Abstract: A system for digitally representing a warehouse or a material storage facility (1) with a computing unit (1) in which a digital twin (12) of the warehouse and/or material storage facility (1) is created which contains information regarding the warehouse and/or material storage facility (1) that includes position data of infrastructure elements (4) as well as goods (2, 17) and/or materials in the warehouse and/or material storage facility (1). The computing unit (10) has an available computer program with an algorithm that is configured to detect changes in the information data regarding the warehouse and/or material storage facility (1) and, continuously or at regular intervals, to represent a current status of the goods (2, 17) and/or materials in the warehouse and/or material storage facility (1) and to output a current inventory result.

Abstract: A method for the production of an environment map (5) for a mobile logistics robot includes sensing an environment by use of a sensor system (2). The sensor data is evaluated in a processor unit (3), and a virtual grid of the environment is produced using cells. The cells in which objects (1) are detected are labeled as occupied cells and the cells in which no objects (1) are detected are labeled as free cells, as a result of which a representation of the environment is generated. The objects (1) that occupy the cells are identified in the processor unit (3). The mobile logistics robot carries out the method.

Abstract: Embodiments described herein generally relate to a method and system for calibrating measured values for ambient air parameters using trained models. In at least one embodiment, the method includes calibrating measured values for an ambient parameter (AP) by generating training dataset points, each of the training dataset point comprising: (i) a first measured value of an AP, generated by a first sensor, at a time instance, and (ii) a second, time-paired measured value for the AP, generated by at least one reference sensor, at the time instance, wherein the first and second values are measured in a localized area surrounding the at least one reference sensor; training an AP-specific calibration model using the training dataset points to generate a trained AP-specific calibration model; applying the trained AP-specific calibration model to a new measured value of the AP, generated by a second sensor.

Abstract: A method and related system are provided for estimating or predicting an output value of a first ambient parameter (AP). Sensors generate a plurality of time-paired training values of the first AP, a second AP, and accuracy factor(s) affecting the accuracy of the measured first or second APs. A computer uses the training values, and trust coefficients based on the accuracy factor to train an air quality prediction model for estimating or predicting the output value of the first AP based on a measured input value of the second AP. The computer estimates or predicts the output value of the first AP by applying the trained air quality prediction model to the input value of the second AP. The estimated or predicted output value of the first AP may be used to calibrate the accuracy of measured values of the first AP, or forecast future values of the first AP.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency receiver system. The radio frequency receiver system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power received by the antenna is measured after each perturbation at the same node in the matching network. The measured power values are used by an optimizer in converging to a solution that provides the reflection coefficient of the antenna. The reflection coefficient of the antenna may be used to determine the input impedance of the antenna. The elements of the matching circuit are then adjusted to match the input impedance of the antenna.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency receiver system. The radio frequency receiver system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power received by the antenna is measured after each perturbation at the same node in the matching network. The measured power values are used by an optimizer in converging to a solution that provides the reflection coefficient of the antenna. The reflection coefficient of the antenna may be used to determine the input impedance of the antenna. The elements of the matching circuit are then adjusted to match the input impedance of the antenna.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency receiver system. The radio frequency receiver system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power received by the antenna is measured after each perturbation at the same node in the matching network. The measured power values are used by an optimizer in converging to a solution that provides the reflection coefficient of the antenna. The reflection coefficient of the antenna may be used to determine the input impedance of the antenna. The elements of the matching circuit are then adjusted to match the input impedance of the antenna.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency transmitter system. The radio frequency transmitter system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power reflected by the antenna is measured after each perturbation at the same node within the tunable matching network. The power values are used by an optimizer in converging to a solution that provides input impedance of the antenna. The elements of the matching circuit are adjusted to match the input impedance of the antenna.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency receiver system. The radio frequency receiver system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power received by the antenna is measured after each perturbation at the same node in the matching network. The measured power values are used by an optimizer in converging to a solution that provides the reflection coefficient of the antenna. The reflection coefficient of the antenna may be used to determine the input impedance of the antenna. The elements of the matching circuit are then adjusted to match the input impedance of the antenna.

Abstract: Real-time calibration of a tunable matching network that matches the dynamic impedance of an antenna in a radio frequency transmitter system. The radio frequency transmitter system includes two non-linear equations that may be solved to determine the reflection coefficient of the antenna. The tunable matching network is repeatedly perturbed and the power reflected by the antenna is measured after each perturbation at the same node within the tunable matching network. The power values are used by an optimizer in converging to a solution that provides input impedance of the antenna. The elements of the matching circuit are adjusted to match the input impedance of the antenna.