Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Aspects include an apparatus and a method for performing Second Order Input Intercept Point (IIP2) calibration of a Digital-to-Analog (DAC) during a full-duplex mode receive operation. In some aspects, a plurality of correlation values are obtained, indicating an amount of IMD energy of an RF signal, wherein the correlation values are associated with IIP2DAC values of a DAC. In some aspects, the apparatus can calculate a mixer bias value, based on the correlation values, and adjust a bias value of a mixer according to the determined bias value. The apparatus can obtain the correlation values, calculate the bias value, and adjust the bias value of the mixer during the full-duplex mode receive operation. In some aspects, the apparatus can thus improve IIP2 of the mixer and reduce IMD energy in a receive signal, during the receive operation, without the need of standby or factory calibration.

Abstract: Aspects include an apparatus and a method for performing Second Order Input Intercept Point (IIP2) calibration of a Digital-to-Analog (DAC) during a full-duplex mode receive operation. In some aspects, a plurality of correlation values are obtained, indicating an amount of IMD energy of an RF signal, wherein the correlation values are associated with IIP2DAC values of a DAC. In some aspects, the apparatus can calculate a mixer bias value, based on the correlation values, and adjust a bias value of a mixer according to the determined bias value. The apparatus can obtain the correlation values, calculate the bias value, and adjust the bias value of the mixer during the full-duplex mode receive operation. In some aspects, the apparatus can thus improve IIP2 of the mixer and reduce IMD energy in a receive signal, during the receive operation, without the need of standby or factory calibration.

Abstract: Methods and apparatus to manage tickets are disclosed. A disclosed example apparatus includes a ticket analyzer to read data corresponding to open tickets, a machine learning model processor to apply a machine learning model to files associated with previous tickets based on the read data to determine probabilities of relationships between the files and the open tickets, a grouping analyzer to identify at least one of a grouping or a dependency between the open tickets based on the determined probabilities, and a ticket data writer to store data associated with the at least one of the grouping or the dependency.

Abstract: In embodiments, an apparatus for selectively delivering software updates to nodes in a network includes a receiver to receive a software update and a list of nodes of the network scheduled to receive the software update. In embodiments, the apparatus further includes a device management agent (DMA) to: identify a set of traversals to leaf nodes of the list of nodes necessary to traverse all nodes on the list, and distribute the software updates to the nodes on the list using the set of traversals.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.