Abstract: Examples are described herein for detecting, and outputting audio signals to, wearable audio output devices. In various examples, a device may include a time-of-flight (ToF) sensor and a processor. The processor may analyze ToF sensor data generated by the ToF sensor. Based on the analysis, the processor may detect a wearable audio output device worn by an individual. In response to detecting the wearable audio output device, the processor may output an audio signal to the wearable audio output device.

Abstract: In some examples, an electronic device includes a photosensor and a controller. The controller determines a first delta value and a second delta value via the photosensor. The first delta value is associated with a first lighting attribute of a first location, and the second delta value is associated with a second lighting attribute of the first location. The controller determines if the electronic device has moved from the first location to a second location based on the first delta value and the second delta value.

Abstract: An example device comprises: a surface-type sensor to detect a type of surface on which the device is located; a proximity sensor; and a processor. The processor is to: in response to detecting, using the surface-type sensor, that the device is located on a soft-type surface, increase noise output of the device; and, in response to detecting, using the proximity sensor, that a plurality of persons are proximal the device, decrease the noise output of the device.

Abstract: In some examples, an electronic device includes a photosensor and a controller. The controller determines a first delta value and a second delta value via the photosensor. The first delta value is associated with a first lighting attribute of a first location, and the second delta value is associated with a second lighting attribute of the first location. The controller determines if the electronic device has moved from the first location to a second location based on the first delta value and the second delta value.

Abstract: In some examples, an electronic device includes a processor to identify a first location based on an image captured by a camera. In some examples, the processor detects a movement of the electronic device based on sensor data generated by a sensor. In some examples, the processor identifies a second location when the movement satisfies a registration condition.

Abstract: Examples are described herein for detecting, and outputting audio signals to, wearable audio output devices. In various examples, a device may include a time-of-flight (ToF) sensor and a processor. The processor may analyze ToF sensor data generated by the ToF sensor. Based on the analysis, the processor may detect a wearable audio output device worn by an individual. In response to detecting the wearable audio output device, the processor may output an audio signal to the wearable audio output device.

Abstract: An example device comprises: a surface-type sensor to detect a type of surface on which the device is located; a proximity sensor; and a processor. The processor is to: in response to detecting, using the surface-type sensor, that the device is located on a soft-type surface, increase noise output of the device; and, in response to detecting, using the proximity sensor, that a plurality of persons are proximal the device, decrease the noise output of the device.

Abstract: In one example, a notification may be generated to prompt a user to clean a personal computing device. The notification may be based upon usage data of the personal computing device. Multiple types of usage data may be determined and combined to create a composite usage value. The composite usage value may be compared to a threshold usage value. A notification may be generated if the composite usage value exceeds the threshold usage value.

Abstract: In one example, a notification may be generated to prompt a user to clean a personal computing device. The notification may be based upon usage data of the personal computing device. Multiple types of usage data may be determined and combined to create a composite usage value. The composite usage value may be compared to a threshold usage value. A notification may be generated if the composite usage value exceeds the threshold usage value.

Abstract: In one example in accordance with the disclosure, a computing device is described. An example computing device includes a gaze tracking device. An example gaze tracking device identifies, from a captured image, a gaze region for a user viewing a display device coupled to the example computing device. The gaze region indicates a location on the display device where the user is looking. The example computing device includes a controller. An example controller determines a first window on the display device that is aligned with the gaze region and based on a determination that the first window is aligned with the gaze region, adjusts a video setting of a second window that is outside the gaze region.

Abstract: In some examples, an electronic device includes a printed circuit board. In some examples, the printed circuit board includes a conductive base layer. In some examples, the conductive base layer is an electrode to produce a signal indicative of a change in an omnidirectional electrostatic field corresponding to a moving object. In some examples, the printed circuit board includes a via coupled to the conductive base layer. In some examples, the via is disposed through an intermediate layer of the printed circuit board. In some examples, the printed circuit board includes an integrated circuit coupled to the via. In some examples, the integrated circuit is to detect, using a machine learning model, a direction of the moving object in the omnidirectional electrostatic field based on a feature of the signal.

Abstract: In some examples, an electronic device includes a printed circuit board. In some examples, the printed circuit board includes a conductive base layer. In some examples, the conductive base layer is an electrode to produce a signal indicative of a change in an omnidirectional electrostatic field corresponding to a moving object. In some examples, the printed circuit board includes a via coupled to the conductive base layer. In some examples, the via is disposed through an intermediate layer of the printed circuit board. In some examples, the printed circuit board includes an integrated circuit coupled to the via. In some examples, the integrated circuit is to detect, using a machine learning model, a direction of the moving object in the omnidirectional electrostatic field based on a feature of the signal.

Abstract: In some examples, an electronic device includes an image sensor to capture an image of a scene. In some examples, the electronic device includes a machine learning circuit to execute a machine learning model on the image of the scene to determine a shutter activation parameter. In some examples, the machine learning circuit is to activate a shutter of the image sensor in response to determining that the shutter activation parameter satisfies a condition.

Abstract: In an example implementation according to aspects of the present disclosure, a method may include monitoring a measurement from a sensor disposed within a computing device, filtering out noise from the measurement that is generated by a component of the computing device, determining a usage of the computing device, based on the post-filtered measurement, and adjusting a parameter of the computing device, based on the determined usage.