The application of nonlinear transformations to a cyclostationary signal for the purpose of revealing hidden periodicities has proven to be useful for applications requiring signal selectivity and noise tolerance. The fact that the hidden periodicities, referred to as cyclic moments, are often compressible in the Fourier domain motivates the use of compressive sensing (CS) as an efficient acqui...

Broadband wireless interference in a computer platform is the result of multiple dynamic electromagnetic emission sources. This interference is non-Gaussian and a receiver design based on the Gaussian assumption will yield suboptimal performance. In fact, it has a double-sided K-distribution and needs to be treated differently in the design process. When dealing with this type of interference i...

This paper describes a blind algorithm, which is compared with two another algorithms proposed in the literature, for estimating of the minimum phase channel parameters. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. The simulation results in noisy environment, demonstrate that the proposed method could est...

In ultrasonic nondestructive evaluation (NDE) of materials, pulse echo measurements are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic pulses, and are corrupted by additive noise. Deconvolution operation seeks to undo these masking effects and extract the defect impulse response which is essential for identification. In this contributio...

This paper presents an algorithm for reconstructing phase and magnitude responses of the impulse response when only the output data are available. The system is driven by a zero-mean independent identically distributed (i.i.d) non-Gaussian sequence that is not observed. The additive noise is assumed to be Gaussian. This is an important and essential problem in many practical applications of var...

Higher Order Statistical (HOS) techniques were first proposed over four decades ago. This paper is concerned with higher order statistical analysis of closed loop data for diagnosing the causes of poor control loop performance. The main contributions of this work are to utilize HOS tools such as cumulants, bispectrum and bicoherence to develop two new indices: the Non-Gaussianity Index (NGI) an...

In this paper we analyze sleep spindles, observed in EEG recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second and third order correlations to reveal information on the stationarity of periodic spindle rhythms, detecting transitions between multiple activities. The fr...

This paper is concerned with the statistical analysis of closed loop data for diagnosing the causes of poor control loop performance. Higher Order Statistical (HOS) techniques have been developed over the last two decades, but until now have not been applied to the area of process monitoring. The main contribution of this work is to utilize the higher order statistical tools such as cumulants a...

In this paper, we introduce a new blind fractionally spaced equalizer based on the fourth-order statistics of the input symbol sequence. The input symbol sequence is assumed to come from an independent identically distributed finite alphabet with nonzero fourth-order cumulants. We formulate the equalizer as a column vector and compute it by simultaneously diagonalizing a set of matrices obtaine...