11 results about "Histogram method" patented technology

The invention discloses a low-cost rapid test system for an ultrahigh-precision analog-to-digital converter, and the system comprises an upper computer which is used for generating and outputting a test instruction; according to analog-to-digital conversion results output by the to-be-tested ADC under different excitation voltages, differential linear errors and integral linear errors of the to-be-tested ADC are obtained through calculation; the test evaluation board is used for applying different excitation voltages to the to-be-tested ADC according to the test instruction; analog-to-digital conversion results output by the ADC to be tested under different excitation voltages are uploaded to the upper computer; and the power supply module is used for supplying power to the test evaluation board. According to the test system, the requirement that the resolution ratio of the excitation source is higher than that of the to-be-tested ADC is not put forward, and compared with a histogram method, the number of required samples is greatly reduced, so that the economic cost and the time cost can be reduced, and the test efficiency of the ultra-high-precision ADC is improved.

The application discloses a multi-sound source direction-finding positioning method based on a vector microphone in a strong reverberation environment, which comprises the following steps: converting mixed voice signals collected by the vector microphone from a waveform domain to a time-frequency domain to obtain a spectrogram of an observation signal; removing virtual source single time-frequency points by using a frequency smoothing method and iteratively generating low-reverberation self-term source time-frequency point sets; constructing a steering vector dictionary; finding a steering vector most likely to appear in any time-frequency point in the self-term source time-frequency point sets from the steering vector dictionary, and then calculating the STFT coefficient value of the video point corresponding to the source according to the steering vector, determining whether the time-frequency point is a single source time-frequency point not interfered by reverberation according to the STFT coefficient value, and generating a single source time-frequency point set not interfered by reverberation; estimating the number of sources by using a smoothing histogram method; and obtaining multi-source direction-finding results by using MUSIC. The application can accurately extract single source time-frequency points not interfered by reverberation in the time-frequency domain, and then realize accurate multi-source positioning.

The application discloses a passive radar network signal cooperative complementary sorting method and system, wherein the method comprises the following steps: cooperatively complementing mixed alias radiation source pulse sequence signals received by multiple passive radar stations to obtain complemented pulse sequence signals; and sorting the complemented pulse sequence signals by using a cumulative difference histogram method to separate pulse sequences of different radiation sources. Compared with an existing single-station passive radar signal sorting method, the method solves the problem of incomplete pulse sequences caused by pulse loss, greatly improves the correctness of signal sorting, and has better sorting performance, especially when pulse loss is serious.

This disclosure provides a method for consistency correction of elastic parameters of multiple wells considering the influence of burial depth and compaction. The method includes: determining a standard layer within the correction area; obtaining regional correlation by statistically analyzing and trend-fitting the acoustic, density, resistivity, and natural gamma curve data within the standard layer segment; performing histogram correction on the differences generated by the regional correlation; and backfilling the corrected differences to complete the consistency correction of elastic parameters of multiple wells considering the influence of burial depth and compaction. Based on depth trend analysis, this method transforms the original single-point and two-point histogram methods into a depth trend method. This not only improves the problem of histogram correction altering the original well attributes but also solves the problem of "bull's-eye" phenomena in inversion results caused by correction results that do not consider the compaction depth trend. This method can effectively improve reservoir prediction accuracy and has significant application value in the field of oil and gas field exploration.

The application provides a method for extracting submerged vegetation and algal blooms in a river-connected lake based on a Sentinel-2 image, and the method comprises the following steps: acquiring Sentinel-2 image data and performing image preprocessing; acquiring MNDWI, AVI, NDVI, BI, FAI and SABI index images respectively; determining the classification threshold value in the AVI image by using a linear mixing model; determining the classification threshold value in the FAI image and the SABI image by using a maximum gradient histogram method respectively; and taking an empirical threshold value for the remaining classification threshold values; constructing a decision tree classification model based on different index images and corresponding threshold values, identifying water bodies and non-water bodies first, and then performing pixel-by-pixel discrimination on the targets in the water bodies. The method fully considers the characteristics of the river-connected lake, can identify the spatial distribution of the submerged vegetation and the algal blooms in the river-connected lake on a large scale, and has the advantages of a clear classification process, a wide application range and high monitoring efficiency.

The application discloses a student classroom abnormal behavior detection method based on multi-modal data fusion, and the method comprises the following steps: pre-processing student expression data and body data in a classroom video and selecting an interesting area, using an LBP histogram as a classroom student expression feature description, adopting a single-class support vector machine (One-Class SVM) as a classifier to perform expression abnormality detection, using a flow histogram method (HOFO) to calculate a body feature vector in the classroom video stream, using a single support vector machine (One-Class SVM) as a classifier to perform body action abnormality recognition, and finally performing logical decision fusion to obtain a final classroom abnormal behavior detection result. The application adopts a new multi-modal data fusion method, can comprehensively integrate data information of multiple modes, and makes the student classroom behavior detection result more accurate. The application further discloses a detection system for realizing the above method.

A method and device for detecting high-resolution ocean surface physical state based on satellite-borne GNSS-R complex waveform data, the method comprising: acquiring complex waveform data; preprocessing the complex waveform data to obtain a complex waveform data sequence for detecting the ocean surface physical state, dividing the detection complex waveform data sequence into multiple complex waveform groups in chronological order, and determining the coherence coefficient between two consecutive complex waveforms in each complex waveform group; taking the complex waveform data sequence as a test set, selecting 20% of the data in the test set as a training set, determining the threshold value of the average coherence coefficient feature quantity according to the validation data and the training set using the histogram method, taking the threshold value of the average coherence coefficient feature quantity as the demarcation line for judging sea ice and seawater, and obtaining the surface physical state of the detection area determined by the multiple sampling points corresponding to each complex waveform group in the test set according to the threshold value and the average coherence coefficient of each complex waveform group in the test set.

This invention discloses a method and system for predicting ultra-short-term water supply from water plants based on multi-scale temporal features, relating to the fields of water resource management and prediction technology. This invention aims to address the problems of insufficient research on historical water supply data and lack of consideration for multi-scale temporal features in existing water supply prediction methods. First, historical water supply data and related environmental factors from water plants are collected. After preprocessing, the data is divided into training, validation, and test sets. Then, historical water supply characteristic factors and related environmental characteristic factors are selected from a multi-scale temporal perspective to form a feature set. The correlation and potential relationship between the feature set and water supply are verified through correlation analysis, mutual information analysis, and feature histogram methods. Next, seven deep learning models are constructed based on the mean squared error loss function for water supply prediction research, and Bayesian optimization techniques are used for hyperparameter tuning.

The invention discloses a target detection method and system based on an underwater acoustic signal, and the method comprises the steps: dividing the underwater acoustic signal into a high-frequency-band signal and a low-frequency-band signal, carrying out the line spectrum detection of the high-frequency-band signal and the low-frequency-band signal, and extracting the line spectrum feature information; performing azimuth angle estimation on the low-frequency-band signal by adopting a weighted histogram method based on energy search to obtain a vector array multi-target direction finding result; performing azimuth angle estimation on the high-frequency band signal by adopting an adaptive beam forming algorithm to obtain a scalar array direction finding result; filtering a direction finding result, which is close to the direction finding result of the scalar array, in the vector array multi-target direction finding result to obtain a target direction finding result; and performing feature extraction on the low-frequency-band signal corresponding to the target azimuth direction finding result so as to perform identification, classification and judgment on the target corresponding to the low-frequency-band signal. The interference signals can be effectively filtered out, the azimuth information of the target is reserved while the interference signals are removed, the false alarm rate is reduced, and the detection and analysis capability of the weak target is improved.

The invention relates to the technical field of satellite navigation, and discloses a single-antenna satellite navigation signal synchronization method under a carrier high-speed rotation condition. The method comprises the following steps: capturing a navigation signal; tracking the captured navigation signal; and performing bit synchronization on the locked signal in the tracking process by adopting a histogram method. When a satellite signal is received, the rough Doppler frequency offset of the signal is obtained by capturing, then the tracking process is turned to, the Doppler frequency offset is estimated more finely, then bit synchronization can be carried out on the satellite signal, the bit is correctly extracted from the signal, and the normal work of a satellite navigation receiver is ensured.

The application discloses a kind of on-chip histogram circuit applied to SPAD array detection imaging, in which: series-parallel conversion module and decoder module, including shift register and combination logic gate;Signal processing module, through counter and multiplexer control other module operating state and signal input;Mode control module is state machine, controls the working mode of each pixel;Histogram establishment module, peak storage module, through SRAM storage histogram, peak and mode information;Peak detection module, through counter and comparator peak searching is carried out to histogram;Peak correction and output module is state machine, according to the peak correction or peak output of pixel working mode.This application circuit uses scaling histogram method, reduces the storage area required for on-chip histogram, avoids peak value error detection generated by peak value falling at coarse histogram boundary through boundary correction, and increases boundary detection based on traditional scaling histogram, improves frame rate while reducing power consumption.