Abstract:
In some aspects, the present disclosure provides a method for managing a command queue in a universal flash storage (UFS) host device. The method includes determining to power on a first subsystem of a system-on-a-chip (SoC), wherein the determination to power on the first subsystem is made by a second subsystem of the SoC based on detection of user identity data contained in a first image frame during an initial biometric detection process. In certain aspects, the second subsystem is configured to operate independent of the first subsystem and control power to the first subsystem. In certain aspects, the second subsystem includes a second optical sensor, a set of ambient sensors, and a second processor configured to detect, via a set of ambient sensors, an event comprising one or more of an environmental event outside of the device or a motion event of the device.
Abstract:
Disclosed aspects relate to methods and apparatus for correcting a first sensor clock of a first sensor. The disclosed methods and apparatus effectuate receiving first and seconds signals in a sensor from a processor at known different times related to the timing of the processor clock. Based on the measured time interval between the times of the first and second signals as determined by the sensor, a clock correction factor may be determined in the sensor for correcting the timing of the sensor clock to be synchronized with the processor clock.
Abstract:
Systems and methods for external access detection and recovery in a subsystem of a system-on-a-chip (SoC) in a portable computing device (PCD) are presented. In operation, a subsystem of the SoC is operated in an internal mode independently of the SoC while the SoC is in a low power state, such as a non-functional or zero power state or mode. The subsystem comprises a processor in communication with a memory, a sensor, and a monitor module. The monitor module detects when the processor of the subsystem requests access to a component external to the subsystem. In response to this detected request, the SoC is caused to enter into a full power state or mode, and the subsystem is caused to exit the internal mode of operation.
Abstract:
A dynamic bypass capacitance is provided for a power rail. The dynamic bypass capacitance equals a first bypass capacitance during an idle mode for a digital core powered by the power rail. During an active mode for the digital core, the dynamic bypass capacitance equals a second bypass capacitance that is greater than the first bypass capacitance.
Abstract:
System and method for determining positional and activity information of a mobile device in synchronization with the wake-up period of the mobile device to perform antenna beam management and adjusting the wake-up period based on the positional and activity information of the mobile device. A mobile device comprises: a memory; at least one sensor for detecting data: a processor communicatively coupled to the memory, the processor is configured to: synchronize the at least one sensor with a wake-up period of the mobile device; receive the data detected by the at least one sensor; determine positional information based on the received data; determine activity information based on the received data; estimate a forward position of the mobile device based on the positional information and the activity information; and perform a management of antenna beams of the mobile device based on the positional information, the activity information and the forward position.
Abstract:
Systems and techniques are described herein. For example, a process can include obtaining first sensor measurement data associated with a and second sensor measurement from one or more sensors. In some cases, the first measurement data can be associated with a first time and the second sensor measurement data can be associated with a second time occurring after the first time. In some aspects, the process includes determining that the first sensor measurement data and the second sensor measurement data satisfy at least one batching condition. In some examples, the process includes, based on determining that the first sensor measurement data and the second sensor measurement data satisfy the at least one batching condition, generating a sensor measurement data batch including the first sensor measurement data, the second sensor measurement data, and at least one target sensor measurement data. Ins examples the process includes outputting the sensor measurement data batch.
Abstract:
A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.
Abstract:
Various aspects of the present disclosure generally relate to object detection. In some aspects, a device may include a housing; an input device adjoined to the housing, the input device configured to receive an input associated with a press of a key of a plurality of keys; one or more transmitters disposed in the housing, the one or more transmitters configured to transmit one or more signals toward the plurality of keys; one or more receivers disposed in the housing, the one or more receivers configured to receive one or more return signals corresponding to the one or more signals; and a processor configured to determine a location of the key based at least in part on the one or more return signals.
Abstract:
A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.
Abstract:
Disclosed are methods and apparatus for synchronizing a controller and sensors in a system. A timestamp is provided in a host controller of an interface event on an interface coupled with host controller through detecting a message from a sensor on the interface that identifies the issuance of the interface event caused by the sensor at a first time. In response, the controller issues first and second events on the interface at respective second and third times, while concurrently counting cycles of a clock in the controller after each issuance. The controller also receives a first and second sensor counts representing the internal sensor clock times noted for the first and second events. The controller may then accurately calculate the timestamp of the interface event corresponding to the first time based on both internal controller counts and the sensor counts without needing a timestamp from the sensor directly.