Jamming Recognition Based on Feature Fusion and Convolutional Neural Network

The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.

SENSORING DROPLET SPRAY TRANSFER IN MIG WELDING BASED ON ARC SPECTRUM SIGNAL

The method to detect droplet transfer by means of arc spectrum, while the experiment sets, testing principle and data processing procedure,are presented. The experiment and analysis results show that arc spectrum signal can be utilized to detect and measure the transfer procedure, the transfer modes and the transfer parameters. The arc spectrum signal enjoys excellent quality with high signal amplitude. Each transfer mode has its specific typical signal mode, and the pulse outline corresponds to an integrated transferring procedure of one droplet. All these features of arc spectrum signal can be easily applied hi the control of transfer procedure,the identification and stabilization of transfer mode and the measurement of transfer parameters.

Feature extraction of ship-radiated noise using higher-order spectrum

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).

Passive sonar identification (Ⅲ): Feature extraction and pattern plates of double-frequency spectrum as well as average power spectrum

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

TV White Space Spectrum Analysis Based on Machine Learning

Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new applications.In this paper,we present a detailed analysis of spectrum utilisation over TVWS at different locations in London.Both short-term and long-term outdoor measurement campaigns are conducted over large scales to better understand the spectrum features and variations across multiple locations and time periods.Different from most fixed-location-only measurements,we also drive along the main streets of London with a portable moving node to measure the on-route spectrum density along with the corresponding geographical information,which allows us to study the features and variations of spectrum use through a continuous space.To better analyse the dynamic spectrum utilisation,a machine learning based analysis algorithm is developed over the real-world measurements.This approach allows us to characterise the similarity and variability in spectrum usage within and among different channels,locations,and time instances,which is critical for the secondary system deployment to efficiently exploit the white space.

Doppler effect in high-speed rail seismic wavefield and its application

We demonstrate by theoretical analysis that periodically distributed viaduct piers of high-speed rail result in the Doppler effect in the seismic wavefield of high-speed rail at specific frequencies and analyze the Doppler effect’s influence on the wavefield’s spectrum feature.We further verify our theoretical prediction by using observational data of the high-speed rail seismic wavefield in Rongcheng,Hebei Province,China.We find that the wavefield component with a noticeable Doppler effect vibrates in the propagation direction and only has a unique apparent wave speed,indicating that P-wave is dominant.Furthermore,we propose a speed measurement method based on the Doppler effect and measure the wave speed of the medium along the rail.Measurement results are highly stable and consistent.

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.