Timgad Basin is a part of the North-East Algerian Saharan Atlas. It is located at the northern of the Aurès Mountain. It is bordered in the North by Bou Arif, Amrane and Tagratine mountains, while in the South it is limited by Chelia, Rdam and Nerdi Bouhmar mountains. It is shaped as a synclinal gutter oriented East-West and covers an area of about 1000 Km2 . The climate is semi-arid (cold winter and hot summer) with average annual rainfall not exceeding 350 mm. The basin is composed by varied lithology, showing large folded structures down sloping towards the center, which favor groundwater storage. The Miocene sandstone aquifer is the most important. It is characterized by a wavy shape, which form series of shale-marl filling depressions. Recent wells drilled in the basin edges and center, put in evidence its artesian character. The piezometric survey shows a groundwater convergent flow oriented towards the basin east (Bou el Freis), likewise the sandstone aquifer is marked by the presence of a West-East drainage axis which separates the basin northern part characterized by a low reservoirs hydraulic capacity from the southern part distinguished by the groundwater relative abundance.

Electrocardiogram (ECG) signal with artifact might lead to wrong analysis. In this paper, discrete cosine transform (DCT) denoising method for artifacts removal from ECG signals is proposed. Based on the energy properties of DCT transform is located in the low frequency coefficients and baseline wander (BW) noise is also a low frequency signal; therefore we deduce that the firsts coefficients of the DCT of the ECG signal is corresponds to the BW artifact in it. In our approach the BW is estimated from the low coefficients of the DCT transform, and the corrected ECG signal is obtained by its subtraction from noisy ECG signal in the time domain. The simulation results are done based on real ECG signals taken from MIT-BIH arrhythmia database whose corrupted with the BW noise from MIT-BIH noise stress test database. A comparison analysis of the median filtering (MF), discrete wavelet transform (DWT) decomposition and DCT transform for ECG signal denoising is done. The obtained results showed that our proposed method based on DCT transform gives better performances compared to MF and DWT methods in terms of Signal to Noise Ratio (SNR), and Pearson correlation parameter.

Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.

This paper presents a hybrid strategy combining compact analytical models of short channel Gate-All-Around Junctionless (GAAJ) MOSFET and metaheuristic-based approach for parameters optimization. The proposed GAAJ MOSFET design includes highly extension regions doping. The aim is to investigate the impact of this design on the RF and analog performances systematically and to show the immunity behavior against the short channel effects (SCEs) degradation. In this context, an analytical model via the meticulous solution of 2D Poisson equation, incorporating source/drain (S/D) extensions effect, has been developed and verified by comparing it with TCAD simulation results. A comparative evaluation between the proposed GAAJ MOSFET structure and the classical device in terms of RF/Analog performances is also investigated. The proposed design provides RF/Analog performances improvement. Furthermore, based on the presented analytical models, Genetic Algorithms (GA) optimization approach is used to optimize the design of S/D parameters. The optimized structure exhibits better performances, i.e., cut-off frequency and drive current are improved. Besides, it shows superior immunity behavior against the RF/Analog degradation due to the unwanted SCEs. The insights offered by the proposed paradigm will help to enlighten designer in future challenges facing the GAAJ MOSFET technology for high RF/analog applications.