91 results about "Location aware" patented technology

A system and method for contextual three-dimensional messaging in augmented reality (AR) environments is disclosed. The system receives chat messages with specified real-world destinations and stores them associated with those locations. When a user wearing an AR device enters a destination location, the system detects their presence using techniques like GPS, Wi-Fi positioning, or computer vision. It then generates a 3D visual representation of the message and determines an appropriate spatial position within the physical environment based on environmental analysis and object detection. The 3D message is displayed at the determined position in the AR view. The system can analyze message content to identify topics and match them to detected real-world objects for contextual placement. Users can interact with displayed messages through gestures or voice commands to reply, forward, delete, or reposition messages. This enables immersive, location-aware messaging experiences that seamlessly blend digital content with the physical world.

The invention provides a traffic flow prediction method and system for a dynamic association information fusion space-time network, and the method employs a time attention mechanism to adaptively adjust the association strength between different time points, and distributes a relative weight for each time point, so as to achieve the precise modeling of a time sequence dependence relation. And the space-time dynamic module captures directional information and position sensing features of the traffic flow by introducing a space-time feature enhancement mechanism so as to improve the expression ability of the traffic flow data and the extraction effect of the space-time features. Meanwhile, a gating attention unit is provided, the deep dynamic trend in the traffic sequence data is effectively mined, and a potential time sequence change mode is captured. By constructing a dynamic feature matrix, spatial correlation and node features are fused in real time, so that richer and more accurate spatial context information is provided. Associated information extracted by a dynamic feature matrix is introduced into a spatial graph convolutional network to deeply learn dynamic spatial topological structure features in a traffic network.

Systems and methods for location aware access are provided. A location aware access system comprises a card reader and at least one controller. The card reader obtains access credential from a card, generates a corresponding time stamp, ascertains location coordinates representative of a geolocation of the card reader when the access credentials were received, and provides the access credentials, the time stamp, and the location coordinates to the controller. The controller is configured to receive the access credentials, the time stamp, and the location coordinates corresponding to the geolocation of the card from the card reader. Further, the controller is configured to perform authentication of the card based on the access credentials, the time stamp, and the location coordinates corresponding to the geo location of the card, and trigger an unlocking mechanism corresponding to the card reader upon successful authentication of the card.

A system and method for artificial intelligence enhanced mobile energy and data and network control, planning and optimization. The present invention relates to a system and method for optimizing energy and data and network use in mobile platforms, such as facilities, vehicles, tools, and devices. The system leverages AI, data analytics, and context-aware techniques to collect, process, and analyze data from various sources, including sensors, weather data, and spatial and temporal data with locality aware computing, transport, storage and networking across assets that may be owned or operated by multiple stakeholders. By considering a variety of factors, the system generates optimized recommendations for data storage, compute, transmission, device settings, fleet management, and physical and virtual route planning and logic locality planning. The invention offers benefits, including improved energy efficiency, enhanced data and network management, increased operational efficiency, and cost savings.

Approaches presented herein provide for identification of sound from a sound source relative to an array of microphones of a potentially unknown configuration using, in part, differences in the audio signals received by the microphones. In at least one embodiment, audio signals are captured using an array of microphones and audio features are extracted from those signals. The audio features can be processed using a first neural network to generate a feature vector representing a spatial location of an audio source with respect to the plurality of microphones, where the spatial location is inferred based on audio differences and independent of an availability of information indicating a physical configuration of the plurality of microphones. The feature vector can be provided to a task-specific model to perform at least one audio-related task based in part on the spatial location.

Various embodiments employ multiple readers and tags in fixed locations in different geographical areas and / or along different storage units. This allows for more granular or precise location sensing. Particular embodiments employ multiple environment tags in various geographical locations and within multiple storage units. Using this new infrastructure setup, some embodiments can perform new functionality by predicting whether a target asset is in a particular geographical location, predict whether the asset is within a particular storage unit based on the proportion of readers mapped to that shelf which are actually reading the tag on the asset, and / or can predict where an asset's exact location in the located storage unit is based on comparing indications of signal strength values between each reader and tag in the storage unit with other indications of signal strength values between each reader and the tag that is attached to the asset.

The invention provides a remote sensing image ship target detection method based on a geographic information hybrid expert model. The method depends on a hybrid expert model and a Mask-RCNN network in deep learning, and is a visible light remote sensing image ship target deep learning detection method suitable for a multi-spatial resolution and complex geographical environment. In a Mask-RCNN feature extraction link, two expert groups are introduced, and two feature extraction expert groups guided by resolution and latitude and longitude geographic information respectively are constructed. In this way, detection of the ship target in the multi-resolution complex background visible light remote sensing image is achieved. The method not only has an accurate multi-scale target position sensing capability, but also can effectively eliminate ground feature interference in various geographic areas and complex background environments, and makes up for the deficiency of a current deep learning ship detection method in the aspect of multi-resolution complex background image research.

A multi-target tracking and monitoring method and system with high trajectory consistency, belonging to the field of computer vision, solves the problems of identity switching and trajectory fragmentation caused by long-term target occlusion and dense interaction in multi-target tracking, especially in UAV monitoring scenarios. The method includes the following steps: 1. Inputting a video sequence, performing target detection and feature acquisition for each frame; 2. Performing preliminary association through a basic tracker to obtain an initial trajectory; 3. Detecting the disappearance and appearance of abnormal trajectories using a position awareness submodule; 4. Storing abnormal trajectory features using a long-term memory submodule; 5. Performing secondary association using a multi-step cross-frame matching submodule; 6. Completing trajectory updates. This invention is applicable to UAV monitoring scenarios such as high-altitude inspection, low-altitude operations, and outdoor scene monitoring.

The embodiment of the application provides a kind of communication perception integration implementation method, position perception method, device and equipment;The method comprises: using first communication unit to generate probe signal, probe signal is respectively mapped to the multiple first perception symbols of first flexible frame, and probe signal is sent according to preset first frame structure, first flexible frame is a frame in first frame structure;Using second communication unit according to preset second frame structure, receive echo signal at each second perception symbol of second flexible frame, and send to perception processing unit, the structure of second frame structure is identical with first frame structure, the symbol ratio of first flexible frame and second flexible frame is different, echo signal is the signal after probe signal is reflected by target object;Using perception processing unit determines the relative position of communication device and target object.In the perception position, normal communication service is not affected, and communication function and position perception function can be completed in the same communication device.

The invention discloses a vehicle re-identification method based on multidirectional feature and position perception fusion, relates to the technical field of image processing, and realizes multidimensional feature interaction optimization through position coding module structured modeling (horizontal-vertical direction tensor initialization); a DOConv module performs global-local collaboration (capture of fine-grained textures by deep convolution + integration of a global structure by traditional convolution) and deep separable convolution multidirectional feature capture (a square kernel focuses on local details, horizontal bar-shaped kernel extraction depends on a horizontal long distance, and vertical bar-shaped kernel analyzes vertical spatial distribution); the three collaboratively guide a network to accurately focus a vehicle body identification area, suppress background noise interference and enhance robustness to illumination, shielding, extreme viewing angles and similar vehicle types, so that richer feature information is integrated, the problems caused by intra-class similarity and intra-class difference can be further relieved, the robustness of the model is enhanced, and in the aspect of quantitative indexes, the accuracy of the model is improved. And the current advanced comparison method is also exceeded.

The invention relates to the technical field of intelligent voice interaction, in particular to a two-wheeled vehicle renting voice service system based on user portraits and position awareness, which comprises four core modules. The speech enhancement module for multi-sensor data fusion and motion state coupling establishes motion-intention association through motion micro-motion event detection; a position-aware adaptive noise confrontation processing module constructs a geographic noise fingerprint screening optimal voice channel; the acoustic gene fault-tolerant identification module fused with the user portrait improves the identification robustness by means of a personalized gene bank and a voting matching algorithm; and the intention decision and personalized service module based on the multi-dimensional context fuses the multi-dimensional information and updates the user portrait. The system solves the problem that voice recognition is influenced by noise and individual differences in a riding scene, and realizes personalized and high-precision voice interaction service.

A request to activate a service may be received from a user device and a determination may be made as to whether the request is authorized or fraudulent. In particular, a geographical location of the user device may be determined. Whether to activate the service may be determined based on the geographical location of the user device.

The application discloses a remote sensing image language description generation method based on position perception and reinforcement learning. First, a multi-scale feature extraction module is used to obtain global image embedding, CLIP image embedding containing language features and regional image embedding of the remote sensing image respectively. Second, position perception attention processing is performed, the global and regional features are compressed and matrix interaction is performed to generate regional enhanced features containing spatial topological relationship. Then, multi-scale feature fusion is performed, the attention mechanism with random drop strategy and the gating mechanism are used to deeply fuse the regional enhanced features and the CLIP image embedding. Finally, the multi-scale fused features are input into an improved Transformer remote sensing image language description model to generate accurate and spatially logical remote sensing image natural language description. In addition, the application adopts a supervised pre-training and reinforcement learning fine-tuning combined training mode to improve the generation accuracy of the model.

Reconfigurable intelligent surface is considered as one of the key technologies to improve the performance of the system in multiple-input multiple-output communication systems. When the system works at very high frequencies (such as terahertz), the sparsity of channel parameters becomes a significant feature. In order to effectively recover these channel parameters, we introduce an optimization method based on the atomic norm, which uses the sparsity in the angular domain to accurately estimate the channel state information. In the mixed reconfigurable intelligent surface scenario, the selection of randomly activated elements aims to reduce the cost of reconfigurable intelligent surface deployment while maintaining the performance of channel estimation and positioning. Simulation experiments show that this method can achieve super-resolution channel estimation and outperform the existing orthogonal matching pursuit method. Comparison of various performance indicators shows that the proposed method has obvious advantages. This advantage is crucial for achieving accurate positioning services in complex environments.

The invention relates to the technical field of medical first aid, in particular to an intelligent AED trigger system which comprises a data acquisition and sensing unit, an intelligent matching and decision unit, a communication transmission unit, an actuator unit and a feedback and learning unit. Standardized data is generated through a work order creating module to guarantee integrity and traceability, high-precision position sensing is achieved by means of multi-source positioning, risk grading is completed through information preprocessing, an optimal AED operator is accurately screened through a distributed database and a multi-dimensional self-adaptive matching algorithm, a scheduling sequence is dynamically generated, and the accuracy and reliability of the system are improved. The AED scheduling response speed, the matching accuracy and the rescue operation specialty are remarkably improved, and the rescue timeliness and the success rate in emergency scenes such as sudden cardiac arrest are effectively guaranteed.

Aspects of the subject disclosure may include, for example, identifying a location associated with digital content, augmenting the digital content according to the location to obtain augmented digital content, and configuring a generative artificial intelligence (AI) model according to the augmented digital content to obtain a location-aware, generative AI model. The location-aware, generative AI model is configured to generate a solution according to the augmented digital content. Other embodiments are disclosed.

The application discloses a self-distillation action quality evaluation method based on position awareness and reward feedback, which firstly acquires an action quality evaluation data set, obtains space-time features through a multi-scale space-time feature extraction backbone network based on a deep three-dimensional convolution; secondly, the space-time features are respectively input into corresponding time-channel attention modules to obtain enhanced space-time features; the enhanced space-time features are reconstructed to a standard feature dimension space through a feature reconstruction module, and adaptive weights are obtained by an adaptive weight generator; finally, the reconstructed space-time features are respectively input into an action category classifier and a shared regression head, and a loss is calculated by combining the adaptive weights for training; in the inference stage, the action category classifier and the adaptive weight generator are removed to obtain an action quality evaluation result. The application fuses bottom-level detail features and high-level abstract representations, guarantees the precision, improves the operation efficiency and enhances the overall knowledge distillation effect.

A system having a system backend having a backend processor configured to define a patient clinical team associated with a patient clinical process comprising of a series of patient clinical tasks effected within the healthcare facility, each patient clinical task with an associated patient clinical task location within the healthcare facility, a group of mobile devices communicably connected via a network to the system backend, each mobile device being associated with and configured for use by at least one care member of the patient clinical team, a low energy (LE) beacon array arranged in a predetermined relationship with and differentiating between different patient clinical task locations, and communicably connected to each of the mobile devices that is configured to resolve its proximity, from the beacon array, relative to the at least one patient clinical task location. The backend processor is configured to dynamically enhance the patient clinical team by dynamically changing in real-time a predetermined characteristic of the patient clinical team based on the resolved proximity of each mobile device associated with each care member of the patient clinical team.

The embodiment of the invention provides a data labeling method and device, a storage medium and a program product. The method comprises the steps that robot collection data obtained when a robot executes a target task in an indoor area is received; under the condition that the target task is a cruise task, three-dimensional space sensing is carried out based on robot collection data to obtain position sensing data; under the condition that the target task is an operation task, target object tracking is carried out based on robot collection data to obtain object tracking data; and under the condition that the position sensing data and / or the object tracking data pass the quality inspection, constructing, expanding or updating a three-dimensional sensing data set for training a specified model by utilizing the position sensing data and / or the object tracking data. According to the embodiment, the robot can automatically complete data labeling under the condition that the robot executes the target task in the indoor area, the three-dimensional perception data set is constructed, expanded or updated accordingly, and a data closed loop which is low in cost, extensible and capable of being automatically iteratively optimized is formed.

The invention is suitable for the technical field of scenic spot intelligent explanation, and provides a scenic spot automatic voice explanation method and system based on position awareness, and the method comprises the steps: carrying out the statistical analysis based on the photographing alignment deviation degree corresponding to each exhibition object, and when the photographing alignment deviation degree is in a preset deviation threshold value interval and exceeds a preset specific value, carrying out the voice explanation of the exhibition object. And comprehensively evaluating the alignment deviation degree of each time of shooting so as to calculate a correction factor related to semantic hierarchy correction. According to the invention, by introducing a user shooting behavior analysis mechanism based on location awareness, the conversion of a voice explanation system from static content presentation to dynamic semantic self-adaptive adjustment is realized. After the system detects the shooting behavior of a user on an exhibition object, the shooting alignment deviation degree is calculated, a correction factor is generated according to the shooting alignment deviation degree, and a semantic hierarchy adaptability numerical value of explanation data content is dynamically corrected, so that subsequent explanation content is adaptively matched with a user cognition hierarchy in semantic depth and visual guidance intensity.

A location-aware search assist capability identifies location-aware search query suggestions using location information associated with the location-aware search query suggestions. A user's search query input and location and a location associated with each location-aware search query suggestion candidates may be used to identify a set of search query suggestions for presentation to the user. Location-aware search query suggestion candidates may be ranked in accordance with a closeness of each one's location to the user's location. The ranking may be performed using a score, such as a popularity score associated with each search query suggestion candidate. The location-aware search query suggestion candidates having a location closer to the user's location may be promoted by adjusting each candidate's popularity score upward, and the search query suggestion candidates that are farther away from the user's location may be demoted by adjusting each such candidate's popularity score downward.

This disclosure provides systems, methods, and devices for machine learning techniques that enhance vehicle perception using multi-view feature analysis. In one aspect, a method is provided that includes determining, based on sensor data, a first set of features for an area surrounding a vehicle and determining a second set of features for a top view representation of the area surrounding the vehicle. The second set of features may include center locations for cells within the top view representation. Output data may be determined based on the second set of features and the center locations. Other aspects and features are also claimed and described.

The invention discloses a hyperspectral image super-resolution method based on position-aware double-domain hybrid Transform, which belongs to the technical field of computer vision and comprises the following steps: grouping spectral bands; capturing multi-scale space-spectrum features by using a position-aware cavity convolution module; splicing each group of extracted features along channel dimensions, and performing dimension mapping to obtain hyperspectral fusion features; introducing N DHTMs and then introducing local residual connection, and fusing the original features and the output features of the stacked DHTMs; target spatial resolution is recovered from the features through an up-sampling module; residual features are obtained; and outputting a final prediction SR result. According to the hyperspectral image super-resolution method based on the position sensing double-domain hybrid Transform, a super-resolution reconstruction effect superior to that in the prior art is achieved, and the practical value and reliability of a super-resolution reconstruction result are greatly improved.

Systems and methods of the present disclosure receive source code having multiple code fragments including parts of speech having syntactic types. Based on the syntactic types, the parts of speech in each code fragment are identified to generate nodes of a parsed data tree for each code fragment. Based on the parsed data tree, a processing location for each part of speech is identified to produce, for each node, a location-aware code state indicating a first execution location or a second execution location. A software interface call between a first code segment and a second code segment is determined, the first code segment including one or more first nodes having the first execution location and the second code segment including one or more second nodes having the second execution location based on the location code states. The software interface call may be generated and inserted into the source code.

The invention relates to the field of intelligent traffic operation and maintenance and computer vision, and provides an anti-disturbance automatic registration and impact damage detection method for three-dimensional facilities (guardrails, street lamps and the like) along a highway. Aiming at pain points that facilities are difficult to overlap and registration errors and real deformation are difficult to distinguish due to view angle difference in unmanned aerial vehicle inspection, an integrated solution is provided: firstly, sub-pixel-level automatic alignment is realized by utilizing Loftr depth feature flow matching and RANSAC verification, and feature capture of a slender rod structure is optimized; secondly, designing a position-aware large-scale feature fusion module, recombining tile features through position coding, and keeping semantic continuity of linear facilities; and finally, constructing a multi-scale detection network based on twinborn OfficientNet, and accurately identifying morphological changes such as inclination, fracture and deletion of the facility by using a mixed attention mask module as a core decoding unit, so as to realize automatic high-precision assessment of the damage of the highway facility.

This invention relates to the field of web front-end development technology, proposing an intelligent element sorting and focus switching control component based on Vue.js. The component includes: an entry component module providing external service interfaces for the navigation system; a control and coordination module implementing complete component lifecycle management, DOM structure monitoring, and event binding control; a basic service module providing element scanning, position calculation, and availability detection technologies for other modules; an intelligent spatial navigation algorithm module based on a geometric center point-based intelligent directional navigation algorithm; and an interaction control module for keyboard.js event control, implementing cursor position awareness, dual-mode navigation control, and event lifecycle management. This solution addresses the problems of high development cost and high technical complexity in web form directional key navigation technology.

Systems and methods are directed to securing network access using a digital gate constructed by position-aware compute beacons that form a beacon mesh. A compute beacon in the beacon mesh, detects a request for access from a client device. In response, the beacon mesh, performs a validation process to validate that the client device is located within a digital gate formed by the beacon mesh. The validation process includes detecting, by at least three compute beacons within the beacon mesh, a received signal strength indicator (RSSI) from the client device; triangulating a position of the client device based on the RSSI; and determining whether the position of the client device is within the digital gate. Based on a result, the compute beacon provides a reply to the client device either comprising a challenge request or a session rejection. Subsequently authentication of the client device results in network access.