In a data processing method, a processing device performs encoding, based on spatial position data and time position data of a trajectory point of a trajectory according to an encoding rule of a hybrid code of a preset level in an index database, to obtain a hybrid code of each trajectory point. The hybrid code includes temporal information and spatial position information of the trajectory point. The processing device then queries a similar trajectory in the index database based on the hybrid code of each trajectory point. The index database includes hybrid codes of a plurality of levels, and the index database includes hybrid codes with trajectory information that includes a trajectory identifier and a trajectory length.

A respiratory pressure therapy system for providing continuous positive air pressure to a patient via a patient interface configured to engage with at least one airway of the patient. The system includes: a flow generator configured to generate supply of breathable gas for delivery to the patient via the patient interface; at least one sensor; a display; and a computing device. The computing device is configured to: receive sensor data that is based on measured physical property of the supply of breathable gas; control, based on the received sensor data, the flow generator to adjust a property of the supply of breathable gas; receive, an input indicating assistance is needed with using the patient interface; receive one or more images of the patient with the patient interface; analyse the received one or more images; and based on the analysis, display instructions for positioning the patient interface.

A data processing method includes: obtaining a defect type of a sample set in response to a first input of a user on a first interface, the sample set including samples, each sample having a first parameter used to represent a defect degree of the sample with regard to the defect type and a second parameter used to represent device informations of sample production devices through which the sample passes; calculating yield purity indexes of sample production devices on the samples based on first parameters and second parameters of the samples, so as to obtain influencing parameters of the sample production devices, an influencing parameter of each sample production device being used to represent an influence degree to which the sample production device affects an occurrence of the defect type on the samples; and displaying the influencing parameters of the sample production devices on a second interface.

The present disclosure relates to methods and systems for data visualization. The systems may perform the methods to obtain a video having a plurality of frames including a plurality of objects; identify a target object from the plurality of objects according to the plurality of frames; determine one or more track points of the target object, each of the one or more track points being corresponding to the target object in one of the plurality of frames; determine a first track of the target object based on the track points, the first track including at least one of the one or more track points of the target object; determine a second track of the target object based on the first track, the second track including at least one of the track points of the first track; generate a video analysis result by analyzing the second track; and visualize the video analysis result.

Generally, a system and method for an automatic detection of referee's decisions during a ball-game match are provided. The method may include receiving a plurality of images of a ball-game field generated during the ball-game match; determining, based on predetermined ball-game rules, a first subset of images of the plurality of images representing a first event that is suspected as a specified rule-based event; determining, based on the predetermine ball-game rules, a second subset of images of the plurality of images that represents a second event, wherein the second event is subsequent to the specified rule-based event according to the predetermined ball-game rules; and analyzing, based on the predetermined ball-game rules, the images of the second subset and further determining, based on the analysis thereof, a referee's decision concerning whether the first even is the specified rule-based event.

A method trains a neural network to identify an event based on discrepancies in answers to factoid questions at different times. One or more processors identify answers to a series of factoid questions. The processor(s) compare the answers from the series of factoid questions in order to determine discrepancies in the answers at different times, and then train a neural network to identify an event based on the discrepancies in the answers at the different times.

An optical tracking system includes optical source devices. The optical source devices are configured to emitting optical signals. A control method, suitable for the optical tracking system, includes following operations. A dimensional scale to be covered by the optical tracking system is obtained. Signal strength of the optical signals provided by the optical source devices is adjusted according to the dimensional scale. The signal strength of the optical signals is positively correlated with the dimensional scale.

Detecting a chewing noise from a user during a chewing session, triggering operation of a camera, obtaining image data capturing a food product, identifying the food product based on image data, determining a measurement of the chewing session, determining a volume of the food product based on the measurement of the chewing session, and determining a calorie intake based on the food product, the volume of the food product, and the measurement of the chewing session.

Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.

Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.