The S-transform is a time-frequency representation known for its local spectral phase properties. A key feature of the S-transform is that it uniquely combines a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information. This allows one to define the meaning of phase in a local spectrum setting, and results in many desirable characteristics. On...

Time-frequency transforms represent a signal as a mixture of its time domain representation and its frequency domain representation. We present efficient algorithms for the quantum Zak transform and quantum Weyl-Heisenberg transform.

K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies inneurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this patternare of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysisof this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocat...

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...

The TT-transform stands for time-time transform and has been derived as an inverse Fourier transform of the time-frequency S-transform. So far, it has been proposed that the diagonal of the TT-transform can be used for signal characterisation. We show here an alternative and simplified derivation of the TT-transform which enables a better understanding of this transform.

In this paper, we propose Speaker Identification using the frequency distribution of various transforms like DFT (Discrete Fourier Transform), DCT (Discrete Cosine Transform), DST (Discrete Sine Transform), Hartley, Walsh, Haar and Kekre transforms. The speech signal spoken by a particular speaker is converted into frequency domain by applying the different transform techniques. The distributio...

In this paper, a method of super-resolution image reconstruction based on wavelet transform and edge-directed interpolation is put forward. Firstly the low resolution image is decomposed by the stationary wavelet transform; meanwhile, the image obtained from edge-directed interpolation is decomposed by discrete wavelet transform, then the three high frequency sub bands obtained through stationa...

In this paper we present a new methodology for computing a time-frequency map for non-stationary signals using the continuous wavelet transform (CWT). The conventional method of producing a time-frequency map using the Short Time Fourier Transform (STFT) limits the time-frequency resolution by a pre-defined window length. In contrast, the CWT method does not require pre-selecting a window lengt...

A new signal-adaptive joint time-frequency distribution for the analysis of nonstationary signals is proposed. It is based on a fractionalFourier-domain realization of the weighted Wigner distribution producing auto-terms close to the ones in the Wigner distribution itself, but with reduced cross-terms. Improvement over the standard time-frequency representations is achieved when the principal ...

Properties determining optimal behavior of continuous analytic wavelet functions are investigated, and wavelet functions nearly obtaining such behavior are identified. This is accomplished through detailed investigation of the generalized Morse wavelets, a highly flexible family of exactly analytic continuous wavelets. The degree of time/frequency localization, the existence of a unique interpr...