Applications of Time-Frequency Analysis to Structures With Internal Resonance

1995 ◽  
Author(s):  
Kyle D. Dippery ◽  
Suzanne Weaver Smith
Keyword(s):  
Structural Dynamics ◽  
Frequency Analysis ◽  
Internal Resonance ◽  
Signal Analysis ◽  
Frequency Content ◽  
Modal Interactions ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Transition To Chaos ◽  
Changes Over Time

Abstract Time-frequency analysis is an approach to characterizing the nature of signals whose frequency content changes over time. Although the primary applications of this field have, to date, been in the area of communications and signal analysis, it is becoming known in the field of structural dynamics. This paper explores the application of two straightforward time-frequency techniques to several structures that exhibit internal resonance. In particular, the systems analyzed exhibit simple modal interactions and, in one case, a transition to chaos. While other methods exist for analysis of these types of behaviors, larger systems with more complex resonances maybe better analyzed with time-frequency techniques.

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 Joint Time-Frequency And Wavelet Analysis - An Introduction

2014 ◽  
Vol 21 (4) ◽  
pp. 741-758 ◽  
Author(s):  
Andrzej Majkowski ◽  
Marcin Kołodziej ◽  
Remigiusz J. Rak
Keyword(s):  
Wavelet Analysis ◽  
Frequency Analysis ◽  
Fourier Spectrum ◽  
Frequency Content ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Signals ◽  
Frequency Window ◽  
Short Time

Abstract A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f) algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency). The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components.

scholarly journals ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Pierre-Emmanuel Aguera ◽  
Karim Jerbi ◽  
Anne Caclin ◽  
Olivier Bertrand
Keyword(s):  
Frequency Analysis ◽  
Software Package ◽  
Signal Analysis ◽  
Large Data ◽  
Multidimensional Analysis ◽  
Spatiotemporal Resolution ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strong Trend ◽  
Wide Range

The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes.

Research on Joint Time-Frequency Analysis Methods of Adaptive Radar Signal

2014 ◽  
Vol 989-994 ◽  
pp. 4009-4013 ◽  
Author(s):  
Qiang Xing ◽  
Wei Gang Zhu ◽  
Yuan Bo ◽  
Kang Wang
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Radar Signal ◽  
Analysis Method ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Adaptive Radar ◽  
Short Time

Faced with complex electromagnetic environment and varieties of adaptive radar waveforms, radar signal analysis and identification becomes more and more complex. Considering two important physical quantities - time and frequency in modern signal processing methods, this paper proposes that the joint time-frequency analysis (JTFA) method based on fractional Fourier transform (FrFT) and short-time Fourier transform (STFT) is applied to adaptive radar signal processing. The simulation results show that the joint time-frequency analysis method is superior to single short-time Fourier transform, getting a better analysis of results. The joint time-frequency analysis method provides the joint distribution of the time domain and frequency domain for adaptive radar signal analysis and describes the relationship between signal frequency and time.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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