1077 results about "Multiple input" patented technology

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 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.

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.

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.

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.

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.

Apparatuses, systems, and techniques to detect uplink data in a multi-user multiple input multiple output wireless communication system. In at least one embodiment, uplink data is detected using one or more graphics processing units to perform parallel computations in order to form a consensus belief about information received by one or more base stations in a wireless network.

According to an embodiment, a test system for serially testing of finite state machine circuits using scan chains is proposed. Each scan chain is coupled to a finite state machine circuit to test the finite state machine circuit coupled thereto. Each scan chain has a number of input terminals and one output terminal. The test system includes a dispatcher input interface with a number of input terminals and multiple sets of output terminals, where each set of output terminals includes multiple output terminals coupled to the input terminals of an associated scan chain. The test system further includes a dispatcher output interface with multiple input terminals and one output terminal. The output terminal of each scan chain is coupled to one of the input terminals of the dispatcher output interface.

Examples of wearable systems and methods can use multiple inputs (e.g., gesture, head pose, eye gaze, voice, totem, and / or environmental factors (e.g., location)) to determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on. The wearable system can detect when different inputs converge together, such as when a user seeks to select a virtual object using multiple inputs such as eye gaze, head pose, hand gesture, and totem input. Upon detecting an input convergence, the wearable system can perform a transmodal filtering scheme that leverages the converged inputs to assist in properly interpreting what command the user is providing or what object the user is targeting.

The invention provides a cellular-free mMIMO (multiple input multiple output) system computing power resource allocation and beam forming optimization method and a cellular-free mMIMO system computing power resource allocation and beam forming optimization system, and belongs to the technical field of wireless communication. The method specifically comprises the following steps of: respectively establishing a double-queue model based on a first-in first-out criterion at a central processing unit (CPU) and an access point (APs), taking a dequeue rate at the CPU as an enqueue rate at the AP, constructing a data volume relationship in a queue of a current frame and a previous frame, and analyzing system downlink end-to-end time delay based on an M / D / 1 queuing model. In a first layer queue, a binary search algorithm is provided to design computing power resource allocation at a CPU, and in a second layer queue, a path tracking algorithm is provided to optimize a beam forming vector at an AP. According to the method, the instantaneous channel state information is utilized, the worst user time delay is minimized under an alternating iteration algorithm framework, the calculation complexity is reduced, and meanwhile an effective solution is provided for ultra-low time delay communication.

Certain aspects of the present disclosure provide techniques for wireless communication by a user equipment (UE). For example, the UE may transmit, to a plurality of transmission and reception points (TRPs), a layer parameter based on a maximum number of multiple input multiple output (MIMO) uplink layers that are supported by the UE for uplink communication to the plurality of TRPs. The UE may receive, from at least one TRP of the plurality of TRPs, uplink scheduling information responsive to transmitting the layer parameter. The UE may further transmit, to each of the plurality of TRPs, a physical uplink shared channel (PUSCH) communication according to the uplink scheduling information.

The invention provides an intelligent metasurface enabled cellular-free large-scale MIMO (Multiple Input Multiple Output) network resource efficiency optimization method. The method comprises the following steps: establishing an intelligent metasurface enabled cellular-free large-scale MIMO network model under a Rician fading channel; based on the cellular-free large-scale MIMO network model, performing channel estimation by using a linear minimum mean square error method; based on the cellular-free large-scale MIMO network model, constructing a resource efficiency closed expression when a maximum ratio merging receiving scheme is used; constructing an optimization problem formula for maximizing the network resource efficiency according to the resource efficiency closed expression; and according to the optimization problem expression, designing an optimization method for cooperatively controlling a user power control coefficient and intelligent metasurface phase shift. The spectrum efficiency and the energy efficiency of the intelligent metasurface energized cellular-free large-scale MIMO network are improved, the compromise between the spectrum efficiency and the energy efficiency is effectively realized, the optimization of the network performance is realized, and an important theoretical support is provided for the development of a 6G Internet of Things network.

Various aspects of the present disclosure relate to orthogonal time domain reference signal transmission. A user equipment (UE) may receive signaling configuring a set of parameters corresponding to one or more orthogonal time domain reference signals (e.g., unique words (UWs) in a time domain). The UE supports multiple input multiple output (MIMO) transmission and reception using an orthogonal frequency division multiplexing (OFDM) waveform. The UE transmits or receives symbols in a time domain including the one or more orthogonal time domain reference signals based on the set of parameters and the OFDM waveform.

The embodiment of the invention provides a data processing method based on a grouped hybrid expert model, electronic equipment and a medium. According to the method, multiple pieces of input feature data are acquired, and for each piece of input feature data in the multiple pieces of input feature data, at least one target expert group matched with the input feature data is determined from multiple routing expert groups through a group routing unit; and determining at least one activation expert matched with the input feature data from each target expert group through an expert routing unit, and performing data processing on the input feature data through the sharing expert and the at least one activation expert to obtain a corresponding processing result. According to the method, the routing experts with the high adaptation degree with the input feature data are determined as the activated experts, distribution of the routing experts according to needs is achieved, the method only needs to carry out weight calculation on the routing experts in the target expert group, the calculation amount in the data processing process is reduced, the calculation cost is reduced, and the calculation efficiency is improved.

The invention provides a delay circuit applied to a time-interleaved analog-to-digital converter, the circuit comprises a first delay module, a second delay module and a delay selector, the first delay module is composed of multiple stages of delay units connected in series, each stage comprises a fixed inductor connected in series and a variable capacitor connected in parallel, and is used for providing large-range adjustable delay; the second delay module is connected to the output end of the first delay module and comprises a parallel combination of an adjustable constant current source and a programmable capacitor array, high-precision delay adjustment is achieved, the minimum adjustment step length of the second delay module is smaller than that of the first delay module, and multiple input ends of the delay selector are connected with output nodes of all stages of delay units respectively. The selection module is used for selecting coarse delay signals in different stages; the circuit realizes low-noise coarse delay adjustment through the inductance-capacitance network, completes high-precision fine delay adjustment by using current-capacitance combination, and has the advantages of wide delay range, high adjustment precision, low noise, good stability and the like.

This application describes methods and apparatus to allocate uplink (UL) power dynamically across multiple parallel radio frequency links for wireless devices. The wireless device sets an initial maximum transmit power level (MTPL) for each UL radio link of multiple radio links used for UL traffic based on a duty cycle prediction for the respective UL radio link. The wireless device adjusts the initial MTPL for each UL radio link based on a predicted data throughput for the UL radio link relative to a total predicted data throughput for all UL radio links. The adjusted MTPL for each UL radio link can be also reduced when the UL radio link uses UL multiple input multiple output (MIMO) transmission via multiple antenna ports, e.g., by apportioning the adjusted MTPL among the multiple antenna ports of the UL radio link equally.

The invention discloses a multi-channel measurement time difference fusion method for Kalman filtering, and the method comprises the steps: obtaining the historical data of multi-channel measurement time differences, and obtaining a series of historical mean values; monitoring multi-path measurement time difference input, judging interruption when any path of judgment observation value is missing or invalid based on a historical mean value, performing interruption compensation, and dynamically adjusting process noise covariance according to interruption duration; based on historical data, dynamically distributing weights; and fusing the multiple paths of time difference data through Kalman filtering, and outputting a smoothed time difference measurement result. Through an interruption compensation strategy (virtual observation and weight adjustment) and sliding window smoothing, even if single-path or multi-path input is interrupted, continuous fusion metering time difference can still be output; the dynamic weight distribution mechanism makes full use of statistical correlation of multi-path time difference, and the interrupt path dynamically adjusts the weight according to real-time reliability; and dynamically updating the process noise covariance Q so as to adapt to a time-varying noise environment and realize adaptive noise processing.

The subject technology receives a set of inputs from multiple input sources. The subject technology determines a set of input features based on the set of inputs from the multiple input sources. The subject technology performs a time window-based aggregation on the set of input features to generate a set of aggregated features. The subject technology performs feature extraction, using a set of modular components of a modular classifier network, on the set of aggregated features to generate a set of extracted features. The subject technology generates, using a pinch detection head, a probability score indicating the likelihood of an occurrence of a pinch gesture based on the set of extracted features. The subject technology determines, using triggering logic, whether a pinch gesture has occurred based at least in part on the probability score. The subject technology provides a pinch detection output based at least in part on the determining.

A user equipment (UE) includes: a first subscriber identity module (SIM) associated with a first service provider subscription and a second SIM associated with a second service provider subscription; the US is configured to operate the first service provider subscription in an active-mode and operating the second service provider subscription in an idle mode; and the UE is further configured to signal a changing quantity of multiple input, multiple output (MIMO) layers available to the first service provider subscription in response to radio frequency (RF) resource sharing with the second service provider subscription for a paging operation of the second service provider subscription.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit an indication of a capability of the UE to communicate in accordance with a polar multi-layer coding (MLC) multiple input multiple output (MIMO) communication scheme. The UE may receive a configuration for communication in accordance with the polar MLC MIMO communication scheme, wherein the configuration indicates a sub-block size threshold. The UE may communicate a message in accordance with the polar MLC MIMO communication scheme based at least in part on a determination that a sub-block size for the message satisfies the sub-block size threshold.