2018
Ben Attia H, KAHLOUL L, BENHARZALLAH S.
A new hybrid access control model for security policies in multimodal applications environments. J. Univ. Comput. SciJ. Univ. Comput. Sci. 2018;24 :392-416.
Ben Attia H, KAHLOUL L, BENHARZALLAH S.
A new hybrid access control model for security policies in multimodal applications environments. J. Univ. Comput. SciJ. Univ. Comput. Sci. 2018;24 :392-416.
Ben Attia H, KAHLOUL L, BENHARZALLAH S.
A new hybrid access control model for security policies in multimodal applications environments. J. Univ. Comput. SciJ. Univ. Comput. Sci. 2018;24 :392-416.
Boutabba T, Drid S, Alaoui LC, Benbouzid M.
A New Implementation of Maximum Power Point Tracking based on Fuzzy Logic Algorithm for Solar Photovoltaic System. International Journal of Engineering, Transactions AInternational Journal of Engineering, Transactions A. 2018;31 :184-191.
Boutabba T, Drid S, Alaoui LC, Benbouzid M.
A New Implementation of Maximum Power Point Tracking based on Fuzzy Logic Algorithm for Solar Photovoltaic System. International Journal of Engineering, Transactions AInternational Journal of Engineering, Transactions A. 2018;31 :184-191.
Boutabba T, Drid S, Alaoui LC, Benbouzid M.
A New Implementation of Maximum Power Point Tracking based on Fuzzy Logic Algorithm for Solar Photovoltaic System. International Journal of Engineering, Transactions AInternational Journal of Engineering, Transactions A. 2018;31 :184-191.
Boutabba T, Drid S, Alaoui LC, Benbouzid M.
A New Implementation of Maximum Power Point Tracking based on Fuzzy Logic Algorithm for Solar Photovoltaic System. International Journal of Engineering, Transactions AInternational Journal of Engineering, Transactions A. 2018;31 :184-191.
Mechaour SS, DERARDJA A, Deen JM, Selvaganapathy PR.
New Morphology of a Silver Chloride Surface Grown on Silver Wires. In: Improved Performance of Materials. Springer ; 2018. pp. 63-71.
Mechaour SS, DERARDJA A, Deen JM, Selvaganapathy PR.
New Morphology of a Silver Chloride Surface Grown on Silver Wires. In: Improved Performance of Materials. Springer ; 2018. pp. 63-71.
Mechaour SS, DERARDJA A, Deen JM, Selvaganapathy PR.
New Morphology of a Silver Chloride Surface Grown on Silver Wires. In: Improved Performance of Materials. Springer ; 2018. pp. 63-71.
Mechaour SS, DERARDJA A, Deen JM, Selvaganapathy PR.
New Morphology of a Silver Chloride Surface Grown on Silver Wires. In: Improved Performance of Materials. Springer ; 2018. pp. 63-71.
Briki L, Lahbari N.
New plasticity model using artificial neural networks. International Journal of Structural EngineeringInternational Journal of Structural Engineering. 2018;9 :258-271.
Briki L, Lahbari N.
New plasticity model using artificial neural networks. International Journal of Structural EngineeringInternational Journal of Structural Engineering. 2018;9 :258-271.
F.Menacer, Zohir D, A.Kadri, Fayçal DJEFFAL.
A new smart nanoforce sensor based on suspended gate SOIMOSFET using carbon nanotube ISSN / e-ISSN 0263-2241 / 1873- 412X. MeasurementMeasurement. 2018;Volume 125 :Pages 232-242.
AbstractThis 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.
F.Menacer, Zohir D, A.Kadri, Fayçal DJEFFAL.
A new smart nanoforce sensor based on suspended gate SOIMOSFET using carbon nanotube ISSN / e-ISSN 0263-2241 / 1873- 412X. MeasurementMeasurement. 2018;Volume 125 :Pages 232-242.
AbstractThis 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.
F.Menacer, Zohir D, A.Kadri, Fayçal DJEFFAL.
A new smart nanoforce sensor based on suspended gate SOIMOSFET using carbon nanotube ISSN / e-ISSN 0263-2241 / 1873- 412X. MeasurementMeasurement. 2018;Volume 125 :Pages 232-242.
AbstractThis 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.
F.Menacer, Zohir D, A.Kadri, Fayçal DJEFFAL.
A new smart nanoforce sensor based on suspended gate SOIMOSFET using carbon nanotube ISSN / e-ISSN 0263-2241 / 1873- 412X. MeasurementMeasurement. 2018;Volume 125 :Pages 232-242.
AbstractThis 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.