12 results about "Reference noise" patented technology

The application provides a kind of adaptive gain noise reduction control method and system based on balance detector, it is related to automatic noise reduction technical field.The method comprises first obtaining the original differential current output by balance detector, obtains the amplified current with noise after being amplified by back-end hardware gain, and determines compensation current data.Subsequently, the fluctuation amplitude of amplified current is monitored in real time, and when the fluctuation amplitude exceeds the preset wake-up threshold, the noise reduction process is started: control output compensation current, couple and superimpose it with amplified current to obtain mixed signal; the digital mixed signal is obtained by analog-digital conversion to the mixed signal, and then the preset multi-modal digital filter is used to decouple it, to generate the corresponding virtual reference noise signal in the digital domain, and finally the digital mixed signal and the virtual reference noise signal are digitally subtracted, to obtain the high signal-to-noise ratio target signal.This realizes the cooperative control of gain amplification and noise reduction, solves the problem of bottom noise lifting accompanied by high gain, and improves the signal-to-noise ratio.

The application provides a high-density surface electromyography signal hardware filtering device and method based on FPGA, comprising: an sEMG signal input module for collecting multi-channel noisy surface electromyography signals and at least one reference noise signal; an LMS adaptive filtering calculation module for receiving the noisy surface electromyography signals and the reference noise signal, adopting a fixed-point number operation system and executing an LMS adaptive filtering algorithm through a parallel pipeline structure to output a filtered error signal; a data buffer and output interface module for buffering and outputting the filtered surface electromyography signals; and an FPGA main control module connected with the sEMG signal input module, the LMS adaptive filtering calculation module and the data buffer and output interface module for controlling the work of each module. The application realizes the LMS adaptive filtering algorithm on the FPGA platform, and solves the problems of insufficient real-time performance and large processing delay of software filtering when facing high-density multi-channel sEMG signal processing.

The application relates to a vehicle sealing performance detection method and device, a vehicle, a storage medium and a product. The method comprises the following steps: acquiring original acoustic signals collected by a main microphone array arranged in a to-be-detected area of a vehicle and reference noise signals collected by a reference microphone array arranged in a reference area of the vehicle; performing background noise filtering processing on the original acoustic signals based on the reference noise signals to obtain target acoustic signals; extracting target acoustic features corresponding to the target acoustic signals; determining a feature similarity between the target acoustic features and reference acoustic features corresponding to a current running state of the vehicle; and in a case where the feature similarity is smaller than a similarity threshold, determining a sealing performance detection result of the to-be-detected area according to the target acoustic features. The method can continuously and comprehensively detect the sealing performance of the vehicle during driving.

The invention relates to a pressure sensor and a sensor. The pressure sensor comprises a sensor element (WB) in the form of a Wheatstone bridge and having four piezoresistive resistors (R1, R2, R3, R4), and a reference element (RW) in the form of a reference Wheatstone bridge and having four piezoresistive reference resistors (R5, R6, R7, R8), the reference resistors (R5, R6, R7, R8) being arranged in the same way as the resistors (R1, R2, R3, R4), the pressure sensor being arranged on a single crystal and comprising a cavity closed on one side by a diaphragm, the resistors (R1, R2, R3, R4) being arranged at least partially on the diaphragm, the reference resistors (R5, R6, R7, R8) not being arranged on the diaphragm, the first to fourth resistors (R1, R2, R3, R4) respectively being identical to the fifth to eighth resistors (R5, R6, R7, R8) in that they are constructed in the same way, and the reference Wheatstone bridge (RW) serving as a reference noise source for subsequent signal processing.

The application discloses a continuous monitoring single-arm multi-modal acquisition system and a feature fidelity denoising method, and relates to the physiological signal monitoring technical field.The system comprises an electrocardiosignal acquisition module, a multi-modal signal acquisition module, a communication module, a power module and an upper computer.The method is applied to the system and specifically comprises the following steps: taking a motion signal as a reference input, modeling and adaptively suppressing motion noise in an electrocardiosignal to obtain a signal after preliminary denoising; and inputting the signal after preliminary denoising and reference noise information into a pre-trained reconstruction model to generate a reconstructed signal.The application overcomes the technical problem that it is difficult to balance portability and signal acquisition quality in an existing off-site scene.The application can be widely applied to the physiological signal monitoring technical field.

This application relates to a method, apparatus, and computer device for cross-domain adversarial example detection. The method includes: acquiring an unknown sample set and the signal-to-noise ratio (SNR) of the transmission channel for each unknown sample in the unknown sample set; filtering adversarial examples and samples to be detected from the unknown sample set according to a preset reference noise distribution; inputting the samples to be detected into multiple pre-trained adversarial detection models to obtain multiple adversarial probabilities for the samples to be detected; the adversarial detection models are used to predict the probability that the sample to be detected is an adversarial example, and different adversarial detection models target different types of interference; and obtaining the adversarial detection result of the sample to be detected based on the SNR of the transmission channel of the sample to be detected and the adversarial probabilities of the sample to be detected. This method can improve the accuracy of adversarial example detection.

This specification provides one or more embodiments of an in-vehicle audio equalization method, apparatus, storage medium, and program product. The method includes: acquiring a background noise signal of the current in-vehicle environment and determining the gain difference spectrum of the background noise signal relative to a reference noise signal of a quiet environment; performing track-by-track processing on the currently playing media audio signal to obtain multiple independent audio tracks corresponding to different frequency ranges; for each independent audio track, determining a corresponding gain adjustment amount from the gain difference spectrum based on its frequency range, and applying a corresponding gain adjustment to the independent audio track when the gain adjustment amount meets a gain condition; merging the signals of each independent audio track to output as a processed media audio signal, thereby improving speech clarity and music listening experience while significantly improving the accuracy and naturalness of audio equalization.

The application discloses a kind of steel rail surface damage multichannel magnetic flux leakage detection signal denoising methods, with steel rail surface multichannel magnetic flux leakage signal as input: first, the original magnetic flux leakage signal of each channel is decomposed into several intrinsic mode components using VMD, adaptive separation of noise and damage characteristics is realized, and the component selection method based on distance correlation coefficient is used to remove the component mainly containing noise;Second, the remaining components are denoised by the reference noise elimination algorithm of distance correlation coefficient and normalized energy;Finally, the remaining components after denoising are reconstructed to obtain the denoised magnetic flux leakage signal.The method provides a better denoising method for screening and identifying the magnetic flux leakage signal of rail surface defects, significantly improves the signal-to-noise ratio and reduces the root mean square error, and significantly removes the magnetic flux leakage vibration noise.

This invention discloses a continuous monitoring single-arm multimodal acquisition system and a feature-fidelity denoising method. The system includes: an electrocardiogram (ECG) signal acquisition module, a multimodal signal acquisition module, a communication module, a power supply module, and a host computer. The method, applied to the system described above, specifically includes: using a motion signal as a reference input, performing online modeling and adaptive suppression of motion noise in the ECG signal to obtain a pre-denoised signal; inputting the pre-denoised signal and reference noise information into a pre-trained reconstruction model to generate a reconstructed signal. This invention overcomes the technical problem of balancing portability and signal acquisition quality in existing out-of-hospital scenarios. This invention can be widely applied in the field of physiological signal monitoring technology.

The application provides a DAS signal denoising method, device, medium and product, relates to the technical field of signal processing, and the method comprises the following steps: when a target vibration signal meets an abnormal feature, denoising strategies are executed in turn according to priorities. The execution process comprises the following steps: reference noise data is used to denoise the signal, the denoised signal is input into a neural network classifier, and the maximum classification probability value output by the neural network classifier is used as a denoising score. If the denoising score obtained by a certain strategy exceeds a preset threshold value, subsequent strategies are immediately stopped, and the denoised signal and the classification result corresponding to the strategy are output as the final result. If all the strategies are executed and none of the denoising scores exceeds the threshold value, the signal and the classification result corresponding to the strategy with the highest denoising score are selected as the final result. The scheme solves the technical problem that the denoising effect of a monitoring signal is poor when a spatial reference noise denoising scheme is used for a DAS system that encounters large-scale and continuous environmental noise interference.

This application provides a data processing method, apparatus, device, medium, and product, relating to the field of wireless communication technology. Applied to lightweight RedCap terminals, the method includes: calculating the theoretical reference signal received power when the moving speed exceeds a preset speed threshold or the service demand is identified as an emergency location service; calculating the residual characteristic between the actual measured primary cell reference signal received power and the theoretical reference signal received power; determining the actual noise floor based on the actual received bandwidth, a preset reference bandwidth, and a preset reference noise floor; and inputting the actual measured primary cell reference signal received power, neighboring cell reference signal received power, residual characteristic, the terminal's original signal-to-interference-plus-noise ratio (SINR) measurement, and the actual noise floor into a pre-trained random forest regression model to obtain the predicted distance between the terminal and the primary serving cell base station. The method of this application improves the accuracy of RedCap terminal distance prediction.