56 results about "Spatial noise" patented technology

The invention discloses a method, equipment and medium for automatically testing the sensitivity of a broadband radio frequency system, and relates to the technical field of broadband radio frequency testing, and the method comprises the steps: starting a testing process, and coordinating a plurality of calibration equipment and equipment to be tested in a wireless network in the same testing time slot, synchronously sampling spectrum data of each calibration device through an air interface and processing the spectrum data into a noise distribution parameter set; and dynamically adjusting the output power of the vector signal generator and synchronously monitoring the bit error rate change according to the received power predicted value until the difference value between the bit error rate and a preset target bit error rate threshold value meets a convergence condition, recording the received power value during convergence as a sensitivity test result, and generating a test report. By generating the high-precision dynamic noise map and calculating the compensation transmitting power calibration value, accurate sensing and active compensation of the space noise of the test environment can be realized.

The invention relates to the field of array signal processing, and discloses an underwater target azimuth estimation method based on spherical harmonic noise model sparse reconstruction, which comprises the following steps of: calculating array output data to obtain a sample covariance matrix, establishing a spherical harmonic noise model, expressing spatial noise power density by adopting a finite-order spherical harmonic function, determining an initial power spectrum, and estimating the azimuth of an underwater target according to the initial power spectrum. Initializing the spherical harmonic noise model to obtain a noise covariance matrix, determining an expected model covariance matrix according to the noise covariance matrix, performing peak search on the initial power spectrum to obtain an estimated orientation, and refitting based on the estimated orientation to update the noise covariance matrix and the expected model covariance matrix; the initial power spectrum is subjected to sparse reconstruction based on the expectation model covariance matrix and the sample covariance matrix before updating to obtain an updated power spectrum, when the difference between the updated power spectrum and the initial power spectrum meets the convergence condition, the peak value of the updated power spectrum is searched to determine the target orientation, and super-resolution and robust orientation estimation is achieved.

The invention belongs to the technical field of earphones, and provides earphone equipment and an earphone noise reduction method. According to the method, the multi-channel environmental noise signals are collected through the multi-microphone array of the earphone equipment, the multi-microphone array comprises n microphones of different types, and the earphone equipment can obtain the multi-channel environmental noise signals through the noise signals collected by the multiple microphones; then extracting and fusing spatial features and spectrum features in the multi-channel environmental noise signals to generate a fused feature vector; inputting the fusion feature vector into a deep learning model to separate a target voice signal and an environmental noise signal; and finally, determining the type and azimuth information of the environmental noise signal, and setting a target noise reduction parameter according to the type and azimuth information of the environmental noise signal, so that the spatial noise can be accurately positioned, the full-band deep noise reduction of the earphone equipment is realized, and the requirements of people on high-quality noise reduction in a complex noise environment are met.

The invention discloses a magnetic field inversion current method based on near-field scanning, and the method comprises the steps: obtaining magnetic field data in a circuit, and taking the magnetic field data as input; expanding the measurement plane of the magnetic field to obtain a magnetic field matrix, and enabling the current of the original magnetic field matrix H on the boundary to be continuous; constructing a relationship between the magnetic field and the current in the frequency domain space; a Tikhonov regularization method is used to reconstruct current distribution, and a GCV method is used to optimally select regularization parameters required by the Tikhonov regularization method; extracting structure information of the static current model, taking the structure information as a mask, and acting the structure information on a radio frequency current model result; and outputting the current in the original measurement window as the current reconstructed by the final method. Current artifacts on a boundary are eliminated by expanding a measurement plane, current distribution is automatically solved through regularization, errors caused by empirical selection of filtering parameters are avoided, the precision of reconstructed current is improved, and finally, spatial noise is suppressed through a mask method, and accurate reconstruction of radio-frequency current is achieved.

The spatial noise sub-path is used for carrying out adaptive filtering on an image block through frequency domain disturbance and texture statistics so as to effectively suppress tunnel background noise; the motion blur repairing sub-path is used for completing blur kernel estimation of the image block through feature residual reconstruction and repairing vehicle motion blur; the brightness re-adjustment sub-path is used for repairing the uneven brightness phenomenon of the image block through a piecewise gamma function; the contrast enhancement sub-path is used for enhancing the marginal definition of the image block through nonlinear reconstruction and improving the quality of the image in the tunnel weak light environment; besides, an enhanced perception module is integrated in the detection model, the enhanced perception module comprises a structure guiding normalization unit, a local brightness deviation calibration unit, a multi-head space attention unit and an edge semantic fusion unit, and the multi-head space attention unit comprises a multi-receptive-field branch subunit and a cross channel fusion subunit. The precision of the detection model is improved, and the precision of detecting the vehicle position and the vehicle appearance is further improved.

The invention relates to the technical field of data processing, in particular to an efficient monitoring and intelligent irrigation management platform for radar rainfall data. Obtaining a first abnormal point location and a non-initial abnormal point location according to a data volume feature in an intensity data cluster of the echo intensity and a data difference feature of the intensity data cluster; obtaining a point location connected domain according to the intensity data cluster of the non-initial abnormal point location; obtaining a spatial noise feature value according to the position distribution feature between the point location connected domains and the data difference feature in the point location connected domains; obtaining a time noise characteristic value according to the historical echo intensity of the non-initial abnormal point location; obtaining a second abnormal point location according to the spatial noise characteristic value and the time noise characteristic value; correcting the corresponding echo intensity according to the position features of the first abnormal point location and the second abnormal point location; and the rainfall is determined according to the corrected echo intensity, and the irrigation rule is adjusted, so that the accuracy of rainfall judgment and irrigation regulation is improved.

This invention discloses a lightweight watermarking method for robust image attribution based on latent space diffusion models. The method uses low-dimensional feature vectors as the watermark carrier: first, a feature watermark library is constructed; then, a feature-noise encoder and a noise-feature decoder are trained. The encoder maps the feature watermark to initial latent space noise that satisfies a standard Gaussian distribution and inputs it into the latent space diffusion model to generate an image. When attribution verification is required, the image to be verified is inverted to recover the initial latent space noise, and the corresponding feature watermark is reconstructed by the decoder. The identity is determined using the cosine similarity between the reconstructed watermark and the original watermark in the feature watermark library. This invention maintains the quality of the generated image while exhibiting strong robustness to common distortions such as JPEG compression, cropping, random discarding, blurring, and brightness variations, and significantly reduces the overhead of watermark storage and matching computation.

This application discloses a method and system for estimating the direction of arrival (DOA) of weak targets based on Riemannian manifold background suppression and adaptive sparse Bayesian learning. The method includes: acquiring time-series signals using a sensor array to construct a series of sample covariance matrices, mapping them to a point sequence on a Hermitian positive definite matrix manifold space; iteratively calculating the background interference covariance matrix using the non-Euclidean geometric properties and logarithmic shielding effect of the Riemannian metric; mapping the background interference covariance matrix back to Euclidean space, adaptively performing background subtraction based on an energy decision mechanism to reconstruct a positive definite covariance matrix to be measured; inputting the covariance matrix to be measured into a sparse Bayesian learning framework, first iteratively recovering the signal power through adaptive mesh refinement sparse Bayesian learning, then performing a closed-loop iteration of subspace noise cleaning while keeping the mesh fixed to recover the sparse spatial spectrum of the target signal; and finally, using local analytical interpolation techniques to eliminate mesh quantization errors and calculate the precise DOA of the target.

The present application relates to the technical field of electron-optical imaging, and particularly relates to a backscattered electron scintillator detector device and a detection method, which comprises a backscattered electron receiving sensor, an optical condensing group, an optical transmission group, a photoelectric conversion system and a detector protection mechanism. By using a scintillator as a sensor, a backscattered electron signal generated by an industrial electron gun electron beam is received, and then the electronic information carried by the backscattered electron is converted into an optical signal and transmitted to an area far away from the workpiece. Finally, a high signal-to-noise ratio voltage analog signal is formed by the photoelectric conversion system, avoiding the influence of spatial noise and other interference signals on weak signal transmission in the existing analog signal transmission process. For detailed topographic features, a backscattered electron image with high resolution can be displayed.

The application discloses a rPPG signal extraction network based on a CBAM attention mechanism and a bidirectional Mamba, and a non-contact physiological monitoring method and device, wherein spatial noise is filtered by introducing a CBAM attention mechanism, long time sequence dependence is modeled at low cost by using a Mamba architecture, and signal purity is improved by combining a frequency domain and a confidence gate. The rPPG signal extraction network effectively extracts weak skin color change features through an adaptive differential fusion mechanism of the fusion backbone network, realizes long time sequence global modeling through a Mamba architecture (with linear calculation complexity) in the space-time encoder, performs double cleaning on the features in the spatial domain and the frequency domain through a confidence perception gate module and a frequency domain gate module, and finally reconstructs a high-fidelity rPPG waveform through the bidirectional Mamba regression head.

Embodiments of the present application disclose a hyperspectral image clustering method and device, electronic equipment and storage medium. The method comprises: extracting a first feature vector from a pixel set corresponding to a pixel in a hyperspectral image through a trained feature extraction network model, wherein the feature extraction network model comprises a convolutional autoencoder (CAE) and an asymmetric autoencoder; and clustering the hyperspectral image according to the first feature vector to obtain a clustering result of the hyperspectral image. The method extracts the first feature vector through the feature extraction network model constructed based on the CAE and the asymmetric autoencoder, can weaken the spatial noise in the first feature vector, effectively improves the accuracy of feature vector extraction, and on this basis, further clusters the hyperspectral image using the first feature vector obtained after optimization, can further remove part of the spatial noise in the first feature vector, thereby improving the accuracy of the clustering result.

The application provides an image editing method and system based on a text-to-image large model. The method includes inputting text describing a source image and an edited image and a mask of a target editing area by a user, using the text-to-image large model to edit the image, and performing optimization processing on a latent space noise graph within a preset time step in a latent space noise graph denoising process. The optimization processing includes: first, inputting the latent space noise graph of the current step and the text prompt into a noise prediction network; then, extracting a cross-attention graph and a self-attention graph generated in the noise prediction network, and calculating cross-attention consistency constraints and self-attention reservation constraints; second, calculating gradients of the cross-attention consistency constraints and the self-attention reservation constraints on the latent space noise graph through back propagation, respectively, and updating the latent space noise graph using an adaptive time step scheduler. The application can balance the fidelity and editing effect of the target image.

The utility model relates to the technical field of noise acquisition, and discloses a near-field noise acquisition cover which comprises an acquisition cover and an expansion cover, the acquisition cover is of a conical cylinder structure, the radius of the top end of the expansion cover is smaller than that of the tail end, and the tail end of the acquisition cover is connected with the top end of the expansion cover to form a large conical cylinder structure. The outer sides of the collection cover and the expansion cover are sleeved with a first sound insulation layer and a second sound insulation layer respectively, the edges of the first sound insulation layer and the second sound insulation layer extend to the outer sides of the collection cover and the expansion cover respectively, and the first sound insulation layer covers the outer side of the top of the expansion cover and makes contact with the outer wall of the expansion cover. Compared with the prior art, the noise collecting cover is composed of the collecting cover body and the expanding cover body, the noise collecting distance can be prolonged through the expanding cover body, noise in a narrow space can be collected only through the collecting cover body, the structure can be adjusted according to use scenes, and practicability is higher.

Provided herein may be an image processing device and an image processing method. The image processing device may include a motion manager configured to generate movement information of an image sensor based on pixel values of a first frame image received from the image sensor, pixel values of a second frame image received from the image sensor prior to the first frame image, and velocity information of an inertia sensor configured to sense a velocity of the image sensor, a similarity map generator configured to generate a similarity map indicating a weight of a noise removal filter based on the pixel values of the first frame image, and a noise remover configured to remove temporal and spatial noise based on pixel values of the first frame image to which the noise removal filter is applied based on the similarity map and pixel values of the second frame image.

A noise-resistant integrated communication ranging and phase measurement method comprises the following steps: generating two squared signals from the reference and measurement electrical signals of the heterodyne interferometric communication ranging integrated optical system; mixing the two squared signals with sine and cosine signals generated by an FPGA to obtain four sine and cosine mixed signals; calculating the phase of the difference frequency signal between the two complex exponential signals generated by the mixed signals using a full-phase FFT algorithm; and subtracting the phases of the two difference frequency signals to complete the phase measurement. This invention solves the problems of excessive logic resource consumption and ineffective suppression of spatial noise in FPGAs. It can suppress the severe interference of communication signal modulation on phase measurement in displacement measurement while reducing the use of logic resources in the FPGA, achieving a 2×10⁻⁶ phase ratio at a 50dB signal-to-noise ratio. ‑ 4 Phase measurement accuracy in rad.

The application provides a spatial noise annoyance modeling method based on binaural psychoacoustic parameter difference, and the method uses the binaural psychoacoustic parameter difference as a bridge, establishes a spatial noise annoyance model based on noise self annoyance by using multivariate linear regression, and realizes correction of noise self annoyance in the sound source direction, so that the annoyance of noise in different directions is more reasonably and accurately predicted, the use range of the noise annoyance model is widened, the physical mechanism of the spatial noise annoyance is explained, and the application value of the noise annoyance model is enhanced.

The present invention provides a video generation method and system based on a large-scale text-based video model. Based on the descriptive text of multiple subjects in the generated video, their corresponding actions, and backgrounds, as well as the bounding boxes of the multiple subjects in the generated video in space, the large-scale text-based video model is used to generate videos for multiple subjects, especially those with different actions. The method first inputs the current step's latent space noise map sequence and text description into a noise prediction network; then extracts a cross-attention map sequence, and uses it to calculate spatial position constraints and grammar-aware contrast constraints; finally, backpropagation is used to calculate the gradient of the attention adjustment constraint with respect to the latent space noise map sequence, and this gradient is used to update the latent space noise map sequence. The method can achieve the effect of aligning the subjects and actions in the generated video while ensuring consistency between the generated video and the descriptive text.

The present invention proposes a method and system based on dual-relationship temporal alignment and adaptive region optimization. The method comprises: obtaining a video and a question prompt text, extracting features from the video using a visual encoder to obtain visual features, extracting features from the question prompt text using a text encoder to obtain text features, performing global average pooling on the visual features, and calculating cosine similarity with the text features to obtain a set of correlation scores between the visual feature representation and the text features of each frame, and screening based on the correlation scores between the visual feature representation and the text features of each frame to obtain a set of highly correlated frames and a set of moderately correlated frames. The present invention introduces a dynamic spatial sampling module to extract the maximum connected region from the candidate heat map region associated with the prompt to eliminate scattered interference and enhance the compactness of the spatial feature expression, thereby greatly reducing the interference of spatial noise.

A camera may capture reflected light from the surface of the wafer during a semiconductor process that adds or removes material from the wafer, such as an etch process. To accurately determine an endpoint for the process, a camera sampling rate and light source intensity may be optimized in the process recipe. Optimizing the light source intensity may include characterizing light intensities that will be reflected from the waiver using an image of the wafer. Pixel intensities may be used to adjust the light source intensity to compensate for more complex wafer patterns. Optimizing the camera sampling rates may include nondestructively rotating a view of the wafer and converting the sampled intensities to the frequency domain. The camera sampling rate may be increased or decreased to remove spatial noise from the image without oversampling unnecessarily. These optimized parameters may then generate a clean, repeatable trace for endpoint determination.

The invention belongs to the technical field of radio frequency fingerprint identification, and provides a radio frequency fingerprint identification method and system based on a feature latent space diffusion model in order to solve the problem that the identification rate of an existing method is reduced in a low signal-to-noise ratio environment. The method comprises the following steps: after receiving a radio frequency signal, firstly carrying out classification pre-training on a system, and updating parameters of a feature extraction module and a classification module; then, carrying out submerged space diffusion pre-training, training a noise time step prediction module and a submerged space diffusion noise prediction module based on a feature extraction and classification module, and optimizing parameters in combination with feature reconstruction, time step prediction and classification loss; and finally, systematic fine adjustment is carried out, and all modules are integrally optimized. And repeatedly training until the neural network converges, and outputting an optimal network parameter for testing. According to the method, the robustness and the accuracy of radio frequency equipment identification under the condition of low signal-to-noise ratio are effectively improved by combining feature extraction and submerged space noise suppression.

The application discloses a kind of multi-vehicle coordination 3D target detection method and system based on radar data, method includes: obtaining the local environment information of main vehicle and each coordination vehicle, and obtains radar data by adding noise;The radar data of coordination vehicle is shared to main vehicle, and radar data is converted to the coordinate system of main vehicle;Radar data is converted into pseudo image, and the feature of coordination vehicle and main vehicle is extracted;The feature of extracted coordination vehicle removes time and space noise;Make the feature of coordination vehicle and main vehicle interact fusion;Generate the three-dimensional target detection frame of surrounding environment;Through calculating loss function, the target detection model is cyclically optimized, and the 3D target detection of multi-vehicle coordination is completed.The application embodiment can improve the precision and robustness of target detection, so that automatic driving system can identify target in real time, accurately in dynamic, complex road conditions, enhance the safety of automatic driving system, and can be widely applied in intelligent traffic technology field.

The invention provides a self-adaptive integrity monitoring system for a low-altitude city canyon, and the system carries out the real-time modeling of non-Gaussian noise in the city canyon through a Gaussian mixture model (GMM), achieves the high-precision state estimation through unscented Kalman filter (UKF), and provides a high-quality innovation sequence. The method comprises the following steps: dividing observation subspaces based on a satellite elevation angle and a carrier-to-noise ratio, dynamically updating each subspace noise model by adopting an online learning algorithm, feeding back the optimized model to a filter and an integrity monitoring module, and finally calculating more reliable horizontal and vertical protection levels based on a self-adaptive GMM and a filter covariance matrix. The system forms'estimation-monitoring-learning 'closed-loop optimization, effectively solves the problems of fault detection performance reduction and protection level conservative caused by noise model mismatch of a traditional autonomous integrity detection system, and remarkably improves the positioning precision, integrity and availability in a complex urban environment.

Embodiments are disclosed for spatial noise filling in multi-channel codecs. In an embodiment, a method of regenerating background noise ambience in a multi-channel codec by generating spatial hole filling noise comprises: computing noise estimates based on a primary downmix channel generated from an input audio signal representing a spatial audio scene with background noise ambience; computing spectral shaping filter coefficients based on the noise estimates; spectrally shaping the multi-channel noise signal using the spectral shaping filter coefficients and a noise distribution, the spectral shaping resulting in a diffused, multi-channel noise signal with uncorrelated channels; spatially shaping the diffused, uncorrelated multi-channel noise signal with uncorrelated channels based on a noise ambience of the spatial audio scene; and adding the spatially and spectrally shaped multi-channel noise to a multi-channel codec output to synthesize the background noise ambience of the spatial audio scene.

This invention provides a system and method for estimating and suppressing spatial noise using machine learning support. [Solution] The noise estimation and suppression method includes the steps of: estimating the probability of speech and noise for each band of the input audio signal using a machine learning classifier; estimating a set of averages of speech and noise or a set of averages and covariances of speech and noise based on the probability and microphone covariance over the bands using a directional model; estimating the average and covariance of noise power based on the probability and power spectrum using a level model; determining a first noise suppression gain based on the directional model; determining a second noise suppression gain based on the level model; selecting the first noise suppression gain or the second noise suppression gain or their sum based on the signal-to-noise ratio of the input audio signal; and scaling the time-frequency representation of the input signal by the selected noise suppression gain.

The application discloses a kind of multi-modal information fusion based on pulse neural network emotion recognition method, belong to artificial intelligence, brain-like intelligence and biomedical signal processing technical field.The purpose of the present application is to introduce mutual mode dynamic threshold adjustment and collaborative coordinate attention mechanism, improve the accuracy, robustness and energy efficiency of multi-modal emotion recognition, realize the efficient alignment and adaptive deep fusion of heterogeneous physiological signals in pulse domain based on pulse neural network multi-modal information fusion emotion recognition method.The application respectively inputs each mode signal into the pulse encoder with the same structure and parameter-configurable to extract aligned features, inputs the mutual attention output of two directions after splicing into collaborative coordinate attention module to generate joint pulse gating map, and finally performs selective gating and fusion on enhanced features.The application effectively eliminates the inherent spatial noise and time-domain artifacts in physiological electrical signals, while ensuring the sparsity of pulse features, greatly improving the information density of the fused features.

The application discloses a kind of magnetic field inversion current methods based on near-field scanning, including steps: obtaining magnetic field data in circuit, and as input;The magnetic field is extended measurement plane, and the magnetic field matrix is obtained, so that the original magnetic field matrix H is continuous on the current of boundary;The relationship between magnetic field and current in frequency domain space is constructed;Using Tikhonov regularization method to reconstruct current distribution, and using GCV method to optimize the regularization parameter required by Tikhonov regularization method;Extract the structural information of static current model, use it as a mask, and apply it to the result of radio frequency current model;Output the current in the original measurement window as the current reconstructed by the final method.The application eliminates the current artifact on the boundary by extending the measurement plane, automatically solves the current distribution by regularization, avoids the error caused by empirical selection of filter parameters, improves the accuracy of reconstructed current, and finally suppresses spatial noise by mask method to realize accurate reconstruction of radio frequency current.

In observation of a distant object with a telescope or the like, it is required to remove noise in a visible light region such as atmospheric turbulence. Although a method using SPI has been proposed as a method for removing noise due to atmospheric turbulence, the resolution of observation in the visible region is low and atmospheric turbulence has not been removed, although remote monitoring is barely possible with the current SPI.SOLUTION: In order to solve the above problem, optical signals are acquired using a DMD and a plurality of photodetectors, and in a correlation process of imaging using the optical signals, optical correlation signals are acquired from selective wavelengths of the optical signals from the plurality of photodetectors, thereby simultaneously acquiring reconstructed images in a wide wavelength band. By mounting the DMD, the plurality of photodetectors, and the correlation process of this configuration on an imaging device or a magnifying optical device, it is possible to perform imaging instead of adaptive optics for suppressing spatial noise. As a light source, a reconstructed image is acquired for a wide range of wavelengths such as visible light, infrared light, and ultraviolet light.SELECTED DRAWING: Figure 1

The application is suitable for the technical field of noise monitoring, and provides a noise monitoring method and system based on multi-modal fusion and a storage medium. The method comprises the following steps: in response to detecting a preset trigger signal, collecting environmental data of an indoor site; the environmental data comprises at least one of the following: effective noise data, contact vibration data, non-contact vibration data and three-dimensional point cloud data. According to the three-dimensional point cloud data, a three-dimensional structure model of the indoor site is constructed; according to the three-dimensional structure model of the indoor site, the effective noise data, the contact vibration data, the non-contact vibration data and a sound transmission law, noise propagation path data is generated; the sound transmission law is a physical law of noise propagation in a building solid structure or an air medium. The effective noise data and the noise propagation path data are subjected to compliance review, and a qualification recognition compliance report is generated. The three-dimensional positioning of full-space noise can be realized, and the noise sound source of the upper floor, the lower floor, the left neighbor and the right neighbor can be accurately locked.

The application discloses a kind of broadband radio frequency system's sensitivity automatic test method, equipment and medium, it is related to broadband radio frequency test technical field, including, starting test procedure, coordinate multiple calibration equipment and to be measured equipment in wireless networking in same test time slot, through air interface synchronous sampling the spectrum data of each calibration equipment and processing into noise distribution parameter set;According to the predicted value of receiving power, the output power of vector signal generator is dynamically adjusted and the change of bit error rate is synchronously monitored, until the difference of bit error rate and preset target bit error rate threshold meets convergence condition, the receiving power value when converging is recorded as sensitivity test result and test report is generated.The application can realize accurate perception and active compensation to test environment space noise by generating high-precision dynamic noise map and calculating compensation transmission power calibration value.

The application relates to the field of array signal processing and discloses an underwater target direction estimation method based on spherical harmonic noise model sparse reconstruction. Sample covariance matrices are obtained by calculating array output data, a spherical harmonic noise model is established, spatial noise power density is represented by using limited order spherical harmonic functions, an initial power spectrum is determined, the spherical harmonic noise model is initialized to obtain a noise covariance matrix, and an expected model covariance matrix is determined based on the noise covariance matrix. A peak value of the initial power spectrum is searched to obtain an estimated direction. The noise covariance matrix and the expected model covariance matrix are updated based on the estimated direction. The initial power spectrum is sparsely reconstructed based on the expected model covariance matrix before updating and the sample covariance matrix to obtain an updated power spectrum. When the difference between the updated power spectrum and the initial power spectrum meets a convergence condition, a peak value of the updated power spectrum is searched to determine a target direction. The application realizes super-resolution and robust direction estimation.