Abstract: A method is described for using artificial intelligence (AI) components in association with first a transceiver node in a wireless network, where the first node is configured to send data over a wireless channel and to initiate a training procedure for an artificial intelligent component in a second node. The first node, having an encoder, transmits, to a decoder in the second node, a plurality of ordered training pairs, the transmission in response to a detection of a trigger condition based on a reconstruction loss value determined by the first node. The first node receives, from the second node, partially processed training information corresponding to the transmitted training pairs. The first node updates learnable parameters of the encoder based on the received partially processed training information to reduce the reconstruction loss value.

Abstract: A method includes determining, using a physics-based model and based on a plurality of observations, first solution data. The first solution data is descriptive of a first estimated solution to an inverse problem associated with the plurality of observations, and the first solution data includes artifacts due, at least in part, to a count of observations of the plurality of observations. The method also includes performing a plurality of iterations of a gradient descent artifact reduction process to generate second solution data. The artifacts are reduced in the second solution data relative to the first solution data. A particular iteration of the gradient descent artifact reduction process includes determining, using a machine-learning model, a value of a gradient metric associated with particular solution data and adjusting the particular solution data based on the value of the gradient metric to generate updated solution data.

Abstract: A method includes using a machine-learning model to determine multiple sets of image data, each representing an estimated solution to an inverse problem associated with multiple waveform return measurements. First image data are based on a first set of waveform return measurements and first model parameters of the machine-learning model, and second image data are based on a second set of waveform return measurements and a second model parameters of the machine-learning model. The method also includes determining, based on the multiple sets of image data, a representative image. The method further includes generating output data that identifies a first area of the representative image as less reliable than a second area of the representative image based on a statistical evaluation of two or more sets of image data of the multiple sets of image data.

Abstract: A method includes obtaining first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data. The method also includes performing a plurality of deep image prior operations, using an image prior based on the first image data, to generate filter data. The method further includes modifying the first image data based on the filter data to generate second image data. The method also includes performing an artifact reduction process based on the second image data to generate third image data.

Abstract: A method includes obtaining waveform return data including waveform return records for multiple sampling events associated with an observed area and determining a relevance score for the waveform return records of the waveform return data. The relevance score for a particular waveform return record is based, at least partially, on estimated information gain associated with the particular waveform return record. The method also includes, based on the relevance scores, selecting a first subset of waveform return records, where one or more waveform return records are excluded from the first subset of waveform return records. The method also includes generating image data based on the first subset of waveform return records.