Abstract: Aspects of the present disclosure provide an automated labeling system. For example, the automated labeling system can include an automated labeling module (ALM) configured to receive wireless signals and ground truth of learning object and label the wireless signals with the ground truth when receiving the ground truth to generate labeled training data. The automated labeling system can also include a training database coupled to the ALM. The training database can be configured to store the labeled training data.

Abstract: A fatigue data generation method, comprising: obtaining, by the camera device, a target image; obtaining, by a processor, a target feature data from the target image, and inputting the target feature data to a fatigue analysis model which stored in a storage unit, wherein the fatigue analysis model comprises a plurality of reference physiological signals, a plurality of reference feature data, a plurality of reference fatigue data and a plurality of correlation parameters; and generating a target fatigue data according to the target feature data, the plurality of reference feature data and the plurality of correlation parameters.

Abstract: A device produces a dolly zoom effect with automatic focal length adjustment. The device uses a camera to capture an initial image including at least a foreground object and a background. The device includes a size tracking circuit to identify the size of the foreground object in the initial image. The device further includes a focal length control circuit. The focal length control circuit calculates an adjusted focal length of the camera to maintain the size of the foreground object in subsequently captured images.

Abstract: An image processing circuit stores a training database and models in memory. The image processing circuit includes an attribute identification engine to identify an attribute from an input image according to a model stored in the memory. By enhancing the input image based on the identified attribute, a picture quality (PQ) engine in the image processing circuit generates an output image for display. The image processing circuit further includes a data collection module to generate a labeled image based on the input image labeled with the identified attribute, and to add the labeled image to the training database. A training engine in the image processing circuit then re-trains the model using the training database.