It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a s...

Various time-frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. ...

This paper describes techniques for obtaining high accuracy estimates, including those of non-stationarity, of parameters for sinusoidal modelling using a single frame of analysis data. In this case the data used is generated from the time and frequency reassigned short-time Fourier transform (STFT). Such a system offers the potential for quasi real-time (frame-by-frame) spectral modelling of a...

1. THE RIEMANN–LEBESGUE LEMMA. In its usual form, the Riemann– Lebesgue Lemma reads as follows: If f ∈ L1 and f̂ (s) = ∫∞ −∞ eisx f (x) dx is its Fourier transform, then f̂ (s) exists and is finite for each s ∈ R and f̂ (s) → 0 as |s| → ∞ (s ∈ R). This result encompasses Fourier sine and cosine transforms as well as Fourier series coefficients for functions periodic on finite intervals. When the i...

For time-invariant degradation models and stationary signals and noise, the classical Fourier domain Wiener filter, which can be implemented in O(N logN) time, gives the minimum mean-square-error estimate of the original undistorted signal. For time-varying degradations and nonstationary processes, however, the optimal linear estimate requires O(N2) time for implementation. We consider filterin...

Center of pressure (COP) measurements are often used to identify balance problems. A new method for COP signal analysis using mean frequency is proposed in this paper. The computation of mean frequency is based on the Fourier–Bessel (FB) expansion that is suitable for use in non-stationary COP signals. In addition, FB expansion provides better frequency resolution and low frequency detectabilit...

Does it make sense to make impedance spectroscopy in non-stationary conditions? And if yes, how should one analyze the results? The main goal of this work is to give a satisfactory answer to these two questions. Regarding the first question and according to the usual interpretation of the concept of impedance, the answer should be no, because impedance is not defined as timedependent and, there...

Work in Artificial Intelligence often involves search algorithms. In many complicated problems, however, local search algorithms may fail to converge into global optimization and global search procedures are needed. In this paper, we investigate the Fourier Amplitude Sensitivity Test (FAST) as an example of a global sensitivity analysis tool for complex, non-linear dynamical systems. FAST was o...

Time-varying autoregressive (TVAR) modeling approach for the representation of complex non-stationary process is presented, and applied to the classiication of High Range Resolution (HRR) radar signatures. HRR radar signatures are multi-channel nonstationary complex processes, and features extracted under the stationary assumption often result in unsatisfactory performance. In a TVAR modeling a...

In this paper, we present upper bounds for the rate distortion function (RDF) of finite-length data blocks of Gaussian wide sense stationary (WSS) sources and we propose coding strategies to achieve such bounds. In order to obtain those bounds, we previously derive new results on the discrete Fourier transform (DFT) of WSS processes.