The first-order small-slope approximation (SSA-1) model is used for numerical predictions of the normalized radar cross section (NRCS) of an anisotropic ocean surface in bistatic configurations for the K u -band radar frequency. The calculations were made by assuming the Elfouhaily et al. surface-height spectrum for fully developed seas. In the forward-backward case, the SSA-1 presents an agreement with the geometric optics limit of the Kirchhoff approximation results in the near-specular directions where it is well known that the last model works well. In the fully bistatic case, SSA-1 numerical results are compared with those of the two-scale model in several configurations as a function of wind speed, wind direction, incident/scattering angles and for co-and cross-polarization states. Good agreement between the two models is noted in the co-and cross-polarization case with a small difference of about 1-2 dB. But in certain configurations, the SSA-1 model tends to overestimate the radar cross section peak behaviour. This irregularity is discussed and interpreted. The main purpose of this paper is to analyse NRCS predictions based on the SSA-1 model in a fully bistatic configuration.

A new method for predicting the scattered acoustic field due to a plane wave incident upon an infinitely long cylinder lying near an penetrable plane interface is presented. The method generalizes the method of images which is restricted to rigid and soft plane interfaces. Validity domains, physical interpretations, simulations and numerical results are described for sedimentary medium-fluid plane interfaces. And, they are well compared with high frequency asymptotic results based on the Geometrical Theory of Diffraction(G.T.D.).

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This article deals with a new approach in sleep characterization that combines EEG source localisation methods with standard frequency analysis of multielectrode EEGs. First, we describe the theoretical methodology and the benefits that we get from a three-dimensional image (LORETA) of the cerebral activity related to a frequency band. Then, this new application is used as signal-processing technique on sleep EEG recordings obtained from young male adults using four frequency bands (δ 0.5-3.5 Hz, θ 4.0-7.5 Hz, α 8.0-12.5 Hz and β 13.0-32.0 Hz) in different sleep stages. Finally, we show that the obtained results are highly consistent with other physiological assessments (standard EEG mapping, functional magnetic resonance imaging, etc.), but give us more realistic additional information on the generators of electromagnetic cerebral activity.