Abstract: Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

Abstract: A channel decoding method includes constructing a maximum likelihood decoding problem including an objective function and a parity check constraint; converting the parity check constraint in the maximum likelihood decoding problem into a cascaded form, converting a discrete constraint into a continuous constraint, and adding a penalty term to the objective function to obtain a decoding optimization problem with the penalty term; obtaining ADMM iterations according to a specific form of the penalty term, and obtaining a channel decoder based on the ADMM with the penalty term; constructing a deep learning network according to the ADMM iterations, and converting a penalty coefficient and a coefficient contained in the penalty term into network parameters; training the deep learning network with training data offline and learning the network parameters; and loading the learned network parameters in the channel decoder based on the ADMM with the penalty term, and performing real-time channel decoding.

Abstract: This disclosure provides a data retransmission method and apparatus. The method includes: A transmitting device obtains information to be transmitted for a tth time, where the information to be transmitted for the tth time includes Rt extension locations and information to be transmitted for a (t?1)th time, and the extension locations include Mt information bits and Lt check bits corresponding to the Mt information bits. The transmitting device then performs Polar encoding on the information to be transmitted for the tth time, to obtain a codeword after the Polar encoding, obtains a codeword for (t?1)th retransmission based on the codeword after the Polar encoding, and transmits the codeword for (t?1)th retransmission. A receiving device performs polar decoding after receiving the codeword for (t?1)th retransmission, to obtain a decoding result of codewords for t times of transmission.

Abstract: A channel decoding method includes constructing a maximum likelihood decoding problem including an objective function and a parity check constraint; converting the parity check constraint in the maximum likelihood decoding problem into a cascaded form, converting a discrete constraint into a continuous constraint, and adding a penalty term to the objective function to obtain a decoding optimization problem with the penalty term; obtaining ADMM iterations according to a specific form of the penalty term, and obtaining a channel decoder based on the ADMM with the penalty term; constructing a deep learning network according to the ADMM iterations, and converting a penalty coefficient and a coefficient contained in the penalty term into network parameters; training the deep learning network with training data offline and learning the network parameters; and loading the learned network parameters in the channel decoder based on the ADMM with the penalty term, and performing real-time channel decoding.

Abstract: Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

Abstract: In a data-processing system a frame generator generates frames from the reflection data representing successive patterns of reflection that result from having launched a transmitted radio signal into a space. Each frame indicates a pattern of reflection at a particular time that corresponds to the frame. A trajectory generator receives the frame data and uses it to identify of a subject's trajectory through the space. An identification module identifies the subject based at least in part on the trajectory.

Abstract: This disclosure provides a data retransmission method and apparatus. The method includes: A transmitting device obtains information to be transmitted for a tth time, where the information to be transmitted for the tth time includes Rt extension locations and information to be transmitted for a (t?1)th time, and the extension locations include Mt information bits and Lt check bits corresponding to the Mt information bits. The transmitting device then performs Polar encoding on the information to be transmitted for the tth time, to obtain a codeword after the Polar encoding, obtains a codeword for (t?1)th retransmission based on the codeword after the Polar encoding, and transmits the codeword for (t?1)th retransmission. A receiving device performs polar decoding after receiving the codeword for (t?1)th retransmission, to obtain a decoding result of codewords for t times of transmission.

Abstract: A method for pose recognition includes storing parameters for configuration of an automated pose recognition system for detection of a pose of a subject represented in a radio frequency input signal. The parameters having been determined by a first process including accepting training data including a number of images including poses of subjects and a corresponding number of radio frequency signals and executing a parameter training procedure to determine the parameters. The parameter training procedure including, receiving features characterizing the poses in each of the images, and determining the parameters that configure the automated pose recognition system to match the features characterizing the poses from the corresponding radio frequency signals.

Abstract: A method for tracking a sleep stage of a subject takes as input a sequence of observations sensed over an observation time period. The sequence of observation values is processed to yield a corresponding sequence of encoded observations using a first artificial neural network (ANN) and the sequence of encoded observation values is processed to yield a sequence of sleep stage indicators using a second artificial network. Each observation may correspond to an interval of the observation period (e.g., at least 30 seconds). The first ANN may be configured to reduce information representing a source of the sequence of observations in the encoded observations.

Abstract: A method for determining an emotional state of a subject includes receiving the motion based physiological signal associated with a subject, the motion based physiological signal including a component related to the subject's vital signs, and determining an emotional state of the subject based at least in part on the component related to the subject's vital signs.