31 results about "Nonlinear noise" patented technology

The invention relates to the technical field of optical communication, in particular to a wavelength adaptive configuration method of a CWDM / DWDM hybrid wavelength division system, which comprises the following steps of: acquiring physical layer parameters of an optical fiber link in real time; outputting the OSNR margin and the nonlinear noise prediction value of each wavelength position; dynamically adjusting the reservation proportion of the channel; collaboratively optimizing bandwidth, time delay, damage and cost constraints in path calculation through a reinforcement learning algorithm; splitting the CWDM channel into virtual sub-channels, or combining the virtual sub-channels to generate a super-channel; compared with the prior art which depends on an empirical formula to estimate transmission damage and has the defects of extensive modeling and cross-band crosstalk missing detection, the method provided by the invention has the advantages that a power spectrum, a temperature gradient and nonlinear interaction are analyzed in real time through an LSTM-GNN combined architecture, and a mixed channel damage map covering a full frequency band is constructed; the method has the advantages that cross damage of a CWDM wide channel and a DWDM dense area is accurately quantified, and high-crosstalk wavelength combination is dynamically avoided.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The invention discloses a graph signal blind source separation method in a white noise environment, and belongs to the technical field of signal processing. Aiming at the problem that the traditional blind source separation performance is reduced due to additive white noise, the invention provides a joint optimization scheme combining blind compression nonlinear noise suppression and image smoothing regularization. Firstly, a blind compression function is applied to a noisy observation image signal for preprocessing; secondly, carrying out mean value removal and whitening processing on the compressed signal; then, constructing a graph Laplacian matrix as a smoothing operator, establishing a joint diagonalization objective function fusing graph autocorrelation, a FastICA item and a graph smoothing regular item, and adopting a Givens rotation algorithm to iteratively optimize and solve a separation matrix; and finally, reconstructing a source signal through multiplication of the separation matrix and the whitening data. According to the method, noise interference is effectively suppressed through blind compression, signal structure consistency is maintained by using graph smoothing prior, and separation robustness, precision and convergence stability in a strong noise environment are remarkably improved.

Data signals transmitted over cable networks experience degradation, such as frequency-dependent loss, spectral tilt, signal-dependent nonlinear noise, distortion, and compression, which vary with cable characteristics, temperature, electrical loading, and traffic conditions. The present invention relates to improving network performance by estimating cable signal-degradation using physics-based nonlinear system modeling and telemetry data and applying corrections to operating parameters of cascading amplifiers to offset the signal degradation, thereby improving overall network efficiency and reliability.

The application provides an air conditioning system active noise reduction method and system based on adaptive order, relates to the active noise reduction technical field, and comprises the following steps: collecting environmental noise signals and pre-processing to obtain input signals; the input signals are subjected to adaptive order Volterra nonlinear expansion to obtain expanded signals; the expanded signals are input into a non-normal FIR filter, the filter output signals are obtained through non-normal FIR filter processing, and the filter output signals pass through a secondary path to obtain reverse noise signals; the reverse noise signals are input into a noise environment for noise cancellation, and residual error signals after noise cancellation are collected; filter parameters of the non-normal FIR filter are updated according to the residual error signals. The foregoing steps are repeatedly executed until a preset noise reduction requirement is reached. The application improves the real-time performance, stability and nonlinear noise suppression capacity of the air conditioning system active noise reduction under complex working conditions.

The invention relates to the technical field of communication transmission, and provides an equivalent simulation method and device for transmission performance evaluation of an optical fiber communication system, and the method comprises the steps: generating a signal power density spectrum of each span according to a link parameter; calculating a nonlinear noise power density spectrum according to the link parameters and the signal power density spectrum of each span; and taking the signal power density spectrum as an input parameter of the equivalent simulation model, and calculating a nonlinear damage index at the tail end of the optical transmission system to obtain transmission performance evaluation. A simulated signal power density spectrum is used for replacing a tested signal power density spectrum, the nonlinear damage simulation precision is still kept under the condition that model parameter dependence is reduced, and a nonlinear model is more flexibly applied to various scenes. According to the calculation method, the algorithm is optimized and upgraded from the two aspects of operation efficiency and input parameter dependency, the problems that a non-linear noise algorithm is low in operation speed and input parameters are difficult to obtain are solved, and the algorithm implementation complexity is reduced while the algorithm simulation precision is guaranteed.

The invention provides an unsupervised small sample internal defect detection method, and belongs to the technical field of intelligent manufacturing, and the method comprises the following steps: based on a pulse thermal imaging system, regulating thermal imaging parameters, and obtaining time sequence thermal image sequences of different defect samples; carrying out time domain compression and reconstruction on the time sequence thermal image sequence by adopting a depth self-encoder, eliminating nonlinear noise and reserving defect features; and inputting the image denoised by the depth auto-encoder into a Swin Transformer Wnet for extracting potential defect features from a spatial domain, performing post-processing on the potential defect features, and outputting a pixel-level defect segmentation result. According to the method provided by the invention, the limitation of traditional supervised defect detection can be avoided, potential space images are generated through a continuous coding and decoding structure, and the feature images are optimized into an image segmentation result, so that unsupervised internal defect detection under the condition of scarce samples is realized.

The invention discloses an industrial fault detection method based on joint sparse low-rank double-dictionary learning, relates to the technical field of engineering fault detection, and is technically characterized in that sparse representation and low-rank representation are combined, single-dictionary learning is expanded, and the industrial fault detection method based on double-dictionary learning is constructed; according to the method, the problems of nonlinearity, noise interference, high-dimensional data and the like in a complex industrial process are solved, and the fault detection rate of the system is remarkably improved; in a high-dimensional complex industrial process including noise pollution and a multivariable coupling relationship, the scheme can effectively detect system faults.

The invention relates to the technical field of sensor measurement, discloses a multi-modal sensor azimuth measurement fusion system, and aims to solve the problems of insufficient time synchronization precision, limited sensor anomaly detection and compensation capability and fusion performance reduction in a nonlinear noise environment in multi-modal sensor azimuth measurement. The system comprises a sensor array module, a space-time alignment preprocessing module, a multi-modal feature extraction and coding module, a self-adaptive weighted fusion decision module and a system output and feedback control module. Through hardware synchronous sampling, phase-locked loop clock compensation, external parameter calibration, adaptive feature extraction, sensor health degree evaluation and improved volume Kalman filtering fusion and in combination with a closed-loop correction mechanism, high-precision, robust and adaptive azimuth measurement is realized, and the state estimation precision and the system stability are effectively improved.

This invention relates to the field of sensor measurement technology and discloses a multimodal sensor orientation measurement fusion system, aiming to solve the problems of insufficient time synchronization accuracy, limited sensor anomaly detection and compensation capabilities, and degraded fusion performance under nonlinear noise environments in multimodal sensor orientation measurement. The system includes a sensor array module, a spatiotemporal alignment preprocessing module, a multimodal feature extraction and encoding module, an adaptive weighted fusion decision module, and a system output and feedback control module. Through hardware synchronous sampling, phase-locked loop clock compensation, extrinsic parameter calibration, adaptive feature extraction, sensor health assessment, and improved capacitive Kalman filter fusion, combined with a closed-loop correction mechanism, it achieves high-precision, robust, and adaptive orientation measurement, effectively improving state estimation accuracy and system stability.

The invention provides a signal processing method based on a Brillouin optical time domain reflectometer system, which comprises the following steps: filtering an input signal to obtain an output signal, comparing an expected signal with the output signal to obtain an error signal, iteratively updating a weight coefficient of an adaptive filter, and repeatedly executing filtering operation by using the iteratively updated weight coefficient. Enabling an error signal to approach 0; and performing square operation on the filtered and denoised signal to obtain a square filtered and denoised signal, and performing envelope demodulation processing on the square filtered and denoised signal to obtain a root-mean-square envelope demodulation signal to determine a mutational sensing optical fiber signal. And according to the suddenly changed sensing optical fiber signal, determining a suddenly changed sensing optical fiber detection point. According to the method, the carried sensing information can be effectively extracted from a Brillouin optical time domain reflectometer signal containing a large amount of nonlinear noise, and demodulation and extraction of event points by a Brillouin optical time domain reflectometer system under a low signal-to-noise ratio are realized.

The invention provides a slow wave structure and a terahertz traveling wave tube frequency doubling amplifier. According to the invention, the slow-wave structure designed in a segmented manner is adopted, and independent modulation and amplification of the fundamental frequency signal and the harmonic signal are combined, so that the problem of non-uniform energy distribution of electron beams in a traditional traveling wave tube is effectively solved. The first sub-slow-wave structure carries out shallow modulation on a fundamental frequency signal, excessive consumption of electron beam energy is avoided, meanwhile, a harmonic amplification section in the second sub-slow-wave structure can effectively amplify a harmonic drive signal, and the output power of higher harmonics is ensured. The electron beam gradually-changing drift channel is designed through gradually-changing diameters, energy transfer of fundamental frequency and harmonic wave sections is smoothly transited, the system efficiency, bandwidth and stability are further improved, and meanwhile nonlinear noise and oscillation risks are reduced.

The present application relates to a kind of method and device for calculating the nonlinear noise of Raman amplification scene.The method part mainly includes: obtaining the equivalent in-fiber optical power spectrum of each channel according to Raman coupling equation or obtaining the equivalent in-fiber optical power spectrum of each channel according to Raman analytical equation;According to the equivalent in-fiber optical power spectrum of each channel and related fiber parameters, the equivalent nonlinear noise signal ratio of each channel is obtained by calculation, and the equivalent signal noise ratio caused by nonlinearity in Raman amplification scene is finally obtained.The present application can solve the problem that traditional model is too complex and backward Raman amplification scene is difficult to solve, and is more practical.

The application discloses an optical OFDM modulation method combining a probability shaping technology and a subcarrier grouping transmission technology, and belongs to the technical field of optical communication, and comprises the following steps: optimizing a QAM constellation diagram based on a probability shaping method, acquiring optimal probability distribution of constellation points, mapping the optimal probability distribution of the constellation points based on a constant distribution matcher, and obtaining OFDM symbols; performing frequency domain symbol transformation on the OFDM symbols, generating transformed frequency domain signals; converting the transformed frequency domain signals into LED group signals based on a filtering block; and processing the LED group signals based on a DCO modulation mode, and completing OFDM modulation. Different subcarriers are modulated on multiple LEDs, so that the peak-to-average power ratio of the OFDM signals modulated on each LED is reduced, and the nonlinear noise interference of the system is further resisted.

This invention discloses an active noise reduction algorithm based on a convolutional-fuzzy neural network without secondary path sensors. The algorithm establishes a correlation model between noise sources at different locations within a vehicle and the noise in the target noise reduction area using a convolutional-fuzzy neural network. It calculates and fits a virtual error noise signal for the target noise reduction area, replacing the error signal obtained through secondary path sensors in traditional methods. This reduces the dependence of active noise reduction in large vehicle interior spaces on multiple secondary path sensors. Furthermore, the invention uses the virtual error noise signal as input to the fuzzy layer, iteratively calculating and identifying secondary paths, and outputting the optimal noise cancellation signal in real time, improving computational efficiency and enhancing the ability to handle nonlinear noise.

This application discloses a noise joint processing method, apparatus, electronic device, and readable storage medium. The noise joint processing method includes: extracting a first sub-band feature signal and a first speech signal from a nonlinear noise residual signal, and extracting a second sub-band feature signal and a second speech signal from a far-end signal; fusing the first sub-band feature signal and the second sub-band feature signal to obtain a first input feature signal, and fusing the first speech signal and the second speech signal to obtain a second input feature signal; fusing the first input feature signal and the second input feature signal to obtain a fused feature signal; determining a target sub-band gain and target speech filter coefficients based on the fused feature signal, the first input feature signal, and the second input feature signal; and obtaining a sound signal based on the target speech filter coefficients and the target sub-band gain. This application solves the technical problem of difficulty in jointly processing echo, noise, and reverberation while achieving good noise reduction effect.

The application discloses a transmitting power optimization method and system of an improved simulated annealing algorithm, and the method comprises the following steps: modeling self-emission noise of an amplifier of an ultra-wideband wavelength division multiplexing system, obtaining linear noise of the ultra-wideband wavelength division multiplexing system, and obtaining nonlinear noise of the ultra-wideband wavelength division multiplexing system according to an ISRS GN model; determining the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power through a Shannon formula and the linear noise and the nonlinear noise; and optimizing the transmitting power of the ultra-wideband wavelength division multiplexing system through the improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power. The application can improve the convergence speed of the algorithm while searching for the optimal transmitting power distribution.

The invention discloses a pod rapid self-alignment method based on multi-sensor joint estimation, and relates to the technical field of safety management, and the method comprises the following steps: I, initializing a multi-source sensor and collecting environment information; iI, constructing an adaptive filtering structure and estimating the initial attitude of the pod; iII, analyzing the causal relationship of the multi-source observation data and evaluating the credibility of the multi-source observation data; iV, correcting the postures of the pods and eliminating redundant information in the multi-source observation data; the method can significantly shorten the overall alignment time, improve the system starting efficiency, avoid the influence of single sensor failure or noise increase on the system, improve the data fusion effect, dynamically adjust the filtering structure according to the actual operation condition, improve the adaptability to the complex environment, and improve the reliability of the system. A non-linear noise mode which is difficult to find by a traditional statistical method can be identified, abnormal data are stripped through a noise index positioning and data reconstruction mode, and the observation data quality is improved.

The application discloses a signal clustering decision method and system, and relates to the technical field of digital signal processing. The signal clustering decision method comprises the following steps: according to the signal modulation format and the nonlinear noise size, adjusting the proportion of the modulus difference value and the angle difference value of the constellation point and the current clustering center when calculating the distance, so as to calculate the distance between the constellation point and the current clustering center; according to the distance between the constellation point and the current clustering center, determining the final clustering center and the constellation points included in each final clustering center through iterative calculation; and based on the symbol decision attempt, taking the symbol relationship corresponding to the minimum bit error rate as the decision symbol to which each final clustering center and the included constellation point belong. The application can perform clustering on the constellation diagram with large linear noise and nonlinear noise.

The application discloses an enhanced model day-ahead electricity price prediction method based on dynamic feature selection, which extracts features from the original data set through a multi-head attention mechanism for multidimensional correlation analysis, generates a feature weight vector in real time to realize dynamic screening of the optimal feature subset, thereby suppressing redundant information and nonlinear noise features at the source; then, an enhanced XGBoost model is used to deeply mine the structured relationship of power market data, simultaneously combined with a Transformer model to capture the long-range time series dependence characteristics of the electricity price sequence, and through a gating mechanism to perform dynamic weighted fusion, to realize the complementary integration of spatial features and time series features; finally, a multi-objective Bayesian optimization algorithm is introduced to obtain a trained enhanced model through multi-objective collaborative optimization, and then prediction is performed. The application significantly improves the prediction accuracy and robustness, effectively solves the deployment problem of the model in a resource-limited environment, and has high engineering application value and power market prediction practicality.

A SOA arrangement (100) including a first polarization-diversity SOA pair (102) and a second polarization-diversity SOA pair (104) arranged on a common optical path with a correlation reducing optical arrangement 106 The correlation reducing optical element is configured to receive a first and a second input component from the first SOA pair and to mix the first and second input components into a first and second output components. The first output component is a combination of the first and second input component from the first polarization-diversity SOA pair. The second output component is a combination of the first and second input component from the first polarization-diversity SOA pair. The SOA arrangement thereby being arranged to reduce the correlation between the nonlinear noise generated by the first SOA and the second SOA.

A method for computing a set of optimal amplification parameters for a span of a multi-span optical fiber network. Each span has an associated optical transmission fiber connected to associated amplifiers. The associated amplifiers include a forward Raman amplifier and at least one discrete amplifier. Gain and output power of the at least one discrete amplifier is respectively controlled independently. The method includes the steps of providing an analytical form of the signal power evolution along the corresponding span; computing a linear noise from amplifier noise figures and a non-linear noise generated in the corresponding span based on information about the span and the analytical form of the signal power evolution and using the computed linear noise and non-linear noise to compute the impact on the span performance; and computing a set of optimal amplification parameters for the corresponding span based on the impact on the span performance.

The application provides an electric field sensing signal denoising method combining Kalman filtering and a neural network, and relates to the field of signal denoising. The method comprises the following steps: obtaining a recurrent neural network (RNN) through back propagation training based on a first frequency domain signal; receiving a second frequency domain signal to extract a first feature vector, generate a first distorted observation signal through nonlinear distortion modeling; performing Kalman filtering state estimation to obtain a first estimated state; mapping the first estimated state back to a signal space through a two-layer fully connected network to obtain an output mapping signal; converting the output mapping signal from the frequency domain to the time domain through inverse short-time Fourier transform, and performing inverse normalization processing to obtain a target electric field sensing signal. The application combines Kalman filtering and an improved recurrent neural network for joint optimization, estimates and captures the nonlinear noise characteristics of the electric field sensor signal through a fully connected network, enhances the nonlinear modeling capability, and realizes efficient and high-precision denoising of the electric field sensing signal.

The invention discloses an EBAPS-oriented low-light image denoising method and system and a data set construction method, which are used for solving the problem of nonlinear noise of an electron bombardment active pixel sensor (EBAPS) under an extremely low-light condition. According to the data set construction method, clear-noisy image pairs of pixel-level alignment are obtained by adjusting the aperture of a light source instead of changing exposure at a fixed geometric position. The denoising method adopts a two-stage neural network, and the core of the denoising method is a frequency domain-based noise estimation module (FANS). The FANS module generates a noise priori parameter accurately corresponding to illumination by calculating an under-curve area (AUC) of a normalized radial power spectrum of an input image. According to the network, by using the parameter, through low-rank guided decomposition (LROD), two-way adaptive spatial fusion (DASF) and other modules, denoising is carried out on a noisy image. According to the method, EBAPS noise under 10 <-5 > lx limit illumination can be effectively removed, and image details are reserved.

The application relates to the technical field of image processing, and discloses a noise model calibration method, an image denoising method, a device, equipment and a medium. The method comprises the following steps: acquiring multiple RAW domain images; performing nonlinear noise denoising processing based on the multiple RAW domain images to generate a target image; and calibrating a preset RAW domain noise model based on the target image to generate a target RAW domain noise model. Through the technical scheme, the denoising precision of the target RAW domain noise model is improved, and thus the image definition and the image quality of the RAW domain image are improved.

The invention relates to the technical field of power equipment noise control, in particular to a power equipment active noise reduction method and system based on a noise inversion algorithm. The system collects noise signals through a sensor array arranged around power equipment, multiple rows and columns of noise sensors are arranged around the power equipment to form a three-dimensional monitoring sensor array, each noise sensor collects noise of the power equipment, the noise is preprocessed by a data acquisition unit, and then the noise of the power equipment is processed by a data processing unit. A noise inversion algorithm fusing diffusion posteriori sampling and a reliability theory is adopted to accurately identify the position, the intensity and the spectral characteristics of a noise source, and an optimal noise reduction strategy is generated according to the position, the intensity and the spectral characteristics; the active noise reduction actuator generates and emits anti-phase sound waves in real time according to the inversion result, and efficient noise offset is achieved. According to the invention, linear and nonlinear noise inverse problems can be processed at the same time, and the noise source identification precision and the system adaptability are significantly improved.

The invention discloses a satellite green wave band water bottom trend normalization model inversion water depth aiming at water surface fluctuation, water body suspended matter scattering, non-uniform water bottom substrate reflection and non-linear noise superposition characteristics. According to the characteristics that low-resolution water depth data points are lack of underwater topographic data but can provide the underwater trend and water area satellite images are interfered by noise but have high-resolution remote sensing data, advantages are complementary, noise is suppressed, an underwater trend green band normalized image is constructed, and in combination with priori water depth point data, an underwater trend green band normalized image is obtained. And inverting the parameters of the linear water depth inversion model through a least square method to invert the water depth. Through verification of a Sailey island test area, compared with a single-band model (RMSE = 4.19 m, R = 0.79), a logarithmic ratio model (RMSE = 5.62 m, R = 0.62), a ratio logarithmic model (RMSE = 6.02 m, R = 0.56) and a multi-band model (RMSE = 3.88 m, R = 0.82), the method has the advantages that the RMSE is 1.40 m (the precision improvement rate is 63.9%-76.7%) and the R is 0.97, the water depth inversion precision is improved, and a new technology is provided for navigation safety and engineering construction application water depth acquisition.

A method for monitoring impairment includes: receiving a first received waveform sent by the coherent receiver; obtaining a second received waveform including a first sub-waveform and a second sub-waveform with a relative first delay therebetween according to the first received waveform; obtaining a template at a predetermined location on the fiber link according to the second received waveform, wherein the template is used for representing a nonlinear noise by a first sub-signal waveform and a second sub-signal waveform at the predetermined location that are respectively obtained from the first sub-waveform and the second sub-waveform and have the first delay therebetween; obtaining a correlation between the second received waveform and the template; changing a value of the first delay to obtain values of the correlation corresponding to different values of the first delay; and outputting a value of the first delay corresponding to a maximum value of the correlation as impairment estimation.

This invention discloses a method and apparatus for calculating nonlinear noise in a high-bandwidth optical fiber communication system. The method includes: calculating the SPM noise of a monitoring channel in the high-bandwidth optical fiber communication system; calculating the sum of first XPM noise caused by crosstalk channels with a frequency interval less than a preset frequency threshold on the monitoring channel; calculating the sum of second XPM noise caused by crosstalk channels with a frequency interval greater than or equal to a preset frequency threshold on the monitoring channel, based on relative nonlinear noise figures; and adding the SPM noise, the sum of the first XPM noise, and the sum of the second XPM noise to obtain the nonlinear noise corresponding to the monitoring channel. This method separately calculates the large-interval crosstalk noise component, which is computationally time-consuming but has a small impact on overall noise levels. This improves computational efficiency without significantly affecting computational accuracy, thus addressing the shortcomings of the integral-method GN model.