Abstract:
Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.
Abstract:
Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for scheduling an execution of a task, such as a non-real time, non-latency sensitive background task on a computing device. In one implementation, the technique includes detecting a first state of a device, wherein the first state of the device is associated with a first power level and a first task, wherein the first power level is at least partially based on power consumption of a first task, determining that the first power level associated with the first state is above a threshold, and in response to determining that the first power level associated with the first state is above the threshold, and scheduling an execution of a second task on the device, wherein the second task is associated with automatically collecting of calibration data using at least one sensor.
Abstract:
Step detection accuracy in mobile devices is increased by determining whether swinging is taking place. According to the invention, swinging can be detected using threshold detection, Eigen analysis, hybrid frequency analysis, and/or gyroscope-based analysis, for example. The determination that swinging is (or may be) occurring can impact how the mobile device reports detected steps for step detection. A count of missteps and/or a level of certainty, based on swing detection, can be provided with a step count.
Abstract:
A user equipment (UE) controls power consumption of the UE based on mobility information and channel conditions experienced by the UE. In one instance, the UE determines its level of mobility based on a Doppler frequency spread of received communications. The UE disables a motion sensor when the level of mobility is above a first threshold. The UE then controls the communications based on the motion sensor and channel conditions experienced by the UE when the level of mobility is below the first threshold.
Abstract:
Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for improving calibration data by increasing the diversity of orientations used for generating the calibration data. In one embodiment, the computing device receives a plurality of calibration measurements associated with one or more sensors of a device, determines a degree to which the plurality of calibration measurements were captured at different orientations of the device, and determines, based on the degree, whether to update one or more calibration parameters.