16 results about "Time spectrum" patented technology

The described systems and related test methods provide a significant improvement in test accuracy, as well as a reduction in test time for system-level electromagnetic environmental effects (E3) testing. The systems may include a real-time spectrum analyzer, a network analyzer, and a switching / filtering / coupling network, controlled by an information processing device such as a general-purpose computer. These systems can scan all frequencies at each receiver.

This invention discloses a hardware-software co-processing method for real-time spectral signal processing in Raman microscopy, belonging to the field of spectral signal processing technology. The method includes: establishing strict temporal constraints between physical displacement and cloud inference, and initiating asynchronous communication using the physical gaps in stage movement; simultaneously, deploying a deep learning model (F2P) pre-trained with a self-supervised masking strategy in the cloud. This pre-trained model utilizes the structural correlation between the Raman broadband background and narrowband characteristic peaks to directly perform real-time forward inference on the input single-frame noisy spectrum, achieving millisecond-level high-fidelity reconstruction of a single frame without any complex parameter tuning or the need for a clean control spectrum. This invention completely eliminates the physical lag in multi-frame processing and the general technical bias of supervised learning algorithms, achieving true "synchronization between acquisition and display," and significantly improving the real-time processing performance and cross-device automated deployment capabilities of in-situ Raman imaging.

The application provides a process state real-time correction method based on acoustic characteristics, and relates to the technical field of process control, and comprises the following steps: collecting acoustic signals generated by interaction between a processing medium and a processing unit when the processing unit is executing a processing task; performing real-time spectrum analysis on the acoustic signals to extract characteristic frequencies and energy distribution characteristics thereof which are related to the current physical state of the processing medium; determining whether the current physical state of the processing medium is in an abnormal critical region in real time based on changes of the characteristic frequencies and the energy distribution characteristics thereof relative to a preset reference; the abnormal critical region comprises a first precursor state in which the processing medium tends to be excessively dried or a second precursor state in which the processing medium tends to be excessively accumulated; and if it is determined that the processing medium is in the abnormal critical region, the physical state of the processing medium being currently processed is corrected in real time by adjusting at least one execution parameter which influences the physical state of the processing medium before the processing medium being currently processed reaches a target region.

The application discloses a GPU-based multi-client real-time spectrum monitoring system and a method thereof, and belongs to the technical field of spectrum analysis.The method comprises the following steps: acquiring the spectrum state, behavior information, host state and service quality requirement of a client; calculating the interference intensity coefficient, behavior state coefficient, host state coefficient and service quality coefficient of the client through a corresponding model; dynamically allocating the computing power of each client by using a power distribution model based on the above coefficients; and finally outputting the target refresh rate of each client by a refresh rate optimization model in combination with the allocated power and service quality coefficient.The application solves the problem that the resource allocation in the prior art is rigid and cannot adapt to the differentiated requirements of multi-clients by multi-dimensional state sensing and modeling, realizes intelligent, dynamic and efficient allocation of system computing resources, guarantees the service quality of multi-services, and improves the overall energy efficiency and stability of the system.

This application relates to the technical field of partial discharge (PD) detection, and in particular to a PD signal denoising method and system based on time-frequency domain coordination. The method includes: acquiring a prediction model built on an encoder-decoder framework and training it using a composite loss function; acquiring the PD signal and performing a time-frequency transformation to obtain the time spectrum of the noisy signal, the time spectrum of the noisy signal including the clean PD signal and noise components in the time spectrum; inputting the time spectrum of the noisy signal into the trained prediction model and outputting a corresponding prediction signal mask matrix, and multiplying the prediction signal mask matrix element-wise with the time spectrum of the noisy signal to generate the time spectrum of the denoised signal; and performing an inverse time-frequency transformation on the time spectrum of the denoised signal to obtain the denoised time-domain signal. This application aims to improve the noise suppression effect of PD signals in noisy environments.

This invention discloses a high-frequency signal monitoring method, device, and medium based on real-time spectrum analysis, relating to the field of signal quality control technology. The method includes: acquiring high-frequency signal data and performing time base correction and continuous sequential arrangement to generate high-frequency time-series data; using a filter topology reconstruction algorithm to reorganize the filter structure and reconfigure parameters of the spectral gradient data to obtain a filtered signal, and performing spectrum-driven filtering processing on the filtered signal to generate filtered monitoring data; extracting the target signal from the filtered monitoring data, combining the target signal with the filtered signal, and performing difference separation to generate decomposed signal data; continuously organizing and determining the status of anomaly identification data to obtain anomaly determination data, performing correlation analysis on the anomaly determination data, and generating a high-frequency signal monitoring report. This invention improves the reliability of signal separation and identification.

This invention discloses a signal processing method applicable to various signal characteristics, relating to the fields of communication and radar signal processing technology. By combining the construction of a multi-dimensional signal feature library with real-time spectrum sensing, this invention achieves rapid analysis and accurate characterization of deep-space radar signal characteristics. The feature library is generated based on unsupervised clustering of historical data, covering various signal templates such as transient impulses, periodic coherence, and steady-state noise. It not only provides rich prior knowledge references but also supports online sliding window incremental updates, ensuring that feature representation always conforms to actual environmental changes. The real-time spectrum sensing employs adaptive window time-frequency analysis, with the window length dynamically adjusted according to the signal's instantaneous bandwidth and local stationarity, effectively balancing the contradiction between frequency resolution and time resolution and overcoming the limitations of traditional fixed-window analysis in non-stationary scenarios.

This application relates to the field of speech signal processing technology and provides a line spectrum purification method for purifying the radiated noise spectrum of ships. The method includes: performing time-frequency analysis and preprocessing on the original ship radiated noise time-domain signal to obtain an amplitude spectrum; the amplitude spectrum is a two-dimensional matrix; performing low-rank reconstruction of the amplitude spectrum with rank to obtain a low-rank approximate time spectrum; and applying a rectangular filter kernel to the low-rank time spectrum for guided filtering to obtain a purified line spectrum, wherein the size of the rectangular filter kernel is , where is the time-direction filtering radius and is the frequency-direction filtering radius.

This application discloses an automated and adaptive test sequence generation system for EMI testing, belonging to the field of automation technology. By fusing domain knowledge with real-time spectrum data and test progress data, it generates a domain-specific state representation including interference type identifiers and compliance status parameters. Combined with a rule engine and an online reinforcement learning model, it achieves automated and adaptive generation of EMI test sequences. This method effectively solves the problems of low efficiency and insufficient completeness of traditional fixed test sequences in complex interference scenarios. It can understand the test status in real time, dynamically plan and optimize the test path, thereby ensuring that the testing process is compliant with standards, efficient, and comprehensive.

The present application relates to the technical field of space-time spectrum remote sensing data fusion, and especially relates to a space-time spectrum fusion method for hyperspectral images and multispectral images, which reserves the hyperspectral resolution of a reference time and the multispectral high spatial resolution of a prediction time by preprocessing GF1WFV images and GF5AHSI images and analyzing the spatial resolution of the spectral images and the spectral resolution of the hyperspectral images, constructs a space-time spectrum comprehensive prediction model, obtains a new time-phase high spatial high spectral image, and verifies the new time-phase high spatial high spectral image by using the multispectral image of the prediction time. The present application has the beneficial effect that when a hyperspectral image at a certain time cannot be obtained, the multispectral image and the hyperspectral image at a past time can be used for prediction to obtain a hyperspectral image with high spatial resolution at a required time, thereby solving the problem of data sources and serving numerous hyperspectral application fields.

This invention discloses a low-power FFT chip for real-time spectrum analysis of electroencephalogram (EEG) signals and its usage method. The chip includes an analog front-end module, a digital processing module, an interface and control module, and a low-power management and clock control module. The analog front-end module amplifies, filters, performs power frequency notch filtering, and performs analog-to-digital conversion on the input microvolt-level analog EEG signals. This invention integrates a low-noise analog front-end, a dedicated digital processing module, and a fine-grained power management module into a single chip, constructing a dual anti-interference link that combines analog and digital processing. While ensuring signal purity, the digital processing module accelerates real-time processing speed, and the overall power consumption is controlled through fully custom circuitry and dynamic power management. This solves the three major problems of poor real-time performance, high power consumption, and weak noise immunity in general-purpose processor solutions for EEG spectrum analysis, thus meeting the demanding requirements of wearable devices.

This application provides an adaptive inverse Q-filtering method and system based on time-frequency signal-to-noise ratio (SNR), belonging to the field of oil and gas exploration and development technology. The method includes: acquiring seismic data to be compensated; calculating the synchronous compression transform domain time spectrum of the seismic data to be compensated; calculating the time-frequency SNR of the seismic data to be compensated based on the synchronous compression transform domain time spectrum; calculating the statistical characteristics of the time-frequency SNR of the seismic data to be compensated and mapping them to a preset regularization parameter range to obtain adaptive regularization parameters; and performing inverse Q-filtering on the seismic data to be compensated based on the adaptive regularization parameters to obtain adaptively compensated seismic data, thus completing the adaptive inverse Q-filtering method. This application can solve the technical problems of noise amplification and insufficient effective signal compensation caused by improper selection of regularization parameters in existing technologies.

This invention relates to the field of access control and security monitoring technology, and discloses a method and device for real-time status monitoring of electromagnetic locks supporting three-wire control. The method includes acquiring electromagnetic lock current waveform segments and dry node status data streams, fusing them to form an original signal sequence; performing spectral analysis on the original signal sequence to obtain the frequency distribution characteristics of transient oscillations and steady-state noise; determining the dynamic transition process based on the transient oscillation amplitude, and obtaining the start and end points of multi-stage features; decomposing the steady-state noise frequency distribution characteristics to obtain the time-frequency domain sub-band energy distribution of the noise; determining the interference type based on the time-frequency domain sub-band energy distribution; comparing the interference type with a preset transient interference spectrum pattern library, filtering the original signal sequence to obtain a purified status signal; extracting time-frequency domain features from the purified status signal to obtain real-time spectrum data, and outputting the real-time status monitoring result of the electromagnetic lock. This method can enhance the anti-interference capability of status monitoring.

The application relates to the technical field of electromagnetic spectrum management and control, in particular to a spectrum map construction method based on a dynamic window tensor ring low-rank factor, which comprises the following steps: modeling missing spectrum data by using a tensor; introducing a dynamic window mechanism based on the correlation of space-time spectrum data; solving a target function of the model by using an alternating direction multiplier method; and realizing the completion of the missing spectrum data and the real-time construction of a spectrum map. The application solves the target function of the DW-TRLRF model by using ADMM, converts the optimization problem of the target function into a plurality of sub-problems for solving, outputs target spectrum tensor data after multiple iteration convergences of all observed spectrum data when the motion time of a radiation source is less than or equal to the length of the dynamic window, and only needs to take the observed spectrum data of the length of the dynamic window as the input of the DW-TRLRF algorithm when the motion time of the radiation source is greater than the length of the dynamic window, and the iteration number of the algorithm is 1, so that the spectrum map reconstruction can be quickly realized.

This invention discloses a method for classifying deep carbonate sedimentary cycles based on matched Chirplet compression transform (MCST). The method includes: inputting the seismic signal to be analyzed; calculating the instantaneous frequency and group delay in the short-time Fourier transform domain to obtain the chirp rate function; deriving the IF band center and constructing the time-frequency energy focusing domain in the chirplet transform domain; accurately obtaining the local rate of change of the instantaneous frequency trajectory in the chirplet transform domain by constructing a matched Chirplet rate estimator; redistributing the time-frequency coefficients to the true instantaneous frequency trajectory in the time-frequency energy focusing domain through compression transform, thereby obtaining the time spectrum of matched chirplet compression transform (MCST); applying this method to perform time-frequency characterization of seismic trace data, and classifying deep carbonate sedimentary cycles by analyzing the changing trends of the tuned frequencies in the time spectrum.