The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The m...The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The main frequencies of the first class ships are less than 120 Hz, while the second class ships drop in 130 Hz -- 320 Hz. The different relationship between w1 and w2 corresponds to different bispectrum graph. There are the same results in the trispectrum. The feature vector is consist of the wls which correspond to the maximum bispectrum B(wl, wl) and the maximum trispectrum B(wl, w1,wl) respectively, the al, w2 which correspond to the maximum bispectrum B(wl, w2).展开更多
This series of papers deal with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. Based on the studies of a large amount of ship radiat...This series of papers deal with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. Based on the studies of a large amount of ship radiated-noise data, which has been collected from actual ships on the sea, effectively recognizable features are extracted. Such features include line-spectrum features, stationary and nonstationary spectrum features as well as rhythm features. Finally the categorization are tested by unknown samples on the sea, including 33 surface vessels, 8 underwater vessels in 30 operating conditions. Methods for memorization and classilication are also explored in the project. Paper (Ⅲ) is the thirird in the series. It deals with the extraction method of modulation information in double-frequency power spectrum and the establishment of pattern plate of double-frequency spectrum as well as average power spectrum. To extract features from double-frequency spectrum, the tendency of wave is subtracted from the wave of each channel and the modulation of high frequency is compensated. The modulation degree of lines is shown by relative Value and converted to fuzzy value by fuzzy function. The pattern-plate of double-frequency spectrum memorises stable line and its respective modulation strength. The pattern-plate of average power spectrum memorizes the spectra mean of typical samples and the standard variance展开更多
The publisher regrets that the abstract for Research Highlight were repeated twice in the original version.However,there should be no abstract for Research Highlight type.The publisher would like to apologise for any ...The publisher regrets that the abstract for Research Highlight were repeated twice in the original version.However,there should be no abstract for Research Highlight type.The publisher would like to apologise for any inconvenience caused.展开更多
Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adap...Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adaptability,high latency,and excessive power consumption for deployment.Here,we propose an orbital angular momentum(OAM)-mediated optoelectronic neural network(OOENN)that integrates a diffractive optical module for OAM spectrum feature extraction with a shallow electronic module for turbulence diagnostics,leveraging OAM spectrum data transformation.The optical module extracts turbulence-encoded features from distorted Laguerre–Gaussian(LG)beams and decomposes its output field into OAM spectrum data.These data are then fed into an electronic module that diagnoses turbulence strength using a minimal fully connected network with 9 input neurons and nonlinear activation.The OOENN performs feature extraction at light speed while enabling ultra-efficient electronic processing,thereby alleviating the latency and power constraints.Experimental results demonstrate diagnostics of five turbulence strengths within C_(n)^(2)=10-16 to 10-12 m^(-2∕3),achieving 82.4%accuracy at 80 ms latency per diagnosis.This fusion of structured light fields with optoelectronic intelligence establishes a technological foundation for next-generation adaptive systems in turbulence-resilient optical communications,remote sensing,and quantum information transfer.展开更多
基金The project is supported by National Education Ministry Doctor Foundation of China
文摘The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The main frequencies of the first class ships are less than 120 Hz, while the second class ships drop in 130 Hz -- 320 Hz. The different relationship between w1 and w2 corresponds to different bispectrum graph. There are the same results in the trispectrum. The feature vector is consist of the wls which correspond to the maximum bispectrum B(wl, wl) and the maximum trispectrum B(wl, w1,wl) respectively, the al, w2 which correspond to the maximum bispectrum B(wl, w2).
文摘This series of papers deal with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. Based on the studies of a large amount of ship radiated-noise data, which has been collected from actual ships on the sea, effectively recognizable features are extracted. Such features include line-spectrum features, stationary and nonstationary spectrum features as well as rhythm features. Finally the categorization are tested by unknown samples on the sea, including 33 surface vessels, 8 underwater vessels in 30 operating conditions. Methods for memorization and classilication are also explored in the project. Paper (Ⅲ) is the thirird in the series. It deals with the extraction method of modulation information in double-frequency power spectrum and the establishment of pattern plate of double-frequency spectrum as well as average power spectrum. To extract features from double-frequency spectrum, the tendency of wave is subtracted from the wave of each channel and the modulation of high frequency is compensated. The modulation degree of lines is shown by relative Value and converted to fuzzy value by fuzzy function. The pattern-plate of double-frequency spectrum memorises stable line and its respective modulation strength. The pattern-plate of average power spectrum memorizes the spectra mean of typical samples and the standard variance
文摘The publisher regrets that the abstract for Research Highlight were repeated twice in the original version.However,there should be no abstract for Research Highlight type.The publisher would like to apologise for any inconvenience caused.
基金National Natural Science Foundation of China(62422509,62575180)Natural Science Foundation of Shanghai Municipality(23ZR1443700,25ZR1402386)+3 种基金Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23SG41)Young Elite Scientist Sponsorship Program by Cast(20220042)Shanghai Municipal Science and Technology Major ProjectShanghai Frontiers Science Center Program(2021±2025 No.20)。
文摘Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adaptability,high latency,and excessive power consumption for deployment.Here,we propose an orbital angular momentum(OAM)-mediated optoelectronic neural network(OOENN)that integrates a diffractive optical module for OAM spectrum feature extraction with a shallow electronic module for turbulence diagnostics,leveraging OAM spectrum data transformation.The optical module extracts turbulence-encoded features from distorted Laguerre–Gaussian(LG)beams and decomposes its output field into OAM spectrum data.These data are then fed into an electronic module that diagnoses turbulence strength using a minimal fully connected network with 9 input neurons and nonlinear activation.The OOENN performs feature extraction at light speed while enabling ultra-efficient electronic processing,thereby alleviating the latency and power constraints.Experimental results demonstrate diagnostics of five turbulence strengths within C_(n)^(2)=10-16 to 10-12 m^(-2∕3),achieving 82.4%accuracy at 80 ms latency per diagnosis.This fusion of structured light fields with optoelectronic intelligence establishes a technological foundation for next-generation adaptive systems in turbulence-resilient optical communications,remote sensing,and quantum information transfer.
