Example implementations relate to determining a location using time difference of arrival (TDOA). For example, a computing device may include a first sensor to receive a signal at a first time, where the signal is generated by a user contact at a particular location on a keyboard associated with the computing device. The computing device also includes a second sensor to receive the signal at a second time and a third sensor to receive the signal at a third time. The computing device may also include a processor. The processor may calculate a set of TDOAs associated with the first time, the second time, and the third time. The processor may determine the particular location of the user contact using a triangulation based on the set of TDOAs and may identify a character on the keyboard, where the character is associated with the particular location.

Registration of a person with an intelligent assistant computer includes obtaining one or more image frames captured via one or more cameras that depict an initially unregistered person. Facial recognition data for the initially unregistered person is extracted from the one or more image frames. A spoken command to register the initially unregistered person is received via one or more microphones. Upon determining that the spoken command originated from the registered person having the pre-established registration privilege, the initially unregistered person is registered as a newly registered person by associating one or more additional privileges with the facial recognition data in a person profile for the newly registered person.

An angle of arrival estimation method and device, where a first device: collects a first signal from a second device using a reference antenna and a first antenna group; determines first channel state information (CSI); collects a second signal from the second device using the reference antenna and a second antenna group; determines second CSI; determines a phase difference vector between the first signal and the second signal based on a vector corresponding to the reference antenna in the first CSI and a vector corresponding to the reference antenna in the second CSI; compensates for the second CSI using the phase difference vector; and estimates an angle of arrival of the second device based on the first CSI and compensated second CSI.

The present invention provides a method and system for real-time high-precision positioning in the deep sea. The present invention, based on a ray theory model, uses an azimuth angle, a transmission delay, a deep-sea vehicle depth and a depth of an acoustic transducer of a water surface monitoring platform as an eigenray emergence angle, an eigenray transmission time, eigenray emergence depth and an eigenray end point depth respectively, quickly calculates an eigenray that connects the water surface monitoring platform with the deep-sea vehicle, accurately calculates a position of the deep-sea vehicle relative to the water surface monitoring platform, and converts the position into absolute position information of the deep-sea vehicle through the latitude and longitude of the water surface monitoring platform, thereby achieving real-time high-precision positioning.

A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

An apparatus has circuitry which determines a location of a user, based on sound data representing speech of the user, and identifies the user based on the determined location of the user and user identification information and location information received from a mobile device.

Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

An unmanned aircraft includes: a sensor that includes at least a microphone that generates sound data; and a processor. The processor determines quality of a target sound using the sound data generated by the microphone, obtains a positional relationship between the unmanned aircraft and a sound source of the target sound using data generated by the sensor, and controls movement of the unmanned aircraft to control a distance between the unmanned aircraft and the sound source of the target sound, in accordance with the quality of the target sound and the positional relationship.