A new multispectral InGaZnO (IGZO) thin-film phototransistor (TF PT) based on a graded band-gap (GBG) SiGe capping layer with metallic nanoparticles (MNPs) is proposed. An accurate drain-current model is developed to investigate the device performances, where the optical characteristics under different light excitations (530 nm, 820 nm, and 1550 nm) are analyzed using the 3-D Finite-difference time-domain method (FDTD). It is found that the proposed device shows high photoresponse characteristics. Besides, it is revealed that the GBG configuration, MNPs spatial distribution and size can induce a complex behavior, which influences the device photoresponse over multiple spectral bands. Importantly, an iterative decision-maker framework based on the Multi-Objective Genetic Algorithm (MOGA) metaheuristic approach is implemented to design efficient multispectral IGZO TF PT. It is demonstrated that the proposed MOGA-based scheme paves the way for the designer to identify the appropriate GBG profile and MNPs spatial distribution for highly-responsive devices at selective Visible and IR wavelengths and to realize high-performance multispectral sensors. The proposed approach based on combining the proposed IGZO TF PT structure with MOGA metaheuristic computation opens up a new strategy for the design and experimental fabrication of high-performance multispectral optoelectronic devices.

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.