This paper presents an efficient method for recovery of SIMS signals from strongly noised blurred discrete data. This technique is based on Tikhonov-Miller regularization where a priori model of solution is included. The latter is a denoisy signal obtained using the Kalman filter. This is an interesting estimation method, but it can only be used when the system is described precisely. By comparing the results of the proposed technique with those of the literature, our algorithm gives the best results without artifacts and oscillations related to noise and significant improvement of the depth resolution. While, the gain in FWHM is less improved than those obtained by the wavelet technique. Therefore, this new algorithm can push the limits of SIMS measurements towards its ultimate resolution.

In this paper, we propose a new numerical method for acoustics microwaves detection of an acoustics microwaves signal during the propagation of acoustics microwaves in a piezoelectric substrate zinc oxide (ZnO). We have used support vector machines (SVMs), the originality of this method is the accurate values that provides this technique help to identify undetectable waves that we can not identify with the classical methods. We classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the types of microwaves acoustics (bulk waves or surface waves or leaky waves). We obtain accurate values for each of the coefficient attenuation and acoustic velocity. This study will be very interesting in modelling and realisation of acoustics microwaves devices (ultrasound, radiating structures, filter SAW…) based on the propagation of acoustics microwaves.

Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3) ,during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

In this paper, we present a rigorous full-wave analysis able to estimate exactly the resonant characteristics of stacked high T_c superconducting circular disk microstrip antenna. The superconducting patches are assumed to be embedded in a multilayered substrate containing isotropic and/or uniaxial anisotropic materials (the analysis is valid for an arbitrary number of layers). London’s equations and the two-fluid model of Gorter and Casimir are used in the calculation of the complex surface impedance of the superconducting circular disks. Numerical results are presented for a single layer structure as well as for two stacked circular disks fabricated on a double-layered substrate.