By use of window functions, time-frequency analysis tools like Short Time Fourier Transform overcome a shortcoming of the Fourier Transform and enable us to study the timefrequency characteristics of signals which exhibit transient oscillatory behavior. Since the resulting representations depend on the choice of the window functions, it is important to know how they influence the analyses. One ...

The notions of time, frequency, and scale are generalized using concepts from unitary operator theory and applied to time-frequency analysis, in particular the wavelet and short-time Fourier transform orthonormal bases and Cohen's class of bi-linear time-frequency distributions. The result is an infinite number of new signal analysis and processing tools that are implemented simply by prewarpin...

A time-frequency analysis, which represents the time change of a signal, is significant in all fields. Wigner distribution, a short time Fourier transform (STFT), a kernel method, and a characteristic function method etc. are known as a time-frequency analyzing method, and those methods have merits and demerits. STFT method has been used as ultrasound blood-flow imaging for a long time, because...

one of the most fundamental reservoir characteristics is the thickness. the analysis of thin bed tuning on seismic reflectivity has been studied extensively by wides (1973) and neidel and pogiaggliomi (1977), who discussed the limits of seismic resolution. during the past decade, the industry has developed a plethora of new attributes in studying thin beds by employing spectral decomposition (p...

Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternativ...

Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary exhibiting statistical variation that changes in space and time. Time-frequency characterization is useful for analyzing such data, seismic traces in particular. We present a novel time-frequency decomposition, which aims at depicting the nonstationary character of seismic data. The proposed ...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

A generalized S transform based on energy normalized windows is presented, which avoids the weighting effect of existing S transform influenced by the time-frequency spectral energy. The transform can reflect the spectral energy distribution of different frequency components while maintaining the excellent time-frequency resolution. While TT transform is a new approach to process non-stationary...