)日:2022-01-20

    申请号:PCT/EP2020/069909

    申请日:2020-07-14

    Abstract: There is provided a method comprising: acquiring (110) sensor data related to an object; using the first learning module, identifying (120) the object based on the acquired sensor data using a first learning module and determining (130) a user associated with the identified object; determining (140) a timestamped location of the object based on at least one of the acquired sensor data and one or more locations of the one or more sensors; performing (150) a first analysis to determine whether the current status of the object contains an anomaly based on one or more predefined rules stored in a knowledge base; performing (160) a second analysis to determine whether the current status of the object contains an anomaly, using a second learning module; and validating (170) whether the current status of the object contains an anomaly based on results of the first analysis and results of the second analysis.

    ACCESS CONTROL NODE, ACCESS DEVICE, TETHERING DEVICE, AND METHODS THEREIN, FOR PROVIDING WIRELESS ACCESS
    9.
    发明申请
    ACCESS CONTROL NODE, ACCESS DEVICE, TETHERING DEVICE, AND METHODS THEREIN, FOR PROVIDING WIRELESS ACCESS 审中-公开
    接入控制节点,接入设备,收听设备及其中用于提供无线接入的方法

    公开(公告)号:WO2017129226A1

    公开(公告)日:2017-08-03

    申请号:PCT/EP2016/051460

    申请日:2016-01-25

    CPC classification number: H04W48/17 H04W16/26 H04W24/02 H04W88/04

    Abstract: Access control node (200), access device (202A), tethering device (204), and methods therein, for enabling wireless access to a communications network (208). One or more access devices (202) having a wireless connection to the network (208) provide (2:1) relay properties to an access control node (200). When detecting (2:2) that network access is wanted for the tethering device (204), the access control node (200) selects (2:3) an access device (202A) based on the obtained relay properties, to be used for sharing wireless connection with the tethering device (204). The access control node (200) then instructs (2:4) the selected access device (202A) to be available as a relay to the communications network (208) for the tethering device (204) via a wireless link between the access device (202A) and the tethering device (204). The tethering device (204) can then access (2:8) the communications network over the wireless link. By using the relay properties as a basis for selecting the access device (202A), the performance of the wireless network access can be improved and unwanted battery consumption can be avoided. Furthermore, no manual actions are required to achieve the wireless network access.

    Abstract translation: 访问控制节点(200),访问设备(202A),共享设备(204)及其中的方法,用于实现对通信网络(208)的无线访问。 具有到网络(208)的无线连接的一个或多个接入设备(202)向接入控制节点(200)提供(2:1)中继属性。 当检测到(2:2)网络接入对于共享设备(204)是想要的时,接入控制节点(200)基于获得的中继属性选择(2:3)接入设备(202A),以用于 与共享设备(204)共享无线连接。 接入控制节点(200)然后通过接入设备(204)之间的无线链路指示(2:4)选择的接入设备(202A)作为到网络共享设备(204)的通信网络(208) 202A)和束缚装置(204)。 共享设备(204)然后可以通过无线链路访问(2:8)通信网络。 通过使用中继属性作为选择接入设备(202A)的基础,可以改善无线网络接入的性能并且可以避免不希望的电池消耗。 此外,无需手动操作即可实现无线网络访问。

    ENERGY-EFFICIENT DEEP NEURAL NETWORK TRAINING ON DISTRIBUTED SPLIT ATTRIBUTES

    公开(公告)号:WO2022173356A1

    公开(公告)日:2022-08-18

    申请号:PCT/SE2022/050144

    申请日:2022-02-11

    Abstract: A method of operating a master node in a vertical federated learning, vFL, system including a plurality of workers for training a split neural network includes receiving layer outputs for a sample period from one or more of the workers for a cut-layer at which the neural network is split between the workers and the master node, and determining whether layer outputs for the cut-layer were not received from one of the workers. In response to determining that layer outputs for the cut-layer were not received from one of the workers, the method includes generating imputed values of the layer outputs that were not received, calculating gradients for neurons in the cut-layer based on the received layer outputs and the imputed layer outputs, splitting the gradients into groups associated with respective ones of the workers, and transmitting the groups of gradients to respective ones of the workers.

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