2361 results about "Identity recognition" patented technology

To solve the problems of fractured identity across multiple blockchains, these methods and systems allow for a single unified identity to be managed and resolved across multiple chains within an interop network. By leveraging message passing, identity records can exist securely across different chains. The system incorporates counterfactual blockchains, enabling trust-minimized name registration that reduces costs while maintaining decentralization and security. ENS name resolution is supported across both onchain and offchain environments, with verifiable proofs ensuring efficient resolution. By structuring message passing and utilizing decentralized indexers, these methods and systems provide a scalable and reliable framework for cross-chain identity.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention provides an identity recognition method and system based on unmanned aerial vehicle management and control. The method comprises the following steps: acquiring encrypted identity data based on unmanned aerial vehicle state monitoring, and generating an initial code containing a unique identifier, a position and a state parameter; dynamically adjusting a coding rule according to the acceleration variation, and generating a differential sequence matched with the motion state; generating a composite identifier containing a timestamp and a geo-fence through irreversible conversion; fusing the positioning data to dynamically bind the identifier and the track to generate synchronous track data; when the geofence is triggered, redundancy calibration is activated to generate anti-interference data; the composite identification and the anti-interference data are subjected to fragmentation packaging, and a topology dependence encryption block is constructed through a Hash abstract chain; and recombining the encrypted blocks in sequence based on the Hash chain, and checking the space-time consistency of the encrypted blocks and the geofence during recombining. According to the invention, the identity anti-counterfeiting and cross-region transmission security in the dynamic flight of the unmanned aerial vehicle is improved.

The invention relates to the technical field of face recognition, in particular to a face recognition method and system for a dynamic environment, and the method comprises the steps: collecting a face video stream through a multispectral imaging device, and carrying out the preprocessing of dynamic noise reduction, distortion correction and the like; constructing a multi-scale space-time fusion feature extraction network to extract dynamic space-time features and fuse cross-modal features; establishing an environment disturbance simulation generation model to generate a virtual sample, and performing domain adaptive alignment; designing an online incremental feature updating mechanism to optimize parameters of the feature encoder; deploying a heterogeneous graph neural network to carry out multi-modal decision fusion; and a hierarchical verification architecture is adopted to complete identity recognition. The system comprises a data acquisition and preprocessing module, a multi-scale space-time fusion feature extraction module and the like. According to the method, the problem of face recognition in a dynamic environment is effectively solved, the recognition accuracy, robustness, real-time performance and reliability can be remarkably improved in the scenes of complex illumination, posture expression change, shielding, background noise and the like, and the method has a wide application prospect.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention provides an intelligent tool warehouse management system, which belongs to the field of tool management and comprises a multi-mode sensing module, an intelligent access control module, an edge calculation module, an anomaly analysis module and a visualization module. The multi-mode sensing module realizes identity recognition, real-time positioning and integrity verification of the tool through RFID and UWB dual-mode positioning, visual recognition and sensor fusion technologies; the intelligent access control module is based on dynamic authority control and gravity sensing goods shelves, and tool storing and taking compliance is ensured; the edge computing module adopts localized data processing and low-power-consumption communication, and supports network disconnection disaster recovery; the abnormity analysis module identifies violation operation through an LSTM model and triggers grading alarm; the visualization module displays the tool state and the warehouse thermodynamic diagram in real time based on the digital twinning technology. The problems that traditional warehouse management depends on manpower, efficiency is low and errors are prone to occurring are solved, and high-precision tracking, intelligent early warning and optimal scheduling of the tool in the whole life cycle are achieved.

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

A system executes an authorization process for initiating a session with a computing entity. Executing the authorization process includes determining an identity associated with the computing entity, identifying a current set of access policies associated with the identity, and determining, based on the current set of access policies, a first set of actions that the computing entity is authorized to perform. While executing the session, the system executes a first action in accordance with the current set of access policies. Subsequent to executing the first action, the set of access policies is modified. The system detects an occurrence of a trigger condition, and in response, re-executes the authorization process for the session, including determining, based on the modified set of access policies, a second set of actions the computing entity is authorized to perform that differs from the first set of actions.

The invention provides a PPG big data driven biological feature recognition method, which comprises the following steps: acquiring PPG signal data of trainees, and performing high-pass filtering and low-pass filtering preprocessing on the PPG signal data to obtain preprocessed PPG signals; framing is carried out on the preprocessed PPG signal, a Mel frequency cepstral coefficient feature and a Gammatone frequency cepstral coefficient feature are extracted respectively, the Mel frequency cepstral coefficient feature and the Gammatone frequency cepstral coefficient feature are fused, then principal component analysis dimension reduction is carried out, and a fusion feature is obtained; a universal background model UBM of a Gaussian mixture model is constructed based on the fusion features, zero-order, first-order and second-order Baum-Welch statistics are calculated, a global difference space matrix is estimated from the Baum-Welch statistics, and i-vector identity authentication vectors are extracted; and inputting the i-vector identity authentication vector into a long short-term memory network for training and classification to obtain an identity recognition result of the trainees. According to the invention, the influence of motion artifacts can be eliminated, the overall variability of PPG signals is captured, and an identity authentication scheme is provided for wearable equipment.

The invention discloses a non-inductive identity recognition and real-time tracking method based on personnel in a video. The method comprises the following steps: S1, acquiring video stream data and extracting a key frame image; s2, key point coordinates are extracted, and a time sequence skeleton sequence is constructed; s3, decomposing gait, action and attitude features to generate standardized features; s4, inputting the standardized features into an improved multi-scale image convolutional neural network, extracting identity representation features, and generating a target identity code; s5, matching identity codes based on similarity measurement, calculating a consistency score, and establishing an identity tracking trajectory; s6, tracking is carried out in combination with identity codes and consistency scores, identity mapping is dynamically updated, and non-inductive recognition is achieved; and S7, updating a track in real time, adjusting a tracking state, and ensuring identity stability. According to the method, the improved graph convolutional network and the consistency measurement technology are combined, the identity recognition precision and tracking stability are improved, and cross-scene non-inductive identity recognition is achieved.

The invention provides a family blood pressure measurement and identity recognition system and method based on oscillatory wave characteristics, and relates to the technical field of household medical equipment. The method comprises the following steps: firstly, acquiring original oscillatory wave signals during blood pressure measurement, judging whether the signals are available or not, further fusing time domain and frequency domain features to perform quality evaluation on available oscillatory wave signals, and screening high-quality signals; filtering and noise reduction preprocessing is carried out on the oscillatory wave signals with good quality; then respectively extracting time domain, frequency domain and time-frequency joint features from the preprocessed oscillatory wave signal to obtain multi-index features of the oscillatory wave signal; carrying out standardization processing and unsupervised feature fusion on the multi-index feature data; finally, non-invasive blood pressure measurement and identity recognition are completed. According to the method, the oscillation wave signals are subjected to fusion analysis according to the multi-index characteristics, non-invasive blood pressure measurement is achieved, the accuracy and calculation efficiency of identity recognition are enhanced, historical blood pressure measurement results of fixed members are conveniently stored, and a family blood pressure monitoring system is constructed.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent rationing system for pig house feed supply, which comprises the following steps: performing identity recognition on pigs in a feeding area through radio frequency identification tags, and extracting weight, growth cycle and behavior characteristics; based on historical ingestion records, health data and environmental factors, a dynamic nutritional requirement model of the pigs is constructed, and the current feed requirement is predicted in combination with a multi-factor regression algorithm; according to the feed demand result, the feeding amount and the feeding frequency of the corresponding trough are adjusted in real time; dynamically adjusting the power of a conveying motor and the pressure difference of a pipeline section by utilizing a variable-frequency driving control technology; and collecting the ingestion response and the feeding execution state of the pigs, and outputting an updated pig allocation result in combination with online feedback and historical data differences. The feeding device has the advantage that the feeding stability is improved.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention relates to the technical field of indoor old people safety monitoring, and discloses an indoor old people tumble detection system based on a hybrid mode, which comprises a video acquisition module, an audio acquisition module, an identity recognition module, a tumble detection module, an emotion recognition module and an alarm module. According to the method of the indoor old people tumble detection system based on the mixed mode, video and audio data are creatively fused, multi-dimensional monitoring of the state of the old people, capturing of action postures of the old people by the video acquisition module and obtaining of voice and environment sound information by the audio acquisition module are achieved, and the two complements each other; abnormal sound in the audio, such as collision sound of falling and shouting sound, can assist in judgment, the multi-dimensional data acquisition and analysis mode greatly reduces misjudgment and missed judgment caused by single data, the accuracy and reliability of detection are remarkably improved, and the safety of old people is comprehensively guaranteed.

The invention relates to the technical field of image recognition, and discloses a laboratory animal identity recognition method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of an animal motion image sequence collected by a high-speed camera in a laboratory, and obtaining the image data of a target animal; performing two-dimensional discrete Fourier transform and inverse Fourier transform to obtain animal image high-frequency feature data and animal image low-frequency feature data; respectively inputting into a double-branch diffusion network for feature processing to obtain fused animal feature data; constructing a positive sample pair, constructing a negative sample pair through difficult sample mining, calculating a feature distance based on cosine similarity and performing momentum updating to obtain an animal identity feature template; and based on the double-branch diffusion network and the animal identity feature template, feature extraction and similarity calculation are performed on the real-time animal image sequence to obtain animal identity data, so that the distinguishing capability of identity features is improved, and the time sequence consistency of an identity recognition result is enhanced.

The invention discloses an RSIC-V CPU security chip based on authority management, and belongs to the technical field of security startup. Comprising an embedded RSIC-V CPU core, and the embedded RSIC-V CPU core is connected with an ROM authority management module, a safety management system register and a safety authority management module. The ROM authority management module is connected with the secure ROM firmware; the security authority management module is connected with a plurality of security modules; when users with different identities access the secure ROM firmware, identity recognition is carried out, and security services with different levels are provided according to the permission of the recognized user identities. According to the method, only the request passing signature verification and security authority management authentication is allowed to access the security module, the service uniqueness of the security module is ensured, the chip security module can be pertinently accessed only through authorized upper software, the access is non-direct access, security service is carried out through ROM firmware, and a service result is fed back to the upper software, so that the security module can be accessed in a targeted manner. The security of the security module is ensured from the circuit, and the attack difficulty of the chip is greatly increased.

A system, method, and program product are presented for causing a processor of a computing device to receive content, determine a context of the content, and when the determined context is relevant for obfuscation, scan the content to identify information that matches, within the context, one of a plurality of data patterns indicative of being sensitive information. The processor further executes to assign a confidence value to the identifying process. When the sensitive information is identified with the confidence value above a confidence threshold, the processor executes to generate an obfuscated version of the sensitive information within the content, replace the sensitive information with the obfuscated version to form updated content, and output the updated content. The context may include at least one of a feature or a subject, a manner of use, and / or a determination of a user accessing, transmitting, or receiving the received content.

The invention relates to the technical field of laboratory management, in particular to a paperless office-based laboratory whole-process quality control management system, which comprises an abnormity matching unit, an abnormity early warning unit, an experiment monitoring unit, an experiment reexamination unit and a score evaluation unit, and further comprises an identity recognition unit, a data processing unit and a control unit, and the identity recognition unit is used for carrying out face image acquisition on an experimenter by arranging face image acquisition equipment outside a laboratory so as to obtain a face image of the experimenter, and carrying out feature extraction on the face image through a trained feature extraction model so as to obtain a face feature set. The abnormal flow track of the experiment sample is monitored and recognized in real time through the abnormal matching unit and the abnormal early warning unit, and once the abnormity is detected, abnormal information is generated immediately and early warning is performed, so that laboratory management personnel can take measures in time, and potential problems in the experiment process are prevented from expanding.

The invention discloses a large factory cross-platform user drainage and growth promotion system based on artificial intelligence, and relates to the technical field of monitoring analysis, and the system comprises a data collection and processing module which is used for obtaining user cross-platform interaction behavior data, and generating a multi-platform user behavior feature vector set; the data mining module is used for performing cross-platform user identity identification code matching processing on the multi-platform user behavior feature vector set to generate a user cross-platform behavior feature map; the promotion strategy generation module is used for constructing a dynamic diversion strategy generation matrix according to the user cross-platform behavior characteristic spectrum and generating a user cross-platform diversion promotion strategy; and the optimization module is used for acquiring the real-time user feedback data flow, constructing a flow guide strategy dynamic correction model based on the real-time user feedback data flow, optimizing the cross-platform flow guide promotion strategy of the user, and deploying the cross-platform flow guide promotion strategy to a target platform to execute user growth operation.

The invention discloses a shielded pedestrian re-identification method based on attitude guidance and feature fusion. The method comprises the following steps: S1, constructing a preprocessing module; s2, constructing an attitude estimation module, and generating attitude features and heat map features; constructing a feature extraction module for obtaining local visual semantic features; and S4, constructing a feature fusion module for obtaining a local semantic feature set and a posture information fusion set. And S5, constructing prediction for pedestrian identity prediction. S6, taking a plurality of pedestrian color images as input and a corresponding pedestrian identity recognition result as output, constructing and training to obtain an occluded pedestrian re-recognition model based on attitude guidance and feature fusion, and optimizing feature learning by using a joint loss function in the training process; and S7, identifying the pedestrian color image by using the shielded pedestrian re-identification model. According to the method provided by the invention, the recognition effect in a shielding scene is remarkably improved, and high-robustness technical support is provided for a monitoring system in a complex scene.

The invention discloses an electric power artificial intelligence model security protection method and system based on a zero-trust architecture, and belongs to the technical field of power grid security protection. The method comprises the following steps: constructing an encryption service request according to identity recognition information and environment information of an access user; verifying the request by adopting a multi-factor identity verification method, and generating an authentication credible score; performing weighted calculation by combining the user historical behavior score, the equipment safety score and the environmental factor score to obtain a dynamic comprehensive credibility score; and generating a permission configuration strategy based on the score to realize fine-grained access control. The system comprises a user request access module, an information collection module, a credibility evaluation module and an access control module, and through continuous verification and dynamic permission adjustment, safe access of the electric power artificial intelligence model is ensured. The defect that a traditional security technology depends on static authentication and boundary protection is overcome, data leakage, model tampering and illegal access are effectively prevented, and the overall security and stability of a power system are improved.

The invention relates to the technical field of sports artificial intelligence and data management, in particular to a physical exercise AI standing long jump evaluation data management system which comprises a dynamic environment calibration module, a multi-mode identity recognition module, a three-dimensional posture analysis module and the like. The dynamic environment calibration module uses a multispectral sensor array and a deep learning algorithm to establish a dynamic coordinate system without fixing a marker; the multi-mode identity recognition module fuses gait and face features to realize non-contact identity authentication; the three-dimensional attitude analysis module performs high-precision modeling and analysis on the long jump action through a multi-view camera and an advanced algorithm; the intelligent violation determination engine combines rules and an AI model, automatically identifies violation behaviors and can dynamically adjust determination criteria. The method gets rid of the limitation of a traditional evaluation mode, realizes high-precision and intelligent evaluation, effectively solves the problems of poor environmental adaptability, inaccurate identity authentication, unguaranteed data security and the like, and is scientific and reliable in evaluation result and safe and efficient in data management.

The invention relates to the technical field of data acquisition and information security, and discloses a block chain tamper-proof data acquisition method and device, and the device comprises a control chip, an identity recognition module, a local tamper-proof module, a time sequence processing module (including a parallel uploading unit), a clock calibration module, a storage monitoring module and the like. The method comprises the steps of hardware-level identity verification, operator verification, generation of a data packet containing metadata, encryption and tamper prevention, networking / offline uplink evidence storage, association uplink approval and hash comparison verification. By applying the method and the system, the tamper-proofing of the data in the whole process can be realized, the offline scene is adapted, and the high-credibility data acquisition requirement is met.

The invention discloses a multi-scene gait monitoring and motion function assessment method and system, a multi-camera cooperation system is deployed in a life scene to accurately identify an identity and extract multi-scene motion features, and an accurate old people gait function assessment scheme is provided through key motion hierarchical modeling, multi-scene data fusion and a risk suppression mechanism. Specifically, 2D human body key points are estimated based on an OpenPose algorithm, and a PoseLifter model is utilized to lift the 2D key points to 3D, so that a three-dimensional motion track of a target is estimated; dynamically adjusting the weights of the face features and the skeleton features during identity recognition according to ambient light; designing an independent long-short-term memory network for different actions to evaluate the motion function score and the fall risk rating of the actions; and designing a risk sensitivity mechanism and fusing multi-scene data to carry out comprehensive scoring. The gait function abnormity of the old people can be found in time, and a basis is provided for health management.

The invention relates to the technical field of data processing, in particular to an operation and maintenance object information query and work order automatic generation system based on NFC (Near Field Communication) equipment, which comprises an NFC identity recognition module, an equipment parameter summarization module, a work order information verification module, a work order structure generation module, a work order push confirmation module and a work order submission terminal based on NFC, and reading the NDEF data of the NFC tag of the equipment through the NFC reader-writer. According to the invention, automatic acquisition of equipment identity is realized through near field communication, multi-dimensional data consistency verification is carried out in combination with a chip sequence, all equipment information is automatically linked with a background data set, multiple parameters acquired on site are subjected to system logic screening, and work order key information is automatically distributed to a standard structure field. A data packaging link is controlled by a unified standard template, multiple safety verification and full-process state monitoring are executed in a submission stage, error risks are reduced in each link by means of systematic processing, a data tracing link is continuously improved, and field response and overall circulation efficiency are remarkably improved.

The invention belongs to the technical field of radar human body tracking and recognition, and particularly relates to a rotary millimeter wave radar human body tracking and recognition device and method. The millimeter-wave radar module is mounted on the rotating holder module, and the main beam direction of an antenna in the millimeter-wave radar module is perpendicular to the rotating shaft; the millimeter wave radar module is connected with the control module, and the embedded processor is internally provided with a point cloud data processing algorithm and an identity recognition neural network which are used for overcoming the influence of environmental noise and multipath effect on data quality. The problems of fixed field angle and limited monitoring range of the traditional millimeter wave radar are effectively solved; and through an improved data processing algorithm and an identity recognition neural network, the influence of environmental noise and a multipath effect on data quality is overcome, and the reliability and adaptability of the system in a complex scene are remarkably improved.

The present application provides an iris recognition method and apparatus, an electronic device, and a storage medium. The method comprises: obtaining an iris image group acquired for an object to be recognized, wherein the iris image group comprises at least two iris images acquired at different acquisition angles for a same eye region of said object; performing stereo matching on the at least two iris images to obtain a disparity map between the at least two iris images, wherein the disparity map comprises disparity elements, and each disparity element represents a displacement amount in a specified direction between two pixel points corresponding to a same eye region element in the at least two iris images; in one iris image among the at least two iris images, determining a pupil edge on the basis of the displacement amounts; determining an iris region in the iris image on the basis of the pupil edge, and performing feature extraction on the iris region to obtain an iris feature; and performing identity recognition on said object on the basis of the iris feature.

Methods and apparatus for biometric data maintenance, access and distribution across two or more experiential and / or network domains. In one embodiment, a 5G NR-based network architecture is provided which allows ultra-low latency and effectively user-imperceptible biometric data use for e.g., authentication and maintenance of user state across multiple domains via multiple constituent user devices (e.g., UEs). The network architecture includes both (i) a distributed biometric database (BDB) model wherein relevant biometric data for individuals / UEs is intelligently cached in various portions of the distributed database, and (ii) centralized and local BAEs (biometric analytics entities) which manage the aforementioned intelligent caching, as well as network configuration using one or both of 5G NR network “slicing” and CU / DU split options to optimize end-user biometric-related applications such as those providing identification / authentication, AR functions, VR functions or yet others.

The invention relates to the field of network live broadcast, and discloses a multi-person identity recognition method and device in a live broadcast stream, equipment and a medium, and the method comprises the steps: determining a data distance between each face image in the live broadcast stream of a live broadcast room and a plurality of face templates corresponding to a plurality of anchor persons in a preset live broadcast person library corresponding to the live broadcast room, obtaining a distance matrix; the optimal matching between the face images and the face templates is solved according to the distance matrix, and the identity label of the identity of the anchor person to which the uniquely matched face template of each face image belongs is determined through the optimal solution; and responding to an operation instruction acting on the identity of the target anchor person according to the position information of each face image in the video frame and the mapping relation data between the identity label of the face template matched with the face image. According to the method, the anchor person identity is determined by searching the optimal matching, so that the accuracy of multi-person identity recognition in the live broadcast stream and the user experience are remarkably improved.