Frequency Analysis Of Nonstationary Signals Research Articles

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

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.

Understanding THz and IR signals beneath time-resolved fluorescence from excited-state ab initio dynamics.

The detailed interpretation of time-resolved spectroscopic signals in terms of the molecular rearrangement during a photoreaction or a photophysical event is one of the most important challenges of both experimental and theoretical chemistry. Here we simulate a time-resolved fluorescence spectrum of a dye in aqueous solution, the N-methyl-6-oxyquinolinium betaine, and analyze it in terms of far IR and THz frequency contributions, providing a direct connection to specific molecular motions. To obtain this result, we build up an innovative and general approach based on excited state ab-initio molecular dynamics and a wavelet-based time-dependent frequency analysis of nonstationary signals. We obtain a nice agreement with key parameters of the solvent dynamics, such as the total Stokes shift and the Stokes shift relaxation times. As an important finding, we observe a strong change of specific solute-solvent interactions upon the electronic excitation, with the migration of about 1.5 water molecules from the first solvation shell toward the bulk. In spite of this event, the Stokes shift dynamics is ruled by collective solvent motions in the THz and far IR, which guide and modulate the strong rearrangement of the dye microsolvation. By the relaxation of THz and IR contributions to the emission signal, we can follow and understand in detail the molecularity of the process. The protocol presented here is, in principle, transferable to other time-resolved spectroscopic techniques.

A New Regularized Adaptive Windowed Lomb Periodogram for Time–Frequency Analysis of Nonstationary Signals With Impulsive Components

This paper proposes a new class of windowed Lomb periodogram (WLP) for time-frequency analysis of nonstationary signals, which may contain impulsive components and may be nonuniformly sampled. The proposed methods significantly extend the conventional Lomb periodogram in two aspects: 1) The nonstationarity problem is addressed by employing the weighted least squares (WLS) to estimate locally the time-varying periodogram and an intersection of confidence interval technique to adaptively select the window sizes of WLS in the time-frequency domain. This yields an adaptive WLP (AWLP) having a better tradeoff between time resolution and frequency resolution. 2) A more general regularized maximum-likelihood-type (M-) estimator is used instead of the LS estimator in estimating the AWLP. This yields a novel M-estimation-based regularized AWLP method which is capable of reducing estimation variance, accentuating predominant time-frequency components, restraining adverse influence of impulsive components, and separating impulsive components. Simulation results were conducted to illustrate the advantages of the proposed method over the conventional Lomb periodogram in adaptive time-frequency resolution, sparse representation for sinusoids, robustness to impulsive components, and applicability to nonuniformly sampled data. Moreover, as the computation of the proposed method at each time sample and frequency is independent of others, parallel computing can be conveniently employed without much difficulty to significantly reduce the computational time of our proposed method for real-time applications. The proposed method is expected to find a wide range of applications in instrumentation and measurement and related areas. Its potential applications to power quality analysis and speech signal analysis are also discussed and demonstrated.

Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm

Problem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of wavelet transform was to distribute the signal energy with the change of time in different frequency bands. This will showed the characteristic of the signals since energy was an important physical variable in signal analysis. The EOG signals were captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features used to determine the characteristic of eye movement waveform. This technique adopted because it was a non-invasive, inexpensive and accurate. The new technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200. The recorded data was composed of an eye movement toward four directions, i.e., downward, upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT) with energy algorithm and by comparing the energy distribution with the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to display the different percentages of energy for each wavelet coefficient towards different movement. Conclusion: From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding scale such as leftward with scale 6 (8- 16Hz), rightward with scale 8 (2-4Hz), downward with scale 9 (1-2Hz) and upward with level 7 (4-8Hz). Statistically, the results in this study indicate that there are 93% (averages) significance differences in the extracted features of wavelet Scalogram analysis.