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.

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.

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.

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.

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.

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.

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.