Abstract:
A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to obtain electrical activity data corresponding to electrical signals from one or more electrical sources within a user's head. The one or more processors are also configured to execute the instructions to render, based on the electrical activity data, audio data to adjust a location of a sound source in a sound field during playback of the audio data.
Abstract:
Disclosed is a method and apparatus for power-efficiently processing sensor data. In one embodiment, the operations implemented include: configuring a sensor fusion engine and a peripheral controller with a general purpose processor; placing the general purpose processor into a low-power sleep mode; reading data from a sensor and storing the data into a companion memory with the peripheral controller; processing the data in the companion memory with the sensor fusion engine; and awaking the general purpose processor from the low-power sleep mode.
Abstract:
Various embodiments include systems and methods for generating a prompt for a large generative AI model (LXM). A computing device may be configured to receive a user prompt, obtain user context information from one or more sources of physical context information and user background information, use the received user prompt and the obtained user context information to generate a contextualized prompt for submission to an LXM, and submit the generated contextualized prompt to the LXM
Abstract:
Various embodiments include systems and methods for generating a prompt for a large generative AI model (LXM). A computing device may be configured to receive a user prompt, process the received user prompt to recognize whether the prompt includes privacy information or will cause an LXM to provide a response that will reveal privacy information; and use the LXM to provide a response to the user prompt in a manner that will avoid disclosure of privacy information.
Abstract:
A method for energy-efficient state change detection and classification of streaming sequential data includes receiving via a first prediction model, sequential data from a sensor. The first prediction model determines a change in an activity state based on the sequential data. An indication that the activity state has changed is transmitted to a second prediction. The second prediction model determines an updated activity state based on the sequential data. The updated activity state is sent to the first prediction model, after which the second prediction enters an inactive state.
Abstract:
In some aspects, a pose tracking device may receive usability information from a sensor system that includes a plurality of sensors based on current operating conditions associated with the plurality of sensors. The pose tracking device may select a set of sensor modalities associated with the sensor system based on the usability information. The pose tracking device may select a pose tracking model based on the set of sensor modalities selected and one or more key performance indicator (KPI) requirements related to a current context associated with a pose tracking configuration for a client application. The pose tracking device may estimate a pose associated with an object using the pose tracking model based on sensor inputs associated with the one or more sensors selected from the plurality of sensors. Numerous other aspects are described.
Abstract:
Systems and techniques are provided for calibrating tracking devices. For example, a process can include generating, at a primary tracking device of a tracking device network, pose information of the primary tracking device; obtaining relative pose information of a secondary tracking device of the tracking device network, wherein the relative pose information of the secondary tracking device comprises a relative position and orientation between the primary tracking device and the secondary tracking device; obtaining absolute pose information of the secondary tracking device using the pose information of the primary tracking device and the relative pose information of the secondary tracking device; and transmitting, at the primary tracking device, the absolute pose information to the secondary tracking device for performing a calibration action.
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:
Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.
Abstract:
In mobility scenarios, a user equipment (UE) may enter an airplane mod in which the UE has limited or no connectivity to a network. Cell selection latency after terminating the airplane mode may result in a delay in providing network or communication services. Some aspects described herein enable a reduction in latency associated with cell selection after operation in a mobility scenario. For example, a UE may determine that the UE is operating in a mobility scenario, and perform a receive-only cell selection procedure. Additionally, or alternatively, the UE may predict a destination associated with the mobility scenario and may identify a band list for performing a cell selection procedure. In this way, when the UE exits the mobility scenario, the UE reduces a time to successfully complete cell selection.