Estimation of Multiple, Time-Varying Motions Using Time-Frequency Representations and Moving-Objects Segmentation

D.S. Alexiadis; G.D. Sergiadis

doi:10.1109/tip.2008.922437

Estimation of Multiple, Time-Varying Motions Using Time-Frequency Representations and Moving-Objects Segmentation

IEEE Transactions on Image Processing ◽

10.1109/tip.2008.922437 ◽

2008 ◽

Vol 17 (6) ◽

pp. 982-990 ◽

Cited By ~ 3

Author(s):

D.S. Alexiadis ◽

G.D. Sergiadis

Keyword(s):

Moving Objects ◽

Multiple Time ◽

Time Varying ◽

Time Frequency

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Analysis and classification of time-varying signals with multiple time-frequency structures

IEEE Signal Processing Letters ◽

10.1109/97.995826 ◽

2002 ◽

Vol 9 (3) ◽

pp. 92-95 ◽

Cited By ~ 35

Author(s):

A. Papandreou-Suppappola ◽

S.B. Suppappola

Keyword(s):

Multiple Time ◽

Time Varying ◽

Time Frequency

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Multiple Time-frequency Curve Classification for Tacho-less and Resampling-less Compound Bearing Fault Detection under Time-varying Speed Conditions

IEEE Sensors Journal ◽

10.1109/jsen.2020.3035623 ◽

2020 ◽

pp. 1-1

Author(s):

Gang Tang ◽

Yatao Wang ◽

Yujing Huang ◽

Han Wang

Keyword(s):

Fault Detection ◽

Multiple Time ◽

Time Varying ◽

Frequency Curve ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Detection

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Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions

MethodsX ◽

10.1016/j.mex.2019.05.020 ◽

2019 ◽

Vol 6 ◽

pp. 1415-1432 ◽

Cited By ~ 3

Author(s):

Huan Huang ◽

Natalie Baddour ◽

Ming Liang

Keyword(s):

Fault Diagnosis ◽

Multiple Time ◽

Time Varying ◽

Frequency Curve ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Matlab Code

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Estimation of time-varying velocities of moving objects by time-frequency representations

IEEE Transactions on Image Processing ◽

10.1109/tip.2003.811503 ◽

2003 ◽

Vol 12 (5) ◽

pp. 550-562 ◽

Cited By ~ 25

Author(s):

I. Djurovic ◽

S. Stankovic

Keyword(s):

Moving Objects ◽

Time Varying ◽

Time Frequency ◽

Estimation Of Time

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Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction

Journal of Sound and Vibration ◽

10.1016/j.jsv.2017.11.005 ◽

2018 ◽

Vol 414 ◽

pp. 43-60 ◽

Cited By ~ 35

Author(s):

Huan Huang ◽

Natalie Baddour ◽

Ming Liang

Keyword(s):

Fault Diagnosis ◽

Rotational Speed ◽

Multiple Time ◽

Time Varying ◽

Frequency Curve ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Continuous time-varying Q-factor estimation method in the time-frequency domain

Applied Geophysics ◽

10.1007/s11770-020-0869-8 ◽

2021 ◽

Author(s):

Wang Qing-Han ◽

Liu Yang ◽

Liu Cai ◽

Zheng Zhi-Sheng

Keyword(s):

Frequency Domain ◽

Continuous Time ◽

Estimation Method ◽

Time Varying ◽

Q Factor ◽

Time Frequency ◽

Factor Estimation

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Extended dissipativity and non-fragile synchronization for Recurrent neural networks with multiple time-varying delays via sampled-data control

IEEE Access ◽

10.1109/access.2021.3060044 ◽

2021 ◽

pp. 1-1

Author(s):

R. Anbuvithya ◽

S. Dheepika Sri ◽

R. Vadivel ◽

Nallappan Gunasekaran ◽

P. Hammachukiattikul

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Multiple Time ◽

Time Varying ◽

Sampled Data ◽

Data Control ◽

Sampled Data Control

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Stability analysis in Lagrange sense for a class of BAM neural networks of neutral type with multiple time-varying delays

Neurocomputing ◽

10.1016/j.neucom.2014.07.041 ◽

2015 ◽

Vol 149 ◽

pp. 930-939 ◽

Cited By ~ 32

Author(s):

Jigui Jian ◽

Baoxian Wang

Keyword(s):

Neural Networks ◽

Stability Analysis ◽

Neutral Type ◽

Multiple Time ◽

Time Varying ◽

Bam Neural Networks

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Delay-dependent exponential stability for neutral stochastic system with multiple time-varying delays

IET Control Theory and Applications ◽

10.1049/iet-cta.2014.0169 ◽

2014 ◽

Vol 8 (17) ◽

pp. 2092-2101 ◽

Cited By ~ 11

Author(s):

Huabin Chen ◽

Jinjing Wang ◽

Peng Hu

Keyword(s):

Exponential Stability ◽

Stochastic System ◽

Multiple Time ◽

Time Varying ◽

Delay Dependent

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A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.650 ◽

2013 ◽

Vol 333-335 ◽

pp. 650-655

Author(s):

Peng Hui Niu ◽

Yin Lei Qin ◽

Shun Ping Qu ◽

Yang Lou

Keyword(s):

Signal Processing ◽

Real Time ◽

Phase Difference ◽

Time Signal ◽

Time Interval ◽

Time Varying ◽

Signal Model ◽

Time Frequency ◽

Mass Flowmeter ◽

Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

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