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.

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.

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.

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.

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.