Why use the S-transform?

Stockwell, R. G.

doi:10.1090/fic/052/12

Cited by 64 publications

(71 citation statements)

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“…Using the equivalent frequency domain definition of the Stockwell transform [8,9], the improved discrete S-Transform is given as For n ≠ 0;…”

Section: Physical Methods For Proposed Algorithmmentioning

confidence: 99%

“…The STransform (ST), introduced by Stockwell et al [5] is advanced method to short time Fourier transform(STFT) and continues wavelet transform(CWT) for progressive time frequency resolution [6] [7]. The invariable resolution of STFT and insufficient of phase data in CWT results in development of ST [8] [9]. ST provides cumulative resolution property of CWT with globally referenced phase output property [9].…”

Section: Introductionmentioning

confidence: 99%

“…The invariable resolution of STFT and insufficient of phase data in CWT results in development of ST [8] [9]. ST provides cumulative resolution property of CWT with globally referenced phase output property [9]. During this progressive resolution energy concentration is major important factor for signal analysis [10].…”

Section: Introductionmentioning

confidence: 99%

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Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

Krishna¹,

Srinivas²,

Gopal³

et al. 2018

IJET

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The S transform is an extension of Short Time Fourier Transform and Wavelet transform, has a time frequency resolution which is far from ideal. A differential frequency window is proposed in this paper to enhance the time frequency energy localization. When a non stationary signal consists of abrupt amplitude variation equal to peak of Gaussian function at initial intervals of chosen guassian window, then some part of the signal amplitude will be nullified during transform projection. The major function of differential frequency window is to track all abrupt amplitude-frequency variations which exploits in non -stationary signals. A mathematical method namely Newton Raphson method is adopted for this trace. The proposed scheme is tested for ECG data in presence of noise environment and results shows that proposed algorithm produces better enhanced energy localization in comparison to the standard S -Transform, STFT, and CWT. Furthermore the above algorithm is implemented on FPGA for real time applications.

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“…Using the equivalent frequency domain definition of the Stockwell transform [8,9], the improved discrete S-Transform is given as For n ≠ 0;…”

Section: Physical Methods For Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

Krishna¹,

Srinivas²,

Gopal³

et al. 2018

IJET

View full text Add to dashboard Cite

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“…The short term Fourier transforms (STFT) is commonly used in time-frequency signal processing (Stockwell 1991;Stockwell & Mansinha 1996). However, one of its drawbacks is the fixed width and height of the analyzing window.…”

Section: The Discrete S-transformmentioning

confidence: 99%

“…Like the STFT, it is a time-localized Fourier spectrum which maintains the absolute phase of each localized frequency component. Unlike the STFT, though, the S-transform has a window whose height and width frequencyvarying (Stockwell 1991).…”

Section: The Discrete S-transformmentioning

confidence: 99%

Application of Signal Processing in Power Quality Monitoring

Moravej¹,

Pazoki²,

Abdoos³

2011

Power Quality Â€“Â Monitoring, Analysis and Enhancement

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Spectral estimation-What is new? What is next?

et al. 2014

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Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10-15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.

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Why use the S-transform?

Cited by 64 publications

References 0 publications

Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

Application of Signal Processing in Power Quality Monitoring

Spectral estimation-What is new? What is next?

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