Communications

2018
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N. Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical and Engineering,University of , Algeria , December 17-18 . 2018 :1-4.
Bouguerra F, Saidi L. Simplified ANN for 256 QAM Symbol Equalization Over OFDM Rayleigh Channel. 7th IEEE International Conference on Smart Communications in Network Technologies (SACONET’18), October 27-31. 2018.
2017
Bouguerra F, Benacer I, Saidi L. MLP and RBF Symbol Tracking with 16 QAM Modulation Over Multipath Distorted Channel. International Conference on Advanced Systems and Electric Technologies (IC_ASET), January 14-17. 2017 :182-187.
Bouguerra F, Benacer I, Saidi L. MLP and RBF Symbol Tracking with 16 QAM Modulation Over Multipath Distorted Channel. International Conference on Advanced Systems and Electrical Technologies, (IC-ASET'2017) 14-17 January . 2017.
2016
Bouguerra F, Saidi L. Artificial Neural Network Applied on Channel Equalization. 1st National Seminar on Numeric Simulation in Applied Sciences (SNSA I-2016), December 15. 2016.
Bouguerra F, Saidi L. RBF Applied on Interference Cancellation on Distorted Channel. 1st National Seminar of mathematics (SNM’01),December 13. 2016.
BOUNOUARA N, CHAFAA K. Particle swarm optimization of a PD controller for robotic manipulators. The 9th International conference on Electrical Engineering and First Workshop on Robotics and Controls, , 02-04  Oct. 2016.
Douak F, Tafsast A, Fouan D, Ferroudji K, Bouakaz A, Benoudjit N. A wavelet optimization approach for microemboli classification using RF signals, in IEEE International Ultrasonics Symposium (IUS). September 18-21. Tours, France: IEEE ; 2016 :1-4.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.

Khelalef A, Ababsa F, Benoudjit N. A Sample Human Activity Recognition Technique Using DCT, in International Conference on Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Springer ; 2016.Abstract

In this paper, we present a simple new human activity recognition method using discrete cosine transform (DCT). The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.

Bouguerra F, Saidi L. ANN Symbol Decision Making in Multipath Distorted Channel with QPSK Modulation. 2nd International Conference on Pattern Analysis and Intelligent Systems (PAIS’16),November 16-17. 2016.
Bouguerra F, Saidi L. High Order Modulation BP-ANN Symbol Decision Making Over OFDM AWGN Channel. 3rd International Conference on Embedded Systems in Telecommunications and Instrumentation (ICESTI’16), October 24-26. 2016.
Bouguerra F. RBF Applied on Interference Cancellation on Distorted Channel. First National Seminar of MathematicsSeminars of laboratory MAM, Mentouri University Of Constantine ,- Tuesday 13 December . 2016.
Dib A, Hassam A, Srairi K, Saidi L. Numerical Modeling and Heuristic Algorithms for Nanogenerator Behavior Analysis. The Fourth International Conference on Advances in Information Processing and Communication Technology - IPCT2016. 2016 :86 – 90.