Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: A mounting assembly includes a mounting block. A first shaft is supported by the mounting block and movable about a first rotational axis of the first shaft and a first extension axis with respect to the mounting block. A clamp is attached to the first shaft, wherein the clamp interfaces with the mounting block to limit movement of the first shaft along the extension axis while allowing rotation of the first shaft about the first rotational axis. A second shaft is coupled to the first shaft and movable about a second rotational axis of the second shaft.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: A mounting assembly includes a mounting block. A first shaft is supported by the mounting block and movable about a first rotational axis of the first shaft and a first extension axis with respect to the mounting block. A clamp is attached to the first shaft, wherein the clamp interfaces with the mounting block to limit movement of the first shaft along the extension axis while allowing rotation of the first shaft about the first rotational axis. A second shaft is coupled to the first shaft and movable about a second rotational axis of the second shaft.

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: A mounting assembly includes a mounting block. A first shaft is supported by the mounting block and movable about a first rotational axis and a first extension axis with respect to the mounting block. A second shaft is coupled to the first shaft and movable about a second rotational axis.

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, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: A mounting assembly includes a mounting block. A first shaft is supported by the mounting block and movable about a first rotational axis and a first extension axis with respect to the mounting block. A second shaft is coupled to the first shaft and movable about a second rotational axis.

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 method and system for transmitting a warning message to a driver of a vehicle is provided. Authorization for uploading message data is provided followed by the provision of message data indicative of a plurality of pre-recorded warning messages. The message data are then stored in memory of the transmitter. A responder of the emergency vehicle selects one of the plurality of pre-recorded warning messages. The message data associated with the selected warning message are then encoded and encrypted. The encrypted message data are then processed to generate a RF signal in dependence thereupon which is then transmitted to the vehicle. Upon receipt of the RF signal at the vehicle message data are generated in dependence thereupon which are then decrypted and decoded.

Abstract: A method and system for transmitting a warning message to a driver of a vehicle is provided. Authorization for uploading message data is provided followed by the provision of message data indicative of a plurality of pre-recorded warning messages. The message data are then stored in memory of the transmitter. A responder of the emergency vehicle selects one of the plurality of pre-recorded warning messages. The message data associated with the selected warning message are then encoded and encrypted. The encrypted message data are then processed to generate a RF signal in dependence thereupon which is then transmitted to the vehicle. Upon receipt of the RF signal at the vehicle message data are generated in dependence thereupon which are then decrypted and decoded.

Abstract: A system and method are provided for actively illuminating and monitoring a subject, such as a driver of a vehicle. The system includes a video imaging camera orientated to generate images of the subject eye(s). The system also includes first and second light sources offset from each other and operable to illuminate the subject. The system further includes a controller for controlling illumination of the first and second light sources such that when the imaging camera detects sufficient glare, the controller controls the first and second light sources to minimize the glare. This is achieved by turning off the illuminating source causing the glare.

Abstract: A system and method of assessing the driving task demand on the driver of a vehicle, and further controlling one or more devices on the vehicle as a function of the assessed driver demand is provided. The method includes sensing a coverage zone in relation to a vehicle, determining a presence of one or more objects in the sensed zone, measuring speed of each detected object in the sensed zone, determining a variation in speed of one or more sensed objects, and determining a driving task demand signal indicative of driving task demand of the vehicle as a function of the measured speed variability. The method controls one or more devices on the vehicle based on the driving task demand signal. Alternately, the driving task demand signal is determined based on vehicle speed.

Abstract: A system and method are provided for monitoring an eye. The system includes a video imaging camera oriented to generate images of an eye. The camera is aligned along an imaging axis. A first light source is located at an angle less than 2.5 degrees of the imaging axis for illuminating the eye. A second light source is arranged at an angle greater than 4.5 degrees from the imaging axis for illuminating the eye. The system includes a processor for processing the images generated with the video imaging camera during illumination of the eye with the first light source and during illumination of the eye with the second light source.