This paper extends Thomson's multitaper spectrum estimation method [17] to nonstationary signals. The method uses a newly{derived set of basis functions which generalize the concentration properties of the prolate spheroidal waveforms [15] to the time{frequency case. We solve for the basis which diagonalizes the nonstationary spectrum generating operator over a nite region of the time{frequency...

The generalized entropies of Rényi inspire new measures for estimating signal information and complexity in the time–frequency plane. When applied to a time–frequency representation (TFR) from Cohen’s class or the affine class, the Rényi entropies conform closely to the notion of complexity that we use when visually inspecting time–frequency images. These measures possess several additional int...

This article gives an overview on nonparametric modelling of nonstationary time series and estimation of their time-changing spectral content by modern denoising (smoothing) methods. For the modelling aspect localized decompositions such as various local Fourier (spectral) representations are discussed, among which wavelet and local cosine bases are most prominent ones. For the estimation of th...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

Abstract: This paper presents a delay-dependent robust stochastic active queue management (AQM) scheme that can be implemented in the routers for stabilizing queues in transmission control protocol (TCP) communication networks. The linearized TCP/AQM system is modelled as a uncertain time-delay system with stochastic perturbations and time-varying network parameters. In this paper, a new flow m...

A highly accurate motion segmentation technique for time-varying mesh (TVM) is presented. In conventional approaches, motion of the objects was analyzed using shape feature vectors extracted from TVM frames. This was because it was very difficult to locate and track feature points in the objects in the 3D space due to the fact that the number of vertices and connection varies each frame. In thi...