Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for detecting and classifying radio signals. The method includes obtaining one or more radio frequency (RF) snapshots corresponding to a first set of signals from a first RF source; generating a first training data set based on the one or more RF snapshots; annotating the first training data set to generate an annotated first training data set; generating a trained detection and classification model based on the annotated first training data set; and providing the trained detection and classification model to a sensor engine to detect and classify one or more new signals using the trained detection and classification model.

Abstract: A utility monitoring application device is disclosed, which is a software and/or mobile application for monitoring all home and business utilities (i.e., gas, electric, water, etc.) on one site. The utility monitoring application device includes a wall display sensor which is tied to each utility at the entrance of the home or business to monitor usage and leaks and transmit the information wirelessly to the software application. The user will input his or her normal monthly usage of electricity, gas, and water into the unit or application. If the user exceeds the normal monthly usage, he/she will be notified through the application or sensors. The device will prevent overuse and overcharging. Further, monthly reminders can be sent for bills. Users may also monitor the application for potential leaks.

Abstract: A method includes obtaining, using a specified protocol of a radio access network, low-level signal data corresponding to a radio frequency (RF) signal processed in the radio access network; providing the low-level signal data as input to at least one machine learning network; in response to providing the low-level signal data as input to the at least one machine learning network, obtaining, as an output of the at least one machine learning network, metadata providing information on one or more characteristics of the RF signal; and controlling an operation of the radio access network based on the metadata.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: A method includes obtaining samples of radio-frequency (RF) uplink data signals received wirelessly at a radio unit of a radio access network, the RF uplink data signals including a first RF uplink data signal received from a user device; providing the samples of the RF uplink data signals as input to at least one machine learning model; in response to providing the samples of the RF uplink data signals as input to the at least one machine learning model, obtaining based on an output of the at least one machine learning model, recovered data of the RF uplink data signals; and sending the recovered data of the RF uplink signals to a destination device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.

Abstract: A method includes obtaining, using a specified protocol of a radio access network, low-level signal data corresponding to a radio frequency (RF) signal processed in the radio access network; providing the low-level signal data as input to at least one machine learning network; in response to providing the low-level signal data as input to the at least one machine learning network, obtaining, as an output of the at least one machine learning network, metadata providing information on one or more characteristics of the RF signal; and controlling an operation of the radio access network based on the metadata.

Abstract: Aspects of the present disclosure provide a betatron for accelerating electrons. For example, the betatron can include magnet core parts spaced apart by an air gap. At least one main coil can be arranged on the magnet core parts. A betatron tube can be arranged in the air gap for electrons to circulate therein. A control circuit can be electrically coupled to the main coil. The control circuit can be configured to control a main coil current flowing through the main coil, such that as the control circuit increases the main coil current during a current ramp up period, the control circuit maintains the main coil current at a constant level during an injection period when the electrons are injected into the betatron. The current ramp up period can include a short pause and the injection period.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for detecting and classifying radio signals. The method includes obtaining one or more radio frequency (RF) snapshots corresponding to a first set of signals from a first RF source; generating a first training data set based on the one or more RF snapshots; annotating the first training data set to generate an annotated first training data set; generating a trained detection and classification model based on the annotated first training data set; and providing the trained detection and classification model to a sensor engine to detect and classify one or more new signals using the trained detection and classification model.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; generating a first RF signal by processing using an encoder machine-learning network; determining a second RF signal that represents the first RF signal altered by transmission through a communication channel; determining a first property of the first signal or the second RF signal; calculating a first measure of distance between a target value of the first property and an actual value of the first or second RF signal; generating second information as a reconstruction of the first information using a decoder machine-learning network; calculating a second measure of distance between the first information and the second information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the first and second measures.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; generating a first RF signal by processing using an encoder machine-learning network; determining a second RF signal that represents the first RF signal altered by transmission through a communication channel; determining a first property of the first signal or the second RF signal; calculating a first measure of distance between a target value of the first property and an actual value of the first or second RF signal; generating second information as a reconstruction of the first information using a decoder machine-learning network; calculating a second measure of distance between the first information and the second information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the first and second measures.

Abstract: Methods and systems including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; generating a first RF signal by processing using an encoder machine-learning network; determining a second RF signal that represents the first RF signal altered by transmission through a communication channel; determining a first property of the first signal or the second RF signal; calculating a first measure of distance between a target value of the first property and an actual value of the first or second RF signal; generating second information as a reconstruction of the first information using a decoder machine-learning network; calculating a second measure of distance between the first information and the second information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the first and second measures.