656 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.

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 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 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.

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.

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.

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.

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.

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.

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.

A method performed by a user equipment (UE) for dynamically allocating data transmission resources in a massive multiple input multiple output (MEMO) system is disclosed. The UE receives from a network node at least one initial channel state information reference signal (CSI-RS) resource set comprising at least an initial number of ports. A sounding reference signal (SRS) is transmitted to the network node. The network node transmits to the UE a dynamically allocated number of CSI-RS ports, or alternatively, a trigger state for a predefined CSI-RS resource set and a beamformed CSI-RS. The UE computes one or more parameters corresponding to channel state information (CSI) from the beamformed CSI-RS. A CSI report comprising the one or more parameters is transmitted to the network node. The UE then receives user data over a data traffic channel using the dynamically allocated number of CSI-RS ports, or alternatively the pre-defined CSI-RS resource set.

A method for determining an uplink multiple input multiple output (MIMO) transmission codeword, includes: determining at least one of a candidate codeword of 4 transmit antenna ports (Tx) for uplink MIMO transmission or a candidate codeword of 2 Tx for uplink MIMO transmission; dividing 8 Tx into K antenna port groups, wherein K is a positive integer less than 8; determining a third codeword from the candidate codeword, and determining a fourth codeword corresponding to the third codeword; and obtaining a first codeword for the antenna partial coherence transmission of 8 Tx L-layer for the uplink MIMO transmission by splicing, based on the antenna port groups and a co-phasing coefficient, the third codeword and the fourth codeword, wherein L is less than or equal to 8.

Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

Systems, methods, apparatuses, and computer program products for enabling efficient cell-free MIMO transmissions. One method may include receiving a downlink reference signal, determining at least one cell-free multiple input multiple output transmission strategy associated with at least one access point based upon at least the downlink reference signal, and transmitting to a network entity at least one of the at least one cell-free multiple input multiple output transmission strategy and enabling information.

A power transformation equipment oil temperature prediction and abnormity early warning method based on a fuzzy algorithm comprises the following steps that real-time operation parameters and oil temperature historical data of power transformation equipment are collected, the data are preprocessed, and a high-quality input data set is constructed; constructing a fuzzy inference system considering multiple input factors, and setting a fuzzy membership function and an inference rule base; according to the fuzzy prediction result and the real-time oil temperature change, a self-adaptive dynamic safety threshold model is compared, a potential abnormal trend is identified, and automatic grading early warning is carried out according to the overtemperature grade; and corresponding exception type identifiers and disposal suggestions are generated and are pushed to the operation and maintenance platform through the communication module, so that remote operation and maintenance scheduling and response control are supported, and intelligent cooperative processing is realized. According to the invention, intelligent prediction of the oil temperature change trend under the influence of multiple factors is realized, the capability of sensing temperature rise abnormity in advance is improved, the accuracy of sensing the operation state of the equipment and the timeliness of early warning response are also remarkably improved, and the risk of equipment failure caused by the abnormal oil temperature is reduced.

An example apparatus for decoding media data includes: a memory configured to store video data; and a processing system comprising one or more processors implemented in a circuit, the processing system configured to instantiate a first number of video decoder instances to be executed by the processing system; determining an attribute of the plurality of video media streams, the attribute indicating that each of the plurality of video media streams is available for stream selection; selecting the second number of input video media streams from the plurality of video media streams according to the determined attributes of the second number of input video media streams; executing the video decoder instance to decode the second number of input video media streams to form a second number of decoded video media streams; and outputting data of the second number of decoded video media streams.

A high gain multiple input, multiple output antenna which has a feeding board; a ground plane; a low band patch radiator; a first printed circuit board; a second printed circuit board; and a high band dipole patch radiator. The high band dipole patch radiator is a co-located stacked array on top of the low band patch radiator. The high band dipole patch radiator and the low band patch radiator are proximity coupled with the first printed circuit board and the second printed circuit board. The low band patch radiator covers low band frequencies in the range of 2.4 GHz to 2.5 GHZ and the high band dipole patch radiator cover high band frequencies in the range of 4.9 GHZ to 7.125 GHz. The antenna assembly has a gain of above 7.5 dBi and a well-directed radiation patten with no side lobes.

The invention discloses a wind power multi-step prediction method based on deep learning and interpretability analysis, and the method comprises the following steps: S1, collecting the operation data of a wind power plant unit, and forming a structured input sequence through cleaning, missing value processing, abnormal value elimination and smooth downsampling; s2, screening features through correlation and mutual information conjoint analysis to obtain a key feature set; s3, constructing a TSBM model, wherein the TSBM model comprises an input layer, a time sequence convolutional network, a stacked bidirectional gating circulation unit, a multi-head attention distribution layer and an output layer; s4, training verification is carried out by adopting a multi-input multi-output and recursive correction hybrid multi-step strategy, and short-term precision and long-term stability are balanced; and S5, outputting a prediction result, establishing a unified interpretation framework in combination with MHA and SHAP, and revealing contribution of key historical moments and features. The method provided by the invention can solve the problems of poor prediction stability, more error accumulation and non-uniform interpretability in the prior art, and is high in precision and good in stability.

The invention discloses a model prediction task operation method and device, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: receiving a plurality of prediction requests, wherein the prediction requests comprise input contents; generating a content feature representation of the input content; and in at least one of the pre-filling stage and the decoding stage, adopting a preset constraint size to respectively execute a reply prediction process on the content feature representations corresponding to the plurality of input contents to obtain reply contents respectively corresponding to the plurality of input contents. In the embodiment of the invention, the preset constraint size is adopted to execute reply prediction represented by the content features respectively corresponding to the multiple input contents, and the constraint size is used to eliminate dynamic problems during calculation of the processor in a pre-filling stage or a decoding stage, so that the calculation efficiency of the processor is improved.

The invention provides a method for carrying out channel estimation on a large-scale MIMO-OTFS (Multiple Input Multiple Output-OTFS) system by utilizing a DML (Discrete Multiplexing Language) combined training layered global DLCE (Digital Light Control Element) network, which comprises the following steps of: 1, inputting a received pilot signal into a scene classifier to obtain a channel scene index; and inputting the pilot signal to a corresponding feature extractor to extract channel features based on the predicted channel scene index. And step 3, inputting the output of the feature extractor into a feature mapper to reconstruct the whole downlink cascade channel. The HGDLCE network extracts different channel characteristics for different channel scenes and finally reconstructs the channel, so that the channel estimation precision under different channel scenes is improved. Simulation results show that the scheme can obtain better channel estimation precision than a traditional scheme.

The invention provides an intelligent form filling method and device for a browser plug-in based on multi-modal interaction, and relates to the technical field of data processing.The method comprises the steps that to-be-filled information and a to-be-filled form are obtained, and the to-be-filled information has multiple input formats; converting the to-be-filled information into a filling field in a text format; converting the form to be filled into a structured form, wherein the structured form has a filling position corresponding to the filling field; filling the filling field into the corresponding filling position to obtain a filling template; and matching a corresponding backfilling position for the content in the filling template in the to-be-filled form, and filling the content in the filling template into the corresponding backfilling position to obtain a complete form. According to the intelligent filling method, after the to-be-filled information and the to-be-filled form input by the user are received, the to-be-filled information can be automatically filled into the to-be-filled form, the manual filling process is reduced, and the filling efficiency is improved.

Energy consumption of antennas within an antenna array used for multiple-input multiple output communications can be reduced through the use of an antenna optimization system. The antenna optimization system can determine a pattern of antennas to be used to reduce energy while maintaining required levels of cellular communication performance metrics. The antenna optimization system can take in multiple inputs, such as the number of connected devices, climate, time of day, total demand on network, and provide an output which can comprise an instruction set on which antennas should be activated and how they may be configured or used. The antenna optimization system may use a machine learning model.

The invention provides an interaction system and method of a vehicle-mounted screen, a vehicle, computer equipment and a computer readable storage medium. In one embodiment, an interaction system includes: input modules including first and second input modules, the first input module configured to provide a first input signal from a user, the second input module configured to provide a second input signal from the user; an intent recognition module configured to generate a wake-up intent signal based on one of the first and second input signals; a verification module configured to generate a wake-up instruction in response to the wake-up intent signal and based on the other of the first and second input signals; and the execution module is configured to wake up the vehicle-mounted screen in response to the wake-up instruction. According to the invention, the vehicle-mounted screen is awakened by providing a plurality of input signals, and the awakening action is carried out by firstly determining the awakening intention of the user and then carrying out verification again, so that the awakening action can be ensured to be intentional, wrong awakening is reduced, and the intelligence and user friendliness of an interaction system are improved.

A communication control main control board disclosed by the present invention comprises a board body, a power supply module, a main control module, a communication module and a signal control module, the communication module comprises a wired communication sub-module and a wireless communication sub-module, the signal control module comprises an input control unit and an output control unit, and the main control module is used for generating a first control instruction. The communication module is used for switching a communication mode between the communication control main control board and external communication equipment according to a first control instruction, the signal control module is used for controlling input and output of a communication signal according to the first control instruction, and the main control module comprises a fault self-diagnosis sub-module; and the alarm module is used for detecting working states of the power supply module, the communication module and the signal control module and generating corresponding alarm information. The technical scheme of the invention has multiple communication functions, can be compatible with the interconnection requirements of heterogeneous devices, realizes effective control of multiple input and output devices, is beneficial to finding abnormal working states in time, and is beneficial to improving stability and reliability.

The utility model provides a direct liquid cooling optical fiber beam combining device, which comprises an outer package, a sleeve and an optical fiber beam combiner, a liquid cooling flow channel is arranged in the outer package, the sleeve extends along the direction of an optical path, a through hole extending along the direction of the optical path is arranged on the sleeve in a penetrating manner, and the sleeve penetrates through a main flow channel and the through hole to be isolated from the liquid cooling flow channel; the fused biconical taper ends of the input optical fibers are welded with the output optical fiber, and the optical fiber beam combiner is arranged in the through hole. According to the scheme, heat generated by the cone area of the beam combiner is directly taken away through water flow, then the heat dissipation capacity of the beam combiner is improved, the temperature of the cone area is reduced, optical fiber packaging and pipe sleeve packaging can be connected through dispensing or welding, and the leakproofness of a device flow channel is improved.

The present invention discloses a communication system including WLAN communication systems comprising transmitter including an OTFS grid based Orthogonal Time Frequency Space (OTFS) waveform generator and Multiple Input Multiple Output (MIMO) streams. The transmitter comprises signal processor including OTFS modulator having FEC (forward error correction) encoder for pre-FEC bit appended input data stream and transmission of encoded input data stream in a selective sequence vide OTFS waveform modulated OTFS grid-partially loaded with QAM / PSK symbols of said encoded input data stream through MIMO transmitter and receiver streams including MIMO pre-coder based multi antenna support for said transmission and reception of said OTFS waveform thereby enabling OTFS waveform transmission and retrieval of input data stream.

Various aspects of the present disclosure generally relate to communication. In some aspects, a first device may determine a frequency division multiplexing (FDM) configuration for a set of component carriers associated with a cable frequency spectrum, wherein each component carrier in the set of component carriers is associated with a number of multiple input multiple output (MIMO) layers. The first device may communicate with a second device over a cable infrastructure according to the FDM configuration for the set of component carriers. Numerous other aspects are described.

The present disclosure relates to a machine learning (ML)-based receiver that is computationally efficient, irrespective of a number NR of receiver antennas used in a Multiple Input Multiple Output (MIMO) scenario. To achieve this, the ML-based receiver is configured to obtain a modified matrix and a modified array of symbols based on a channel state information (CSI) matrix and a received array of symbols. The modified matrix has a dimension NT×NT, where NT is a number of MIMO layers. The modified array of symbols has a dimension NT. After that the ML-based receiver is configured to restore a transmitted array of symbols from the received array of symbols by applying a pre-trained ML model that receives the modified matrix and the modified array of symbols as input data and outputs a set of bit log-likelihood ratio (LLR) estimates for the transmitted array of symbols.