Abstract: Embodiments disclosed herein relate to methods and apparatus for managing a sensor grid system including configuring and using sensor grids for various tasks. In one embodiment there is provided a method of configuring a sensor grid system having a plurality of sensors arranged to collect data from a working space. The method includes applying an output from the sensors to a task model for performing a task associated with the working space. A task accuracy parameter is corresponding to the accuracy with which the task model performs the task is determine. In response to the task accuracy parameter being below a task accuracy parameter threshold, the resolution of the output from the sensors is increased, and the complexity of the task model is increased.

Abstract: Methods, systems, and apparatuses for augmenting a quantum approximate optimization algorithm (QAOA) quantum circuit. Augmenting the quantum circuit may include adding to the quantum circuit one or more encoders (312) configured to perform unitary mapping of one-hot encoding basis states to basis states of a reduced number of qubits padded with one or more padding qubits in the zero computational basis state, mid-circuit measurements, and conditional resets. The mid-circuit measurements may be configured to cause one or more quantum processing units (QPUs) to measure one or more qubits of the augmented quantum circuit in the computational basis state. The one or more conditional resets may be configured to reset the augmented quantum circuit without full execution of a variational loop if one or more mid-circuit measurement results indicate that one or more qubits of the augmented quantum circuit are in an invalid state.

Abstract: A technique for determining a relative position of a preceding vehicle with respect to a succeeding vehicle in a vehicle platoon is disclosed, wherein the succeeding vehicle comprises at least two front tags and the preceding vehicle comprises at least two back tags, the front and back tags being configured to perform ranging measurements among each other using radio technology. A method implementation of the technique comprises determining (S202) pairwise distances between the front and back tags using ranging measurements performed by the front and back tags using the radio technology, and determining (S204) the relative position of the preceding vehicle with respect to the succeeding vehicle based on the determined pairwise distances.

Abstract: A system can be configured to provide root cause analysis (“RCA”) of an issue associated with a label generated by a machine learning (“ML”) model. The system can perform operations that include determining a plurality of categories associated with a plurality of features of the ML model. The operations can further include determining a causality relationship between each category of the plurality of categories. The operations can further include determining data associated with each feature of the plurality of features. The operations can further include determining the root cause of the issue using a model explainer with ordering constraints based on the causality relationship between each category of the plurality of categories. The operations can further include performing an action associated with the issue based on the root cause of the issue.

Abstract: A technique for locating a target tag (110) using a short range radio based positioning system comprising a plurality of localization components (120) is provided, wherein the target tag (110) and the plurality of localization components (120) are configured to perform ranging measurements among each other using short range radio technology. A method implementation of the technique is performed by an orchestration component (100) of the positioning system and comprises sending, using long range radio technology, a ranging plan to the target tag (110) and one or more of the plurality of localization components (120), the ranging plan instructing the target tag (110) and the one or more of the plurality of localization components (120) to perform, using the short range radio technology, ranging measurements among each other enabling to locate the target tag (110).

Abstract: Equipment that supports a non-public communication network trains a machine learning model with a training dataset to make a prediction or decision in the network. The equipment determines whether the trained model is valid or invalid based on whether predictions or decisions that the trained model makes from a validation dataset satisfy performance requirements. Based on the trained model being invalid, the equipment analyzes the training dataset and/or the trained model to determine what additional training data to add to the training dataset. The equipment transmits signaling for configuring one or more autonomous or automated mobile devices served by the network to help collect the additional training data. The equipment then re-trains the model with the training dataset as supplemented with the additional training data.

Abstract: The correlation of per-session network logs with trouble reports provided by the users/user devices via a trouble reporting app generates labeled training information used to train Machine Learning (ML) models that are subsequently used to infer, explain, and address service issues associated with an application service session. As such, the level of automation for Quality of Experience (QoE) management increases, while the accuracy of such QoE management increases and a subscriber's experience improves.

Abstract: We generally describe a detection system and a method for tracking a moving object. The detection system (101) comprises a sensor (102) which is configured to sense an event. The system further comprises a trigger detection module (108a) which is coupled to the sensor (102), wherein the trigger detection module (108a) is configured to identify the sensed event to be a trigger event. The system further comprises an imaging device (114) for imaging the trajectory of an object, and an imaging device control unit (112) for controlling the imaging device (114). The imaging device control unit (112) is coupled to the trigger detection module (180a), wherein the imaging device control unit (112) is configured to control the imaging device (114) in response to a trigger event being identified by the trigger detection module (108a).

Abstract: Methods and apparatus for root cause analysis. A method includes obtaining measurement data including measurements data of features of a system. The method further includes generating a prediction value by applying a trained machine learning model to the measurement data, and also generating feature impact values by applying a generated machine learning model explainer to the measurement data. The method further includes updating an ontological representation of connections between the features of the system and the prediction value using the generated feature impact values and, based on the updated ontological representation, outputting a proposed root cause that is responsible for the prediction value.

Abstract: The correlation of per-session network logs with trouble reports provided by the users/user devices via a trouble reporting app generates labeled training information used to train Machine Learning (ML) models that are subsequently used to infer, explain, and address service issues associated with an application service session. As such, the level of automation for Quality of Experience (QoE) management increases, while the accuracy of such QoE management increases and a subscriber's experience improves.

Abstract: A sensor control system (202) for managing at least a first set of one or more sensors (101) for monitoring a first domain of an industrial process and a second set of one or more sensors (102) for monitoring a second domain of the industrial process, wherein the sensor control system (202) comprises at least a first reinforcement learning, RL, agent (A1) and a second RL agent (A2), wherein the first and second RL agents were trained using reinforcement learning and a process graph (196) representing the industrial process.

Abstract: There is provided a method for assessing network performance. The method includes acquiring network measurements indicative of any changes in a network following a change to a configuration of the network and data indicative of one or more factors capable of causing the changes in the network. The one or more factors are independent of the change to the configuration of the network. The method includes analysing the acquired network measurements and data to identify a contribution of the one or more factors to a key performance indicator (KPI) and a contribution of the change to the configuration of the network to the KPI. The KPI is predicted by a machine learning model and is a measure of the network performance following the change to the configuration of the network.

Abstract: The present disclosure relates an evacuation management system, EMS, (40, 52, 60, 00, 200) operative to provide real-time evacuation information for evacuation of a building, triggered by alarm notification (S202). Based on determined (S214) precise real-time positions of personal ultra-wide band, UWB, devices, as determined by UWB real-time localization system, RTLS, a controller determines (S210) evacuation plans being personal UWB device specific in real-time. Based on individual evacuation plans and input from building facilities, real-time guidance information is defined (S208) and sent to each personal UWB device, providing guidance to person carrying said personal UWB device, in what direction to move (S212), to reach a safe area. Based on localization signals the UWB RTLSA then calculates updated positions of the personal UWB devices and sends to the evacuation management system. It is an advantage that trapped or injured people can be found, even in low visibility areas.

Abstract: A method for controlling a handling of a further object (90) which is handled at a site (10) by at least two different workstations (51, 52) and which is moved between the at least two different workstations,—determining a site plan of the site (10), the site plan indicating the at least two workstations at predefined positions in the site plan,—determining a trajectory of a first object (90) moving in the site (10) in an absolute coordinate system of the site,—deducing absolute positions of the at least two workstations (51, 52) in the absolute coordinate system from the determined trajectory of the first object,—using the absolute positions of the at least two workstations (51, 52) for controlling the handling of the at least one further object handled by the at least two workstations.

Abstract: An anchor system for a location system tracks a location of a tag device mounted to a mobile entity using a plurality of anchor devices. The anchor system comprises an anchor cable, a first anchor device affixed to the anchor cable, one or more second anchor devices affixed to the anchor cable, and a power supply circuit. Each of the second anchor device(s) is spaced from the first anchor device along a length of the anchor cable by a predetermined distance. The power supply circuit is operatively connected to the anchor cable and is configured to supply power to each of the anchor devices via the anchor cable. Each of the anchor devices is configured to wirelessly communicate with the tag device, e.g., which enables a remote network of processors in communication with the anchor devices to determine location data for the tag device.

Abstract: A technique for assessing positioning qualities within a localization area of a positioning system comprising a plurality of anchor nodes for determining positions of tag devices within the localization area using radio technology is disclosed. A method implementation of the technique comprises determining (S202) a positioning deviation between an absolute position of a tag device and a relative position of the tag device, the absolute position of the tag device being determined by the positioning system using the plurality of anchor nodes and the relative position of the tag device being determined based on movement related measurements performed by the tag device relative to a previously determined absolute position of the tag device, and assessing (S204) a positioning quality for the absolute position based on the determined positioning deviation.

Abstract: A technique for updating a positioning configuration in a positioning system comprising a plurality of anchor nodes for determining positions of tag devices within a localization area using radio technology is disclosed. A method implementation of the technique comprises determining (S202) an updated positioning configuration for at least one of a set of tag devices and a set of anchor nodes of the positioning system based on predefined positioning requirements that depend on a zone of the localization area in which the set of tag devices is currently located, and applying (S204) the updated positioning configuration to the at least one of the set of tag devices and the set of anchor nodes.

Abstract: A technique for determining a relative position of a preceding vehicle with respect to a succeeding vehicle in a vehicle platoon is disclosed, wherein the succeeding vehicle comprises at least two front tags and the preceding vehicle comprises at least two back tags, the front and back tags being configured to perform ranging measurements among each other using radio technology. A method implementation of the technique comprises determining (S202) pairwise distances between the front and back tags using ranging measurements performed by the front and back tags using the radio technology, and determining (S204) the relative position of the preceding vehicle with respect to the succeeding vehicle based on the determined pairwise distances.

Abstract: We generally describe a collision prevention system (100) comprising: a localization system (402) for determining positions of an autonomous vehicle (104) and a human (106); and a collision determination unit (404) coupled to or in communication with the localization system (402), wherein the collision determination unit (404) is configured to determine, based on the determined positions of the autonomous vehicle (104) and the human (106), whether a predefined condition for an anticipated collision of the autonomous vehicle (104) with the human (106) is met; wherein the collision prevention system (100) is configured to: lock the autonomous vehicle (104) if the predefined condition is met; alert the human (106) for whom the predefined condition for colliding with the autonomous vehicle (104) is met; and allow unlocking of the autonomous vehicle (104) to be performed or initialized by the alerted human (106) only.

Abstract: A technique for locating a target tag (110) using a short range radio based positioning system comprising a plurality of localization components (120) is provided, wherein the target tag (110) and the plurality of localization components (120) are configured to perform ranging measurements among each other using short range radio technology. A method implementation of the technique is performed by an orchestration component (100) of the positioning system and comprises sending, using long range radio technology, a ranging plan to the target tag (110) and one or more of the plurality of localization components (120), the ranging plan instructing the target tag (110) and the one or more of the plurality of localization components (120) to perform, using the short range radio technology, ranging measurements among each other enabling to locate the target tag (110).