479 results about "Nonlinear distortion" patented technology

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The invention relates to a retiming circuit applied to an ultra-high-speed and high-precision DAC (Digital-to-Analog Converter), which comprises an alignment module, a cross point adjusting module, a coding module, a phase detection module and a digital module, the coding module is connected with the alignment module, the phase detection module is connected with the cross point adjusting module and the coding module, and the digital module is connected with the phase detection module and the alignment module. According to the circuit structure provided by the invention, the sampling clock can be accurately aligned to the center of a data eye pattern, so that the delay difference between a clock tree and a data path caused by process deviation and the like is effectively reduced, the high-frequency spurious suppression performance is improved by eliminating nonlinear distortion caused by time sequence errors, the SFDR is greatly improved, and compared with a traditional scheme, the SNR is effectively improved.

The invention provides a communication method and device, which can be applied to the technical field of wireless communication, and can enable a compensation model to be matched with a power amplifier (PA) in actual work, thereby accurately compensating nonlinear distortion of a signal caused by the PA, and not needing to carry out approximate mathematical modeling on the PA. The method comprises: a first device receiving a first signal and first information; wherein the first signal is a signal which is amplified by the first PA through adoption of the first gain, and the first information is used for indicating the first gain. And the first device determines a first compensation model according to the first signal and the first gain. Wherein the first compensation model is used for compensating non-linear distortion, and the non-linear distortion comprises the non-linear distortion on the signal in the process that the first PA adopts the first gain to execute amplification processing. For example, the first compensation model is a digital pre-distortion (DPD) model.

The invention discloses an electroencephalogram signal artifact removal method and an acquisition system. The method comprises the following steps: synchronously acquiring sensor data of a testee and each lead electroencephalogram signal by utilizing a unified trigger signal; performing timestamp alignment verification on the two types of synchronous data; a motion state quantitative index is determined, and if the motion state quantitative index does not exceed a preset motion threshold value, the collected electroencephalogram signals are output; if yes, an exception processing flow is triggered and comprises the steps that self-adaptive filtering and nonlinear compensation are sequentially executed on the electroencephalogram signals, static electroencephalogram data of the testee serve as reference in the self-adaptive filtering process, an NLMS filter with the step length dynamically adjusted is adopted, a motion artifact linear part and base noise are restrained, a self-adaptive filter is constructed based on the second-order Volterra series in the nonlinear compensation process, and the motion artifact linear part and the base noise are restrained; a kernel coefficient is optimized through an RLS algorithm, and nonlinear distortion is processed; and outputting the electroencephalogram signal after the exception processing. The method is suitable for non-linear and non-stationary motion artifact signals, signal synchronism and quality can be improved, and processing efficiency is considered.

The invention provides an image correction method and system based on a nonlinear distortion distribution elliptical fisheye lens, and a medium. The method comprises the following steps: establishing a nonlinear mixed distortion imaging model based on a mapping relation between image coordinates and a spherical direction; defining a distortion vector, and performing fitting processing on the distortion vector based on a two-dimensional B-spline function to obtain a distortion function; the method comprises the following steps: acquiring images in a five-surface three-dimensional angle calibration space based on an elliptical fisheye lens, analyzing lens imaging parameters, and generating parameter values of a distortion function; analyzing parameter values of the distortion function based on a nonlinear mixed distortion imaging model to generate calibration parameters, and remapping pixel points in the distorted image based on the calibration parameters; performing image reconstruction on the remapped distorted image to obtain a corrected image; improving the sampling density of a target area by constructing a nonlinear mixed distortion imaging model; the system adapts to complex monitoring environments such as galleries and entrances and exits; and spatial distortion is flexibly expressed through a B spline, so that the modeling capability is improved.

The invention discloses a method and a system for recovering time domain nonlinear distortion of an OFDM (Orthogonal Frequency Division Multiplexing) signal of satellite communication, which mainly solve the problem that the nonlinear distortion of the OFDM signal of the satellite communication is difficult to recover in the prior art. According to the scheme, the method comprises the following steps: acquiring a data set of satellite communication OFDM signal time domain nonlinear distortion recovery; constructing a deep learning network model composed of an optimized multi-layer one-dimensional convolutional neural network and a bidirectional long-short-term memory network module; inputting the acquired data set into a deep learning network model for training, verification and testing so as to recover a time domain distortion signal; and carrying out channel equalization and demodulation on the recovered time domain signal, completing demodulation from a modulation symbol to bit information, and realizing effective recovery and transmission of data. The method has good mean square error and bit error rate performance, the complexity of a network model can be reduced, signal distortion caused by nonlinear distortion can be effectively recovered, the precision and efficiency of signal recovery can be improved, and the method can be used for recovering nonlinear distortion signals in a satellite communication system.

The invention relates to the field of lithium battery intelligent monitoring, in particular to a low-temperature-resistant lithium battery BMS monitoring method and system based on cooperative sensing. The method comprises the following steps: acquiring surface temperature distribution, environment temperature and electrochemical parameters of the low-temperature tolerant lithium battery, and constructing a multi-source state characteristic matrix through space-time alignment and data fusion; applying a composite current excitation signal, and combining impedance analysis and spectrum analysis to extract impedance parameters, nonlinear distortion components and electrochemical stress factors to form a multi-dimensional feature vector; obtaining a state monitoring result by utilizing decision level fusion; through multi-sensor fusion and multi-dimensional feature fusion, the special internal state of the low-temperature tolerant lithium battery is comprehensively represented, early risk identification and early warning are carried out aiming at unique electrochemical behavior features of the low-temperature tolerant lithium battery, and compared with a common lithium battery monitoring method, the method has the advantages that the safety is high; and the accuracy, the robustness and the real-time performance of monitoring the state of the low-temperature tolerant lithium battery in the low-temperature environment are remarkably improved.

The invention relates to the technical field of digital image processing, in particular to an unmanned aerial vehicle image distortion correction and coordinate positioning compensation method, which comprises the following steps: collecting a large amount of unmanned aerial vehicle image data containing a damaged house, labeling the damaged house in the image, and generating a labeling file required by a YOLOv5 model; performing parameter calibration on the unmanned aerial vehicle lens to obtain a distortion coefficient of the lens, and constructing a nonlinear distortion correction model based on a lens parameter calibration result; establishing a dynamic affine transformation matrix in combination with the real-time flight attitude data of the unmanned aerial vehicle and the pixel coordinates of the detection frame; in a YOLOv5 detection stage, fusing feature maps with different resolutions, and fusing corrected coordinate data with a damaged house detection frame output by a YOLOv5 model; calculating coordinates of a center point of the damaged house according to the real-time latitude and longitude of the unmanned aerial vehicle; through dual optimization of image distortion correction and dynamic positioning compensation, the problem of coordinate misalignment caused by hardware limitation and unstable flight attitude in traditional unmanned aerial vehicle detection is solved.

The invention discloses a wide temperature range compensation method for resistive current of a lightning arrester, which belongs to the technical field of online monitoring of power equipment, and comprises the following steps: acquiring waveform data under a reference low-temperature working condition, and establishing an initial capacitive harmonic base; acquiring working condition data at any temperature in real time, performing harmonic decomposition, and analyzing to generate a nonlinear distortion feature vector; dynamically correcting the initial base by using the feature vector, and carrying out subtraction operation on the total current harmonic wave to obtain a final resistive current harmonic wave vector; and reconstructing an instantaneous resistive current waveform through inverse transformation and calculating a characteristic value. According to the technical scheme, the initial capacitive harmonic wave base is established, the nonlinear distortion feature vector is generated based on the harmonic distortion of the real-time working condition quantification temperature and valve plate nonlinear coupling, and dynamic feedback correction is conducted on the base, so that the real resistive current waveform in the wide temperature range can be accurately separated and reconstructed, and the accuracy of the real resistive current waveform in the wide temperature range is improved. And the accuracy and reliability of lightning arrester state evaluation are improved.

The invention relates to a narrowband minimum mean square error echo suppression method introducing a complex neural network, and belongs to the field of adaptive echo cancellation. An existing neural network method still has the problem of inaccurate priori knowledge modeling, and phase information in complex signals is not fully utilized. According to the method, a plurality of DNNs are adopted to carry out modeling and estimation on step length, loudspeaker nonlinear distortion and error signals respectively. And the adaptive capacity to an echo channel in a complex environment is improved by utilizing the efficient parameter learning capacity of a plurality of neural networks. According to the method, the phase information of complex data is fully utilized, the nonlinear distortion problem of the loudspeaker can be effectively suppressed, meanwhile, the small model parameter quantity is kept, efficient echo suppression is achieved, high practicability and hardware adaptability are achieved, and the method is suitable for various practical application scenes.

The invention discloses a high-frequency spark machine calibration method based on machining precision and stability control, and relates to the field of mechanical calibration, and the method comprises the steps: analyzing collected operation data, and building a data analysis model; the resonant frequency is dynamically adjusted based on the output frequency, and electromagnetic interference is eliminated through optical fiber transmission signals; an environment compensation mathematical model is constructed, and voltage deviation caused by environment change is automatically corrected; fundamental frequency and harmonic components are detected at the same time, and frequency deviation caused by nonlinear distortion is identified and compensated; a self-adaptive sensitivity adjusting mechanism is designed, a detection threshold value is dynamically adjusted based on the defect type and size, and sensitivity parameters of the high-frequency spark machine are automatically adjusted; the stability of the mechanical structure is predicted by simulating the assembly process; damping parameters of a mechanical structure are automatically adjusted, and the stability of high-frequency spark machine equipment is improved. The product quality and the production efficiency are guaranteed by improving the performance and the stability of the high-frequency spark machine.

The invention relates to the technical field of wireless communication, in particular to an OTA upgrading method and system for Bluetooth sound equipment. The method comprises the following steps: acquiring to-be-upgraded firmware data and currently played audio data, and acquiring power amplifier current feedback data of a sound speaker by sending a double-frequency detection signal containing 21kHz and 22kHz; acquiring equipment transmission capability confirmation data according to a silent response mode of the sound equipment to the preset handshake signal; and calculating a nonlinear distortion coefficient of the loudspeaker according to the intensity of the 1kHz difference frequency component in the power amplifier current feedback data, and obtaining real-time channel evaluation data based on current waveform deviation analysis. According to the method, the nonlinear distortion degree of the loudspeaker is accurately evaluated through the 1kHz difference frequency component generated by the 21kHz and 22kHz dual-frequency detection signals, so that the reliability of data transmission is more accurately predicted.

The invention is suitable for the technical field of semiconductor testing, and provides a data acquisition method and device based on a DDIC chip, and testing equipment, and the data acquisition method comprises the steps: receiving a test pattern, controlling the DDIC chip to output a corresponding gray scale voltage signal, and achieving the comprehensive testing of the response capability of the chip in a full gray scale range; secondly, a target acquisition link composed of functional units including impedance conversion, single-ended to differential conversion, programmable gain amplification, filtering, analog-to-digital conversion and the like is utilized, so that the signal driving capability and the anti-interference capability are effectively improved, meanwhile, high-frequency noise and nonlinear distortion are reduced, and the basic quality of sampled data is guaranteed; on the basis, sampling optimization processing is carried out on the sampling data based on the FPGA unit, and the data stability is improved. According to the scheme, through high-precision transmission of the signal link, the data acquisition precision of the DDIC chip is improved, and then the technical requirements of high-precision DDIC chip testing are met.

The invention relates to the technical field of surface mounting, and discloses an SMT (Surface Mount Technology)-based patch vision correction method, which comprises the following steps: S1, a calibration stage: constructing a double-coordinate system mapping model based on a PCB (Printed Circuit Board) plane, and establishing a nonlinear distortion compensation matrix according to the patch thickness; s2, a pickup stage: a CCD shooting camera is arranged at the top of the suction nozzle, a top CCD is triggered to shoot when the suction nozzle picks up an element, and pin coplanarity data is extracted; s3, a positioning stage: setting a reference point at the top of the PCB, scanning the PCB reference point through a CCD, and calculating a deviation between an actual position and a theoretical coordinate by using an algorithm processor; and S4, compensation execution: the motion shaft system dynamically adjusts the mounting path according to the deviation vector. The visual correction method based on SMT pasting has the advantages of accurate pasting and the like, and solves the problems of pasting position offset, low efficiency and unstable precision caused by factors such as vibration of an element feeder, mechanical transmission errors and thermal deformation of a PCB (Printed Circuit Board) due to dependence on manual calibration in the existing SMT pasting process.

The invention relates to the technical field of signal correction, in particular to a nonlinear distortion correction method for a vibration monitoring signal, and provides the following scheme: carrying out broadening and sparse reconstruction on a vibration signal through dispersion Fourier transform and compressed sensing, and extracting a high-frequency local structure; on the basis of phase-space reconstruction and a Lyapunov exponent, nonlinear distortion segments are identified; further performing orbit guiding modeling and degradation path reasoning on the orbit cluster corresponding to the segment, and selecting a reference orbit matched with the structure from the residual linear segments; and constructing an affine mapping relation between orbits and back-projecting the affine mapping relation to a time domain to generate a correction signal. According to the invention, high-precision identification and low-error correction of millisecond-level local nonlinear distortion are realized, and the stability and accuracy of vibration signal analysis are improved.

The invention relates to the technical field of aluminum alloy forming processes, and discloses an intelligent optimization method of an aluminum alloy round aluminum rod forming process. The method comprises the following steps: acquiring process parameter monitoring data of smelting, casting and rolling stages, wherein the process parameter monitoring data comprises temperature gradient, pressure fluctuation value and alloy component real-time variation; based on the time sequence relevance of the technological parameter monitoring data of the smelting stage and the casting stage, the technological parameter transmission delay amount between the two stages is calculated, and the parameter fluctuation compensation factor of the casting stage is determined accordingly. And determining the energy interference intensity of each process stage in the corresponding frequency domain according to the temperature gradient difference value, the pressure fluctuation change rate and the alloy component offset of the adjacent process stages in the short-time window. And generating a process nonlinear distortion probability based on the energy interference intensity distribution of all adjacent process stages. And the pressure control reference value in the rolling stage is dynamically adjusted according to the process nonlinear distortion probability. By means of the method, intelligent optimization of the aluminum alloy round aluminum rod forming process is achieved.

The invention relates to the technical field of radio frequency signal processing, and discloses a digital filtering repair method and system for signal distortion, and the method comprises the steps: obtaining a frequency domain amplitude-frequency response and a phase response; constructing a phase compensation amount by adopting piecewise polynomial fitting and a phase inversion mechanism; a complex number logarithm domain mapping mechanism is adopted to execute composite frequency response inversion; constructing a switchable hybrid digital filter with an amplitude path and a phase path coupled in parallel; multi-band adaptive scheduling is executed; and outputting the radio frequency baseband signal stream. The technical problems that in the prior art, frequency response nonlinear distortion in a broadband range cannot be dealt with by adopting a fixed template or a single-path filter, and particularly, a high-precision and low-delay dynamic compensation effect cannot be realized in a scene that multi-band coupling interference exists in a radio frequency ADC channel with the bandwidth exceeding 800MHz are solved. Due to the fact that the switchable hybrid digital filter is constructed, accurate modeling and flexible recovery of distortion characteristics in broadband signals are achieved.

The invention discloses a three-dimensional geometric reconstruction camera calibration method and system, and relates to the technical field of three-dimensional geometric reconstruction, and the method comprises the steps: S1, image collection; s2, automatic calibration point selection; s3, estimating camera parameters; s4, distortion correction is carried out; and S5, performing three-dimensional reconstruction verification. According to the three-dimensional geometric reconstruction camera calibration method and system, a calibration point does not need to be selected manually, the calibration process is simplified, the complexity and time cost of the calibration process are reduced, a high-precision calibration result can be obtained without shooting a plurality of plane images and carrying out a plurality of optimization iterations, and the calibration efficiency is improved. The calculation amount and the time cost are further reduced, and camera calibration can be quickly and accurately realized; in addition, nonlinear distortion of a camera lens is fully considered, the calibration precision is further improved, and the method is particularly suitable for the situation that a wide-angle lens is used or an image contains large distortion.

The invention discloses a preprocessing method for a digital baseband signal of a receiver, which comprises the following steps of: quantifying the sparse degree of the signal by selecting a proper sparsity measurement index, constructing an optimization problem based on the selected sparsity measurement index, and then minimizing the sparsity index of the signal in a transform domain, namely searching a group of compensation parameters. The sparsity of the compensated signal in the transform domain is restored to the level close to that of the original signal, and then the established optimization problem is solved by using a numerical optimization algorithm to find the optimal compensation parameter. According to the method, the sparsity of the signal can be effectively recovered, so that the nonlinear distortion at the front end of the receiver is compensated, and the signal quality and the system performance are improved.

The invention provides a phase generated carrier demodulation method based on two-parameter estimation. The method comprises the following steps of: firstly, realizing estimation of a C value by extracting an amplitude ratio of a carrier frequency multiplication component in an interference signal frequency spectrum and combining a Bessel function characteristic; then, carrying out operation processing by utilizing a first frequency multiplication signal and a second frequency multiplication signal to extract carrier phase delay theta c; and finally, eliminating the influence through dynamic phase compensation. According to the demodulation method provided by the invention, the nonlinear drift of the modulation depth parameter C and the synchronous estimation and compensation of the carrier phase disturbance signal theta c can be realized, the cross interference error in the traditional demodulation system is effectively inhibited, the phase demodulation robustness is remarkably improved, and the harmonic nonlinear distortion is effectively inhibited.

The invention provides a method for automatically determining the content of main elements in lithium battery black powder, and belongs to the technical field of lithium batteries. Characteristic spectral line intensity is collected by adopting an inductively coupled plasma emission spectrometer, background signals are deducted through wavelet multi-resolution decomposition, and the content of the main elements in the lithium battery black powder is obtained. Spectral overlapping peaks are separated through Fourier transform, an internal standard element is used for correcting a matrix effect, signal fluctuation introduced by pulse of a peristaltic pump is eliminated by adopting Kalman filtering, and relative intensity subjected to filtering smoothing is input into a multi-scale concentration calibration model with a layered soft maximum value classification layer; according to the model, interval affiliation probability distribution is output through a coarse-grained classifier, a fine-grained classifier is selected to carry out local concentration prediction and weighted fusion is carried out to obtain a concentration prediction value, and quality control standard sample monitoring and an instrument recalibration mechanism are combined; the technical problem that the measurement precision is reduced due to nonlinear distortion of a calibration curve when the multi-element concentration in the lithium battery black powder crosses a wide concentration range is solved.

The invention provides an in-vehicle acoustic enhancement method and device fusing determinacy-randomness hybrid modeling, and the method comprises the steps: collecting a fitted vehicle acceleration sound sample, extracting audio data, carrying out the analysis, constructing a determinacy component synthesis algorithm, and associating with a vehicle working condition; the method comprises the following steps: constructing a multi-physics-field coupled random noise generation module, wherein the multi-physics-field coupled random noise generation module comprises an aerodynamic noise module for simulating air inlet turbulence, a low-frequency pulse module for representing mechanical shock, a detonation module for simulating combustion uncertainty and a nonlinear distortion module for enhancing exhaust roughness and generating high-order harmonics; each random noise generation module is matched with the deterministic component, and mixing of the deterministic component and the random component is realized through weight adjustment; details are enhanced through dynamic compression, and target sound waves are generated; through collaborative optimization of determinacy-randomness components, the naturalness and brand identification degree of synthetic sound waves are improved, the vehicle working condition and driving information can be converted into perceptible sound effect characteristics, and the problem of information perception simplification in the electric vehicle driving process is solved.

The invention discloses a parametric array loudspeaker nonlinear distortion modeling and compensation method based on deep learning. The method comprises the following specific steps: (1) recording an audio sound signal output by a parametric array loudspeaker system and an input signal of the audio sound signal at a listening position where nonlinear distortion needs to be suppressed, and making a data set for modeling; (2) performing overall modeling on the parametric array loudspeaker system by using a neural network F, and training the neural network F by using the data set; modeling a linear part of the system by using an FIR (Finite Impulse Response) filter model Flin; (3) using the trained neural network F and the model Flin to train a neural network G as an inverse filter of the system; and (4) preprocessing an audio signal expected to be replayed through the neural network G, and inputting the preprocessed audio signal into a parametric array loudspeaker system to realize nonlinear compensation of the system. According to the method, accurate modeling and effective compensation can be carried out on the nonlinear distortion of the parametric array loudspeaker, and the nonlinear distortion of the parametric array loudspeaker is remarkably inhibited.

The invention relates to the technical field of ultrahigh-order QAM (Quadrature Amplitude Modulation) signal nonlinear compensation, in particular to an ultrahigh-order QAM signal nonlinear compensation method and system based on a time characteristic memory neural network, and the method comprises the steps: S1, obtaining a plurality of groups of orthogonal amplitude modulation signals under the condition of different optical signal-to-noise ratios, performing zero mean normalization processing on the quadrature amplitude modulation signal to obtain a pre-processed quadrature amplitude modulation signal; s2, dividing the preprocessed orthogonal amplitude modulation signal into a training set and a test set, constructing a signal nonlinear compensation model, and training the signal nonlinear compensation model by using the training set to obtain a trained signal nonlinear compensation model; and S3, based on the trained signal nonlinear compensation model, performing nonlinear compensation on a quadrature amplitude modulation signal to be compensated. According to the method, the time sequence characteristics of the signals can be extracted, the two-way time dependence relation can be captured, and the purpose of nonlinear distortion compensation is achieved.

A signal nonlinear distortion compensation method, a model, and a communication system, relating to the technical field of wireless communications. In the present disclosure, on the basis of a preset nonlinear compensation fusion model, DPD processing, CFR processing, and error compensation processing are respectively performed on an initial OFDM signal, so as to obtain the initial OFDM signal that has been subjected to nonlinear distortion compensation; in this way, by connecting in parallel a BL-DPD module, a BL-CFR module, and an error compensation module in the nonlinear compensation fusion model, the technical disadvantages in the prior art of relatively high requirements on the sampling rate of ADC / DAC, increased requirements on hardware and algorithm convergence speed, and increased system implementation difficulty and cost caused by a DPD module being applied after a CFR module are avoided. Therefore, the requirements on the sampling rate of the ADC / DAC are reduced, the nonlinear compensation performance of DPD for a power amplifier is improved, and the communication performance and the sensing performance of an OFDM system are also improved.

The invention relates to the technical field of transmit-receive converters, in particular to a short wave transceiver common antenna transmit-receive converter based on a PIN diode. The transmit-receive converter comprises a power control module used for monitoring the output power of a power amplifier in real time and dynamically adjusting the output power through a variable attenuator; the cooperative control unit is configured to adjust the attenuation amount of the output power according to the power control module; determining an adjustment strategy of a pre-distortion coefficient in a digital pre-distortion algorithm; and the digital pre-distortion module is used for determining a pre-distortion coefficient in a digital pre-distortion algorithm according to the adjusted output power and the integer strategy so as to compensate nonlinear distortion of the power amplifier through the pre-distortion coefficient.

The invention provides a multi-sound-source arrival direction estimation method and device based on a frequency focusing spatial spectrum. The method comprises the following steps: firstly, generating microphone array data and constructing a training data set by using a preset simulation environment and a microphone array signal simulator; secondly, constructing a mask estimation network model through sound source masking training; then, sub-bandwidth division is carried out on the enhanced multi-sound-source signals in a frequency band splicing mode, and a weighted focusing covariance matrix is obtained; thirdly, broadband focusing spatial spectrum features are constructed through a sub-bandwidth frequency focusing covariance matrix obtained through beam forming, and a convolutional neural network is constructed based on the features; and finally, according to the mask estimation network model and the multi-sound-source DOA spatial spectrum estimation network model, decoding to obtain a multi-sound-source DOA spatial spectrum, and completing multi-sound-source DOA estimation. According to the invention, the intermediate representation based on the broadband frequency focusing spatial spectrum reduces the influence of nonlinear distortion of the neural network on DOA estimation; and the stability of algorithm performance and the fault tolerance of DOA estimation are improved.

The invention provides an OTFS (Orthogonal Time Frequency Space) channel estimation method based on a bidirectional gating loop unit (BiGRU) and a multi-head attention mechanism, and belongs to the technical field of channel estimation. The method comprises the following steps: determining time delay-Doppler domain parameters according to a channel scene, and constructing a TDL (Tap Delay Line) channel model; designing a time-frequency domain pilot frequency structure; carrying out time-frequency domain modulation on a sending signal and adding high-power amplifier (HPA) nonlinear distortion; obtaining an initial channel state by using LS estimation; a BiGRU network and multi-head attention mechanism joint model is constructed, and channel state prediction is realized through bidirectional time sequence feature extraction and multi-dimensional attention weight distribution; and time delay-Doppler domain channel response reconstruction is completed by using the optimized model. Bidirectional dependency of channel time-varying features is captured through BiGRU, and a multi-head attention mechanism is combined to enhance the key path feature extraction capability, so that the channel estimation precision in a high-mobility scene is remarkably improved, and the influence of HPA nonlinear distortion on the system performance is effectively inhibited.

The invention discloses a visual compensation alignment method and system for a special-shaped screen, and belongs to the technical field of image processing, and the method comprises the steps: obtaining physical geometric parameters of the special-shaped screen, carrying out the mathematical simulation, and generating a physical perception deviation map; receiving a content image, performing multi-scale semantic analysis, and generating a content perception joint constraint tensor; constructing and iteratively solving an energy function, and generating an optimal visual compensation flow field; performing nonlinear distortion and reconstruction, and outputting a pre-compensated content image; and performing sub-pixel-level processing through the flexible edge mixed mask, and outputting a final display picture. According to the method, a joint optimization strategy fusing a screen physical perception model and content semantic analysis is adopted, the key visual content can be intelligently protected while the physical fidelity is maximized, and seamless and high-fidelity fusion of a display picture and a special-shaped screen physical form is achieved.

The invention discloses a visible light communication method based on precoding and adaptive dynamic compression. The method comprises the following steps: data layering and precoding processing; signal conversion and clipping; regulating and controlling a dynamic peak value and generating a signal; receiving end signal recovery and processing; decoding and recovering the signal; and separating and demodulating the signal. The method comprises the following steps of: layering original data by adopting a modulation mode of mixing ACO-OFDM and PAM-DMT at a transmitting end; layered signals are preprocessed through a precoder, the peak-to-average power ratio of the signals is reduced, converted time domain signals are input into a generative adversarial network-based framework, and the framework adaptively performs amplitude compression and dynamic clipping on the signals through a feedback mechanism of a convolution feature extraction module and a discriminator, so that the peak-to-average power ratio is effectively reduced; a convolutional feature extraction module at a receiving end generates a recovered signal, and nonlinear distortion is reduced; the signal recovery precision and the spectrum efficiency of a communication system are remarkably improved, and meanwhile the reliability and stability of communication are guaranteed.