7 results about "Amplitude histogram" patented technology

The invention discloses a crop phenotype in-situ analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a field crop image acquired by a multi-view depth camera carried by an unmanned vehicle, performing semantic segmentation on the crop image to obtain a plant region and a background region, and dividing to obtain a plurality of grids; determining a dynamic response threshold value of the grid by adopting a gradient magnitude histogram mode, screening feature points from the grid according to the dynamic response threshold value, calculating ORB descriptors, and combining to form an ORB feature point set of the crop image; three-dimensional point cloud reconstruction is carried out based on IMU data and the ORB feature point set of the multi-view depth camera, and a global dense three-dimensional point cloud map is generated; identifying the global dense three-dimensional point cloud, removing ground points, reserving a crop vegetation point cloud, and carrying out single plant instance segmentation on the crop vegetation point cloud to obtain a point cloud cluster of a single crop plant; and performing phenotypic parameter calculation on the point cloud cluster of the single crop plant to obtain phenotypic parameters.

The embodiment of the application provides a kind of device fault detection method and device based on vibration image, the vibration image of equipment is obtained by image acquisition, after carrying out feature extraction to vibration image, the frequency amplitude two-dimensional histogram of vibration frequency and amplitude value about vibration image is obtained;According to and pre-set through classification convolutional neural network model, the vibration image, frequency amplitude histogram obtained is classified and calculated, and the classification result for vibration image and frequency amplitude histogram is obtained, finally, according to classification result, equipment is judged to be abnormal fault;Around the vibration image feature of improved classification convolutional neural network model analysis equipment, with non-contact information acquisition method, equipment vibration is analyzed, and vibration signal detection means for industrial equipment is expanded.

The invention provides a signal processing method and device for a photoelectric detector, equipment and a storage medium, and the method comprises the steps: extracting amplitude histograms and preliminary motion features of an original signal frame, carrying out the weighted combination of the amplitude histograms according to a partition weighting matrix, obtaining a partition weighting amplitude sequence, and carrying out the calculation of the partition weighting amplitude sequence; performing time sequence analysis on the partition weighted amplitude sequence to obtain a stable amplitude sequence, and inputting the stable amplitude sequence into a preset Gaussian mixture model to perform kernel density estimation to obtain amplitude probability features; according to the first change feature of each sampling point in the digital signal frame, obtaining a third change feature according to the first change feature, the amplitude probability feature and a clustering analysis algorithm; determining a pre-selected sampling time zone according to the third change feature and the partition weighted amplitude sequence; and constructing a probability prediction model according to a preset Gaussian distribution function, the partition weighted amplitude sequence and the third change feature, inputting the pre-selected sampling time zone into the probability prediction model, and outputting signal detection information.

The present application provides a signal processing method, device, equipment and storage medium for a photoelectric detector, the method comprising: extracting an amplitude histogram and preliminary preliminary motion features from an original signal frame, weighting and combining the amplitude histogram according to a partition weighted matrix to obtain a partition weighted amplitude sequence, performing time series analysis on the partition weighted amplitude sequence to obtain a stable amplitude sequence, inputting the stable amplitude sequence into a preset Gaussian mixture model for kernel density estimation to obtain an amplitude probability feature; according to a first change feature of each sampling point in a digital signal frame, obtaining a third change feature according to the first change feature, the amplitude probability feature and a cluster analysis algorithm; determining a preselected sampling time zone according to the third change feature and the partition weighted amplitude sequence; constructing a probability prediction model according to a preset Gaussian distribution function, the partition weighted amplitude sequence and the third change feature, inputting the preselected sampling time zone into the probability prediction model, and outputting signal detection information.

The invention discloses a robust noise floor estimation method based on a histogram, and belongs to the field of electronic reconnaissance, and the method comprises the steps: firstly carrying out the preprocessing of a received radar signal, i.e., A / D sampling and FFT conversion; extracting the FFT data every a plurality of channels, and performing logarithm conversion and amplitude histogram statistics on the extracted channel data; then, carrying out smooth filtering processing to obtain an amplitude distribution envelope of the extracted channel, searching an envelope extreme point, and determining a noise floor estimation value of the extracted channel according to the found local minimum point; and finally, carrying out abnormal value detection, nonlinear conversion and linear interpolation on the noise floor estimation values of the extracted channels, and outputting noise floor estimation results of all channels. The method is insensitive to non-flat noise, missing detection and false alarm in a complex electromagnetic environment are effectively reduced, the calculation complexity is optimized while the detection accuracy is ensured, the data processing pressure of electronic reconnaissance equipment is greatly reduced, and the target signal detection and tracking efficiency is improved.

The invention discloses an SAR image domain false target generation method based on time-frequency rotation, and the method comprises the steps: carrying out the local processing on the 1A-level SLC image data of an SAR, embedding a false target template, and controlling the defocusing degree of a false target in the azimuth direction through the fractional Fourier transform. Fine generation of deception jamming is achieved through fusion of a false target and an image background, it is guaranteed that the amplitude distribution of the false target is consistent with that of a real image through amplitude histogram matching, meanwhile, target phase features are reserved, and the false target close to a real target in the geometrical shape, the defocusing degree and the statistical property is generated in an SAR image. According to the method, histogram matching is carried out by fusing the image background frequency spectrum and utilizing the real target amplitude statistical characteristics, so that the problems that the difference between the false target image after defocusing processing and the real target amplitude characteristics is large and the background splitting is obvious are effectively avoided, and the fineness and the sense of reality of the false target in the image are improved.

This invention relates to the field of optical fiber communication technology, specifically to an efficient optical signal-to-noise ratio (OSNR) monitoring method and system for small sample scenarios. The key technical points include the following steps: dividing the received signal into a test set and a training set; normalizing the received signal and extracting amplitude histogram features; constructing a random forest regression model based on the amplitude histogram features; establishing a mapping relationship between the amplitude histogram features and the OSNR using a small number of labeled samples; inputting the amplitude histogram features of the signal to be tested into the trained random forest model to output the corresponding OSNR estimate. This invention effectively reduces the risk of single-model overfitting under small sample conditions by performing amplitude statistical modeling of the received signal and combining it with the ensemble learning mechanism of random forests, achieving high-precision OSNR estimation without requiring complex feature design or additional hardware support, thus realizing stable and reliable OSNR monitoring in high-speed coherent optical communication systems.