Bicoherence in electrostatic turbulence driven by high magnetohydrodynamic activity in Tokamak Chauffage Alfven Bresilien

During some discharges in Tokamak Chauffage Alfven Bresilien [R. M. O. Galvao et al., Plasma Phys. Controlled Fusion 43, 1181 (2001)] high magnetohydrodynamic activity may appear with a peaked frequency spectrum. Whenever this peak occurs, the ambient broadband electrostatic turbulence is remarkably modified, synchronizing into the dominant magnetic fluctuation frequency and presenting high bicoherence in the whole plasma edge with a maximum bicoherence inside the plasma. A phenomenological model is introduced to investigate this driven turbulence bicoherence, consisting of nonlinearly coupled phase-randomized drift modes with time-periodic external driving at the dominant magnetic fluctuation frequency. The bicoherence spectrum of this model can mimic features of the experimental results. (C) 2009 American Institute of Physics. [DOI: 10.1063/1.3099701]

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features