Abstract: A computing system comprises a data storage and at least one processor communicatively coupled to the data storage. The at least one processor is configured to execute program instructions to cause the system to perform the following steps. A deep neural network (“DNN”) model is trained using training data. Next, additional scenes are determined based on the DNN model and the training data. The determined scenes are generated, and then used to augment the training dataset. The DNN model is then retrained using the augmented training dataset and stored in a data storage for deployment.

Abstract: Technologies and techniques for operating a sensor system including an image sensor and a light detection and ranging (LiDAR) sensor. Image data associated with an image scene of a landscape is received from the image sensor, and LiDAR data associated with a LiDAR scene of the landscape is received from the LiDAR sensor, wherein the LiDAR scene and image scene of the landscape substantially overlap. A machine-learning model is applied to (i) the image data to identify image points of interest in the image data, and (ii) the LiDAR data to identify LiDAR features of interest in the LiDAR data. The LiDAR features of interest and the image points of interest are fused, utilizing an attention mechanism, and generating an output, wherein new LiDAR data is produced, based on the fusing output.

Abstract: In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

Abstract: Technologies and techniques for converting sensor data, used in a vehicle or other device. A machine-learning model is applied to first sensor data, including a first operational characteristic capability and first sensor label data, wherein the machine-learning model is trained to second sensor data including a second operational characteristic capability. New sensor data is generated that corresponds to the applied machine-learning model, wherein the new sensor data includes translated first sensor label data. A loss function may be applied to the new sensor data to determine the accuracy of the new sensor data and translated first sensor label data. In some examples, a multi-dimensional matrix of camera sensor parameters may be applied to the first sensor data labels to transform the first sensor data labels to second sensor data labels.

Abstract: Technologies and techniques for operating a sensor system including an image sensor and a light detection and ranging (LiDAR) sensor. Image data associated with an image scene of a landscape is received from the image sensor, and LiDAR data associated with a LiDAR scene of the landscape is received from the LiDAR sensor, wherein the LiDAR scene and image scene of the landscape substantially overlap. A machine-learning model is applied to (i) the image data to identify image points of interest in the image data, and (ii) the LiDAR data to identify LiDAR features of interest in the LiDAR data. The LiDAR features of interest and the image points of interest are fused, utilizing an attention mechanism, and generating an output, wherein new LiDAR data is produced, based on the fusing output.

Abstract: Technologies and techniques for operating a sensor system. First sensor data is received that is generated using a first sensor. Second sensor data is received that is generated using a second sensor, wherein the first sensor data includes a first operational characteristic capability, and the second sensor data includes a second operational characteristic capability. A machine-learning model may be trained/applied, wherein the machine-learning model is trained to output the second sensor data based on input of the first sensor data. New sensor data is generated using the applied machine-learning model. A loss function may be applied to the new sensor data to determine the accuracy of the new sensor data relative to the first sensor data and the second sensor data.

Abstract: A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence (“AI”) model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Abstract: A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence (“AI”) model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Abstract: A computing system comprises a data storage and at least one processor communicatively coupled to the data storage. The at least one processor is configured to execute program instructions to cause the system to perform the following steps. A deep neural network (“DNN”) model is trained using training data. Next, additional scenes are determined based on the DNN model and the training data. The determined scenes are generated, and then used to augment the training dataset. The DNN model is then retrained using the augmented training dataset and stored in a data storage for deployment.