Time-frequency analysis

By A. Rao, Khaled H. Hamed, Huey-Long Chen. 2003

2,299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7d5d7b13dd02f5bb7e50f2fda678dfa7985cc0dd

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In Artificial Intelligence

In Control and Systems Engineering

In Signal Processing

Citations in the corpus

A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals (2008, background): “Though the Hilbert transform is a simple approach for instantaneous frequency calculation [17], it results in a considerable error in discrete time domain and with the presence of noise.”
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding (2009, methodology): “INTRODUCTION The Empirical mode decomposition (EMD) method [3] is an algorithm for the analysis of multicomponent signals [4] that breaks them down into a number of amplitude and frequency modulated (AM/FM)”
Intelligent prognostics tools and e-maintenance (2006, background): “Time series analysis [27] or frequency domain analysis [28] could be used to process stationary signals (signals with time invariant frequency content), while wavelet [29], or joint timefrequency domains [30] could be used to describe nonstationary signals (signals with time-varying frequency conten”
Reply to ``Comment on `Performance of different synchronization measures in real data: A case study on electroencephalographic signals' '' (2001, methodology): “y Re[W x](t)+i W˜ x(t) = AH Re[Wx] (t) ·e iφH Re[Wx] (t). (23) Let us now recall the deﬁnition of analytic signals. A complex function g(t) is an analytic signal if it satisﬁes (Fg)(ω) = 0 ∀ ω < 0 [36]. If g is analytic, then Im[g(t)] = ˜g(t) ≡ (H Re[g])(t). If a wavelet function Ψ is analytic, t”