11868results about "Mechanical pattern convertion" patented technology

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

The invention relates to a tunnel illumination control method considering visual adaptability of a driver, and belongs to the technical field of tunnel illumination control, and the method comprises the following steps: S1, employing a volume scattering point location method to obtain visual adaptability parameters of visual adaptation states of all segments in a tunnel; s2, dynamically constructing a predictive visual adaptation model of the driver group based on the visual adaptation parameters, and outputting expected visual states of the drivers according to the predictive visual adaptation model; s3, according to the expected visual state, a parameter control mechanism is adopted to control target illumination parameters of all the sections of the tunnel; s4, according to the target lighting parameters, the working state of lighting equipment in a corresponding section in the tunnel is regulated and controlled, so that the light environment in the tunnel is matched with the dynamic visual adaptability of a driver, and safe visual guidance is achieved; the method has the beneficial effects that the target illumination parameters of all the sections of the tunnel are controlled by adopting a parameter control mechanism according to the expected visual state according to the target illumination parameters.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention discloses a robot control system and method based on vision and force sense fusion and a robot, and relates to the technical field of robots. The robot control system based on vision and force sense fusion and the control method thereof analyze visual features in real time through an artificial intelligence processing unit to predict mechanical parameters; and the self-adaptive impedance control unit is combined to dynamically fuse a position instruction and force sense feedback, so that precise adaptation of target object characteristics and compliant regulation and control of an interaction process are realized, real-time sensing and dynamic adjustment capabilities are achieved, assembly damage caused by mechanical mismatching is effectively avoided, and the reliability and efficiency of precise assembly are improved.

In some embodiments, a computer system performs different object selection-related operations. In some embodiments, a computer system places objects at locations in a displayed region based on attention of a user. In some embodiments, a computer system displays a container virtual object with curvature in a three-dimensional environment.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention relates to a motor imagery ability evaluation and training enhancement system and method based on electroencephalogram and a medium, and belongs to the technical field of brain-computer interfaces. The system comprises an electroencephalogram acquisition device, a processing terminal and a display device. By collecting and analyzing electroencephalogram signals of a subject, the system extracts time-domain, frequency-domain and space-domain features by using a multi-feature fusion technology, so that accurate quantitative evaluation of motor imagery ability is realized. The evaluation core index is a lateral index. A built-in self-adaptive training module dynamically adjusts training difficulty and comprises a basic mode, a middle-level mode and a high-level mode, and personalized efficient training is ensured. The method comprises pre-training guidance, data acquisition and processing in formal training, and adaptive training adjustment based on an evaluation result. By combining multi-feature fusion and an adaptive training mechanism, an efficient and personalized solution is provided for evaluation and enhancement of motor imagery ability, so that the rehabilitation training effect of a motor imagery brain-computer interface system is more effectively improved.

The invention discloses a digital large-screen interaction method and device based on multi-agent cooperation and a medium, and relates to the technical field of digital large-screen interaction.The method comprises the steps that postures, expressions and voice data of a user are collected in real time, the attention weight is calculated in combination with spatial position information, the emotional state change rate is monitored, and the user experience is improved in combination with personal historical browsing preferences; acquiring a main interaction user, an emotional state feature and an emotional change trend; according to the main interaction user, the emotional state features and the emotional change trend, obtaining an initial confidence coefficient of the suspected interested field through a semantic understanding model; and based on the candidate guide images, identifying a user selection intention through a multi-modal fusion processing mechanism, calculating a selection probability, triggering content activation of the main interaction area and content dynamic generation of the auxiliary information area, synchronizing state information, and starting an immersive collaborative interaction process. Through three-level collaborative service configuration, the beneficial effects of improving large screen content organization efficiency and enhancing immersive interaction experience are achieved.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

An XR system is provided. This system captures images including images of a first hand of a user and a second hand of the user using one or more cameras. The XR system generates cropped images using the images, each cropped image including a surface of the first hand. The XR system detects a hand touch of the surface of the hand by a digit of the second hand using the cropped images. The hand touch is used as an input into an XR user interface of the XR system. The surface of the hand can be palmar surface or a hand dorsal surface.

An extended Reality (XR) system is provided that enhances user interaction within XR environments. The XR system captures tracking data using one or more sensors, including hand tracking data of a user's hand and pose data of the XR system itself. By continuously capturing this data, the XR system dynamically generates a hand-located user interface that includes interactive virtual objects associated with specific locations on the surface of the user's hand. Additionally, the XR system generates labels for the interactive virtual objects that are dynamically oriented toward the user as the user moves their hands and head when interacting with an XR environment.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention provides an electroencephalogram-myoelectricity collaborative limb movement intention decoding method and system based on a symmetric cross-modal attention network, and belongs to the technical field of neural rehabilitation engineering and movement intention decoding. Comprising the following steps: acquiring an electroencephalogram signal and an electromyographic signal to be decoded; inputting the electroencephalogram signals into an electroencephalogram channel specificity feature extraction network, and extracting multi-scale space-time oscillation features; the electromyographic signals are input into an electromyographic channel specificity feature extraction network, and dynamic time sequence mode features are extracted; inputting the electroencephalogram features and the myoelectricity features into a symmetric cross-modal attention module, carrying out bidirectional feature interaction and calibration, and generating fusion enhancement features; the symmetric cross-modal attention module realizes mutual enhancement and alignment between the electroencephalogram signals and the electromyographic signals by taking own features as queries and taking another modal feature as a key and a value through a cross attention mechanism; and inputting the fusion enhancement feature into a classifier, and decoding to obtain a corresponding motion intention category.

The invention discloses an emotional interactive intelligent nursing system and method based on multi-modal perception, and belongs to the technical field of intelligent medical treatment and intelligent old-age care crossing. The matrix type pressure sensor array is integrated in the nursing mattress, multi-point pressure distribution of the body of the old people on the bed surface is obtained in real time, and high-resolution body pressure mapping is achieved. Compared with an existing single-point type or linear pressure monitoring structure, the system can accurately recognize the spatial position of a local pressed area, and early recognition and quantitative evaluation of a bedsore risk area are achieved. By fusing the pressure matrix, the heart rate, the blood oxygen, the environment temperature and humidity, the urine wetting and other multi-mode sensing data, the monitoring dimension is remarkably expanded, the health condition can be comprehensively evaluated from multiple physiological indexes, compared with a single physiological parameter monitoring mode, the more comprehensive information obtaining capacity is achieved, and the monitoring precision and the nursing efficiency are effectively improved.

The invention discloses a human body gesture generation method and related equipment, and relates to the technical field of computer vision, and the method comprises the steps: obtaining multi-modal input information, the multi-modal input information comprises a voice audio signal, text transcription information and a reference gesture sequence, the text transcription information comprises a semantic annotation, and the reference gesture sequence comprises a reference gesture sequence; the reference gesture sequence is a basic gesture template in the target scene; generating a multi-modal feature based on the multi-modal input information; performing time alignment processing operation on the multi-modal features to obtain alignment condition features; performing space-time decoupling modeling operation on the alignment condition features to obtain optimized gesture potential features; inputting the optimized gesture potential features into a diffusion model for iterative denoising to obtain denoised gesture potential features; and generating a target human body gesture sequence based on the de-noised gesture potential features.

The invention relates to the technical field of medical hygiene, and particularly discloses a hygiene visual intelligent monitoring and guiding system suitable for surgical hand disinfection, hand motion data is collected by fusing a multi-modal motion capture module with a depth camera and an inertial sensor, and a high-risk area identification module analyzes the risk of microbial residues in finger seams and fingernail edges. The three-dimensional motion modeling module constructs a biomechanical model to calculate a trajectory, the dynamic compliance guiding module marks deviation in real time through optical projection, the multi-source data fusion module integrates a risk value and a spectral data evaluation effect, and the intelligent decision engine module outputs an operation matching degree and a correction instruction based on a neural network. Omnibearing monitoring, accurate risk assessment, real-time action guidance and quantitative effect evaluation of the hand disinfection process of an operator are realized, the standardization and effectiveness of surgical hand disinfection operation are finally improved, and the infection risk of a surgical site is reduced.

The invention belongs to the technical field of brain-computer interfaces, multi-modal feature alignment and information retrieval, and discloses a visual content retrieval method based on electroencephalogram signals. Cross-subject standardized electroencephalogram samples are obtained and input into an electroencephalogram encoder to extract low-dimensional electroencephalogram signal feature vectors; an image encoder is adopted to process the corresponding retrieval images to extract visual feature vectors; the low-dimensional electroencephalogram signal feature vector and the visual feature vector are jointly input into a prototype attention enhancement module to form a dynamic prototype pool, and prototype enhanced electroencephalogram signal representation is obtained through processing; the method comprises the following steps: performing classification training according to existing prototype enhanced electroencephalogram signal representation and image data pairs; for a plurality of time slices of the to-be-queried electroencephalogram signal sample, calculating a reconstruction error or a signal-to-noise ratio of each time slice to obtain a confidence coefficient; and adopting a weighted average or voting mechanism to fuse a plurality of time slice results, and outputting a stable and robust final visual retrieval classification result.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

Various implementations disclosed herein include devices, systems, and methods that initiate an action based on a detected gaze direction oriented towards a target area (e.g., a hot corner / zone). For example, an example process may include producing a reflection by directing light towards an eye using an illuminator, receiving sensor data from a sensor, wherein a direction of sensing by the sensor (e.g., the optical axis of the camera) and a direction from the eye to a target area are approximately aligned, determining a reflective property (e.g., a spectral property) of the reflection based on the sensor data, detecting that a gaze direction of the eye is approximately oriented towards the target area (e.g., a hot zone / spot) based on the reflective property, and initiating an action based on detecting that the gaze direction is approximately oriented towards the target area.

The invention discloses a generative interaction method and device for medium-free aerial imaging, and relates to the technical field of man-machine interaction for medium-free aerial imaging. The method comprises the steps of obtaining a reference description text, hand motion data and hand posture data corresponding to a target user, wherein the hand posture data is used for indicating a final posture of hand motion; inputting the hand motion data and the hand posture data into a motion text generation model, outputting a motion text, inputting the reference description text and the motion text into a text fusion model, and outputting a target description text; the target description text is input into a model generation network, a three-dimensional model is output, and the three-dimensional model comprises at least one entity model and at least one environment scene model corresponding to the entity model; and mapping the three-dimensional model to a medium-free aerial imaging device to obtain an aerial suspension image. The problems of low aerial imaging efficiency, lack of dynamic interaction capability and the like of the existing aerial imaging scheme are solved.

The present disclosure relates generally to implementing biometric authentication, including providing user interfaces for: while the electronic device is in a first state in which a respective function of the electronic device is disabled, detecting one or more activations of the button; and in response to detecting the one or more activations of the button: capturing, with the one or more biometric sensors that are separate from the button, biometric data; if the biometric data satisfies biometric authentication criteria, transitioning the electronic device to a second state in which the respective function of the electronic device is enabled; and if the biometric data does not satisfy the biometric authentication criteria, maintaining the electronic device in the first state and displaying, on the display, an indication that biometric authentication has failed.

Examples relate to systems and methods for enhancing generative AI outputs using eye tracking data. An eye tracking system accesses eye gaze information associated with a field of view of a head-wearable apparatus and generates contextual information associated with the field of view of the head-wearable apparatus based on the eye gaze information. The eye tracking system processes, by a generative machine learning model, the contextual information and at least one image of the field of view of the head-wearable apparatus to generate an output and presents on a display of the head-wearable apparatus the output generated by the generative machine learning model.

The invention discloses an intelligent man-machine interaction system and method based on multi-modal features, and relates to the field of man-machine interaction and artificial intelligence. The problems of low interaction efficiency and frequent errors caused by single intention understanding, feedback lagging and lack of continuous learning ability of a traditional interaction system are solved. According to the method, multi-modal data are collected, and uniform feature representation is generated through feature extraction and time sequence alignment fusion; generating a candidate intention list by using a lightweight encoder and an inference network based on the features; a final execution intention is determined by dynamically generating a multi-modal information probe and analyzing micro-feedback characteristics of a user in combination with iterative screening of a consensus degree calculation model; converting the determined intention into an equipment control instruction and executing the equipment control instruction; a reinforcement learning environment is constructed, a reasoning network and consensus degree model parameters are continuously optimized based on interaction process data and execution results, and accurate understanding of a multi-modal intention, self-adaptive optimization of an interaction process and continuous improvement of system performance are realized.

The application discloses a multimodal interaction method, device, controller, system, automobile and storage medium. The method comprises the following steps: determining a current interaction dialogue according to a current interaction voice at an interaction time; determining a current scene image and current scene data corresponding to a current interaction interface corresponding to the interaction time; adopting a multimodal recognition model to perform multimodal recognition on the current interaction dialogue, the current scene image and the current scene data, and determining a target control instruction; and executing the target control instruction to complete a human-computer interaction operation. The method can guarantee the output efficiency and accuracy of the target control instruction, reduce the complexity of customizing templates, rules and associations and other complex control logics in the development process, and improve the adaptability and generalization ability of voice interaction.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

The invention belongs to the field of man-machine interaction, and particularly discloses a man-machine interaction method for electronic screen control, which comprises the following steps: acquiring touch track data of a user according to a touch sensor, and performing feature extraction on the touch track data to obtain a touch feature vector; the method comprises the following steps: acquiring gesture action data of a user according to image acquisition equipment, and performing key point detection on the gesture action data to obtain a gesture feature vector; the method comprises the following steps: acquiring voice instruction data of a user according to an audio acquisition device, and performing acoustic feature extraction on the voice instruction data to obtain a voice feature vector; acquiring current environment parameters in real time through an environment detection module, wherein the environment parameters comprise illumination intensity, environment noise level and distance between a user and a screen; the objective of the invention is to solve the problem of insufficient reliability of a man-machine interaction mode in a complex environment in the prior art.

The invention relates to a carsickness control method, a storage medium, a program product, equipment, a system and a vehicle, and relates to the technical field of vehicles. The carsickness control method comprises the steps that under the condition that it is determined that a first user is in a carsickness state based on a first electroencephalogram signal, carsickness suppression operation is executed; wherein the first user is a user using the first headrest, and the first electroencephalogram signal is an electroencephalogram signal collected by the first headrest for the first user. The carsickness state can be intelligently detected in real time and effectively relieved in time, and the carsickness state of the user can be efficiently detected and relieved.