Abstract: A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.

Abstract: Training data generated at a particular device deployed in a machine-to-machine network is used to train a first plurality of decision trees for inclusion in a random forest model for use in anomaly detection. Copies of the trained first plurality of decision trees are sent to at least one other device deployed in the machine-to-machine network and copies of a second plurality of decision trees are received from the other device, which were trained at the other device using training data generated by the other device. The random forest model is generated to include the first and second plurality of decision trees. The random forest model is used by the particular device to detect anomalies in data generated by the particular device.

Abstract: A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.

Abstract: An anomaly detection model generator accesses sensor data generated by a plurality of sensors, determines a plurality of feature vectors from the sensor data, and executes a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the plurality of feature vectors to generate a set of predictions. Respective entropy-based weightings are determined for each of the plurality of unsupervised anomaly detection machine learning algorithms from the set of predictions. A set of pseudo labels is generated based on the predictions and weightings, and a supervised machine learning algorithm uses the set of pseudo labels as training data to generate an anomaly detection model corresponding to the plurality of sensors.

Abstract: A radio frequency (RF) front-end circuit and an operating method thereof are provided. The proposed RF front-end circuit includes a first linear amplifier, a second linear amplifier, and a calibration unit. The first linear amplifier performs a high-frequency amplification on a RF signal to generate an amplified RF signal, and down-converts the amplified RF signal into an intermediate frequency (IF) signal. The second first linear amplifier performs a low-frequency amplification on the IF signal to generate an amplified IF signal. The calibration unit is coupled to the first and the second linear amplifiers, and receives a voltage gain fed back from the second linear amplifier. Then, the calibration unit performs an auto-calibration procedure according to the voltage gain fed back from the second linear amplifier to search for an input current value of the first linear amplifier, which correspondingly maximizes the voltage gain of the first amplifier.

Abstract: A radio frequency (RF) front-end circuit and an operating method thereof are provided. The proposed RF front-end circuit includes a first linear amplifier, a second linear amplifier, and a calibration unit. The first linear amplifier performs a high-frequency amplification on a RF signal to generate an amplified RF signal, and down-converts the amplified RF signal into an intermediate frequency (IF) signal. The second first linear amplifier performs a low-frequency amplification on the IF signal to generate an amplified IF signal. The calibration unit is coupled to the first and the second linear amplifiers, and receives a voltage gain fed back from the second linear amplifier. Then, the calibration unit performs an auto-calibration procedure according to the voltage gain fed back from the second linear amplifier to search for an input current value of the first linear amplifier, which correspondingly maximizes the voltage gain of the first amplifier.