Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed for gradient image detection to improve power savings. An example disclosed apparatus includes programmable circuitry to at least one of instantiate or execute the machine readable instructions to identify a region in an image that satisfies a brightness threshold, define a plurality of lines in the image that extend away from the region, and determine the region corresponds to a gradient based on an analysis of pixels along different ones of the plurality of lines.

Abstract: Systems, apparatus, articles of manufacture, and methods to generate digitized handwriting with user style adaptations are disclosed. An example apparatus includes at least one memory, and processor circuitry to train a machine learning model to generate a first digitized handwriting sequence based on a stored handwriting sample. To train the machine learning model, the processor circuitry is to cause a parameterization of a first portion of the machine learning model; and cause a reparameterization of a second portion of the machine learning model. The processor circuitry to re-train the trained machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample.

Abstract: A degraded user experience, such as a user having to wait for an unresponsive application, can be automatically detected and classified. A user experience degradation detection network detects a degraded user experience based on a state of the computing system and a user interaction state. The computing system state can be based on telemetry data provided by the operating system, processor units, and other computing system components and resources, and the user interaction state can be based on user interactions with one or more input devices (e.g., keyboard, touchpad, mouse, touchscreen). A root cause of the degradation event (e.g., hardware, memory, network, or general responsiveness issue) can be classified using a multi-label classifier. An output report can include a snapshot of the system telemetry and user interaction data before, during, and after the degradation event.

Abstract: The present disclosure is directed to systems and methods for reducing display image power consumption while maintaining a consistent, objectively measurable, level of image distortion that comports with a display image quality metric. Raw image data is converted to an HSV format. “V” values are extracted from the HSV format raw image data and a histogram generates a plurality of “V” values. HSV format raw image data is provided to at least one layer of a trained CNN to extract a plurality of features. The plurality of “V” values and the plurality of features are provided to an AI circuit to generate a plurality of distortion class value pairs. Each of the distortion class value pairs is weighted based on proximity of display image distortion and the display image quality metric. The distortion class pair providing a display image distortion close to the display image quality metric is applied to the raw image data to generate the display image data.

Abstract: Apparatus, systems, methods, and articles of manufacture are disclosed for contextual usage control of cameras. An example apparatus includes processor circuitry to execute the instructions to: access one or more images from a camera; detect an object in the one or more images; detect a human activity in the one or more images; build a context based on the object and the human activity; create a visual marker on the one or more images; determine a user voice command; determine a control based on the context, the visual marker, and the voice command; and cause a change in one or more of the one or more images based on the control.

Abstract: The present disclosure is directed to systems and methods for reducing display image power consumption while maintaining a consistent, objectively measurable, level of image distortion that comports with a display image quality metric. Raw image data is converted to an HSV format. “V” values are extracted from the HSV format raw image data and a histogram generates a plurality of “V” values. HSV format raw image data is provided to at least one layer of a trained CNN to extract a plurality of features. The plurality of “V” values and the plurality of features are provided to an AI circuit to generate a plurality of distortion class value pairs. Each of the distortion class value pairs is weighted based on proximity of display image distortion and the display image quality metric. The distortion class pair providing a display image distortion close to the display image quality metric is applied to the raw image data to generate the display image data.

Abstract: The present disclosure is directed to systems and methods for reducing display image power consumption while maintaining a consistent, objectively measurable, level of image distortion that comports with a display image quality metric. Raw image data is converted to an HSV format. “V” values are extracted from the HSV format raw image data and a histogram generates a plurality of “V” values. HSV format raw image data is provided to at least one layer of a trained CNN to extract a plurality of features. The plurality of “V” values and the plurality of features are provided to an AI circuit to generate a plurality of distortion class value pairs. Each of the distortion class value pairs is weighted based on proximity of display image distortion and the display image quality metric. The distortion class pair providing a display image distortion close to the display image quality metric is applied to the raw image data to generate the display image data.

Abstract: The present disclosure is directed to systems and methods for reducing display image power consumption while maintaining a consistent, objectively measurable, level of image distortion that comports with a display image quality metric. Raw image data is converted to an HSV format. “V” values are extracted from the HSV format raw image data and a histogram generates a plurality of “V” values. HSV format raw image data is provided to at least one layer of a trained CNN to extract a plurality of features. The plurality of “V” values and the plurality of features are provided to an AI circuit to generate a plurality of distortion class value pairs. Each of the distortion class value pairs is weighted based on proximity of display image distortion and the display image quality metric. The distortion class pair providing a display image distortion close to the display image quality metric is applied to the raw image data to generate the display image data.