13155 results about "Mobile device" patented technology

A decentralized event detection and geolocation system is disclosed, utilizing a dynamically adaptable Spontaneously Emergent Geolocation Network (SEGNet) comprising mobile, stationary, and other devices equipped with multi-sensor capabilities. The system operates by obtaining sensor data from distributed devices, applying trained models to detect potential events of interest (EOIs), and dynamically forming device networks for collaborative geolocation and event validation. The system employs synchronization mechanisms and geolocation techniques, including Time Difference of Arrival (TDOA) and / or Angle of Arrival (AoA), to determine event locations through multimodal data fusion and iterative validation processes. The architecture supports multiple operational modes, including decentralized, centralized, and hybrid configurations, enabling operation in connectivity-limited environments and enhanced processing when server access is available. The system is designed to accommodate dynamic task delegation and real-time threshold adjustments based on environmental conditions, providing reliable event detection and geolocation across diverse scenarios.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The present application relates to the field of environmental monitoring and governance, and discloses a supervision system and method for agricultural non-point source pollution scenarios. The system includes a perception module, a data management module, an intelligent analysis module, and an application service module. The perception module collects pollutant concentration, hydrological characteristics, and abnormal behavior data through water quality sensors, high-position video monitoring equipment, and drones; the data management module dynamically integrates, stores, and standardizes the data collected by the perception module; the intelligent analysis module predicts the diffusion path of pollutants in the water body based on the pollution diffusion model, and inverts the spatial location and intensity of the pollution source by combining the video monitoring abnormal behavior records with the diffusion model; the application service module displays the analysis results, triggers warnings, and supports remote governance operations through an intelligent early warning system, a GIS visualization platform, and mobile devices. The present invention can accurately monitor agricultural non-point source pollution, improve the efficiency of pollution source positioning and supervision, and provide technical support for the governance of agricultural non-point source pollution.

The invention discloses a lightweight mobile phone screen defect detection method based on improved YOLOv10, and the method comprises the following steps: S1, collecting mobile phone screen image data, carrying out the enhancement processing of a data set, marking the data set, and constructing a mobile phone screen defect training data set; s2, adopting a Starnet module, a C2f-ODConv module and a DetectLSCD module to improve a YOLOv10 network model structure, and constructing a mobile phone screen defect detection model of a YOLO-STAR network structure; s3, using the mobile phone screen defect training data set constructed in the S1 as input, and training the improved YOLOv10 network structure model; and S4, deploying the trained YOLOv10 network structure model to a mobile device, and carrying out real-time detection on a mobile phone screen image. According to the invention, by improving the architecture of the YOLOv10 model, the complexity and the calculation amount of the model are reduced, the training data set and the detection process are optimized, and the deployment efficiency and the real-time detection performance of the model on equipment are improved.

A game controller for a mobile device, the game controller including a first handle, a bridge, and an acoustic waveguide. The first handle is configured to contact and support a mobile device and includes a user-accessible, first hardware interface on a main body portion of the first handle that is configured to accept touch inputs. The bridge extends from the main body portion of the first handle. The acoustic waveguide is at a juncture of the first handle and the bridge. The acoustic waveguide is configured to redirect sound emitted from the mobile device toward at least one of a front side of the game controller, a top side of the game controller, and a bottom side of the game controller.

A system and method(s) to perform operations that include aggregating gaming data associated with a casino network; training, via exploratory data analysis of the aggregated gaming data, a set of developed machine learning models that most accurately predict a target variable output. The operations further include predicting, using a deployed one of the developed machine learning models to analyze user-specific gaming data associated with a specific user account, a user-specific output value. The operations further include determining, via the deployed machine learning model using the user-specific output value, user-specific system-based content to present via a presentation device associated with a location of the specific user account (e.g., via a player interface device that the user account is logged into, via a personal mobile device associated with a user of the user account, etc.). The operations further include presenting (e.g., rendering, animating, etc.), via the presentation device, the user-specific system-based content.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

The invention relates to a flow media bandwidth adapting system, which comprises flow server, media provider, and mobile device, wherein the flow server comprises server flow protocol module, bandwidth analyzing module, bandwidth selecting module, document analyzing module and media sending module; the mobile device comprises terminal flow protocol module, wireless module, bandwidth detecting module, buffer module and play module; the mobile device via wireless module and bandwidth detecting module timely reports received bandwidth state and media receiving state to the server flow protocol module; the server flow protocol module sends said information to the bandwidth analyzing module to find the media bandwidth and sending speed, to be sent by the media sending module. The invention can automatically adjust the sending speed and code bandwidth of media document via the network state and media receiving state, to improve relative play effect.

A computer-implemented method is provided for a mobile device to detect, by a camera of the mobile device, a plurality of checks; determine, by a processing unit of the mobile device, that the image of the plurality of checks is of sufficient quality; instruct, by a display of the mobile device, a user to take a photograph of the plurality of checks; crop, by the processing unit, the photograph of the plurality of checks into a plurality of images, wherein each of the plurality of images contains one of the plurality of checks; and transmit, by a transmitter of the mobile device, the plurality of images to a server via a network. The plurality of images may be transmitted individually (i.e., one at time), or alternatively, collectively and in one payload.

A game controller for a mobile device that includes a first handle, a first bumper, and a first switch. The first handle is configured to contact and support a mobile device. The first bumper is coupled to the first handle. The first bumper is configured to accept touch inputs. The first bumper includes a pivot rod at a first end of the first bumper that is configured to engage a track slot of the first handle. The pivot rod is configured to rotate within the track slot and to translate within the track slot. The first bumper further includes a plunger at a first-span distance from the pivot rod. The first switch is within the first handle and is configured to be activated by touch inputs to the first bumper. The plunger of the first bumper is configured to contact and impart a force to the first switch.

The invention provides a room obstacle detection optimization method and system based on a cross-modal attention mechanism. Wherein a dynamic compensation coefficient is calculated by acquiring motion track data of the mobile equipment, and a stable visual coordinate system is constructed. Visible light and near-infrared wave band acquisition of the multispectral sensor is synchronously triggered, the visible light captures geometric deformation characteristics of a floor projection surface, and the near-infrared detects a local heat radiation abnormal area. And extracting a thermal feature connected domain conforming to periodic biological motion as a living obstacle, and marking a collision risk boundary of a static obstacle. And based on a living body motion law and a collision boundary deviation trend, synchronously optimizing the dual-band exposure parameters and updating the trajectory prediction model, and forming a closed-loop feedback link of sensing and planning. According to the technical scheme provided by the invention, the living body and the static obstacle are accurately distinguished in a dynamic environment, and the sensor parameters are adaptively adjusted to improve the real-time obstacle avoidance response capability.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

A game controller is provided. The game controller includes a first handle body, a second handle body, a first support module, and a second support module. The first support module includes a first support groove, a first clamping member, and a second clamping member. The first clamping member and the second clamping member are disposed at two sides of the first support module. The second support module includes a second support groove, a third clamping member, and a fourth clamping member. The third clamping member and the fourth clamping member are disposed at two sides of the second support module. A mobile device is disposed between the first support module, the first clamping member and the second clamping member of the first support module, the second support module, and the third clamping member and the fourth clamping member of the second support module.

Techniques are provided for enabling on-demand positioning reference signals (PRS) with a user equipment (UE). An example method for determining a location of a mobile device includes sending a request for downlink positioning reference signals to a network server, where the request includes positioning reference signal configuration information, receiving assistance data based on the positioning reference signal configuration information, measuring one or more downlink positioning reference signals based at least in part on the positioning reference signal configuration information, and determining a location of the mobile device based at least on part on the measurements obtained from the one or more downlink positioning reference signals and the assistance data.

The invention discloses a VLM model intelligent decision-making-based driving method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting key visual features from continuous multi-frame driving scene images based on a preset visual feature extraction algorithm; inputting a user instruction and a time sequence visual Token corresponding to the key visual features into a preset VLM model for multi-modal alignment, and generating a target planning Token; inputting the target planning Token and the time sequence vision Token into a preset trajectory generation model to obtain a predicted trajectory; and converting the predicted trajectory into a control signal, and controlling the mobile device to complete a moving action based on the control signal. The problem of modal difference between a semantic space and an action space is solved.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments for providing a mid-stream deposit availability schedule to a customer electronically depositing a check. The deposit availability schedule is generated by activating a mobile financial application, receiving a customer request to deposit a financial instrument, and based on the customer request, activating a camera on the client mobile device to access a field of view of at least one camera and capture one or more images of a financial instrument, store, in a computer memory on the client mobile device, the one or more images. An optical character recognition program, resident on the client mobile device, extracts in real-time, one or more data fields from one or more portions of the financial instrument, communicates the one or more data fields to a remote deposit server and receives a deposit availability schedule, before acceptance of the remote deposit.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

Systems, methods, and non-transitory media are provided for a vehicle and mobile device interface for vehicle occupant assistance. An example method can include determining, based on one or more images of an interior portion of a vehicle, a pose of a mobile device relative to a coordinate system of the vehicle; determine a state of an occupant of the vehicle; and send, to the vehicle, data indicating the state of the occupant and the pose of the mobile device relative to the coordinate system of the vehicle.

A handheld gaming device in which configurations allow for default-open, force-close systems and separate the user's insertion of the mobile device and closure of the handles around the mobile device.

To improve social safety by functioning as a crime prevention buzzer, predicting crimes, and transmitting real-time alerts to nearby pedestrians.SOLUTION: A system comprises: means for functioning as a crime prevention buzzer; preemptive correlation means in which criminal psychology, behavioral psychology, aura, and past crime data (news images(patterns)) are combined; and means for transmitting alerts to mobile devices of nearby pedestrians using information from satellites and big data.SELECTED DRAWING: Figure 1

The invention discloses a spine three-dimensional evaluation method and system based on RGB images and anatomical constraints, and belongs to the field of medical image analysis. The method comprises the following steps: carrying out preprocessing and anatomical symmetry correction on an upright position back RGB image shot by the intelligent mobile equipment for a single time; extracting regions of interest of shoulder blade and iliac crest mark points by using a lightweight segmentation network; generating an initial depth map through a depth estimation model fusing a vertebra attention mechanism and a multi-constraint loss function; carrying out physiological scale calibration on the initial depth map based on height and weight parameters; a cascade GAN is adopted to eliminate clothes interference and reconstruct a back three-dimensional surface; based on biomechanical characteristics, NURBS parameters are adjusted in a self-adaptive mode to fit a spine three-dimensional curve; and outputting a coronal plane Cobb angle, a trunk rotation angle and a sagittal plane curvature parameter. Through monocular depth estimation and dynamic evaluation of anatomical constraint, the problems of radiation risk, subjective error and clothes interference of a traditional method are solved.

There is provided a system for determining a route for an item and / or a passenger, the system comprising a processor. The processor is configured to: determine whether the item and / or passenger has missed or is expected to miss a first predetermined departure time of a first scheduled route; retrieve an origin for the item and / or passenger based on the first scheduled route; determine a destination for the item and / or passenger; determine a new, second, scheduled route between the origin and the determined destination wherein the second scheduled route departs from the origin at a second predetermined time which is different from the first predetermined time; and process the item and / or passenger according to the second scheduled route. A corresponding mobile device and user-accessible device for communicating with the system and corresponding methods are also provided.

An example operation includes one or more of receiving a scan of a cabin of a vehicle, wherein the scan includes a spatial region inside the vehicle and outside the vehicle proximate at least one vehicle door, determining at least one occupant characteristic based on the scan, including a classification detection and a presence detection of at least one seat inside the vehicle, determining a cabin status based on the at least one occupant characteristic and communicating the cabin status to a mobile device.

The invention provides a steel surface defect detection method and device, equipment and a storage medium. Relates to the technical field of image recognition. The method comprises the following steps: collecting steel surface defect images, and performing data enhancement processing on the steel surface defect images to obtain a data set after data enhancement; a backbone and neck network of a reference model YOLOv11s network is improved, an ECS-YOLOv11s model is constructed by adopting a multi-scale feature fusion mode, a data set after data enhancement is input into the ECS-YOLOv11s model to train and shoot and collect steel surface images in real time, the steel surface images are input into the trained ECS-YOLOv11s model, and a steel surface defect detection result is obtained. According to the method, the steel surface defects can be accurately and efficiently recognized, meanwhile, the needed calculation amount and parameters are reduced, and the requirement for deployment on mobile equipment can be met.