@proceedings {62645,
	title = {Modal Analysis of a Two Axis Photovoltaic Solar Tracker},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems},
	year = {2021},
	month = {2021},
	pages = {230-236},
	publisher = {Springer},
	abstract = {
	As compared to a fixed Photovoltaic (PV) system, a two axis solar tracker system can increase electrical energy production from 35\% to 45\% in a year. Vibration characteristic is an essential factor in evaluating the reliability and stability of solar tracker structure during operating course. In this paper, the free vibration behaviour (modal analysis) of 12\&nbsp;kW two axis PV solar tracker structure is investigated numerically. The modal analysis by using commercial finite element package (SOLIDWORKS SIMULATION) to identify the modal parameters of the tracker structure (natural frequencies and corresponding modal shapes). The simulation results obtained for tracker structure at maximum elevation angle (60deg) indicate that no resonant problem (according to ASHRAE Standard) during solar tracker operation under wind load (from 0 to 36\&nbsp;m/s).

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-92038-8_23},
	author = {Karim Ferroudji and Outtas, Touffik and Monkova, K}
}
@proceedings {64650,
	title = {Automatic microemboli characterization using convolutional neural networks and radio frequency signals},
	journal = {2018 International Conference on Communications and Electrical Engineering (ICCEE)},
	year = {2018},
	month = {2018},
	pages = {1-4},
	publisher = {IEEE},
	isbn = {1-72810-112-3},
	author = {Abdelghani Tafsast and Karim Ferroudji and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@proceedings {64651,
	title = {Automatic microemboli classification using convolutional neural networks and RF signals},
	journal = { International Conference on Communications and Electrical Engineering (ICCEE)},
	year = {2018},
	month = {2018},
	pages = {1-4},
	address = {El Oued, Alg{\'e}rie},
	abstract = {
	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.

},
	url = {https://ieeexplore.ieee.org/abstract/document/8634521},
	author = {Abdelghani Tafsast and Karim Ferroudji and Hadjijli, Mohamed Laid and Ayache Bouakaz and Nabil Benoudjit}
}
@article {64397,
	title = {An Automated Microemboli Detection and Classification System using Backscatter RF Signals and Differential Evolution},
	journal = {Australasian Physical \& Engineering Sciences in MedicineAustralasian Physical \& Engineering Sciences in Medicine},
	volume = {40},
	year = {2017},
	month = {2017},
	pages = {85-99},
	author = {Karim Ferroudji and Nabil Benoudjit and Ayache Bouakaz}
}
@mastersthesis {64398,
	title = {Particle characterization by ultrasound using artificial intelligence methods},
	year = {2017},
	month = {2017},
	type = {Doctorat},
	author = {Karim Ferroudji}
}
@proceedings {65493,
	title = {A wavelet optimization approach for microemboli classification using RF signals},
	year = {2016},
	month = {2016},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90\% for 2MHz and to 99.60\% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.},
	author = {Fouzi Douak and Abdelghani Tafsast and Damien Fouan and Karim Ferroudji and Ayache Bouakaz and Nabil Benoudjit}
}
@proceedings {65922,
	title = {MODELLING OF SMART THIN FILM THERMAL-CONDUCTIVITY HUMIDITY SENSOR USING ANN},
	journal = {1er Congr{\`e}s National de Physique et Chimie Quantique CPCQ 2015},
	year = {2015},
	month = {2015},
	author = {Karim Ferroudji and Nabil Benoudjit and Medjili, Fay{\c c}al}
}
@book {66317,
	title = {D{\'e}tection des particules par ultrason via l{\textquoteright}intelligence artificielle: Application {\`a} la caract{\'e}risation des emboles},
	year = {2014},
	month = {2014},
	publisher = {{\'E}ditions universitaires europ{\'e}ennes},
	organization = {{\'E}ditions universitaires europ{\'e}ennes},
	isbn = {3-8417-3012-4},
	author = {Karim Ferroudji and Nabil Benoudjit}
}
@proceedings {66687,
	title = {Empirical mode decomposition based support vector machines for microemboli classification},
	journal = {2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)},
	year = {2013},
	month = {2013},
	pages = {84-88},
	publisher = {IEEE},
	isbn = {1-4673-5540-2},
	author = {Karim Ferroudji and Nabil Benoudjit and Ayache Bouakaz}
}
@article {67338,
	title = {In vitro microemboli classification using neural network models and RF signals},
	journal = {UltrasonicsUltrasonics},
	volume = {51},
	year = {2011},
	month = {2011},
	pages = {247-252},
	isbn = {0041-624X},
	author = {Nabil Benoudjit and Karim Ferroudji and Bahaz, Mohamed and Ayache Bouakaz}
}
