87 results about "Frequency map" patented technology

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention discloses a feature fusion processing method for anesthesia depth multi-modal data, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining a multi-modal physiological signal and an electromyographic signal of a patient; performing time axis calibration on the physiological signal to generate an alignment signal; extracting a multi-modal feature vector and calculating an anesthesia depth index; performing deviation analysis on the basis of the electromyographic signal and the index to obtain an electromyographic response deviation index; performing graphical feature mapping on the real-time electroencephalogram signal to generate a real-time time-frequency map; when the deviation index exceeds a safety threshold value, performing graph pattern matching with a pattern template library to calculate a similarity score; and outputting the current anesthesia state mode in a classified manner. According to the method, a multi-modal signal graphical feature fusion technology is adopted, and a dynamic map generation and pattern matching mechanism is combined, so that the problem of complex pattern recognition of physiological signal graphic data can be solved, and the accuracy and timeliness of anesthesia state classification are improved.

The invention provides a steel shell concrete interface void detection system and method, and relates to the technical field of civil engineering structure health detection, and the system comprises a mobile scanning unit, an excitation unit, an infrared imaging unit, a signal processing and acquisition unit and a comprehensive analysis terminal. The method comprises the following steps: controlling a mobile scanning unit to carry out step-by-step impact excitation and signal acquisition so as to obtain a frequency diagram, and synchronously sampling by using an infrared thermal imager so as to obtain a thermal analysis diagram; and then binarizing the two images respectively, fusing the two images by adopting a noise suppression algorithm to generate a comprehensive image, and judging that the interface is void according to the fused image. According to the method, the high sensitivity of the impact echo method to the layering defect and the accurate description capability of the infrared thermal imaging method to the defect shape are fused, so that nondestructive, efficient and high-precision detection of the steel shell concrete interface void is realized, and the misjudgment and leak detection risks of a single method are effectively reduced.

The invention discloses a radar target template signal establishment method based on depth model adaptive segmentation, mainly relates to the technical field of template signals, and is used for solving the problems that a target contour is easy to fracture or false detection by clutters is easy to cause when a time-frequency map is subjected to binary segmentation by a simple threshold slice in the prior art. And the threshold parameter is very sensitive to the signal-to-clutter ratio and the environmental change. Comprising the following steps: reading a one-dimensional radar echo sequence, and mapping the one-dimensional radar echo sequence to a two-dimensional time-frequency domain to obtain a time-frequency image; multi-scale features are obtained, and a matrix is prompted; obtaining a binary mask corresponding to the mask feature through the multi-scale feature and the prompt matrix; determining the binary mask corresponding to the highest confidence score as a final mask; screening time-frequency transformation data corresponding to the radar echo sequence by using the final mask to obtain output data; and recovering the output data into a time domain signal by using inverse short-time Fourier transform, and taking the time domain signal as a target template signal.

The invention relates to the technical field of signal processing, and provides a hidden weak signal feature recognition method and system, and the method comprises the steps: carrying out the time-frequency transformation of a to-be-recognized signal, and generating a time-frequency diagram; inputting the time-frequency graph into the feature recognition model, and extracting multi-scale features from the time-frequency graph through a backbone network; sending a feature map with the highest semantic hierarchy in the multi-scale features into a convolution attention module, and sequentially executing channel attention weighting and space attention weighting in the convolution attention module to obtain an enhanced feature map; fusing the enhanced feature map and other scale features in a feature fusion layer to obtain a fused feature map; and based on the fused feature map, identifying a weak signal through a detection head. Compared with the prior art, the method has the advantage that the recognition accuracy of weak signals in communication signals is greatly improved.

The invention relates to the field of wind power generation system fault diagnosis, in particular to a wind power generation system fault diagnosis method and system based on multi-source data fusion. The method comprises the following steps: acquiring an original vibration signal in a wind power generation transmission system, and preprocessing the original vibration signal to obtain a two-dimensional frequency diagram of an original domain data sample; constructing a residual network based on separation attention mechanism optimization as a classifier, and obtaining a migration diagnosis teacher model for a target domain through a conditional domain adversarial migration method; a mask information entropy knowledge distillation strategy is adopted, an information entropy graph output by the teacher model is calculated and multiplied by a target area mask, and refined knowledge is migrated to a lightweight student model; and inputting the to-be-tested data sample of the target domain into the lightweight student model to obtain a diagnosis result. The invention aims to provide a wind power generation system fault diagnosis method and system based on multi-source data fusion, and solves the problems that fault data of a wind power generation transmission system is scarce and model calculation resources are limited.

The invention discloses an unmanned aerial vehicle radio frequency signal identification method based on a deep prototype network, and the method comprises the following steps: 1, data preparation: collecting radio frequency signals between different types of unmanned aerial vehicles and a remote controller, generating and preprocessing a time-frequency graph, and dividing the time-frequency graph into a training set and a test set; 2, a model construction and training step: constructing a deep prototype network GLF-SAProNet fusing multi-scale cavity convolution and adaptive attention feature enhancement as a feature encoder, and training and testing the model by using the training set and the test set to obtain a trained recognition model; and 3, an identification application step: preprocessing a to-be-identified unmanned aerial vehicle radio frequency signal through the data preparation step, inputting the to-be-identified unmanned aerial vehicle radio frequency signal into the trained identification model, and outputting a model classification result of the to-be-identified unmanned aerial vehicle radio frequency signal. According to the method, the multi-scale cavity convolution and the adaptive attention feature enhancement are fused, and the small sample learning normal form is utilized, so that high-precision and robust recognition of the model of the unmanned aerial vehicle under the condition of limited samples is realized.

The invention relates to an information processing apparatus, a method of controlling the information processing apparatus, a program product, and a storage medium. The information processing apparatus includes: an obtaining unit that obtains an image in which a work area and an object are captured; a first detection unit that detects a working area from the image; a second detection unit that detects an object region from the image; a frequency map generation unit that generates a frequency map on the basis of the number of times of detection of the object region of each grid in the working region; a heat map generation unit that generates a heat map in a case where a frequency map is caused to correspond to the working area detected by the first detection unit; and a display control unit that superimposes and displays the heat map at a position based on the working area on the display device.

This invention relates to the field of target recognition technology, and in particular to a radar target recognition method and system based on micro-Doppler sensing attention. First, a micro-Doppler time-frequency map is generated based on the radar echo signal. Second, the micro-Doppler time-frequency map is input into a lightweight backbone network for feature extraction to obtain an initial spatiotemporal feature map. Third, a dual attention mechanism is used for the first residual compression feature extraction to obtain a first-stage feature map. Then, the first-stage feature map is input into a max-pooling layer for feature downsampling to obtain a downsampled feature map. Next, a dual attention mechanism is used for the second residual compression feature extraction to obtain a second-stage feature map. Finally, the second-stage feature map is input into a classification output module for classification calculation to obtain the radar target recognition result. This invention achieves high-precision target classification under strong background noise by introducing a time-axis integral pooling mechanism and a channel attention mechanism that conform to the physical laws of incoherent accumulation of radar signals.

This application relates to a terahertz video synthetic aperture radar moving target imaging method based on time-frequency analysis. The method includes: constructing a radar echo model of the moving target based on instantaneous range and radar echo; processing the radar echo model by performing a short-time Fourier transform on the compressed echo signal; obtaining the Doppler center frequency of the scene echo from the obtained time-frequency distribution map; performing Doppler frequency zero-padding, range migration correction, and azimuth processing on the compressed echo signal according to a pre-set function; observing the range cell position of the moving target from the obtained imaging results; and extracting and performing time-frequency analysis on the moving target signal. After determining the Doppler center frequency of the moving target through time-frequency analysis and the time-frequency map, velocity estimation is performed; and the image of the moving target is refocused based on the characteristics of the time-frequency distribution line of the moving target echo. This method can quickly achieve velocity estimation and image refocusing of moving targets.

This invention discloses a signal detection method based on an improved YOLOv5. The method includes: 1. Performing a short-time Fourier transform on the received signal to obtain its time-frequency map, and then converting the time-frequency map to grayscale to construct a signal detection time-frequency map dataset; 2. Introducing the CBAM module into the classic YOLOv5 to improve the feature extraction capability of the deep learning network; 3. Replacing the NMS algorithm in YOLOv5 with the WBF algorithm to improve the accuracy of the final predicted bounding box; 4. Using an improved Focal-EIoU loss function to enhance the influence of high-quality prediction results during the training process; 5. Training the improved YOLOv5 network model using the Adam optimizer and the signal time-frequency map dataset; 6. Inputting the time-frequency map of the signal to be detected into the trained network model to obtain the signal detection result. This invention is the first to propose using an improved YOLOv5 network model to detect target signals present in received broadband data. This method is simple and practical, achieving high signal detection performance with low complexity, and has pioneering significance for the application of deep learning networks in signal target detection.

The invention specifically relates to a radar target identification method based on attitude angle division, and the method comprises the steps: obtaining the actual measurement data of a plurality of types of targets, each target sample comprising a time-frequency map, the real-time distance, azimuth angle and pitch angle of a target relative to a radar, and the distance, azimuth angle and pitch angle of the target in a previous time slice; calculating a course angle based on measured data; the method comprises the following steps of: preliminarily setting and dividing angle areas by utilizing a known course angle and a pitch angle on the basis of target micro-Doppler characteristics embodied by a time-frequency spectrum, and constructing a time-frequency spectrum data set of various types of targets in different angle areas; constructing a data set by using convolutional neural network training, then verifying classification precision, and judging whether a neural network model needs to be retrained by continuing an iterative division method or not according to an identification result; and determining a division boundary, training a neural network model, and finally inputting a time-frequency spectrum of a target to finish accurate classification. According to the method, the classification precision of radar target recognition is improved.

The invention relates to the technical field of high-voltage circuit breaker fault identification, and discloses a high-voltage circuit breaker fault identification method, system and device and a medium, and the method comprises the steps: converting a multi-state vibration signal of a high-voltage circuit breaker into a multi-state signal time-frequency diagram; performing data expansion on the multi-state signal time-frequency diagram, and dividing the multi-state signal time-frequency diagram into a training set and a test set; extracting a time-frequency graph feature vector of the training set, and performing parameter optimization on the fault classifier according to the time-frequency graph feature vector to obtain an optimized classification model; calculating verification model parameters according to the optimization classification model; and constructing a target fault prediction model according to the verification model parameters, performing fault prediction on the to-be-diagnosed high-voltage circuit breaker signal by using a target fault diagnosis model, and calculating a corresponding feature importance thermodynamic diagram and a target fault evaluation factor. According to the method, the accuracy of target fault category prediction can be improved, the fault reason is directly positioned through the feature importance thermodynamic diagram, and accurate identification of high-voltage circuit breaker fault detection is realized.

This invention proposes a soft measurement method for the internal state of a ball mill based on DEM simulation and acoustic signals. The method includes: constructing a DEM simulation model based on the physical parameters of a laboratory ball mill and performing iterative calibration; under different operating parameters, acquiring acoustic signals from the laboratory ball mill experiment and obtaining corresponding internal state data from the calibrated simulation model to collaboratively construct a soft measurement training dataset; generating paired noise reduction module training data through continuous wavelet transform, time-domain superposition, and pairing processing; and training the noise reduction module and the soft measurement module based on this data to achieve noise reduction processing of noisy time-frequency maps and accurate prediction of the internal state variables of the ball mill. This invention uses internal state variables such as filling rate, collision energy distribution, and material particle size distribution extracted from DEM simulation as training labels, fundamentally solving the problem of limited soft measurement accuracy caused by insufficient label accuracy in existing methods.

This invention discloses a method for identifying unipolar and bipolar depression based on deep learning-based multi-state EEG fusion, comprising: Step 1, performing continuous wavelet transform on EEG signals in open and closed states respectively to obtain open-eye time-frequency maps and closed-eye time-frequency maps; Step 2, using a deep learning model to extract features from the open-eye and closed-eye time-frequency maps obtained in Step 1 to obtain feature vectors for open-eye and closed-eye states; then, fusing the feature vectors in open-eye and closed-eye states to obtain a multi-state fused feature vector; finally, using a deep learning classification network to classify and identify the multi-state fused feature vector to obtain the identification result, thus completing the identification. This invention uses a deep learning model based on EEG signals to perform three-class classification identification of unipolar depression, bipolar disorder, and healthy individuals, improving classification performance.

The invention relates to the technical field of smart power grids, and discloses a multi-node harmonic coupling feature recognition method based on a graph convolutional neural network, comprising the following steps: step S101, calculating to obtain an amplitude type edge weight spectrum surface; step S102, constructing a sharp point geometric evidence; step S103, calculating to obtain a gating adjacency matrix; step S104, calculating to obtain a continuous score; step S105, calculating to obtain a smooth score; and step S106, using the global maximum value of the smooth score as an identification result, and forming a positioning triple. According to the method, through joint convolution of line graph gating and a frequency graph, time-frequency positioning of a strongest harmonic influence path in a power distribution network is realized, and a single quantization result and corresponding position and time are given; and converting a double-peak-to-single-peak merging event into a learnable edge-level signal by using a sharp point geometric evidence, and ensuring that the output is unique by using a global extreme value.

The invention provides an intelligent diagnosis device and method for internal defects of concrete. The intelligent diagnosis device comprises an electromagnetic exciter, an accelerometer, a multi-sensor array, a synchronous trigger circuit and a data processing unit, the electromagnetic exciter drives the firing pin with the specific mass, so that the impact energy and frequency are adjustable, the electromagnetic exciter adapts to different concrete strengths and thicknesses, and transient impact on the surface of the concrete is achieved. The accelerometer is used for directly measuring stress wave signals generated and returned by impact; the multi-sensor array comprises an acoustic sensor, a thermal infrared imager, a positioning and distance measuring module and a control and data acquisition unit, and the thermal infrared imager records the full-field temperature change of an impact point and a surrounding area to form a thermal image series; the control and data acquisition unit is internally provided with an embedded system and is responsible for controlling triggering of the electromagnetic exciter and synchronously receiving data of the accelerometer and the thermal infrared imager; and the data processing unit converts the acoustic signal into an acoustic time-frequency graph and converts the thermal image sequence into a time sequence thermal characteristic graph.

This invention relates to a method and system for identifying individual open-set radiation sources based on space-frequency domain fusion and Hybrid-EVT, belonging to the field of radiation source identification technology. It addresses the problems of frequent occurrences of unknown emission sources and the failure of the closed-set hypothesis in radiation source identification under complex electromagnetic environments. The method includes: acquiring raw IQ signals, preprocessing them to obtain a time-frequency map; extracting features from the time-frequency map based on the SFFNet model to obtain a discriminant activation vector; training the SFFNet model and a classification head based on samples of known radiation source categories, and calculating the mean activation vector for each category; constructing a Hybrid-EVT hybrid extremum model based on the training set activation vectors; inputting test samples into the SFFNet model with frozen parameters to obtain the activation vector of the test samples; evaluating the tail probability of the distance between the test samples and candidate categories, introducing unknown channel probabilities; if the unknown confidence level meets preset conditions, it is determined to be an unknown radiation source; otherwise, the corresponding known category is output. This invention is applicable to scenarios involving enhanced security for wireless device authentication.

The present application relates to a kind of frequency hopping method of avoiding co-channel interference, comprising: step S1, in the available frequency band range, plan several carrier frequencies, and these carrier frequencies are divided into first carrier group and second carrier group;Wherein, the number of planned carrier frequency is twice the number of user to be scheduled;Step S2, with two adjacent time slots as a group, the frequency hopping value corresponding to time slot in each group is respectively mapped to the carrier frequency in the first carrier group and the carrier frequency in the second carrier group;Step S3, in each time slot, produce a frequency hopping value for each user to be scheduled in the time slot after a specified number of time slots in advance, obtain several to be adjusted frequency hopping value;Step S4, to the to-be-adjusted frequency hopping value obtained is adjusted, so that the to-be-adjusted frequency hopping value is not mutually different;Step S5, in the no time delay scene, according to the frequency hopping value adjusted in step S2, frequency hopping communication is carried out;In the time delay scene, according to the carrier frequency mapped to in step S4, frequency hopping communication is carried out.

This invention belongs to the field of electromagnetic interference detection technology for the BeiDou Navigation Satellite System, and discloses a method, system, device, and medium for identifying BeiDou multi-domain characteristic interference signals. This invention constructs a two-stage step-by-step identification strategy combining time-frequency maps and fourth-order cumulant maps. In each stage, an interference signal identification model based on a lightweight multi-scale network is built. In the first stage, based on the time-frequency map and the interference signal identification model, highly distinguishable interference types are identified. For easily confused interference signals that cannot be identified in the first stage, the second stage, based on the fourth-order cumulant map and the interference signal identification model, improves the accuracy of identifying easily confused interference types, ultimately improving the overall identification accuracy for complex interference. Simultaneously, the introduction of the lightweight multi-scale network effectively solves the problems of insufficient key feature capture and high computational cost of multi-scale structures in existing neural network technologies.

The present application relates to the technical field of terahertz pulse generation of optoelectronics, and discloses a terahertz field generation method based on two-color field phase locking, comprising: through optical system building, using a beam expander to amplify the fringe spot in the optical path. The frequency map and phase map of the amplified spot are obtained, and the periodic frequency point is extracted. The phase change information of the periodic frequency point is analyzed, compared with the expected phase change threshold, and it is judged whether the position of the piezoelectric ceramic needs to be adjusted. If the phase change information is within the threshold range, it is determined that no adjustment is needed; otherwise, the position of the piezoelectric ceramic is adjusted and moved. Through accurate control of the phase of the optical field, the stability of the pulse is improved, the overall structure is simplified, and the cost of the terahertz magnetic field generation is reduced.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and a convolutional neural network (122) configured for outputting a predetermined number of deblurred magnetic resonance images (126) that are slices of a deblurred magnetic resonance imaging data set in response to receiving a set of partially deblurred magnetic resonance images for each of the slices. The execution of the machine executable instructions causes a computational system (104) to: receive (200) the set of partially deblurred magnetic resonance images; receive (202) the predetermined number of deblurred magnetic resonance images in response to inputting the set of partially deblurred magnetic resonance images for each of the slices into the convolutional neural network; calculate (204) a set of difference images (128) for each of the slices by calculating a difference between the deblurred magnetic resonance image and each of the set of partially deblurred magnetic resonance images; and calculate (206) a determined B0 inhomogeneity map (130) for each of the slices by fitting a smooth manifold to B0 values determined from the set of difference images, the documentation frequency map, and the assigned demodulating frequnecy for each of the set of difference images.

This invention belongs to the field of high-voltage equipment monitoring and processing technology, and discloses a method and system for data enhancement of oil discharge impact signals based on hybrid time-frequency convolution. The method performs two-dimensional processing on the acquired oil discharge impact signals to obtain a two-dimensional time-frequency map; the two-dimensional time-frequency map is used to train a generative adversarial network (GAN); the GAN includes an encoder, a decoder, and a discriminator; the trained decoder is used as a generator to obtain a generated time-frequency map, thereby achieving data enhancement of the oil discharge impact signal. This invention significantly improves the fidelity of the time-frequency features of the generated samples while enhancing the oil discharge impact signal data, and can also achieve fault identification of discharge types.

The application relates to a radar multi-component signal separation method based on an optimized time-frequency distribution, and relates to a radar multi-component signal separation method. The application aims to solve the problems that most of the existing IF estimation algorithms assume that radar signal components do not cross in the time-frequency domain; the radar components may be tracked after the intersection point, error switching occurs, the frequency extraction precision is reduced under the influence of noise in the low signal-to-noise ratio condition; and the current signal overlapping area is still disturbed by the cross term, resulting in distortion of the recovered signal and a large error between the original signal. The process is as follows: 1, obtaining an instantaneous frequency estimation; 2, obtaining an instantaneous frequency estimation; 3, detecting a straight line segment in each block; 4, obtaining a corrected instantaneous frequency estimation; 5, obtaining an instantaneous frequency diagram; 6, determining a time-varying filter; and 7, radar multi-component signal separation. The application is used in the field of radar multi-component signal separation.

The present application relates to the field of seismic signal processing, and particularly relates to a seismic signal denoising method, a storage medium and an electronic device. The method comprises: obtaining a time-frequency feature map corresponding to a real part and an imaginary part of each signal component in a seismic signal to be denoised. The time-frequency feature maps corresponding to three signal components are input into a SEQNet model to generate a seismic signal time-frequency map after denoising of the seismic signal to be denoised. In the present application, a multi-scale feature attention mechanism is introduced between the feature layers of the encoder and the decoder through the skip connection part in the SEQNet to improve the extraction capability of the context features in the seismic signal. The MFA can capture the details of different time periods and frequency components in the signal by processing the seismic signal at multiple scales, and enhance the adaptability of the model to complex noise. Further, the SEQNet can better maintain the integrity of the signal and reduce the interference of noise on the signal when processing complex and diverse seismic signals.

The invention discloses a privacy-enhanced image compression method, which comprises the following steps: constructing a semantic-frequency map based on frequency domain features and sensitive masks, and positioning a semantic unit set; performing spectral domain structure adjustment on the frequency domain features, performing entropy alignment generation in combination with an entropy model, and obtaining adjusted frequency domain features, parameterizing semantic differences and dual compensation; reversible dual decomposition is applied to the adjusted frequency domain features, and public potential features and dual potential features are obtained; an entropy model is utilized to compile the public potential feature entropy into a main bit stream, and the dual potential feature, the parameterized semantic difference and the dual compensation joint entropy are compiled into a safe bit stream; and generating a protected head containing access control information in combination with the access key, and packaging the main bit stream, the security bit stream and the protected head into a container code stream. According to the method, machine semantic recognition can be effectively shielded at a common end, meanwhile, an authorization end is supported to accurately recover semantics at low code rate cost, and multi-level access control of the same code stream is achieved.

The application belongs to the technical field of equipment part fault diagnosis, and discloses a time-frequency diagram fault diagnosis method and equipment based on multi-scale feature fusion and incremental learning, which comprises the following steps: (1) adopting old category data as training data, training an initial neural network to obtain a model ModelZ capable of classifying faults corresponding to the old category data; (2) dividing new category data into a predetermined batch, and then training the model ModelZ based on incremental learning to obtain a model ModelX(k) capable of identifying the new category data and a model ModelY(k) capable of classifying and diagnosing faults corresponding to the new category data and the old category data; (3) inputting a real-time time-domain signal of an equipment to be tested into the model ModelX(k) after converting the real-time time-domain signal into a two-dimensional time-frequency image, so as to obtain current old category data and new category data, and then the model ModelY(k) realizes fault category classification. The application has high efficiency and accuracy.

The invention discloses an HFCT discharge pulse identification method based on an EWT-CNN algorithm, and relates to the technical field of electrical equipment partial discharge detection, and the method comprises the following steps: S01, carrying out the Fourier transform of a partial discharge pulse signal, and obtaining a frequency diagram; s02, based on a plurality of maximum value points obtained in the frequency diagram, determining the maximum value points and then arranging the maximum value points in sequence from large to small; s03, obtaining a corresponding number of maximum value points according to a preset decomposition number; s04, obtaining a frequency spectrum segmentation point by adopting a mode of taking an envelope minimum value from two adjacent maximum extreme points; and S05, decomposing the signal according to an EWT algorithm to obtain components. According to the method, important information existing in the discharge pulse can be effectively extracted, the inaccuracy of manual feature extraction is effectively avoided by depending on the powerful feature extraction and self-learning function of the CNN in the subsequent feature extraction, and the recognition accuracy of the discharge pulse signal is improved.

An example method includes storing reconstructed electrophysiological data sets for at least two baseline time intervals and at least one modulation time interval. The electrophysiological data sets are representative of electrophysiological signals reconstructed at locations across a respective surface of interest within a patient's body based on non-invasively measured electrophysiological signals for respective time intervals. The modulation time interval can overlap with modulating an arrhythmia or arrhythmogenic activity. The method can also include computing dominant frequency maps from the electrophysiological signals reconstructed for at least two baseline data sets and the at least one modulation data set. The method can also include analyzing the dominant frequency maps to determine an indication of dominant frequency stability across at least a portion of the surface of interest.

A non-contact motion physiological sensing method and a motion physiological sensing radar. The non-contact motion physiological sensing method is executed by a processor in a signal processing device, comprising: obtaining a digital signal; obtaining a phase map and a vibration frequency map according to the digital signal, the phase map presenting an energy distribution varying with a distance relative to the motion physiological sensing radar and a phase variation, and the vibration frequency map presenting an energy distribution varying with a distance relative to the motion physiological sensing radar and a vibration frequency variation; selecting at least one candidate position with an energy intensity exceeding an energy threshold from the vibration frequency map; selecting a target position from the candidate positions; obtaining one or more target phase data according to the target position; and inputting the one or more target phase data into a machine learning model to obtain a physiological parameter prediction result. The non-contact motion physiological sensing method and the motion physiological sensing radar can accurately sense the physiological parameter in the motion state of the subject in a non-contact manner.