Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Detection of leak acoustic signal in buried gas pipe based on the time―frequency analysis

Abstract: A time–frequency technique for locating leaks in buried gas distribution pipes involves the use of the cross-correlation on two measured acoustic signals on either side of a leak. This technique can be problematic for locating leaks in steel pipes, as the acoustic signals in these pipes are generally narrow-band and low frequency. The effectiveness of the time–frequency technique for detecting leaks in steel pipes was investigated experimentally in an earlier study. The object of this paper is to identify the characteristics of this dispersive acoustic wave through analysis of the cut-off frequency by using the time–frequency method experimentally and BEM (boundary element method) theoretically for the development of an experimental tool to analyze the leak signals in steel pipe. The tool is based on experimental work and theoretical formulation of wave propagation in a fluid-filled pipe. This tool uses the time–frequency method to explain some of the features of wave propagation measurements made in gas pipes. Leak noise signals are generally passed through a time–frequency filter for detection of impulse signal related leakage.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Abstract: Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Extracting Micro-Doppler Radar Signatures From Rotating Targets Using Fourier–Bessel Transform and Time–Frequency Analysis

Abstract: In this paper, we report the efficiency of the Fourier-Bessel transform (FBT) and time-frequency (TF)-based method in conjunction with the fractional Fourier transform (FrFT), for extracting micro-Doppler (m-D) radar signatures from the rotating targets. This approach comprises mainly of two processes, with the first being the decomposition of the radar return, in order to extract m-D features, and the second being the TF analysis to estimate motion parameters of the target. In order to extract m-D features from the radar signal returns, the time domain radar signal is decomposed into stationary and nonstationary components using the FBT in conjunction with the FrFT. The components are then reconstructed by applying the inverse Fourier-Bessel transform (IFBT). After the extraction of the m-D features from the target's original radar return, TF analysis is used to estimate the target's motion parameters. This proposed method is also an effective tool for detecting maneuvering air targets in strong sea clutter and is also applied to both simulated data and real-world experimental data.