Estimation of the power spectrum S( f ) of a stationary random process can be viewed as a nonparametric statistical estimation problem. We introduce a nonparametric approach based on a wavelet representation for the logarithm of the unknown S( f ). This approach offers the ability to capture statistically significant components of lnS( f ) at different resolution levels and guarantees nonnegati...

We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally stationary wavelet processes and contains processes whose spectral density function may change very suddenly in time. A notion of time-varying wavelet spectrum is uniquely defined as a wavelet-type transform of the autocovariance function with respect to so-called autocorrelation wavelets. This le...

This article develops a wavelet decomposition of a stochastic process which parallels a time{localized Cram er (Fourier) spectral representation. We provide a time{scale instead of a time{frequency decomposition and, hence, instead of thinking as scale in terms of \inverse frequency" we start from genuine time{scale building blocks or \atoms". Using this class of locally stationary wavelet (LSW...

This article develops a wavelet decomposition of a stochastic process which parallels a time{localized Cram er (Fourier) spectral representation. We provide a time{scale instead of a time{frequency decomposition and, hence, instead of thinking as scale in terms of \inverse frequency" we start from genuine time{scale building blocks or \atoms". Using this class of locally stationary wavelet (LSW...

Accelerometers, which can be installed inside a floating platform on the sea, are among the most commonly used sensors for operational ocean wave measurements. To examine the non-stationary features of ocean waves, this study was conducted to derive a wavelet spectrum of ocean waves and to synthesize sea surface elevations from vertical acceleration signals of a wave buoy through the continuous...

Wavelet decompositions of Raman spectra were investigated with respect to their usability for spike removal and denoising of the raw data. It could be shown that those operations should be performed sequentially. Suppression of spikes is not straightforwardly possible by wavelet transformation; however, the wavelet transform may be used to recognize the spikes by their first level detail coeffi...

In recent years, improvement has been achieved in vibration signal processing, using wavelet analysis for condition monitoring and fault diagnosis. The use of wavelet analysis has proven to be efficient to detect faults in vibration signals with nonstationary, transient characteristics/ components. An experimental data set is used to compare the diagnostic capability of the fast Fourier transfo...

obtaining a seismic section with high temporal and spatial resolution was always one of the goals of seismic data processors and interpreters. accurate estimation of the thicknesses of thin beds is an important tool in this regard. the basic problem is that the wavelength of the signal must be similar in dimention to that of the bed thinness. if it is much longer than the bed thinness, the dete...

On the wavelet-packet modulation scheme, wavelet packets are used as carriers, where information to be transmitted is encoded, via an inverse wavelet packet transform, as the coefficient of wavelet packets. The power spectrum density (PSD) of the modulated signals describes the property of modulation in frequency domain, which is discussed by many researchers by simulation. In this paper, the f...

Cognitive radio draw lots of research attentions in recent years for its efficient spectrum utilization. In cognitive radio networks, the first cognitive task preceding any form of dynamic spectrum management is the spectrum sensing and identification of spectrum holes in wireless environment. Spectrum Sensing is an important functionality of Cognitive Radio (CR). Accuracy and speed of estimati...