470 results about "Filter bank" patented technology

The invention discloses a multi-mode counterfeit video detection method based on a multi-head addition cross attention mechanism, and belongs to the technical field of video counterfeit detection. The method comprises the following steps: preprocessing a video stream, decomposing a single-frame positioning face, and extracting an audio to generate a Mel spectrogram slice; the 3D convolutional network extracts video spatio-temporal features and motion differences, and the filter bank extracts audio features in combination with the residual network; audio features are mapped to a video alignment space through asymmetric projection, the video features are subjected to bidirectional interaction with an audio input multi-head addition cross attention module after being subjected to time sequence coding, and audio dominant and video dominant features are generated and are cascaded and fused with original features; and constructing cross-modal similarity loss constraint feature distribution, and fusing feature dynamic weighting and time sequence compression to output four classification probabilities of audio-visual double true, audio-visual double pseudo, video pseudo-audio true and video pseudo-audio pseudo. The multi-mode counterfeiting recognition precision is improved, and texture abnormity and audio and video mismatch features are captured.

The invention discloses a noise signal recognition method based on deep learning. The method comprises the steps of original signal collection, signal preprocessing, noise recognition feature extraction, noise signal recognition model establishment and noise signal intelligent recognition. The invention relates to the technical field of digital signal data processing, in particular to a noise signal identification method based on deep learning, and innovatively provides a self-adaptive signal mode decomposition method based on a combined judgment mechanism of energy threshold value judgment and energy range value judgment. Decoupling and reservation of different frequency bandwidth information in an original signal can be effectively completed, and the recognition accuracy of non-stationary communication noise is improved; a frequency band filter bank constructed based on a Gaussian function is introduced, so that the accuracy of communication signal identification is improved; a method for constructing a noise recognition model by fusing graph neural network structure modeling and an original type reference vector matching mechanism is innovatively proposed, and the recognition robustness of the model to non-stationary communication interference signals is improved.

The invention belongs to the technical field of multimedia security, and particularly relates to a deep forgery detection method and system based on audio and video multi-mode fusion. The method comprises the following steps: extracting lip motion space-time characteristics from a video stream through dynamic ROI (Region of Interest) cutting, and processing an audio stream through fast Fourier transform and a Mel filter bank in sequence to obtain audio spectrum characteristics; reconstructing and generating a corresponding audio feature based on the spatio-temporal feature of lip motion, and fusing the audio spectrum feature and the generated audio feature by adopting a bidirectional cross-modal attention mechanism to obtain an attention fusion feature; a Mel spectrogram of the audio stream is acquired, and feature maps of different scales are extracted from the Mel spectrogram and the video stream by using a feature extraction model formed by a convolutional layer and are fused to obtain multi-scale features; after the attention fusion feature and the multi-scale fusion feature are flattened, performing weighted fusion after channel dimension splicing, and outputting a forgery probability through a multi-layer perceptron. The video detection performance is improved.

The invention discloses a ship equipment vibration test method and system based on frequency domain kurtosis mapping and non-Gaussian signal generation. The ship equipment vibration test method comprises the following steps: collecting an actual ship impact vibration signal and extracting a target power spectral density curve; the method comprises the following steps: identifying an impact energy concentration frequency band based on spectrum energy distribution, carrying out frequency band division by adopting an elliptical filter bank with a second-order section structure, and calculating an envelope kurtosis value of each frequency band to establish a kurtosis-frequency mapping model; after the initial Gaussian driving signal is generated, a non-Gaussian driving signal is generated through a phase randomization and amplitude dynamic scaling algorithm based on a mapping model; a multi-target optimization controller based on Pareto leading edge search is adopted, a power spectral density error and a kurtosis error are taken as optimization targets, synchronous optimization is carried out through fast non-dominated sorting and an adaptive hybrid variation strategy, and a driving signal is output. According to the invention, the broadband and non-Gaussian impact vibration environment of the ship equipment in the actual severe sea condition can be reproduced in a high-fidelity manner, and the authenticity of the test and the fault mode coverage capability are remarkably improved.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention discloses an acousto-optic combined detection and fusion positioning method and device for underground gas leakage, and belongs to the technical field of industrial safety monitoring. In order to solve the problems of high false alarm rate, insufficient positioning precision, poor dynamic environment adaptability and the like of a single sensor in the prior art, the invention provides a multi-modal signal space-time collaborative fusion technical scheme which specifically comprises an acousto-optic heterogeneous sensor, an integrated MEMS ultrasonic array and a tunable laser spectrum module. Microsecond-level synchronous acquisition of acousto-optic signals is realized through a GPS taming clock, and a cross-correlation time delay compensation model is designed to eliminate space-time mismatch errors. And designing a three-stage dynamic fusion framework: improving LMS adaptive filtering to suppress industrial noise, establishing a light intensity attenuation model in combination with dust environment concentration, carrying out multi-modal feature extraction of acousto-optic signals, carrying out signal preliminary fusion, and judging a gas leakage condition. And constructing an acousto-optic Bayesian joint probability model, and calculating the leakage posterior probability. And optimizing positioning parameters based on a double-Kalman filter group, predicting sound wave time delay by forward filtering, and correcting a light diffusion coefficient by backward filtering to realize three-dimensional coordinate solution. Temperature, humidity and dust concentration parameters are fused in real time, a sound velocity calculation model and a light intensity attenuation factor are corrected, and the robustness of complex working conditions is improved. According to the invention, the advantages of acousto-optic signals are combined, errors of various complex environmental factors are further fused and compensated, the overall error is greatly reduced, and the method has the characteristic of dynamic adaptation, and is very suitable for gas leakage detection and positioning in complex environments such as underground environments and the like.

The invention relates to a time domain broadband adaptive beam forming method and system based on Chebyshev weighting, and belongs to the field of array signal processing. Aiming at the technical problems of poor real-time performance and high sidelobe of the existing broadband beam forming, the method comprises the following steps of: dividing a received broadband signal into a plurality of sub-bands, obtaining an optimal weight vector for each sub-band by adopting a minimum linear constraint iterative algorithm after down-conversion, constructing an expected response of an FIR filter according to each weight, obtaining a filter coefficient through L2 norm convex optimization, and finally obtaining a high sidelobe beam according to the expected response of the FIR filter. And performing time domain weighting on each array element signal by using a filter bank, and outputting a directional beam. According to the method, the calculated amount is low, a main lobe and interference null can be formed in real time in a broadband, and a certain side lobe height is kept.

The invention belongs to the technical field of speech recognition and acoustic modeling, and particularly relates to a high-fidelity end-to-end acoustic modeling method combining speech intelligibility reconstruction and autoregressive feedback optimization, which comprises the following steps of: firstly, establishing an intelligibility loss function based on a human speech intelligibility model; subjective definition features of the voice are reconstructed through a frequency spectrum reconstruction network, intelligibility related features are extracted and embedded in combination with a perceptual weighted filter bank, and the intelligibility related features and Mel frequency spectrum features are input into an acoustic encoder in parallel; based on this, by introducing a voice intelligibility reconstruction and autoregressive feedback mechanism, while the conciseness of an end-to-end voice modeling framework is maintained, systematic improvement of voice sharpness, naturalness and stability is realized, and the method can be widely applied to scenes such as intelligent voice assistants, voice transcription, virtual anchors, voice restoration, cross-language voice generation and the like. And the method has extremely high practical application value and popularization prospect.

The invention discloses a wind driven generator large-torque transmission chain resistance increasing control method and system, and relates to the technical field of wind driven generator resistance increasing control. The method comprises the steps that operation parameters of a wind driven generator are collected in real time and preprocessed; calculating a rotation fluctuation frequency set and a corresponding amplitude spectrum of the current time window, and tracking and locking the dominant vibration frequency; constructing a fan state space prediction model containing the torsional vibration state of the transmission chain, outputting the predicted variable quantity of the torque of the transmission chain in the next control period, constructing a filter bank based on the dominant vibration frequency, performing filtering compensation on the predicted variable quantity, and generating an electromagnetic torque given value; and performing phase lead compensation on the electromagnetic torque given value, generating a control instruction, sending the control instruction to the wind power converter, and controlling the wind power generator to output the corresponding electromagnetic torque by using the wind power converter. Through systematic multi-link cooperative processing, the vibration suppression effect and the operation stability of the transmission chain can be comprehensively improved.

Apparatus for retrieving an acoustic room information from a database, comprising: a test fingerprint processor (10) for deriving a test fingerprint from a test acoustic room information; and a database processor (20) for retrieving a reference acoustic room information having a reference fingerprint matching with the test fingerprint, wherein the test fingerprint processor (10) comprises; a filter bank configured for decomposing the test acoustic room information into a plurality of acoustic room information sub-bands; and a parameter calculator (12) configured for calculating a sub-band parameter for each acoustic room information sub-band of the plurality of acoustic room information sub-bands, the sub-band parameter indicating an energy change over time in the acoustic room information sub-band, wherein the test fingerprint is based on the sub-band parameters for the acoustic room information sub-bands.

The invention discloses a micro eye jump detection method and device, near-eye display equipment and a computer readable storage medium. The micro eye jump detection method comprises the following steps: acquiring original data, wherein the original data comprises a time-varying coordinate sequence of an eyeball fixation point; processing the original data based on the filter bank to judge whether micro-eye jump motion exists in the coordinate sequence or not; and controlling the near-to-eye display equipment to execute corresponding operation according to the judgment result. Therefore, noise interference can be effectively avoided, the detection accuracy is improved, the calculation overhead can be reduced, and real-time detection is realized.

The invention provides a ship radiation noise identification method and system based on a double-low-rank adjustment network. A ship radiation noise identification method based on a double-low-rank adjustment network comprises the following steps: performing Mel filter bank transformation on a ship radiation noise signal to obtain a Mel time-frequency spectrogram of the noise signal; inputting the Mel time-frequency spectrogram into a trained noise recognition model, and outputting a target category of the ship radiation noise; the noise recognition model is generated based on a pre-trained reference model containing a plurality of residual blocks, low-rank adaptation is performed on a weight space through a weight low-rank adaptive module, and low-rank adaptation is performed on a feature space through a feature low-rank adaptive module. The method has the advantages that the classification performance of ShipsEar and DeepShip data sets can be remarkably improved under the condition that only 0.55% of parameters are increased, and the method has low overhead and high effectiveness.

The present invention provides a method and system for detecting the switching state of the closing resistor of an AC filter group circuit breaker, which belongs to the technical field of high-voltage AC circuit breakers. The method comprises: loading the closing current recording data of the filter group circuit breaker switching process; performing wavelet decomposition on the closing current recording data of the circuit breaker, obtaining the singular points in the closing current recording data of each layer of the circuit breaker as the starting points of each transient process during the closing resistor switching process of the circuit breaker; performing TLS-ESPRIT decomposition on the recording data of each transient process to obtain the amplitude and attenuation factor of the DC mode, and establishing a corresponding relationship between the amplitude, attenuation factor and the actual resistance value of the closing resistor; fitting two amplitude-resistance curves and two attenuation factor-resistance curves according to the corresponding relationship, as well as the curve function and inverse function of each curve; substituting the amplitude and attenuation factor into the inverse function of the corresponding DC component to calculate the resistance value, and further calculating the number of flashover resistors based on the average resistance value, so as to understand whether a flashover fault occurs.

The invention relates to an underwater acoustic communication and positioning integrated signal design and processing method based on FDMA signal modulation, a medium and a device, and belongs to the field of underwater acoustic communication / positioning. And respectively modulating the integrated baseband signals to a plurality of pre-divided carriers with different center frequencies. Each carrier corresponds to an independent transmitter signal, and the center frequency interval of different transmitter signals is greater than the signal bandwidth. And the modulated signal is transmitted to an underwater environment through the underwater acoustic transducer. In the signal processing process, a band-pass filter bank is used for carrying out frequency separation on received signals, each filter corresponds to the center frequency of a transmitting signal, the separated signals are demodulated and decoded to recover original communication data, and the distance between a transmitter and a receiver is calculated by measuring time delay. And simultaneous communication interaction and high-precision positioning of multiple underwater users are realized.

The invention provides an inter-channel crosstalk suppression method in a multi-channel optical module, which aims at the problems of inter-channel interference and signal overlapping caused by wavelength shift in the multi-channel optical module, calculates the wavelength shift by adopting a proportional integral differential control method through collecting the reading of a temperature sensor and the output wavelength data of a laser device in real time, and finally suppresses the crosstalk in the multi-channel optical module. The threshold value is dynamically adjusted in combination with the environment temperature change, and the interference risk index is quantified; the wavelength drift trend is extracted through time sequence analysis, the channel interval adjustment logic is optimized, the filter bank is dynamically configured to improve the signal purity, meanwhile, the wavelength parameter is corrected through the offset compensation value, and the stability is ensured. If the transmission quality requirement is met, configuration parameters are locked, a correction model is matched and optimized through historical data, potential risks are predicted in combination with state estimation filtering, and the logic adaptability is enhanced and adjusted. According to the invention, efficient correction of wavelength drift and interference minimization are finally realized, and the transmission quality and operation stability of the multi-channel optical module are improved.

The invention relates to the technical field of defect detection, in particular to a corrugated paper defect detection method and system based on machine vision. The method comprises the following steps: acquiring a corrugated paper image and converting the corrugated paper image into a frequency domain; identifying a periodic texture peak point based on amplitude spectrum angle energy distribution, generating a texture suppression filter, and combining with a multi-scale Gaussian high-pass filter to obtain a composite filter bank; the filter bank is used for filtering the frequency domain signals and performing energy weighted fusion; performing inverse transformation on the fused signal to obtain an enhanced image; and segmenting the enhanced image to obtain candidate defects, and verifying by combining the gradient information of the original image to determine a final defect. According to the scheme, the texture background can be adaptively inhibited, the multi-scale defect is enhanced, the false alarm rate is effectively reduced in combination with gradient check, and the detection accuracy and robustness are improved.

The invention discloses a brain-computer interface multi-task fine coding and decoding method and system based on electrical stimulation to induce SSSEP, and the method comprises the steps: in a divergent resting somatosensory stimulation test and a somatosensory attention orientation task, applying different frequencies of electrical stimulation and LED stroboflash to the five fingers of a single hand at the same side by using a multi-mode five-finger stimulator, collecting at least six-lead electroencephalogram signals such as C3, Cz, C4, O1, Oz and O2, and carrying out the detection of the electroencephalogram signals; sSVEP features of O1, Oz and O2 leads are analyzed and extracted through a filter bank and are subjected to weighted fusion, and SSVEP classification probability vectors are obtained; the method comprises the following steps: extracting SSSEP classification feature vectors of C3, Cz and C4 leads through a common spatial mode, performing classification to obtain SSSEP probability vectors, finally performing linear weighted fusion on the two vectors, and taking a maximum element to judge a decoding category. According to the method, multi-cortex areas are activated through somatosensory and visual bimodal neural coding, the decoding precision and response speed are improved, the training period is shortened, and rehabilitation medicine is adapted.

The invention relates to the technical field of broadband signal synthesis and high-speed digital-to-analog conversion, in particular to an oversampling-based real-time frequency interleaving digital-to-analog conversion system aliasing suppression method and system, and aims to solve the problem of spectrum aliasing caused by non-ideal characteristics of a digital sub-band decomposition filter and an analog filter under a real-time architecture of the system. In the invention, the sub-band decomposition is completed by adopting the linear phase complementary filter bank, so that distortion introduced by a transition band of a digital filter is avoided; by restraining system parameters such as passband and stopband frequencies and local oscillation frequency of a sub-band decomposition filter, a protection band is set by an oversampling mechanism, a frequency margin is reserved for a transition band of the filter, and aliasing components are suppressed. According to the method, extraction aliasing of a digital domain and mirror image aliasing of an analog domain are avoided at the same time from the system architecture, the frequency spectrum purity of output signals is improved, the method is suitable for a real-time system for processing continuous data streams, and technical support is provided for achieving a high-performance broadband signal source.

The invention discloses a dynamic reconfigurable signal processing platform based on a ZYNQ architecture, and belongs to the technical field of digital signal processing, and the dynamic reconfigurable signal processing platform comprises a channel preprocessing unit, a high-speed AD sampling unit, a ZYNQ-PL logic unit and a ZYNQ-PS algorithm processing unit. A radio frequency signal is fed in from an antenna end and then enters the channel preprocessing unit, a balun circuit and a filter bank are adopted to carry out preselection filtering, amplification and conditioning on the radio frequency signal, and the signal is extracted and sent to the high-speed AD sampling unit; the high-speed AD acquisition unit converts a received analog signal into a digital signal and transmits the digital signal to the ZYNQ-PL logic unit, and the ZYNQ-PL logic unit is connected with the ZYNQ-PS algorithm processing unit through an AXI bus; wherein the ZYNQ-PL logic unit is composed of a static region and three dynamic regions, and is mainly used for realizing a logic part of a functional waveform and completing reconstruction and loading of the functional waveform; and the ZYNQ-PS algorithm processing unit completes algorithm processing, realizes modulation and demodulation of a functional waveform and realizes a functional algorithm.

The invention discloses a rain detection radar clutter suppression and high-precision echo recovery method and a related device, and belongs to the technical field of weather radar signal processing. According to the method, a non-uniform Doppler filter bank design is adopted, a wide and deep null trap is formed near zero frequency to suppress ground clutters, and meanwhile, a clutter channel of a DFT filter bank is reserved to be used for detecting a low-speed / zero-speed target; self-adaptive cancellation processing is carried out in combination with the dynamically constructed azimuth-distance-velocity three-dimensional clutter map; and finally, recovering a rainfall echo frequency spectrum submerged by clutters through a Gaussian fitting algorithm to realize high-precision estimation of rainfall parameters. According to the method, the problems of insufficient weak rainfall echo detection capability, low-speed / zero-speed target information loss and large spectral moment estimation error of a traditional method under a strong clutter background are effectively solved.

The present invention relates to the field of audio processing technology, and discloses a wireless microphone intelligent control system and method, wherein a wireless microphone intelligent control method includes: collecting multi-point acoustic data through a distributed microphone network to construct a spatial acoustic topology model; constructing an adaptive notch filter group to achieve howling suppression based on the topology analysis results; monitoring environmental changes in real time through a distributed acoustic sensor network to dynamically optimize suppression parameters; integrating a lightweight convolutional neural network on the microphone device to identify acoustic events; and adopting a layered edge computing architecture to achieve low-latency acoustic event processing and howling prevention. The present invention effectively solves the technical problems of unstable howling suppression, weak environmental adaptability and lack of spatial perception in complex environments.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

Aspects of the subject disclosure may include, for example, a digital timing skew compensator, including: a first polyphase adaptive filter bank having a first input and a first output; a first input matrix coupled to the first input of the first polyphase adaptive filter bank; a first subtractor coupled to the first output of the first polyphase adaptive filter bank, wherein the first subtractor removes a first signal generated by the first polyphase adaptive filter bank from digital outputs of sub analog-to-digital converters (ADCs) of a time-interleaved ADC to create a desired signal, wherein the desired signal is coupled to the first input matrix; a modulation matrix that modulates the digital outputs of the sub-ADCs to create a modulated signal vector; a second polyphase adaptive filter bank having a second input and a second output; a second input matrix coupled to the second input of the second polyphase adaptive filter bank; and a second subtractor coupled to the second output of the second polyphase adaptive filter bank, wherein the second subtractor removes a second signal generated by the second polyphase adaptive filter bank from the modulated signal vector to create an error signal, wherein the error signal is coupled to the second input matrix. Other embodiments are disclosed.

The invention discloses an on-line diagnosis method and system based on an intermediate frequency oscillation filter bank capacitor and a medium, and belongs to the technical field of power system equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting the three-phase current intermediate frequency oscillation waveform data of a filter bank branch resistor after a filter bank circuit breaker is opened; performing mathematical fitting on the intermediate frequency oscillation waveform data, and constructing a current attenuation model to extract an attenuation time constant of each phase; the current decay time constant is longitudinally compared with the corresponding historical reference value and transversely compared with the average value of the other two phases; and if the deviation between a certain decay time constant before being equivalent and the historical reference value exceeds a preset threshold value or the deviation between the decay time constant and the average value of other two phases exceeds a proportional threshold value, judging that the phase capacitor corresponding to the current decay time constant is abnormal. According to the invention, unnecessary manual measurement is avoided; by combining a transverse and longitudinal comparison mechanism, the accuracy is high, and the safety and stability of the converter station AC filter bank are improved.

The utility model relates to the technical field of microwave communication, in particular to a cavity filter bank integrated structure, which comprises a main body with a cavity, a top opening of the main body is communicated with the cavity, a cover plate covers the top opening of the main body, and the cover plate is detachably connected with the main body through a plurality of sunk screws. Two tuning modules are integrated in the cavity, the bottom of the main body is provided with an output interface which is inserted with a 0.9 mm signal needle of an external system, the output interface is welded and fixed on the cavity through a customized coaxial connector, and the cover plate is provided with an input interface. According to the utility model, by optimizing the cavity layout and the interface design, high-performance signal filtering under a compact size is realized, and special plug-in requirements are met.

The invention discloses a bearing fault diagnosis method and system based on a multi-source weighted frequency spectrum reconstruction model, and the method comprises the steps: carrying out the Fourier transform of a bearing vibration signal, and obtaining a frequency spectrum; performing weighting processing on the frequency spectrum to generate a weighted entropy spectrum; dividing initial frequency boundary points; iteratively adjusting the initial frequency boundary point to obtain an optimized frequency boundary point; constructing a coupling transfer function; performing iterative calculation to obtain a unified frequency boundary point; constructing a filter bank based on the unified frequency boundary points, and performing sub-band segmentation on each weighted entropy spectrum to obtain a plurality of frequency spectrum sub-bands; overlapping and fusing the corresponding frequency spectrum sub-bands which belong to different weighted entropy spectrums and are in the same frequency range to obtain a reconstructed frequency domain signal; performing inverse Fourier transform on the reconstructed frequency domain signal to obtain a reconstructed time domain signal; performing envelope spectrum demodulation on the reconstructed time domain signal to obtain an envelope spectrum; and determining a bearing fault based on a preset bearing fault type table and the fault characteristic frequency in the envelope spectrum. The method is used for improving the accuracy of bearing fault diagnosis.

The invention belongs to the technical field of extrusion process monitoring, and particularly relates to a cable extrusion process state online monitoring method and system based on acoustic sensing, so as to solve the technical problem of inaccurate cable extrusion process state monitoring in the prior art. The monitoring method comprises the following steps: S1, when a transient impact factor is greater than a preset threshold value, windowing a signal frame by adopting an asymmetric window function with a main lobe in front and a side lobe in back; s2, performing fast Fourier transform on each windowed signal frame to obtain a frequency spectrum of each windowed signal frame; filtering the frequency spectrum of each signal frame by using a filter bank and calculating logarithmic energy of each filtering channel; s3, selecting cepstrum coefficients of which the ratio is greater than a selection threshold to form a process state feature vector; and inputting the process state feature vector into a state classifier to obtain a current cable extrusion process state. According to the method, redundancy and noise interference are eliminated, so that the accuracy of process state monitoring is improved.

The invention discloses a signal reading system and method for superconducting microwave multiplexing device detection, and belongs to the field of superconducting microwave multiplexing device detection, and the system comprises a server which can issue a detection configuration instruction to a signal board, receive and return detection data, operate detection software, execute spectrum analysis and data analysis, and generate a detection result; the signal board is provided with a circuit containing a programmable logic device, and the circuit can output accurate intermediate frequency signals and excitation current signals according to instructions of the server, processes output signals of the detected superconducting microwave multiplexing devices mixed by the frequency mixing board through a critical sampling filter bank algorithm to obtain detection data, and transmits the detection data back to the server; and the frequency mixing plate can perform up-conversion on the intermediate frequency signal generated by the signal plate to radio frequency to form a radio frequency detection signal, output the radio frequency detection signal to each detected superconducting microwave multiplexing device, perform down-conversion on the radio frequency response signal returned from each superconducting microwave multiplexing device to intermediate frequency, and output the radio frequency response signal to the signal plate. The system can detect the quality of the device with low computing resources.

The invention discloses a motor imagery electroencephalogram decoding method based on multi-view space-time convolution and an attention mechanism, which comprises the following steps of: firstly, constructing multi-view data by utilizing a filter bank, and enhancing training data by adopting a frequency band sensing fragment splicing strategy so as to expand sample distribution; secondly, respectively and accurately capturing specific time sequence features and spatial features of different frequency bands through a frequency adaptive convolution kernel and depth separable convolution; meanwhile, in combination with a channel attention mechanism and a time sequence statistics module, feature channel weights are effectively utilized, and a mean value and a logarithmic variance in a time window are calculated, so that the modeling capability of the model for key spatio-temporal information is enhanced. According to the method, the problem of insufficient spatio-temporal feature mining in the motor imagery task is effectively solved, and a competitive result is obtained.

The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.