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 (SVM) ,the originality of this method is the accurate values that provides .this technic help us to identify undetectable waves that we can not identify with the classical methods; in which 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) . By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity. This study will be very interesting in modeling and realization of acoustics microwaves devices (ultrasound ,Radiating structures , Filter SAW ….) based on the propagation of acoustics microwaves.

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n this paper, we propose a new method for Bulk waves detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO₃). 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 in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials.

By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Our work is mainly about detecting 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.