This paper presents a new nanoforce sensor based on a suspended carbon nanotube gate field-effect transistor. To do so, a numerical investigation of Suspended Gate Silicon-on-Insulator MOSFET (SG-SOIMOSFET) is carried out using ATLAS 2D simulator. Based on the relationship between the nanotube’s deflection and the applied force, a comprehensive study of the proposed nanoforce sensor behavior is performed. Moreover, we describe the evolution of the drain current characteristics as a function of the applied force while examining the influence of capacity variation of the insulating gate on the drain current in the saturation region. It is found that the sensor has a good sensitivity of 230.68 ln(A)/pN. Our second contribution in this paper is to develop a model based on artificial neural networks (ANNs). We successfully integrate our neural model of nanoforce sensor as a new component in the ORCAD-PSPICE electric simulator library; this component must accurately express the behavior of the sensor. A second model based on neural networks, which deals with correction and linearization of the sensor output signal, is designed and implemented into the same simulator. The proposed device can be considered as a potential alternative for CMOS-based nanoforce sensing.

Nowadays, the security of communication becomes very important with the rapid development of network technology. So, the transmission and distribution of the several digital information must be protected and secured against other users. Many steganography techniques have been proposed for embedding secret digital data in other digital data. In this article, we propose a new steganography algorithm based on a linear algebraic tool that is the polar decomposition (PD) for hiding secret data in an image. A host image is selected and divided into blocks of size 2 × 2, a PD is applied on each block, and the secret data are embedded in suitable blocks. Experimental results show that our proposed algorithm gives a higher hiding capacity, achieves good imperceptibility, and also provides a high degree of security against common types of attacks such as compression attack with quality 10%, gamma correction attack, and impulse noise attack.

In this investigation a novel type-2 fuzzy model for electrophysiological signals is presented. It is based on interval type-2 fuzzy systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserve the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first is the primary model, which represents an ordinary model with a low resolution for the electrophysiological signal under consideration, the second is a fuzzy sub-model called the error model, which represents uncertainty in the primary model. Identification is achieved by innovative metaheuristic optimisation algorithms. The method’s effectiveness is evaluated through testing on synthetic and real ECG signals. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed method can significantly improve convergence and resolution.

The hydrochemical and multivariate statistical techniques such as the principal component analysis (PCA) and the cluster analysis (CA) were used to identify the hydrochemical processes and their relation with groundwater quality and also to get an insight into the hydrochemical Zana aquifer groundwater chemistry evaluation. Twenty-four samples during the wet season and even during the dry season are analyzed. The Piper diagram showed that water facies are magnesium bicarbonate on the sides of the western reliefs and magnesium chloride-sulfated at the north and the center of the plain. The PCA carried out on three factors revealed that on the factorial design F1-F3, nitrates negatively determine factor 3, indicating the presence of an agriculture pollution. On the factorial design F1-F2, HCO3- positively determine the factor 2, indicating the carbonated origin. However, the CA, based on variables, showed that the waters in the region can be classified into three groups according to flow direction while the CA, based on major ion contents, defined three groups, reflecting the same hydrochemical facies. The first group with dry residue varying between 360 and 1700 mg/l and characterized by Mg2+ and Cl-, HCO3-. Samples of this group are mostly located in the north and northeastern part of the region. The second group with highest dry residue (2080 to 3820 mg/l) characterized by Mg2+ and SO4-, Cl- is located near the Northwestern and western outcrops. The third group coincides with the central part, the lowest of the plain, with heightened dry residue (4140 to 13,950 mg/l), characterized by Mg2+ and SO4-. The hydrochemical study made it possible to allot the evaporitic origin to the elements Na+, Mg2+, K+, Cl-, and SO4-, while for element HCO3-, it results from the carbonated formations. These results showed that the presence of nitrates in the studied area is closely linked to the agricultural activity.

In this paper, a new radiation sensitive field-effect Transistor (RADFET) dosimeter design based on armchair-edge graphene nanoribbon (AGNR), for high performance low-dose monitoring applications, is proposed through a quantum simulation study. The simulation approach used to investigate the proposed nanoscale RADFET is based on solving the Schrödinger equation using the mode space (MS) non-equilibrium Green’s function (NEGF) formalism coupled self-consistently with a two dimensional (2D) Poisson equation under the ballistic limits. The responsiveness of the proposed RADFET to the modulation of radiation-induced trapped charge densities is reflected via the threshold voltage, which is considered as a sensing parameter. The dosimeter behavior is investigated, and the impact of variation in physical and geometrical parameters on the dosimeter sensitivity is also studied. In comparison to other RADFETs designs, the proposed radiation sensor provides higher sensitivity and better scalability, which are the main requirements for futuristic dosimeters. The obtained results make the suggested RADFET dosimeter as a viable and attractive replacement to silicon-based MOS dosimeters.

In this paper, we present a comprehensive investigation the impact of various high-k gate materials on both breakdown voltage and drain current of a vertical 4H-SiC-based power MOSFET, operating in the quasi-saturation regime. The device electrical behavior is numerically investigated using a TCAD-based computation provided by ATLAS 2D simulator. Moreover, the performance parameters, governing the power MOSFET breakdown characteristics are extracted in order to reveal the role of the high-k gate materials in improving the transistor electrical performance. The effect of the dielectric permittivity on the derived current capability in also analyzed. After, we conduct a sensitivity analysis of the breakdown voltage with several high-k materials (Al2O3, HfSiO4, HfO2, and TiO2) and different dielectric thicknesses. It is found that the proposed power MOSFET design exhibits improved electrical behavior not only enables enhancing the drain current but also allows achieving superior breakdown performance as compared to the conventional design, making it suitable for high-performance power electronic applications.