2029 results about "Multiple input" patented technology

In some embodiments, an electronic device facilitates cursor interactions in different regions in a three-dimensional environment. In some embodiments, an electronic device facilitates cursor interactions in content. In some embodiments, an electronic device facilitates cursor movement. In some embodiments, an electronic device facilitates interaction with multiple input devices. In some embodiments, a computer system facilitates cursor movement based on movement of a hand of a user of the computer system and a location of a gaze of the user in the three-dimensional environment. In some embodiments, a computer system facilitates cursor selection and scrolling of content in the three-dimensional environment.

The invention discloses a remote sensing image segmentation method based on enhanced and cross-level multi-input features. The method comprises the following steps: firstly, obtaining a high-resolution remote sensing image training sample set; the method comprises the following steps: designing a Swin Transform encoder based on channel enhancement, a cross-level multi-path input decoder and a feature refining module based on fractional order differential, and constructing a pixel-level semantic segmentation model; then, based on the high-resolution remote sensing image training sample set, training the pixel-level semantic segmentation model to obtain a trained pixel-level semantic segmentation model; and finally, based on the trained pixel semantic segmentation model, performing semantic segmentation on a to-be-processed high-resolution remote sensing image to obtain a semantic segmentation result of the to-be-processed high-resolution remote sensing image. According to the method provided by the invention, the problems of fuzzy boundary, small target loss and insufficient information processing during semantic segmentation of the high-resolution remote sensing image are effectively improved, and the precision of a pixel-level semantic segmentation model is improved.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

An intelligent multi-modal component search and procurement system is provided. The system enables users to search for industrial components through multiple input modalities, including keyword queries, BOM (Bill of Materials) file uploads, and natural language interactions. The system dynamically refines search results using a combination of structured filtering, semantic similarity analysis, and machine learning. Key features include a chat interface for natural language processing (NLP), a selection panel for real-time filtering, and a product listing that adapts to user inputs. The invention improves efficiency in industrial procurement by integrating contextual understanding, schema validation, and embedding vector conversions.

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The invention belongs to the technical field of artificial intelligence, and discloses a government information consultation system based on a large language model, which comprises a user interaction module, a large language model core engine, a knowledge base integration module, a multi-level authority management module, a feedback optimization mechanism and a risk control module. The comprehensive intelligent government affair service system is constructed through the six core modules, remarkable advantages are shown in government affair service digital transformation, the system innovatively adopts multi-mode interactive design, multiple input and output modes of voice, text and images are supported, an intelligent authority management mechanism is matched, and the intelligent authority management mechanism is matched with the intelligent authority management mechanism. According to the technical scheme, the accessibility and convenience of government affair services are greatly improved, precise services for different user groups are achieved, it is guaranteed that sensitive data are safe and controllable while wide spreading of government affair information is guaranteed, and a large language model of a system core is subjected to professional government affair scene optimization training and is combined with a dynamically-updated knowledge graph technology.

The invention discloses a document link construction and evolution relation tracking method based on fragment-level semantic alignment, which relates to the technical field of document management, and comprises the following steps: carrying out structured analysis on a plurality of input documents, and dividing the document content into a plurality of semantic fragments; performing vectorization processing on each semantic fragment, and constructing a document-fragment-vector semantic mapping relationship; different document fragment-level semantic alignment is realized through cross-document semantic vector similarity calculation, a position offset tolerance algorithm and context association analysis, and a semantic link network is established; fusing the link relationship, the timestamp, the version information and the author information, and constructing a document evolution diagram taking the documents or fragments as nodes and evolution links as edges to record semantic flow paths between the documents; and outputting the semantic link network and the document evolution diagram in a structured form. The method can solve the problems that semantic relations between documents are missing, evolution paths are invisible, content reuse cannot be traced, and the structure induction capability is insufficient.

The invention discloses an LSTM photovoltaic generation power prediction method and system fused with multi-modal data, and the method comprises the steps: generating a time-space consistent multi-modal training feature matrix according to historical meteorological data, historical photovoltaic monitoring data and corresponding historical generation power data; based on the multi-modal training feature matrix, constructing a multi-input-channel LSTM prediction model, and outputting a cross-regional generalization pre-training model; according to the real-time meteorological data of the target area, the current photovoltaic monitoring data and the electricity price fluctuation curve, updating the pre-training model on line by adopting a dual reward reinforcement learning strategy, and outputting a dynamically optimized power prediction model; and inputting the meteorological data and the photovoltaic monitoring data at the current moment into the dynamically optimized power prediction model to generate a photovoltaic power generation power prediction sequence in the future preset time. According to the embodiment of the invention, high-precision and high-adaptability photovoltaic power generation power prediction can be realized.

The invention provides a frequency converter IGBT junction temperature prediction method based on a neural network, and relates to the technical field of neural networks, and the method comprises the steps: obtaining a circuit structure and operation parameters of a subway traction transmission system, and constructing a dynamic thermal network model; establishing a simulation working condition set, performing simulation to acquire a real-time parameter set, and establishing a sample data set after preprocessing; a neural network with multiple input layers is established, training is executed to construct an IGBT junction temperature prediction model, each input layer is mapped with a feature extraction sub-network, feature extraction is performed by using the feature extraction sub-networks, and neural network training is performed according to a feature extraction result; and performing data prediction by using the IGBT junction temperature prediction model, and establishing a junction temperature time sequence. The technical problems that dynamic changes of the junction temperature under complex operation conditions are difficult to capture in the prior art, and particularly when load fluctuation is large or multiple working conditions are switched, prediction errors of a traditional model are remarkably increased, and consequently the accuracy of junction temperature prediction is insufficient are solved.

The invention provides a multi-parameter MIMO (Multiple Input Multiple Output) aviation time-frequency detection system and method, which can be applied to the technical field of underground medium detection. The system comprises a flying platform, a transmitting subsystem, a receiving subsystem and a measurement and control subsystem, and the transmitting subsystem is configured to load a first current pulse into a transmitting coil through a first current transmitter to achieve primary electromagnetic field excitation and load a second current pulse into a compensation coil through a second current transmitter to achieve secondary electromagnetic field excitation. Compensation electromagnetic field excitation is realized; the receiving subsystem is configured to be used for sampling the first current pulse and the second current pulse and collecting secondary electromagnetic field response to obtain underground medium electrical information of the target detection area; and the measurement and control subsystem is configured to send a control instruction to the transmitting subsystem and the receiving subsystem according to a sampling result and set detection parameters and monitor the working state of the detection system, and the measurement and control subsystem adjusts the first current pulse and the second current pulse in real time.

The invention relates to a high-speed data synchronous acquisition and emission system suitable for a satellite communication MIMO (Multiple Input Multiple Output) system. The system comprises two acquisition and emission daughter cards and a clock management unit, the acquisition and emission daughter card comprises a multi-channel ADC acquisition unit and a multi-channel DAC refreshing unit; a multi-channel ADC acquisition unit of the acquisition and emission daughter card sends acquired data to an FPGA for processing through a JESD204B interface, and then the data is forwarded through an optical module; a multi-channel DAC refreshing unit of the acquisition and emission daughter card sends data received by the optical module to an FPGA for processing, and then the data is sent to a DAC through a JESD204B interface for interpolation filtering and IQ conversion, refreshed and output to a radio frequency front end; and the clock management unit realigns the phase of the device clock and the phase of the reference clock in each power-on process by using a trigger signal of the clock chip so as to realize inter-board synchronization. The system can improve the synchronization performance and reduce the power consumption.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

Systems and methods for security-based device control are disclosed. For example, a presence-based trigger event is detected and one or more suggestion models are utilized to determine whether a security-based device control suggestion should be sent. Multiple suggestions are arbitrated, and a user device is selected for receiving the suggestion based on multiple inputs. The format of the suggestion is also determined and the suggestion is either automatically output on the selected device or a notification of the suggestion is provided.

A method for measuring the size of a slide block of a guide rail with a complex shape based on intelligent vision belongs to the field of machine vision, and comprises the following steps: DXF file data acquisition: reading by using Python; image acquisition and preprocessing: acquiring image data in real time by using an industrial camera, and performing image preprocessing; hole detection and measurement: adopting a Hough transform algorithm to realize sliding block hole detection, introducing a sub-pixel counting method to improve the algorithm, and realizing aperture high-precision measurement; edge line detection and measurement: extracting edge and line segment features of the sliding block image by adopting Canny edge detection and LSD line segment detection technologies, automatically removing invalid line segments, determining an optimal fitting line segment, and realizing accurate detection and measurement of a target edge line; and detection and measurement result visualization: displaying a sliding block size measurement result in an image, comparing the sliding block size measurement result with a standard parameter of a DXF file, judging the qualification of the sliding block, constructing a visual interface by adopting Pyside2 to record the measurement result, and supporting a plurality of input source detection functions.

An information technology equipment enclosure comprises a plurality of IT components, cold plates disposed over at least two of the IT components, and a cooling fluid manifold disposed within the IT equipment enclosure. The coolant liquid manifold has multiple input connectors and multiple output connectors. Each of the cold plates are coupled to the cooling fluid manifold using a coolant line attached to one of the input connectors and a coolant line attached to one of the output connectors. An input cooling liquid line couples the coolant liquid manifold to an output of an external liquid cooling system, and an output liquid line couples the coolant liquid manifold to an input of the external liquid cooling system. The coolant liquid manifold is configured to receive cooling fluid from the external liquid cooling system and to distribute the cooling fluid to all of the cold plates via the input connectors.

The invention discloses an interactive sand table demonstration system based on artificial intelligence, and relates to the technical field of intelligent interactive sand table demonstration. Comprising a multi-mode perception module, a semantic intention recognition module, a three-dimensional simulation module, a decision feedback module and a visualization module. Accurate recognition and semantic analysis of a user instruction are realized by fusing various input modes such as voice, gestures and images; based on a knowledge graph and a deep learning model, user demands can be intelligently understood, and a three-dimensional sand table is driven to carry out dynamic interactive demonstration. The situation adaptation module automatically adjusts the sand table scene according to the environment and the demonstration progress, and the reality sense and immersion sense of demonstration are improved; the decision feedback module generates demonstration results and strategy suggestions in real time, and supports multi-dimensional and intelligent interactive experience; the system is widely applied to the fields of emergency drilling, military deduction, urban planning, educational training and the like, and the intelligent level and the operation convenience of sand table demonstration are remarkably improved.

A user equipment (UE) receives control signaling for an uplink (UL) multiple input multiple output (MIMO) transmission and transmits the UL MIMO transmission precoded with an 8 port uplink MIMO precoder based on the control signaling and a set of two or more antenna port groups.

A precoder index is derived from a precoder codebook using a low computationally complex process. A plurality of uplink channel matrix estimates are received at a Base Station (BS) from a User Equipment (UE). The plurality of uplink channel matrix estimates are processed to determine an indicator of the precoder having a highest spectral efficiency estimate. The indicator of the precoder having the highest spectral efficiency estimate is selected for transmission by the BS.

A system and method for virtual block stick circuits is presented. The present disclosure implements specialized algorithms adapted to determine the true status of a virtual block based on multiple inputs from different perspectives. In one embodiment, the system can use the far house perspective of that virtual track segment and the PTC hazard for the near virtual track segment directly adjacent to the near house uses the near house perspective of that virtual track segment. For the middle virtual track segments, the near house perspectives of the middle virtual track segments are held ‘TRUE’ if they are already ‘TRUE’ when the train first enters the block, using stick circuits for the near house perspective of the middle track circuits. The vital application can then indicate the true state of the virtual track segment as occupied (FALSE), to protect the train from trains that follow.

The invention discloses a channel estimation method and system with a limited backhaul network in a non-cellular large-scale MIMO (Multiple Input Multiple Output) system, and the method comprises the following steps: receiving a quantized pilot signal sent by an access point through the backhaul network, and taking the quantized pilot signal as a channel observation signal; updating the channel noise variance according to the channel prior mean value and the channel prior variance during the last iteration, and reducing the quantization noise of the channel observation signal according to the updated channel noise variance to obtain a received signal estimation value; inputting the channel noise variance and the received signal estimation value into the trained VAMP network to obtain an estimated channel state matrix, and calculating to obtain a channel prior mean value and a channel prior variance according to the estimated channel state matrix; and outputting the channel state matrix estimated at the moment as a final channel state matrix until the number of iterations reaches a preset number. The method is higher in robustness.

The invention discloses a lightweight real-time radio frequency fingerprint identification method based on streaming jump connection, and belongs to the field of communication signal processing. The implementation method comprises the following steps: adopting a WiSig data set as a training set and a test set of a neural network; and replacing a two-dimensional convolutional layer in the ResNet network with a one-dimensional convolutional layer. The ADC information sampling throughput in the edge device is greater than the reasoning throughput of the radio frequency fingerprint model, a plurality of input branches are added at different depths of the radio frequency fingerprint model, and the multi-input branch structure enables the generation time of the feature patterns needing to be fused to be consistent, thereby avoiding the distribution of the storage space. And receiver distortion features and channel noise features are removed from the same kind of radio frequency fingerprint information in different time periods within the preset time. Fusion feature fingerprint identification information is used as input of a classifier to obtain a more accurate prediction vector, a vector output by a full connection layer is mapped into a probability value through a Softmax function, a neural network is trained through a cross entropy loss function and an SGD optimizer, and radio frequency fingerprints are identified through the trained neural network.

User interfaces for controlling media capture settings are described, including user interfaces for controlling capture of media with associated depth information (e.g., for applying simulated depth-of-field effects), controlling media capture settings with multiple input types, controlling zoom settings for media capture (e.g., conditionally displaying a secondary preview at a different zoom level), capturing and viewing media using internal and external media storage, and controlling an indicator for level capture guidance.

The invention discloses a method for sharing a host based on multiple input devices, and belongs to the technical field of computers, and the method comprises the following steps: after a management user logs in a multi-seat configuration tool, creating second seats with the same number as new input devices according to a Udev rule, and enabling the management user to be a user operating the input device corresponding to a first seat of the host; according to a binding instruction sent by the management user, the second seats and the new input devices are bound one by one, and operation users of the input devices corresponding to the second seats are common users; according to a permission configuration instruction sent by the management user, configuring an application window permission for each common user; and sending the binding relationship between the second seat and the new input device and the application window authority to a window manager, so that the window manager manages the application window according to the binding relationship and the application window authority. According to the invention, the problems of disordered management and complex configuration when multiple users share one host window are solved, and more flexible and efficient equipment management experience is provided for the users.

Embodiments of the present disclosure relate to multi-view LIDAR perception with motion cues for autonomous and semi-autonomous machines and applications. A DNN may be used to detect objects, a navigable space, weather or surface conditions, artifacts, and / or other parts or features of an environment based on multiple views of LIDAR data from multiple time slices. The DNN may include multiple input channels for processing multiple views of sensor data from multiple time slices to provide motion cues, and the extracted features from the different time slices may be geometrically projected from a first 2D view to a second 2D view, combined with features that were extracted from the second 2D view, and applied to a subsequent stage of the DNN. The data generated by the DNN may be provided to the drive stack of an autonomous vehicle or other ego-machine to enable safe planning and control of the vehicle.

The invention discloses a pre-coding method and device for a cellular-free large-scale MIMO (Multiple Input Multiple Output) sensing integrated system, and the method comprises the steps: an access point provides a data service for a communication user through a downlink of the cellular-free large-scale MIMO sensing integrated system, and tracks a point-like sensing target from a reflection echo; the method comprises the following steps: calculating a downlink reachable rate of a communication user and a signal-to-noise ratio of a reflection echo, and constructing a problem function by taking maximization of the downlink sum rate of the communication user as a target and taking the signal-to-noise ratio of the reflection echo as a constraint; and solving the constructed problem function to obtain a precoding scheme of the system. According to the invention, on the premise of not introducing extra system energy consumption and guaranteeing the sensing performance, the trade-off relationship between the communication performance of the system and the display of the communication sensing performance can be greatly improved, so that the utilization efficiency of wireless spectrum resources is enhanced, and the integrity of the system is expanded and improved.

Disclosed are a power line communication method and apparatus, comprising: a first device generating a physical layer protocol data unit (PPDU) frame, the PPDU frame comprising a frame control field, a training symbol field, and a load symbol field, the load symbol field being used for modulating and transmitting a load symbol, the training symbol field being used for bearing a training symbol, and the load symbol field being used for receiving the training symbol; the training symbol field is used for channel estimation of multiple input multiple output (MIMO) and extended bandwidth, the frame control field comprises a variable region field, the variable region field comprises a frame identification field and a frequency band field, the frame identification field is used for indicating a frame type of a frame start (SOF) frame included in the PPDU frame, and the frequency band field is used for indicating an available frequency band of a training symbol and a load symbol; and the first device sends the PPDU frame to the second device. According to the embodiment of the invention, through the redefined PPDU frame, the power line communication supports the characteristics of MIMO, large bandwidth and the like, so that the overall network performance is improved.

The invention discloses an optimization method for secure transmission of an active STAR-RIS assisted RSMA communication system. The optimization method mainly comprises the following steps: S1, constructing an active STAR-RIS assisted multi-input single-output RSMA communication system; s2, the base station transmits the information to legal users in the system through the active STAR-RIS; s3, the base station sends the artificial interference noise while sending the useful signal; constructing an optimization equation for maximizing system private information and safety rate based on beam forming, artificial noise, an active STAR-RIS power amplification matrix, an amplitude matrix and a phase shift matrix as optimization parameters; s4, performing proper conversion on a non-convex objective function and constraints by using a WMMSE criterion, and performing decoupling solution on an optimization problem by using an AO algorithm; s5, updating an auxiliary variable introduced by the WMMSE algorithm according to the closed expression; s6, using an SCA algorithm to optimize and update beamforming and artificial noise of the base station, and a power amplification matrix and an amplitude matrix of the active STAR-RIS; and S7, adopting an SCA algorithm and a PDD algorithm to optimize and update the phase shift matrix of the active STAR-RIS. According to the invention, the security of the communication system is obviously improved.

The invention discloses a low-power-consumption multiple-input-multiple-output thermo-optical switch chip which comprises N optical input ports, an N * N optical beam splitter, N spiral waveguide thermo-optical phase shifters, an N * N optical beam combiner and N optical output ports. The N * N light beam splitter and the N * N light beam combiner adopt the same design structure and comprise N single-mode input / output waveguides, N width gradient waveguide transition areas (taper areas) and a multi-mode waveguide area, and each single-mode input and output waveguide is connected to the multi-mode waveguide area through the width gradient waveguide transition area. The spiral waveguide thermo-optic phase shifter comprises two spiral waveguides with different sizes, an S bend with a gradually-changed width and a circular heating electrode. The transmission path of light in the chip can be changed by adjusting the spiral waveguide thermo-optical phase shifter, and the function of optical switch switching is achieved. The invention has the advantages of high integration level, high expandability and low power consumption, and can be used in the field of integrated photon chips.

A multiple input converter for outputting power from AC power or a battery to positive and negative direct current (DC) buses is provided. The multiple input converter includes at least one bridge arm unit and a bidirectional DC-DC conversion unit. The bridge arm unit corresponds to one phase of the alternating current (AC) power and includes an upper and lower bridge arms that can independently form a two-level converter. In a battery mode, the upper and lower bridge arms discharge power from the rechargeable battery to the positive and negative DC buses, and the bidirectional DC-DC conversion unit discharges power from the rechargeable battery to the positive and negative DC buses or balances voltages of the positive and negative DC buses. In a mains mode, the bridge arm units are connected to the AC power. The bidirectional DC-DC conversion unit determine whether to charge or discharge according to load conditions.

The invention discloses a multi-scale feature fusion-based channel information feedback method for a large-scale MIMO (Multiple Input Multiple Output) system, which comprises the following steps of: 1, constructing a multi-scale feature fusion-based encoder and decoder network, and combining a novel selective state space with a multi-head attention model and hybrid convolution, the channel state information (CSI) remote modeling capability is further enhanced through an input selectivity mechanism, and multi-scale channel features are extracted for CSI (de-compression). 2, constructing a convolution enhanced vector quantization model, and further improving the feedback precision under the same feedback overhead by using dimension reduction convolution; and 3, a channel feature enhancement (CFE) module is constructed, and channel information loss is further reduced by utilizing ascending channel convolution to assist in decompressing full connection. According to the codec, multi-scale CSI features are extracted by using an input selectivity mechanism, multi-head attention and convolution, and high-precision channel information feedback is completed by combining a convolution enhanced vector quantization model and a CFE module.