16 results about "Quantized feedback" patented technology

The invention discloses a capacitor mismatch error shaping circuit and method suitable for a multi-bit quantization Sigma-Delta ADC, and belongs to the technical field of integrated circuits, and the capacitor mismatch error shaping circuit comprises a data weight averaging module which is used for adopting a data weight averaging technology for high-bit quantization code words, and correcting the high-bit quantization code words according to the data weight averaging technology; averaging the mismatch error of the capacitor corresponding to the high-order quantization code word to obtain an averaged high-order capacitor; and the mismatch error shaping module is used for taking the averaged high-order capacitance as a reference, adopting a mismatch error shaping technology, keeping low-order feedback of the current low-order quantization code word of the lower polar plate of the capacitive digital-to-analog converter during Sigma-Delta ADC sampling, and realizing subtraction operation between two adjacent low-order quantization code words. According to the invention, the error caused by the mismatch of the CDAC capacitor fed back by the multi-bit quantization Sigma-Delta ADC can be effectively suppressed, the linearity of the system is improved, and the circuit overhead is remarkably reduced.

The invention relates to the technical field of gold mine filling automatic control, in particular to a gold mine filling automatic control method based on the Internet of Things. The method comprises the steps that original multi-modal data are collected and preprocessed, the preprocessed multi-modal data are subjected to tensor nonlinear transformation, and disturbance characteristic quantities are obtained; constructing a modal input vector based on the disturbance characteristic quantity, and processing the modal input vector to obtain a hidden layer embedding vector; based on the implicit layer embedding vector, carrying out implicit variable path evolution processing to obtain inter-modal path tension; constructing a control instruction based on the inter-modal path tension; after the control instruction is sent to the control equipment, original multi-modal data are collected in real time and predicted; and calculating a composite quantitative feedback error based on the multi-modal data predicted value, and updating and optimizing the control instruction. The problems that the filling quality control precision is low, the filling efficiency is not high, the manual dependence degree is large, filling parameter adjustment lags behind, and the sudden disturbance response capacity is poor in the current gold mine filling operation are solved.

Provided in the embodiments of the present disclosure are a channel information feedback method and apparatus. The method comprises: determining a base vector set on the basis of a dimension parameter set and a real number set, wherein the base vector set is composed of N base vectors; dividing the base vector set into M groups of orthogonal bases, wherein base vectors in each group of orthogonal bases are orthogonal pairwise; selecting k groups of orthogonal bases from among the M groups of orthogonal bases; and constructing a codebook set on the basis of the k groups of orthogonal bases, and indicating the selected codebook set to a receiving end, such that the receiving end uses the codebook set to perform quantized feedback on channel information.

The invention relates to the technical field of artificial intelligence, and relates to an event-driven unmanned ship self-adaptive trajectory tracking control method, which comprises the following steps of: performing quantization operation on a control input signal of an unmanned ship through a uniform quantizer, establishing a linear model to analyze and quantize dynamic characteristics, designing a control law and a self-adaptive rate of the unmanned ship, and performing trajectory tracking control on the unmanned ship. Wherein the control law processes uncertainty and external disturbance of the unmanned ship system through sliding mode control, and the adaptive rate is used for estimating unknown parameters; then a time-varying threshold event triggering mechanism is utilized, the event triggering mechanism and sliding mode control are combined, a quantitative feedback control system is constructed, finally, the stability of the quantitative feedback control system is proved based on the Lyapuno theory, and an efficient control effect can be achieved without knowing quantitative parameters in advance in combination with event triggering and input quantization. And different triggering effects are obtained aiming at different states by utilizing a nonlinear triggering mechanism, so that network resources can be reasonably utilized, and the stability of control is effectively ensured.

The invention discloses a full-performance intelligent detection method and system for electric energy metering equipment, and the method comprises the steps: collecting an observation signal outputted by the electric energy metering equipment, and carrying out the multi-dimensional coupling information filtering, and obtaining a working state estimation value signal of the electric energy metering equipment; calculating the fractal dimension and fractal entropy of the observation signal of the electric energy metering equipment according to the working state estimation value signal; dynamically generating an equipment state quantitative feedback signal according to a time-varying membership function based on the fractal dimension and the fractal entropy; and intelligently identifying equipment faults or performance deviation according to the equipment state quantitative feedback signal, and carrying out equipment full-performance detection when the equipment faults or the performance deviation is identified. According to the invention, potential faults or performance deviations can be intelligently identified, full-performance detection and adjustment can be carried out in time, the precision of the equipment is improved, the self-adaptive capability of the equipment is enhanced, and human intervention and maintenance cost are reduced.

The invention relates to the technical field of network security, and discloses an IPSec VPN encrypted traffic security detection method and system based on an FPGA, and the method comprises the steps: capturing an IPSec VPN encrypted traffic data package through a network interface of the FPGA, and carrying out the detection for a plurality of times, and obtaining a plurality of encrypted traffic feedback parameter groups; performing security analysis on the encrypted traffic feedback parameter group, and calculating a feedback parameter anomaly metric value; obtaining normalized feedback parameter anomaly metric values, determining a feedback parameter anomaly degree value of each normalized feedback parameter anomaly metric value, and calculating a feedback parameter anomaly coefficient; and according to the feedback parameter abnormal coefficient and the preset feedback parameter abnormal coefficient, performing security detection on the IPSec VPN encrypted traffic data packet, so that relevance and volatility among feedback parameters can be quantified, efficient and accurate security detection on the IPSec VPN encrypted traffic is realized, and the detection speed and accuracy are remarkably improved.

An example apparatus includes quantization feedback circuitry (QFC) including an input terminal coupled to an output terminal of voltage-controlled oscillator (VCO) circuitry and an input terminal coupled to an output terminal of first frequency divider circuitry (FDC). The example apparatus also includes second FDC including an output terminal coupled to an input terminal of phase frequency detector (PFD) circuitry and an input terminal coupled to an output terminal of the first FDC. Also, the example apparatus includes masking logic circuitry including an output terminal coupled to an input terminal of the QFC, an input terminal coupled to the output terminal of the VCO circuitry, and an input terminal coupled to the output terminal of the second FDC. The example apparatus also includes adder circuitry including an input terminal coupled to an output terminal of the PFD circuitry and an input terminal coupled to an output terminal of the QFC.

The invention discloses a self-adaptive wireless energy and signal simultaneous transmission device integrated with a QC feedback loop, which relates to the technical field of wireless energy and signal simultaneous transmission and comprises a transmitting end, a receiving end and a wireless channel. The transmitting end comprises an energy information fusion signal generator, a multi-mode transmitting coil array, a quantum channel state decoder and a cognitive adaptive controller. According to the method, channel state parameters are mapped to quantum states of entangled photons through quantum entanglement pair sources of a quantized feedback signal generator, and instantaneous correlation transmission of channel state information is achieved through feedback signals by means of quantum entanglement characteristics. A transmitting end does not need to completely deadjust feedback data packets, the channel state can be rapidly obtained through quantum state comparison, the delay bottleneck of traditional analog and digital feedback links is thoroughly broken through, and a system can capture channel microscopic changes in time.

The embodiment of the invention provides a channel information feedback method and device, and the method comprises the steps: determining a base vector set according to a dimension parameter set and a real number set, the base vector set being composed of N base vectors; dividing the base vector set into M groups of orthogonal bases, wherein the base vectors in each group of orthogonal bases are orthogonal in pairs; selecting k groups of orthogonal bases from the M groups of orthogonal bases; and constructing a codebook set according to the k groups of orthogonal bases, and indicating the selected codebook set to the receiving end, so that the receiving end performs quantitative feedback on the channel information by using the codebook set, thereby solving the problems of relatively large burden and great RRC feedback overhead in the quantitative feedback process of the channel information in the related technology, and improving the channel information feedback efficiency. The base vector set is divided into M groups of orthogonal bases, a part of k groups of orthogonal bases are selected to form the codebook, the size of the codebook is reduced, the constructed codebook is used for carrying out quantitative feedback on the channel information, the burden of the quantitative feedback process of the channel information is greatly reduced, and the RRC feedback overhead is reduced.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive and measure one or more signals for generating channel state information (CSI) associated with a channel for the UE. The CSI may include quantized and unquantized CSI feedback for the channel. The UE may transmit an uplink shared channel that includes a CSI report associated with the channel. The CSI report may include a first set of symbols allocated for the quantized CSI feedback, a second set of symbols allocated for the unquantized CSI feedback, or both. The first set of symbols, the second set of symbols, or both are based at least in part on one or more CSI reporting rules.

The present invention amalgamates the domains of deep brain stimulation, wearable and wireless inertial sensor systems, machine learning, and multidisciplinary design optimization to achieve an optimal parameter configuration. This present invention's respective amalgamation attains a means to automate the acquisition of an optimal parameter configuration for deep brain stimulation for movement disorders, such as essential tremor and Parkinson's disease, in a closed loop context. Wearable inertial sensors provide quantified feedback of movement disorder response to a deep brain stimulation parameter configuration. Using multidisciplinary design optimization, a minimal effective power, which is derived from tremor power and deep brain stimulation power, is acquired constituting an optimal parameter configuration.

The invention provides a downlink channel reconstruction method based on dual-time scale phase calibration for a Cell-Free large-scale MIMO system. The downlink channel reconstruction method comprises the following steps: step S1, uplink pilot frequency transmission and reciprocity structure feature extraction; s2, performing dual-time scale trigger judgment based on a radio frequency nonreciprocity slow variation characteristic; s3, performing downlink phase detection and user side phase error extraction; s4, performing quantization and lightweight feedback on pure phase information; and S5, performing global downlink channel reconstruction and data transmission based on double scales. The uplink feedback overhead is greatly reduced through pure phase feedback, resource waste caused by frequent calibration is avoided through time granularity adjustment, high-performance downlink channel reconstruction and beam alignment are achieved under the condition that a hardware calibration circuit between access points is not needed, and the transmission efficiency of a distributed wireless communication system is remarkably improved.

This invention discloses a desktop table tennis shot quality quantification training device and method, belonging to the technical field of sports training equipment. The device includes a fixed base, a shot response component, a measurement module, a data processing module, and a data feedback module. The measurement module includes at least one of a spin measurement submodule and a velocity measurement submodule, preferably both; the spin measurement submodule collects shot spin parameters, and the velocity measurement submodule collects velocity parameters of the moving component of the moving subunit. The data processing module simultaneously collects, aligns, and fuses the two types of parameters within a sampling window to generate shot quality quantification data, which is output by the data feedback module. This invention also provides a scheme containing only a single submodule and discloses the corresponding quantification method, training data feedback system, and computer-readable storage medium, which can simultaneously acquire spin and velocity parameters, output objective quantitative feedback, and improve the accuracy of training evaluation.

This invention discloses a multi-agent collaborative system and method based on task hierarchical division of labor. The system includes a task parsing module for task attribute extraction and standardization, whose output is connected to the input of a hierarchical decomposition module for complex task hierarchical decomposition and graph modeling. The hierarchical decomposition module is used for multi-dimensional capability quantification modeling of agents, and its output is connected to the input of a task allocation module for optimal task-agent matching. The task allocation module is used for encrypted communication and state quantification feedback, and its output is connected to the input of a monitoring and adjustment module for anomaly identification and dynamic adjustment. Finally, the system's output is connected to a storage module. This invention addresses the technical problems of poor adaptability of existing collaborative mechanisms, insufficient environmental robustness, difficulty in guaranteeing system convergence, and high engineering implementation costs.

The invention relates to a wireless communication technology, in particular to a 6G-oriented lightweight CSI (Channel State Information) feedback method based on deep learning, which comprises the following steps of: deploying an encoder at a user equipment end for compressing a real part and an imaginary part of a truncated complex CSI matrix of an angle-time delay domain into one-dimensional code words, and in the compression process, according to a width scaling factor, carrying out compression on the real part and the imaginary part of the truncated complex CSI matrix of the angle-time delay domain into one-dimensional code words; dynamically adjusting the number of channels of each layer of output feature map, and matching fluctuating computing resources of the user equipment; and deploying a decoder at the base station end, receiving the one-dimensional code word from the feedback link by the decoder, and reconstructing the CSI matrix by the decoder through the one-dimensional code word. According to the method, the value of the width scaling factor is adjusted, so that the system can realize balance between the reconstruction precision and the calculation efficiency, and the problem of universal deployment of the model on diversified equipment is effectively solved.

This invention relates to the field of neural computing technology and discloses a method for steady-state adjustment of a neural computing model with dynamic quantization feedback. The method includes: constructing an error dynamics model integrating cross-node and dimensional topological features; acquiring real-time state errors and generating stepped values ​​using an asymmetric quantization operator with dynamic hysteresis, wherein the dynamic hysteresis threshold shifts asymmetrically with dimensional topological traction; establishing a mapping relationship between convergence time and model parameters through gamma function-based estimation logic to determine the time window; calculating adaptive gain and generating a smoothing adjustment signal to offset threshold jumps and guide the response system convergence. This invention eliminates distortion caused by missing dimensional coupling, achieves accurate estimation of convergence time, effectively suppresses control oscillations, and improves the state evolution accuracy of the computing model when processing high-dimensional data.