SparkCognition, Inc.

A system includes one or more processors configured to obtain first data representing waveform returns in terms of time, location, and ray parameter. The one or more processors are also configured to provide, as input to one or more machine-learning models, input data frames to generate output data in terms of time, location, and velocity. Each input data frame includes a sampled portion of the first data, an interpolated portion of the first data, or both. The one or more processors are further configured to generate, based on the output data, one or more images representing structures of an observed area associated with the waveform returns.

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.

Uses of artificial intelligence in battery technology including a method that includes receiving input data associated with at least one sensor. The method includes executing at least one trained model by a processor. Executing the trained model comprises providing the input data to the at least one trained model to generate a model output. The method further includes determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.

A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes obtaining sensor data distinct from the waveform data. The method further includes generating feature data based on the sensor data and the waveform data and providing the feature data as input to a first machine learning model and initiating a response action based on an output of the first machine learning model.

A method of monitoring behavior of a device includes obtaining, at a computing device, first data based on first sensor data from a first sensor device coupled to the device. The method includes processing, at the computing device, the first data at a first anomaly detection model and at a second anomaly detection model of multiple anomaly detection models trained to detect anomalous behavior of the device. The method also includes determining, based on outputs of the multiple anomaly detection models, whether to generate an alert.

A method of behavior monitoring includes receiving, at a device, sensor data from one or more sensors associated with a monitored asset. The method also includes applying, at the device, a data scaling operation to input data to generate scaled input data for a pre-trained global model. The input data is based on the sensor data. The method further includes providing, at the device, the scaled input data to the pre-trained global model to selectively generate an alert.

A method includes providing input data to one or more machine-learning models to generate output data. The input data includes an input value for each of N features associated, and the output data includes a predicted value of each of M features. The method further includes determining M sets of feature relevance values including a set of feature relevance values for each of the M predicted values. A particular set of feature relevance values is associated with a particular predicted value, and each feature relevance value of the particular set of feature relevance values represents an estimate of a contribution of a respective one of the N input values to the particular predicted value. The method also includes aggregating feature relevance values to generate N aggregate feature relevance values and characterizing the operating state of the monitored asset based on the N aggregate feature relevance values.

A method includes obtaining waveform return data including waveform return records for multiple sampling events associated with an observed area and generating image data based on the first subset of waveform return records. The method also includes reducing imaging artifacts in a region of interest of the image data using beam-domain local correction operations.

A method includes obtaining a query in a base language and translating the query to generate one or more translated queries each in a respective target language. The method also includes searching one or more sets of electronic files based on the one or more translated queries to generate target-language search results, where each translated query is used to search one or more electronic files that include content in the respective target language of the translated query. The method also includes, based on the target-language search results, scheduling one or more electronic files of the one or more sets of electronic files for at least partial translation to the base language.