15 results about "Noise statistics" patented technology

The invention discloses a coherent accumulation measurement method of an S-mode low-power signal and related equipment, and relates to the field of aviation communication signal measurement. Comprising the following steps: performing noise statistics and baseline calibration on digital I / Q sampling data filtered and amplified by an analog front end to obtain a noise mean value and a noise standard deviation; setting a detection threshold value based on the noise standard deviation, and performing sliding correlation on the net signal and the S-mode lead pulse reference template to capture the lead pulse; taking the capturing moment as a starting point, intercepting a preset-frame-length signal sequence covering the S-mode leading pulse and the data segment from the net signal, and distinguishing an S-mode complete frame and an A / C-mode short pulse string through dual-threshold verification; and performing coherent accumulation on the I-path and Q-path sampling points in the complete frame according to a complex number form to obtain a coherent accumulation result, and calculating the average power of the S-mode low-power signal according to the coherent accumulation result. Accurate measurement of an extremely weak S-mode response signal can be realized under a noise background.

The invention relates to an intelligent multi-source navigation method based on inertia and Beidou / visual information fusion, which comprises the following steps: firstly, establishing a multi-source information system of an inertial navigation system, and embedding an IMU (Inertial Measurement Unit) multi-parameter online calibration mechanism based on carrier dynamics in inertial navigation solution; establishing a tight coupling fusion framework taking an INS resolving result as a reference, and constructing tight combination measurement with the original observed quantity of the BDS and constructing re-projection error measurement with the visual feature points by utilizing INS high-frequency pose information; an intelligent adaptive filter based on inertial dynamics error model constraint is adopted, an INS error equation is used as a state prediction model, a Sage-Husa algorithm is introduced to estimate system noise statistical characteristics in real time, estimated error parameters are fed back to INS calculation through closed-loop correction, real-time correction of navigation results is achieved, and the system noise statistical characteristics are estimated in real time. Through deep tight coupling fusion and intelligent adaptive processing, navigation precision and system reliability in a complex environment are effectively improved.

This invention discloses a touch sensing control method, relating to the field of touchscreen technology. The method includes: collecting the capacitance response values ​​of all sensing units on the touchscreen; calculating capacitance mutation based on the capacitance response values ​​and the reference capacitance values ​​of the sensing units; calculating a mutation threshold based on noise statistics in a non-touch state; marking sensing units with capacitance mutation exceeding the mutation threshold as candidate touch points; clustering the candidate touch points using a spatial clustering algorithm, grouping spatially adjacent candidate touch points into clusters; calculating the centroid coordinates of each cluster as the effective touch point position; calculating the capacitance response value offset gradient between the effective touch point and its surrounding neighboring sensing units; identifying false touch points based on the capacitance mutation and offset gradient of the effective touch points, combined with preset high mutation thresholds and high gradient thresholds, and using a compensation algorithm for signal purification; and generating continuous touch trajectories by constructing a cost matrix for trajectory association and matching.

The application discloses a building steel structure weld ultrasonic nondestructive testing and flaw detection method and system, belongs to the technical field of weld detection, and the method comprises the following steps: acquiring weld surface laser point cloud data, calculating local normal direction and converting probe inclination; extracting noise amplitude sequence in a non-welding area, and constructing a noise statistics table; driving an ultrasonic array probe to generate a full matrix echo data set and a plane wave echo data set according to a scanning posture table; mapping into a full focus image and a plane wave superposition image, calculating amplitude difference to generate a fusion weight map; generating a fusion image based on the fusion weight map, performing regional segmentation by using the noise statistics table, and extracting a defect boundary coordinate table. The technical scheme of laser point cloud guided probe posture, dual-mode imaging fusion and adaptive noise segmentation can realize high-precision, automatic identification and objective quantitative evaluation of internal defects of a weld.

The application relates to a quantum perception intelligent key distribution method for an intelligent quantum communication network, which comprises the following steps: inputting quantum channel original observation data into a computer system and extracting a quantum feature vector at each step; stacking the quantum feature vector into a two-dimensional matrix and performing space-time representation through a convolutional neural network; combining a bidirectional long short-term memory network and an attention mechanism to obtain time sequence cleaning features; extracting associated quantum channel features and noise statistics, performing abnormality detection and error correction prediction; inputting both into a proximal policy optimization algorithm to adjust transmission distance and protocol parameters; combining the adjusted parameters to construct a saturated noise channel model, correcting errors, amplifying privacy, and generating a final secure key. The application aims to fully combine quantum feature perception, deep sequence modeling and reinforcement learning optimization mechanism, construct high-dimensional quantum feature mapping, and perform standardization, filtering and abnormal value processing, so that robust feature extraction of quantum channel original observation is realized.

This utility model relates to the field of noise statistics technology and proposes a high-precision noise statistics analysis device, including a fixed tube, a connecting shaft, a mounting plate, a moving block, a connecting pin, a support frame, a protective tube, a first connecting piece, a first shielding piece, a guide block, a second connecting piece, a second shielding piece, and a fixing pin. The connecting shaft is rotatably connected inside the fixed tube, and a mounting plate is installed at the top of the connecting shaft. The moving block is slidably connected inside the mounting plate, and a connecting pin is threaded through the upper surface of the moving block. One end of the connecting pin, which penetrates into the moving block, abuts against the mounting plate. The support frame is installed on the upper surface of the moving block, and a protective tube is installed on the inner side of the support frame. A first connecting piece is installed on the side of the protective tube, and a first shielding piece is slidably connected inside the first connecting piece. This technical solution addresses the problem in the prior art where the receiving head is easily affected by wind when moving through the adjustment mechanism, leading to data deviation.

This paper describes an apparatus (901) for image denoising, comprising a processor (904) for: receiving (701) an input image captured by an image sensor (902); executing a trained artificial intelligence model to: form (702) an estimate of a noise pattern in the input image; form (703) an estimate of at least one noise statistic of the image sensor that captured the input image; refine (704) the noise pattern estimate based on the estimate of the at least one noise statistic; and form (705) an output image by subtracting the refined noise pattern estimate from the input image. A method (800) for training the model is also disclosed herein. Taking into account the noise statistics of the sensor that captured the input image can improve the quality of the denoised image.

The invention discloses a full-spectrum water quality detection method with coupling compensation. The method comprises the following steps: acquiring a multi-source synchronous data packet containing spectral data and physical channel data; processing the data packet, and generating a transient scattering index and an event label; applying a jump Markov state space model, driving noise statistics of the model by using a transient scattering index and an event tag, processing spectral data and generating a transient-free absorption spectrum; and performing parameterized sparse comb demodulation and physical decurling processing on the transient absorption spectrum to generate a decurling absorption spectrum. According to the method, the physical state of the transient event is coupled with the noise statistics, and the problems of interference misjudgment and spectral feature distortion are solved by combining the decurling processing of the physical prior, so that the accuracy and robustness of detection are improved.

The invention discloses an anti-attack windowing Gaussian approximate fusion filtering method under unknown noise statistics. The method comprises the following steps: 1, establishing a multi-sensor nonlinear system dynamic model; 2, establishing a measurement model actually received by a local estimator; 3, designing an anti-attack windowing Gaussian approximation local filter unified framework; 4, calculating, and summing; 5, obtaining an estimator of unknown noise statistics based on a mobile windowing and random weighting estimation method; 6, determining a random weighting factor; seventhly, Gaussian weighted integral involved in the approximate local filtering unified framework is obtained, and local filtering is obtained; and 8, providing an anti-attack windowing Gaussian approximate sequential weighted covariance cross fusion filter in combination with a sequential thought and a weighted covariance cross fusion criterion. According to the method, the accuracy of the filtering performance of the fusion filtering problem of the multi-sensor nonlinear system with attack and unknown noise statistics under the Gaussian approximation filtering framework is effectively improved.

The invention discloses a building steel structure welding seam ultrasonic nondestructive testing flaw detection method and system, and belongs to the technical field of welding seam detection.The method comprises the steps that welding seam surface laser point cloud data is obtained, the local normal direction is calculated, and the inclination angle of a probe is converted; extracting a noise amplitude sequence in a non-welding area, and constructing a noise statistical table; driving the ultrasonic array probe to respectively generate a full-matrix echo data set and a plane wave echo data set according to the scanning attitude table; mapping the image into a full-focus image and a plane wave superposition image, and calculating amplitude difference to generate a fusion weight map; and generating a fusion image based on the fusion weight map, performing region segmentation by using a noise statistical table, and extracting a defect boundary coordinate table. According to the method, the technical scheme of guiding the posture of the probe through the laser point cloud, dual-mode imaging fusion and self-adaptive noise segmentation is adopted, and high-precision and automatic recognition and objective quantitative evaluation of the internal defects of the welding seam can be achieved.

The invention relates to a dynamic system feature extraction method and device and electronic equipment. Comprising the steps that a kinetic equation of a kinetic system about displacement, speed, external force items and random noise is established, and the kinetic equation comprises an external force gain coefficient and a damping coefficient; the external force item is obtained through external force gain coefficient and random excitation calculation; acquiring input characteristics of the dynamic system about time; the input characteristics at least comprise a displacement sequence, a speed sequence, a random excitation sequence and noise statistics of random noise; calculating attention weights of the input features of different time steps through multi-head attention of the large language model; extracting a time sequence feature of the input feature based on the attention weight; the time sequence characteristics are used for solving an external force gain coefficient and a damping coefficient through a large language model, and the external force gain coefficient and the damping coefficient are used for controlling a dynamic system. Through the method, transient motion characteristics of a dynamic system can be captured, a periodic mode can be identified, and the method has the capability of extracting nonlinear interaction.

The invention discloses a nonlinear filtering method and a nonlinear filtering system based on mode matching, which are used for effectively solving the problem of state estimation of additive and asymmetric skew noise in a system model. The core of the method is to construct a mode matching (GeMM) transformation module, through synchronous processing and accurate propagation of three statistics of a mean value, a covariance and a mode of state distribution, and through matching by using closed skew normal (CSN) distribution, skew characteristics are accurately described. The specific implementation comprises the steps of system modeling and filter initialization; in the prediction stage, a prior mean value and a prior covariance are calculated by adopting a traditional moment propagation method, and a prior mode is directly and accurately calculated according to a mode propagation theorem; in the updating stage, the prior statistic, the observation noise statistic and the actual observation value are input into GeMM for transformation, the transformation finally obtains the mean value, the covariance and the mode of posterior condition distribution by constructing CSN distribution of the statistic matched with the joint variable, and state estimation is completed. The method provided by the invention can be used as an enhanced module seamless embedded extended Kalman filter (EKF) or unscented Kalman filter (UKF) framework, the estimation precision in a skew noise environment is remarkably improved, excellent calculation efficiency and robustness are kept, and the method has a good engineering application prospect.

The invention relates to a terahertz medical image denoising method based on an SE module and a DnCNN neural network, and the method comprises the steps: carrying out the simulation generation of a simulation noise which is similar to the statistics of an observation noise for an observed first noise-containing image, defining a second noise-containing image as a noise-containing image added with the simulation noise, inputting the noise-containing image into a DnCNN-SE model, carrying out the noise reduction processing, and training an NAC strategy of a self-supervision network, thereby achieving the denoising of a terahertz medical image. And training a DnCNN-SE model by taking a mean square error MSE between the denoised image and the first noisy image as a loss function, and directly carrying out denoising processing on the original noisy image y to obtain a final denoised image. An external clean sample is not needed in the process, an original noise image is regarded as a training target, and additional noise is superposed on the original noise image to serve as network input. By simulating the noise process, the model learns the general denoising capability, and the noise level does not need to be estimated in advance. And the SE module adaptively emphasizes channels with rich information and inhibits channels dominated by noise through extrusion and excitation operations, so that key information of the medical image is better reserved.

The invention discloses a diffusion time step self-adaptive derivation method based on a potential space PSNR (Potential Signal to Noise Ratio), which comprises the following steps of: under an extremely low bit rate condition, taking compression potential as a condition, adaptively determining a starting time step of diffusion back-stepping by utilizing the potential domain PSNR, and introducing noise to count matching loss in a training stage, so as to obtain the diffusion time step of the diffusion back-stepping. And empirical statistics of sample differences are aligned with theoretical diffusion noise statistics, so that the perception quality and stability of reconstruction are improved while the code rate bpp is ensured. A diffusion time step matched with the PSNR is deduced in a self-adaptive mode according to the PSNR of potential space calculation, noise characteristic matching loss is constructed through theoretical noise statistics corresponding to the time step, joint optimization is carried out on the noise characteristic matching loss and main loss, and therefore the statistical characteristics of compression / condition noise and diffusion noise are aligned in the training process; reconstruction degradation caused by mismatch is relieved, and the reconstruction quality and stability under the extremely low bit rate are improved.

An optical coherence tomography (OCT) system comprises an interferometer configured to interfere a test beam reflected from a specimen with a reference beam to produce an interference pattern. The OCT system also comprises a spectrometer configured to measure a spectrum of the interference pattern to produce measurements of intensities of the interference pattern corresponding to different wavelengths. The OCT system further comprises a processor configured to determine noise statistics of the intensities of the interference pattern and use the noise statistics to set a threshold for detecting a number of layers of the specimen penetrated by the test beam at the location based on a pre-specified probability of false acceptance of noise as a signal. The processor is further configured to determine the profilometry measurements as an estimate of depths of the layers of the specimen causing the intensities of the interference pattern above the threshold.