A Novel Imaging Approach for Dispersive Target Electromagnetic Imaging

2013 ◽  
Vol 427-429 ◽  
pp. 1972-1976
Author(s):  
Guo Rong Huang ◽  
Wei Jun Zhong ◽  
Chao Wang ◽  
Yuan Pei Wu
Keyword(s):  
Frequency Analysis ◽  
Time Domain ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Electromagnetic Wave Scattering ◽  
Electromagnetic Imaging ◽  
Imaging Experiment ◽  
Imaging Approach ◽  
The Time Domain ◽  
Difference Time

A novel imaging approach for dispersive target electromagnetic imaging based on the time-frequency analysis is presented in this paper. On the foundation of researching the time-domain imaging principle of dispersive target, the basic theory of back projection imaging algorithm and time-frequency analysis is discussed. The sampling data obtained by simulation of target electromagnetic wave scattering which is accomplished by finite-difference time domain method (FDTD). According to the simulating data, after disposing by Wigner-Ville transfer, the sampling data is processed integral management in frequency-domain, and the imaging experiment is carried out. The experiment results show that the improved imaging arithmetic could estimate the shape of dispersive target and improve the imaging quality.

Simulation in the Time Domain and Time-Frequency Analysis for Coherent Doppler Wind Lidar

2015 ◽  
Vol 42 (1) ◽  
pp. 0114003
Author(s):  
白雪 Bai Xue ◽  
郭磐 Guo Pan ◽  
陈思颖 Chen Siying ◽  
张寅超 Zhang Yinchao ◽  
陈和 Chen He ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Time Domain ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Wind Lidar ◽  
The Time Domain ◽  
Doppler Wind Lidar

Time Frequency Domain Analysis of the Dispersion of Guided Wave Modes

2004 ◽  
Author(s):  
Youn-Ho Cho ◽  
Yong-Kwon Kim ◽  
Ik-Keun Park
Keyword(s):  
Wavelet Transform ◽  
Group Velocity ◽  
Frequency Analysis ◽  
Time Domain ◽  
Excitation Frequency ◽  
Guided Wave ◽  
Time Frequency Analysis ◽  
Mode Identification ◽  
Time Frequency ◽  
Wave Modes

One of unique characteristics of guided waves is a dispersive behavior that guided wave velocity changes with an excitation frequency and mode. In practical applications of guided wave techniques, it is very important to identify propagating modes in a time-domain waveform for determination of defect location and size. Mode identification can be done by measurement of group velocity in a time-domain waveform. Thus, it is preferred to generate a single or less dispersive mode. But, in many cases, it is difficult to distinguish a mode clearly in a time-domain waveform because of superposition of multi modes and mode conversion phenomena. Time-frequency analysis is used as efficient methods to identify modes by presenting wave energy distribution in a time-frequency. In this study, experimental guided wave mode identification is carried out in a steel plate using time-frequency analysis methods such as wavelet transform. The results are compared with theoretically calculated group velocity dispersion curves. The results are in good agreement with analytical predictions and show the effectiveness of using the wavelet transform method to identify and measure the amplitudes of individual guided wave modes.

scholarly journals Comparison of the Time Domain Windows Specified in the ISO 18431 Standards Used to Estimate Modal Parameters in Steel Plates

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Jhonatan Camacho-Navarro ◽  
R. Guzmán-López ◽  
Sergio Gómez ◽  
Marco Flórez
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Time Domain ◽  
Steel Plate ◽  
Theoretical Method ◽  
Steel Plates ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Time Frequency ◽  
The Time Domain

The procedures used to estimate structural modal parameters as natural frequency, damping ratios, and mode shapes are generally based on frequency methods. However, methods of time-frequency analysis are highly sensible to the parameters used to calculate the discrete Fourier transform: windowing, resolution, and preprocessing. Thus, the uncertainty of the modal parameters is increased if a proper parameter selection is not considered. In this work, the influence of three different time domain windows functions (Hanning, flat-top, and rectangular) used to estimate modal parameters are discussed in the framework of ISO 18431 standard. Experimental results are conducted over an AISI 1020 steel plate, which is excited by means of a hammer element. Vibration response is acquired by using acceleration records according to the ISO 7626-5 reference guides. The results are compared with a theoretical method and it is obtained that the flat-top window is the best function for experimental modal analysis.

Extracting of Micro Doppler Parameter of Helicopter Rotor Based on Time-Frequency Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 2696-2700 ◽  
Author(s):  
Lei Zhang ◽  
Guo Qing Huang
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Good Method ◽  
Helicopter Rotor ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Radar Echo ◽  
Doppler Parameter

The micro Doppler effect of the radar echo signal of helicopter rotor is studied, and the formula of helicopter rotor echo is obtained. Then the received echo signal of helicopter rotor simulated is analyzed in time domain, frequency domain and time-frequency domain respectively, the analysis results show that it is a good method to extract micro Doppler of helicopter rotor echo by time-frequency analysis. According to analysis results, obtained a method to determine parity of blades and velocity of helicopter rotor, these methods can be used to identify helicopter.

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

scholarly journals Clinical usefulness and feasibility of time-frequency analysis of chemosensory event-related potentials

2013 ◽  
Vol 51 (3) ◽  
pp. 210-221
Author(s):  
C. Huart ◽  
Ph Rombaux ◽  
T. Hummel ◽  
A. Mouraux
Keyword(s):  
Frequency Analysis ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Discrimination Performance ◽  
Clinical Usefulness ◽  
Olfactory Stimulation ◽  
Signal To Noise ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Time Domain Averaging

Background: The clinical usefulness of olfactory event-related brain potentials (OERPs) to assess olfactory function is limited by the relatively low signal-to-noise ratio of the responses identified using conventional time-domain averaging. Recently, it was shown that time-frequency analysis of the obtained EEG signals can markedly improve the signal-to-noise ratio of OERPs in healthy controls, because it enhances both phase-locked and non phase-locked EEG responses. The aim of the present study was to investigate the clinical usefulness of this approach and evaluate its feasibility in a clinical setting. Methodology: We retrospectively analysed EEG recordings obtained from 45 patients (15 anosmic, 15 hyposmic and 15 normos- mic). The responses to olfactory stimulation were analysed using conventional time-domain analysis and joint time-frequency analysis. The ability of the two methods to discriminate between anosmic, hyposmic and normosmic patients was assessed using a Receiver Operating Characteristic analysis. Results: The discrimination performance of OERPs identified using conventional time-domain averaging was poor. In contrast, the discrimination performance of the EEG response identified in the time-frequency domain was relatively high. Furthermore, we found a significant correlation between the magnitude of this response and the psychophysical olfactory score. Conclusion: Time-frequency analysis of the EEG responses to olfactory stimulation could be used as an effective and reliable diagnostic tool for the objective clinical evaluation of olfactory function in patients.

Diagnosis of damaged wind turbine blade by noise characteristics

2020 ◽  
Vol 68 (2) ◽  
pp. 146-156
Author(s):  
Chao-Nan Wang ◽  
Tang-Yao Chi
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Frequency Analysis ◽  
Time Domain ◽  
Wind Turbine Blade ◽  
Statistical Regression ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Noise Characteristics

This study has proposed two estimation models of noise signal characteristic diagnosis based on time-domain and time-frequency analysis. The diagnosis of time domain was based on the fractal theory, and the result of fractal dimensions was converted into Gauss distribution, so as to provide a feature extraction for abnormality diagnosis of damaged blade. In addition, for time-frequency analysis, the wavelet methodwas used as the basis of signal analysis. The Morlet transform and mother wavelet were used for wavelet analysis of signal to obtain the result of time-frequency analysis. When the time axis was integrated, the marginal spectrum of frequency domain was obtained, and statistical regression analysis was used to provide another method of feature extraction diagnosis. The wind turbine blade signal was measured in actual wind turbine operation at Changhua Coastal Industrial Park for diagnostic analysis, so as to provide a multi-diagnostic model of wind turbine blade prewarning and health management models.

Scattering Characteristic Extraction and Recovery for Multiple Targets Based on Time Frequency Analysis

2020 ◽  
Vol 35 (8) ◽  
pp. 962-970
Author(s):  
Xiangwei Liu ◽  
Jianzhou Li ◽  
Yi Zhu ◽  
Shengjun Zhang
Keyword(s):  
Frequency Analysis ◽  
Multiple Targets ◽  
Total Scattering ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Time Domain Signal ◽  
Characteristic Extraction ◽  
Single Target ◽  
Scattering Field ◽  
The Time Domain

The multi-target scattering field consists of the scattering fields of each target, but it is difficult to know the scattering characteristics of the specific target from the total scattering field. However, the scattering characteristics of single target embedded in the total scattered field have important research significance for target recognition and detection. In this paper, a method is proposed to extract and recover each target’s scattering characteristics from the total scattering field of multiple targets. The theoretical basis of the method is that the scattering echoes corresponding to different targets reach the receiver at different time. We acquire the total scattering field at first. Then, we perform the signal processing with time-frequency analysis to obtain the arrival time of different scattering echoes. According to the time slot difference, the time domain signal of each target can be extracted to recover its scattering field. Several examples validate the proposed method.

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