scholarly journals A Frequency Domain Extraction Based Adaptive Joint Time Frequency Decomposition Method of the Maneuvering Target Radar Echo

2018 ◽  
Vol 10 (2) ◽  
pp. 266 ◽  
Author(s):  
Guochao Lao ◽  
Canbin Yin ◽  
Wei Ye ◽  
Yang Sun ◽  
Guojing Li
Keyword(s):  
Frequency Domain ◽  
Decomposition Method ◽  
Time Frequency ◽  
Maneuvering Target ◽  
Frequency Decomposition ◽  
Radar Echo

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

2019 ◽  
Vol 233 (5) ◽  
pp. 868-880 ◽  
Author(s):  
Yao Cheng ◽  
Dong Zou
Keyword(s):  
Decomposition Method ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Local Means ◽  
Residual Noise ◽  
Rolling Element ◽  
Noisy Signals ◽  
Mode Mixing

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

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.

Eigenvalue decomposition polarization analysis: A regularized sparsity-based approach

2021 ◽  
Author(s):  
Eduardo Almeida ◽  
Hamzeh Mohammadigheymasi ◽  
Maryam Fathi ◽  
Paul Crocker ◽  
Graça Silveira
Keyword(s):  
Frequency Domain ◽  
Decomposition Methods ◽  
The Body ◽  
Eigenvalue Decomposition ◽  
Seismic Signals ◽  
Polarization Analysis ◽  
Technical Program ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Dependent Polarization

<p>Polarization analysis is a signal processing tool for decomposing multi-component seismic signals to a set of rectilinearly or elliptically polarized elements. Theoretically, time-frequency polarization methods are the most compatible tool to analyze the intrinsically non-stationary seismic signals. They decompose the signal to a superposition of well-defined polarized elements, localized in the time and frequency domains. However, in practice, they suffer from instability and limited resolution for discriminating between interfering seismic phases in time and frequency, as the time-frequency decomposition methods are generally an underdetermined mapping from the time to the time-frequency domain. Our contribution is threefold: Firstly we obtain the frequency-dependent polarization properties in terms of the eigenvalue decomposition of the Fourier spectra of three-components of the signal. Secondly, by extending from the frequency to the time-frequency domain and using the regularized sparsity-based time-frequency decomposition (Portniaguine and Castagna, 2004) we are able to increase resolution and reduce instability in the presence of noise. Finally, by combining directivity, rectilineary, and amplitude attributes in the time-frequency domain, we extend the time-frequency polarization analysis to extract and filter different seismic phases. By applying this method on synthetic and real seismograms we demonstrate the efficacy of the method in discriminating between the interfering seismic phases in time and frequency, including the body, Rayleigh, Love, and coda waves. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017) project.<br><br><strong>REFERENCES</strong><br>Portniaguine, O., and J. Castagna, 2004, Inverse spectral decomposition, in SEG Technical Program Expanded Abstracts 2004: Society of Exploration Geophysicists, 1786–1789.</p>

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

Sign in / Sign up

Export Citation Format

Share Document