Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Abstract: The present disclosure generally relates to a magnetic recording head for magnetic media, such as a magnetic media drive. The head has a main pole, trailing shield, leading shield, first side shield, and second side shield. One side shield is electrically connected to the trailing shield and electrically disconnected from the leading shield. The other side shield is electrically connected to the leading shield and electrically disconnected from the trailing shield. The side shields are electrically connected through a hot seed layer and/or a nonmagnetic electrically conductive layer that is electrically disconnected from the main pole. Such an arrangement results in a current path from the trailing shield, through the side shields, around the main pole, and to the leading shield, which results in reduced resistance.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images with multiple subjects. Some of these subjects may be known to the user capturing an image with the image sensor. The user may prefer to have the captured image data be optimized around the known subjects. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. This inference data can be utilized along with other sensor data, such as a motion sensor, for the generation of one or more image sensor configuration changes that may be implemented to optimize the subsequent capture of image data. This cycle of image data analysis, image sensor optimization, and subsequent capture can continue multiple times until a threshold of optimization or time is met. The captured image data optimized around the known subjects is then stored.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images with multiple subjects. Some of these subjects may be known to the user capturing an image with the image sensor. The user may prefer to have the captured image data be optimized around the known subjects. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. This inference data can be utilized along with other sensor data, such as a motion sensor, for the generation of one or more image sensor configuration changes that may be implemented to optimize the subsequent capture of image data. This cycle of image data analysis, image sensor optimization, and subsequent capture can continue multiple times until a threshold of optimization or time is met. The captured image data optimized around the known subjects is then stored.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images with multiple subjects. Some of these subjects may be known to the user capturing an image with the image sensor. The user may prefer to have the captured image data be optimized around the known subjects. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. This inference data can be utilized along with other sensor data, such as a motion sensor, for the generation of one or more image sensor configuration changes that may be implemented to optimize the subsequent capture of image data. This cycle of image data analysis, image sensor optimization, and subsequent capture can continue multiple times until a threshold of optimization or time is met. The captured image data optimized around the known subjects is then stored.