504 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.

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 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 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 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.

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.

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 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.

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 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.

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.

This invention is entitled "Continuous Touch Input on Multiple Independent Surfaces." A touch input device is provided with the ability to sense capacitive loads in a two-dimensional array while also being able to sense deflection or movement of the input device at multiple different locations beneath the input plate. The touch input device can be advantageously used in a remote control device and can be used with multiple input buttons or platforms that overlay multiple capacitive touch input areas on a capacitive touch sensor substrate. Movement and positioning of the capacitive loads can be tracked and calculated through output signals from a set of electrodes or other capacitive sensors that have irregular shapes, inconsistent distances from the capacitive loads, and are positioned beneath variable materials and material compositions. Thus, track gestures and taps can be detected using the capacitive sensing device and deflection inputs can be detected using switches or other actuators positioned beneath the capacitive sensing device.

Embodiments herein disclose an antenna system comprising a plurality of panel comprising at least one Multiple Input Multiple Output (MIMO) antenna array, including a plurality of microstrip patch antenna elements and stacked patch antenna elements. The microstrip patch antenna elements, configured in a linear array, facilitate a lower operating frequency, while the stacked patch antenna elements, placed atop the microstrip patch antenna elements, achieve a higher operating frequency. The stacked patch antenna elements are connected in series and oriented at a predefined angle of 90° relative to the microstrip patch antenna elements. A panel from the plurality of panels is arranged at a pre-defined angle of 90° relative to an adjacent panel from the plurality of panels for polarization diversity to obtain the multifunctional shared aperture 8N-port MIMO antenna system.

Aspects of the disclosure related to devices, wireless communication apparatuses, methods, and other aspects of passive multiple input multiple output. In some aspects, an apparatus is provided that includes a first radio frequency (RF) transmission line having a first terminated with a quarter wavelength grounded transmission line, and a first array of antennas including a plurality of antenna elements. The apparatus also includes a switch array including a corresponding switch for each antenna element of the plurality of antenna elements of the first array of antennas, to selectively connect each antenna element to the first RF transmission line, and path lengths selectable by the switches at half wavelength distances for passive transmission. The apparatus also includes or more lens elements configured to modify wireless inputs signals to the first array of antennas and to modify wireless output signals from the first array of antennas.

A hardware accelerator and acceleration method based on a VisionTransformer neural network, the accelerator is to deploy the VisionTransformer neural network on a ZYNQ development platform; the acceleration method is: an ARM processor stores a feature picture into a DDR memory, the read data is dispersed to an input cache and a weight cache, the processed feature picture is input to an on-chip cache unit, the processed data is sent to a PL end, the hardware IP of the PL end is configured, and read-write operation is performed at the same time, the final calculation result is obtained, written into the DDR memory, the data in the DDR memory is taken out and probability operation is completed, the probability operation result is transmitted to a PC, a PetaLinux operating system is transplanted to the hardware accelerator system of the VisionTransformer neural network, and a prediction result of inference is obtained from the PC; the neural network structure is optimized through the parallel method of multiple input and multiple output channels, the calculation speed is fast, the hardware resource occupation is low, the recognition accuracy is high, and the image classification task can be efficiently completed.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for group formation and sounding for distributed multi-user multiple input multiple output (MU-MIMO). Some implementations include a method of wireless communication. The method includes transmitting, from a first access point of a plurality of access points, an announcement frame for performing a beamforming procedure for a distributed transmission. The distributed transmission includes a transmission from the plurality of access points. The announcement frame includes at least one identifier of a user terminal in a different basic service set than a basic service set of the first access point. The method further includes transmitting a packet for measuring a channel. The method further includes receiving feedback information from the user terminal based on the packet for measuring the channel. The method further includes transmitting the distributed transmission using the beamforming procedure.

The invention provides a time-space dual-scale SCR denitration system modeling method based on a KAN network, and relates to the technical field of SCR denitration system management, and the method comprises the steps: collecting a plurality of input variables and outlet NOx concentrations of an SCR denitration system; a spatial feature extraction module is adopted to analyze the coupling relation among the multiple input variables, and spatial feature vectors are formed; a time feature extraction module is adopted to analyze the change rule of the NOx concentration of the outlet, and a time feature vector is obtained; fusing the spatial feature vector and the time feature vector by using a feature fusion module to obtain a space-time fusion feature; inputting the space-time fusion features into a KAN network based on a spline function for training until the model converges; the trained spatial feature extraction module, the trained time feature extraction module, the trained feature fusion module and the trained KAN network are jointly integrated into a space-time dual-scale KAN model, the outlet NOx concentration of the SCR denitration system is predicted in real time through the model, and the prediction precision is improved.

The application discloses an optoelectronic computer based on time domain all-optical signal serial-parallel hybrid computing, which is composed of an electrical transceiving module, a light source module, a linear matrix vector computing module, a summation separation module and a nonlinear activation module.The application adopts time sequence encoding serial input, and multiple input signals are simultaneously and parallelly calculated, so that all computing steps of a full connection neural network, i.e., linear calculation, summation operation and nonlinear activation, can be completed in the optical domain, and the host end only needs to send input data and receive the final computing result.The application has the following advantages compared with other comparative applications: multiple matrix-vector multiplication operation can be completed in parallel; only one optical element is used to complete summation and nonlinear activation operation on multiple multiplication vectors, thereby reducing the hardware deployment pressure; and the bandwidth loss of a pure serial time domain computing method is relieved through serial-parallel hybrid encoding in the time domain, and the computer computing speed is improved.

The utility model relates to the technical field of display equipment, and discloses a display control circuit and display equipment, and the display control circuit comprises a display driving circuit, a Type-C transmission control circuit, an HDMI transmission control circuit and a video signal output circuit. The accessed Type-C signal source is used for supplying power to the display driving circuit and / or transmitting a Type-C input signal; an HDMI input signal is transmitted to the display driving circuit through the HDMI transmission control circuit; a video output signal is transmitted to a video signal output circuit through a display driving circuit according to a received input signal; and projecting the video output signal to a display screen through a video signal output circuit. According to the utility model, various input signals can be received through the Type-C interface and the HDMI interface, and video output signals can be efficiently projected to the display screen, so that diversified display requirements can be met, and diversified equipment can be compatible, and more convenient and diversified display functions can be provided for users.

This application relates to a multi-channel fiber optic image transmission device, which belongs to the field of fiber optic image transmission. It includes an image input fiber bundle, an image processing relay, and an image output fiber bundle. Multiple image input fiber bundles are provided. The image output fiber bundle and the multiple image input fiber bundles are respectively connected to the image processing relay. The image processing relay includes an input reflector and an image combining mirror. Multiple input reflectors are provided, and each input reflector is respectively positioned opposite to the multiple image input fiber bundles. These reflectors can reflect the images introduced by the multiple image input fiber bundles to the image combining mirror. The image combining mirror is positioned opposite to the image output fiber bundle, and can combine the images reflected by the input reflectors and then illuminate the image output fiber bundle. This allows for the acquisition of combined images from different locations in a distant region, ensuring the quality of the combined image.

The invention provides a multi-room audio distribution method and system based on voice recognition, and the method comprises the steps: receiving a voice instruction of a user, and converting the voice instruction into text information; semantic analysis and intention recognition are conducted on the text information, and specific operation instructions and operation parameters are extracted; the operation instructions and the operation parameters are distributed to at least one function module related to the multi-room audio system, and the function module comprises an input source selection module, an input source volume control module, a multi-path output volume control module, a multi-path input and output routing module and a sound mixing module; the at least one function module executes corresponding operation according to the received operation instruction and the operation parameter so as to realize distribution of multi-room audios; and generating voice feedback information corresponding to the operation, and returning the voice feedback information to the user.

The disclosure related to a RF front-end device, a RF system and an electronic device. The RF front-end device is configured with an antenna terminal, a control terminal for receiving multiple control signals, and multiple input terminals for receiving multiple RF signals. The RF front-end device includes: a first switch unit having multiple first terminals connected to the multiple input terminals respectively; a power amplification unit, having an input terminal connected to a second terminal of the first switch unit, and an output terminal connected to the antenna terminal; and a control unit, where the control unit is connected to each of the control terminal and the power amplification unit, and is configured to control, based on a received target control signal, the power amplification unit to perform power amplification processing on a received target RF signal. The target control signal is one of the multiple control signals, and frequency bands of the multiple RF signals are different. This can increase the multiplexing efficiency of the power amplifier unit, enhance the integration level of RF front-end device, and achieve a low cost.

The invention provides a multi-link automatic switching device and method for an air traffic control system. A multi-path input interface module accesses a message link, and the output end of the multi-path input interface module is connected with a core processing and control module; the core processing and control module executes data acquisition, state judgment and algorithm decision; the output interface module is used for receiving an instruction of the core processing and control module and transmitting data of a selected link to post-stage communication equipment; the emergency bypass circuit is used for communicating a specified primary link input port with the output interface module through a physical switch; the communication module core processing and control module performs data interaction with a remote monitoring host through the communication module; when the system runs normally, the link state is monitored in real time, and data of the RS-232 interfaces are read; and when the data of the input port is abnormal, the application layer controls the multi-path input module to quickly switch the output signal to the optimal link interface. According to the method, the message data flow can be kept smooth through physical direct connection under the extreme condition of complete downtime of the device.

The invention discloses a novel information system reliability determination method for a multi-input dependent starting scene, and belongs to the technical field of system reliability. Comprising the following steps: drawing a system structure chart containing a multi-input dependency starting module, and dividing a three-stage system structure of a core module-a support signal module-an auxiliary guarantee module; a GO graph is drawn, the multi-input dependent starting module is uniquely identified as a type 29 operator, and the number of input ports of the operator is in one-to-one correspondence with the number of support signals of the corresponding core module; and determining the signal effective probability, the correlation coefficient and the element reliability according to a standardized process, calculating the module reliability by combining a type 29 operator quantitative model, and finally determining the system reliability. According to the method, the problem that an existing GO method operator cannot adapt to a multi-input dependent starting scene is solved, the blank of a special tool for GO method modeling in the scene is filled up, and the application range of the GO method in a novel information system is expanded.

The utility model discloses a DJ sound console. The DJ sound console comprises a shell, a control panel, a channel mixer, a mainboard and a meter head, the number of the control panels is multiple, the control panels are vertically arranged in the shell along the length direction of the shell, the control panels are parallel to each other, and the upper end of each control panel is provided with a knob, a toggle switch, an indicator light and a button which are used for adjusting the frequency band and optimizing the tone; the channel mixer is arranged on the inner wall of the front end of the shell, and multiple input / output channels are formed in the channel mixer and used for being connected with external equipment; the main board is arranged in the shell, and the control panels and the channel mixer are electrically connected with the main board; the meter head is arranged on the top surface of the shell, and the meter head is electrically connected with the mainboard.