CPC Class H04N

Systems and methods for generating synthetic data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset that includes time series data having a plurality of dimensions and generating a transformed dataset by performing a first data transformation. The first data transformation may include a time-based data processing method. The operations may include generating a synthetic transformed-dataset by implementing a data model using the transformed dataset. The data model may be configured to generate synthetic transformed-data based on a relationship between data of at least two dimensions of the transformed dataset. The operations may include generating a synthetic dataset by performing a second data transformation on the synthetic transformed-dataset. The second data transformation may include an inverse of the first data transformation.

An electronic device displays a media capture user interface that includes a media capture preview of objects in a field of view of the camera. While displaying the media capture user interface, the electronic device scans the field of view of the camera for data encoded in an optical machine-readable format. In accordance with a determination that the field of view of the camera includes data encoded in the optical machine-readable format that meets respective notification criteria, the electronic device displays a notification that indicates that the camera application has detected data encoded in the optical machine-readable format. In accordance with a determination that the field of view of the camera does not include data encoded in the optical machine-readable format that meets the respective notification criteria, the electronic device maintains display of the media capture user interface of the camera application without displaying the notification.

A system performs a method for processing an image of a machine-readable code. The method includes receiving an image of a machine-readable code comprising coded information, where the machine-readable code is at least partially obscured. An adjusted image is generated by adjusting a color space of the image. At least a machine-readable code region of the image is binarized, wherein the machine-readable code region of the image depicts the machine-readable code. The binarized machine-readable code region is decoded to determine the coded information. Other apparatus and methods are also described.

A system and methods for upsampling of decompressed financial time-series data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between time-series data sets.