Study of the Impact of Non-linear Piezoelectric Constants on the Acoustic Wave Propagation on Lithium Niobate

Impact of nonlinear piezoelectric constants on surface acoustic wave propagation on a piezoelectric substrate is investigated in this work. Propagation of acoustic wave propagation under uniform stress is analyzed; the wave equation is obtained by incorporating the applied uniform stress in the equation of motion and taking account of the set of linear and nonlinear piezoelectric constants. A new method of separation between the different modes of propagation is proposed regarding the attenuation coefficients and not to the displacement vectors. Detail calculations and simulations have made for Lithium Niobate (LiNbO3); transformations between modes of propagation, under uniform stress, have been found. These results leads to conclusion that nonlinear terms affect the acoustic wave propagation and also we can make controllable acoustic devices

NEW MODEL OF A SOLAR WIND AIRPLANE FOR GEOMATIC OPERATIONS

The ability for an aircraft to fly during a much extended period of time has become a key issue and a target of research, both in the domain of civilian aviation and unmanned aerial vehicles. This paper describes a new design and evaluating of solar wind aircraft with the objective to assess the impact of a new system design on overall flight crew performance. The required endurance is in the range of some hours in the case of law enforcement, border surveillance, forest fire fighting or power line inspection. However, other applications at high altitudes, such as geomatic operations for delivering geographic information, weather research and forecast, environmental monitoring, would require remaining airborne during days, weeks or even months. The design of GNSS non precision approach procedure for different airports is based on geomatic data

Nonlinear wavelet regression function estimator for censored dependent data

Let (Y;C;X) be a vector of random variables where Y; C and X are, respectively, the interest variable, a right censoring and a covariable (predictor). In this paper, we introduce a new nonlinear wavelet-based estimator of the regression function in the right censorship model. An asymptotic expression for the mean integrated squared error of theestimator is obtained to both continuous and discontinuous curves. It is assumed that the lifetime observations form a stationary ..mixing sequence. Resume. Soit (Y;C;X) un vecteur de variables aleatoires ou Y;C et X sont, respectivement, la variable d'inter^et, une censure a droite et une covariable (predicteur). Dans cet article, nous introduisons un nouveau estimateur de la fonction de regression base sur les ondelettes non lineaire dans le modele de la censure a droite. Une expression asymptotique de l'erreur quadratique moyenne integree de l'estimateur est obtenue pour les deux courbes continues et discontinues. On suppose que les observations de la duree de vie forment une suite &#945-melangeante

Нова технологія деконволюції для вдосконалення глибини різкості в мас-спектрометрії вторинних іонів

У роботі запропоновано ефективний метод відновлення сигналів SIMS від сильно розмитих дискретних піків. Ця методика грунтується на регуляризації Тихонова-Міллера, де включена апріорна модель розв’язку. Останній – це шумопригнічуючий сигнал, отриманий при використанні фільтра Калмана. Це цікавий методом оцінки, але він може бути використаний тільки тоді, коли ми можемо точно описати наш зразок. Порівнюючи результати запропонованої методики з результатами літератури, наш алгоритм дає найкращі результати без артефактів і коливань, пов'язаних з шумом, і значного поліпшення глибинного аналізу, у той час як коефіцієнт підсилення менш поліпшений, ніж коефіцієнт, отриманий методом вейвлетів. Таким чином, цей новий алгоритм може розширити межі вимірювань SIMS до граничної роздільної здатності.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

Denoising Medical Ultrasound Images and Error Estimate by

Speckle Noise is a natural characteristic of medical ultrasound images. It is a term used for the granular form that appears in B-Scan and can be considered as a kind of multiplicative noise. Speckle Noise reduces the ability of an observer to distinguish fine details in diagnostic testing. It also limits the effective implementation of image processing such as edge detection, segmentation and volume rendering in 3 D. Therefore; treatment methods of speckle noise were sought to improve the image quality and to increase the capacity of diagnostic medical ultrasound images. Such as median filters, Wiener and linear filters (Persona & Malik, SRAD.....).The method used in this work is 2-D translation invariant forward wavelet transform, it is used in image processing, including noise reduction applications in medical imaging

Tikhonov-Miller regularization with a denoisy and deconvolved signal as model of solution for improvement of depth resolution in SIMS analysis

In this paper the improvement by deconvolution of the depth resolution in Secondary Ion Masse Spectrometry (SIMS) analysis is studied. Indeed, a new Tikhonov-Miller deconvolution method, where a priori model of solution is included. The latter is a denoisy and pre-deconvolved signal obtained firstly by the application of wavelet shrinkage algorithm and after, by the introduction of the obtained denoisy signal in an iterative deconvolution algorithm. The results of the proposed algorithm are compared to those of Tikhonov-Miller regularization where the model of solution is a raw signal. Finally, based on the obtained results the advantages and limitations of the proposed method as well as suggestions for future work are presented and discussed.Anglai