@article {14352,
	title = {An Efficient Human Activity Recognition Technique Based on Deep Learning},
	journal = {Pattern Recognition and Image Analysis},
	volume = {29},
	number = {4},
	year = {2019},
	pages = {702-715},
	abstract = {In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It{\textquoteright}s simple to implement, requires less computing power, and can be used for multi-subject activity recognition.},
	url = {https://link.springer.com/article/10.1134/S1054661819040084},
	author = {Aziz Khelalef and Fakhreddine Ababsa and Nabil Benoudjit}
}
@conference {14368,
	title = {Automatic Microemboli Characterization using Convolutional Neural Networks and Radio Frequency},
	booktitle = {International Conference on Communications and Electrical Engineering (ICCEE)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {17-18 Dec., El Oued, Algeria},
	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/document/8634521},
	author = {Abdelghani Tafsast and Karim Ferroudji and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@article {14403,
	title = {An Automated Microemboli Detection and Classification System using Backscatter RF Signals and Differential Evolution},
	journal = {Australasian Physical \& Engineering Sciences in Medicine},
	volume = {40},
	year = {2017},
	pages = {85-99},
	abstract = {Embolic phenomena, whether air or particulate emboli, can induce immediate damages like heart attack or ischemic stroke. Embolus composition (gaseous or particulate matter) is vital in predicting clinically significant complications. Embolus detection using Doppler methods have shown their limits to differentiate solid and gaseous embolus. Radio-frequency (RF) ultrasound signals backscattered by the emboli contain additional information on the embolus in comparison to the traditionally used Doppler signals. Gaseous bubbles show a nonlinear behavior under specific conditions of the ultrasound excitation wave, this nonlinear behavior is exploited to differentiate solid from gaseous microemboli. In order to verify the usefulness of RF ultrasound signal processing in the detection and classification of microemboli, an in vitro set-up is developed. Sonovue micro bubbles are exploited to mimic the acoustic behavior of gaseous emboli. They are injected at two different concentrations (0.025 and 0.05\&nbsp;{\textmu}l/ml) in a nonrecirculating flow phantom containing a tube of 0.8\&nbsp;mm in diameter. The tissue mimicking material surrounding the tube is chosen to imitate the acoustic behavior of solid emboli. Both gaseous and solid emboli are imaged using an Anthares ultrasound scanner with a probe emitting at a transmit frequency of 1.82\&nbsp;MHz and at two mechanical indices (MI) 0.2 and 0.6. We propose in this experimental study to exploit discrete wavelet transform and a dimensionality reduction algorithm based on differential evolution technique in the analysis and the characterization of the backscattered RF ultrasound signals from the emboli. Several features are evaluated from the detail coefficients. It should be noted that the features used in this study are the same used in the paper by Aydin et al. These all features are used as inputs to the classification models without using feature selection method. Then we perform feature selection using differential evolution algorithm with support vector machines classifier. The experimental results show clearly that our proposed method achieves better average classification rates compared to the results obtained in a previous study using also the same backscatter RF signals.},
	url = {https://link.springer.com/article/10.1007/s13246-016-0512-4},
	author = {K. Ferroudji and N. Benoudjit and A. Bouakaz}
}
@article {14387,
	title = {Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in 18F-FDG-PET images},
	journal = {IET Image Processing},
	volume = {11},
	number = {6},
	year = {2017},
	pages = {389-396},
	abstract = {In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in\&nbsp;18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7\%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58\%), gradient-based method with (19.14\%) and threshold-based methods.},
	url = {http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1024},
	author = {Abdelghani Tafsat and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@article {14360,
	title = {Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning},
	journal = {Periodica Polytechnica Electrical Engineering and Computer Science},
	volume = {61},
	number = {4},
	year = {2017},
	pages = {337-349},
	abstract = {The path-planning problem is commonly formulated to handle the obstacle avoidance constraints. This problem becomes more complicated when further restrictions are added. It often requires efficient algorithms to be solved. In this paper, a new approach is proposed where the path is described by means of Non Uniform Rational B-Splines (NURBS for short) with more additional constraints. An evolutionary technique called Particle Swarm Optimization (PSO) with three options of particles velocity updating offering three alternatives namely the PSO with inertia weight (PSO-W), the constriction factor PSO (PSO-C) and the combination of the two(PSO-WC); are used to optimize the weights of the control points that serve as parameters of the algorithm describing the path. Simulation results show how the mixture of the first two options produces a powerful algorithm, specifically (PSO-WC), in producing a compromise between fast convergence and large number of potential solution. In addition, the whole approach seems to be flexible, powerful and useful for the generation of successful smooth trajectories for robot manipulator which are independent from environment conditions.},
	url = {https://pp.bme.hu/eecs/article/view/8682},
	author = {Nadjib Zerrouki and Noureddine Gol{\'e}a and Nabil Benoudjit}
}
@proceedings {14479,
	title = {A Simple Human Activity Recognition Technique Using DCT},
	journal = {17th International Conference on Advanced Concepts for Intelligent Vision Systems ACIVS 2016},
	volume = {10016},
	year = {2016},
	pages = {37-46},
	publisher = {Springer Nature},
	address = {Lecce, Italy},
	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.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-48680-2_4$\#$citeas},
	author = {Aziz Khelalef and Fakhreddine Ababsa and Nabil Benoudjit}
}
@conference {14476,
	title = {A wavelet optimization approach for microemboli classification using RF signals},
	booktitle = { 2016 IEEE International Ultrasonics Symposium (IUS)},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Tours, France},
	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.},
	url = {https://ieeexplore.ieee.org/abstract/document/7728884},
	author = {Fouzi Douak and Abdelghani Tafsast and Damien Fouan and Karim Ferroudji and Ayache Bouakaz and Nabil Benoudjit}
}
@conference {14690,
	title = {Automatic Gaussian Mixture Model (GMM) for segmenting 18F-FDG-PET images based on Akaike Information Criteria},
	booktitle = { 2015 4th International Conference on Electrical Engineering (ICEE)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Boumerdes, Algeria},
	abstract = {Positron emission tomography (PET) plays an important role in early tumour recognition, diagnosis and treatment. Automated and more accurate biological tumour volume (BTV) detection and delineation from PET is challenging. In this paper, we proposed a new method to segment (BTV) in\&nbsp;18\&nbsp;F-FDG-PET images using an automatic Gaussian mixture model (GMM) based on Akaike information criteria (AIC). The algorithm has been validated on two patients from seven had laryngeal tumours. The volumes estimated were compared with the macroscopic laryngeal specimens in which a 3-D biological tumour volume (BTV) defined by histology served as reference. Experimental results demonstrated that our method was able to segment the (BTV) more accurately than other threshold-based methods.},
	url = {https://ieeexplore.ieee.org/document/7416845},
	author = {Abdelghani Tafsat and Mohamed Laid Hadjili and Hichem Hafdaoui and Ayache Bouakaz and Nabil Benoudjit}
}
@conference {14698,
	title = {Neural Networks based approach for inverse kinematic modeling of a Compact Bionic Handling Assistant trunk},
	booktitle = {23rd International Symposium on Industrial Electronics (ISIE)},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {1-4 June, Istanbul, Turkey},
	abstract = {A common approach to resolve the problem of inverse kinematics of manipulators is based on the Jacobian matrix. However, depending on the complexity of the system to model the elements of the Jacobian matrices may not be calculated. To overcome intrinsic problems related to Jacobian matrix based methods, a new inverse kinematic modeling approach capable to approximate the inverse kinematics of a class of hyper-redundant continuum robots, namely Compact Bionic Handling Assistant (CBHA) is proposed in the present work. The proposed approach makes use of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks as approximation methods. A validation using a rigid 6 DOF industrial manipulator demonstrates the effectiveness and efficiency of the proposed approach.},
	url = {https://ieeexplore.ieee.org/document/6864791/authors$\#$authors},
	author = {A. Melingui and R. Merzouki and J. B. Mbede and C. Escande and N. Benoudjit}
}
@article {14410,
	title = {Kernel Ridge Regression with Active Learning for Wind Speed Prediction},
	journal = {Applied Energy},
	volume = {103},
	year = {2013},
	pages = {328-340},
	abstract = {This paper introduces the active learning approach for wind speed prediction. The main objective of active learning is to opportunely collect training samples in such a way as to minimize the error of the prediction process while minimizing the number of training samples used, and thus to reduce the costs related to the training sample collection. In particular, we propose three different active learning strategies, developed for kernel ridge regression (KRR). The first strategy uses a pool of regressors in order to select the samples with the greater disagreements between the different regressors, while the second one relies on the idea to add samples that are distant from the available training samples, and the last strategy is based on the selection of samples which exhibit a high expected prediction error. A thorough experimental study is presented. It is based on ten different wind speed measurement stations distributed over the vast Algerian territory. Promising results are reported, showing that a smart collection of training samples can be of benefit for wind speed prediction problems.},
	url = {http://www.sciencedirect.com/science/article/pii/S0306261912006964},
	author = {F. Douak and F. Melgani and N. Benoudjit}
}
@article {14434,
	title = {Support Vector Regression in Spectrophotometry: An experimental Study},
	journal = {Critical Reviews in Analytical Chemistry},
	volume = {42},
	number = {03},
	year = {2012},
	pages = {214-219},
	abstract = {In this work, we present a detailed experimental assessment of an interesting regression approach based on support vector machines (SVMs), a technique relatively recently introduced in the literature. The experimental framework reports a thorough investigation of the performance of SVMs from different viewpoints, including: (i) the influence of the kernel type in the SVM regression task; (ii) the sensitivity to the number of input variables (spectra dimension); (iii) the sensitivity to the available number of training samples; and (iv) the overall stability. The obtained results are compared with those yielded by the radial basis function (RBF) and the multilayer perceptron (MLP) neural networks as well as the traditional multiple linear regression (MLR) method on two different spectrophotometric datasets.},
	url = {http://www.tandfonline.com/doi/full/10.1080/10408347.2011.651945},
	author = {L. Douha and N. Benoudjit and F. Douak and F. Melgani}
}
@article {14431,
	title = {Microemboli Classification using Non-linear Kernel Support Vectors Machines and RF Signals},
	journal = {Journal of Automation \& Systems Engineering (JASE)},
	volume = {6},
	number = {2},
	year = {2012},
	pages = {123-132},
	author = {K. Ferroudji and N. Benoudjit and A. Bouakaz}
}
@article {14429,
	title = {A Robust Regression Approach For Spectrophotometric Signal Analysis},
	journal = {Journal of Chemometrics},
	volume = {26},
	number = {07},
	year = {2012},
	pages = {400-405},
	abstract = {The effectiveness of a regression method strongly depends on the characteristics of the considered regression problem. As a consequence, this makes it difficult to choose a priori the most appropriate algorithm for a given dataset. This issue is faced in this work through a novel regression approach based on the fusion of an ensemble of different regressors. In order to implement the proposed robust multiple system (RMS), four different fusion strategies are explored. In this context, we propose a novel fusion strategy named selection-based strategy (SBS) that provides as output the estimate obtained by the regression algorithm (included in the ensemble) characterized by the highest expected accuracy in the region of the feature space associated with the considered model. The SBS is based not on a direct combination of the estimates yielded by all the regressors but on a selection mechanism that identifies the expected best available estimate. For such purpose, it exploits the accuracies of the regressors included in the ensemble in different portions of the input feature space. The experimental assessment of the RMS was carried out on three different datasets: a wine, an orange juice, and an apple datasets. The obtained experimental results suggest that, in general, the fusion of an ensemble of different regression algorithms leads to a regression process that is more robust and sometimes also more accurate than traditional regression methods. In particular, the proposed SBS method represents an effective solution to carry out the fusion process.},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/cem.2455/abstract},
	author = {L. Douha and N. Benoudjit and F. Melgani}
}
@article {14428,
	title = {Active Learning for Spectroscopic Data Regression},
	journal = {Journal of Chemometrics},
	volume = {26},
	number = {07},
	year = {2012},
	pages = {374-383},
	abstract = {In this work, we introduce an active learning approach for the estimation of chemical concentrations from spectroscopic data. Its main objective is to opportunely collect training samples in such a way as to minimize the error of the regression process while minimizing the number of training samples used, and thus to reduce the costs related to training sample collection. In particular, we propose two different active learning strategies developed for regression approaches based on partial least squares regression, ridge regression, kernel ridge regression, and support vector regression. The first strategy uses a pool of regressors in order to select the samples with the greatest disagreements among the different regressors of the pool, while the second one is based on adding samples that are distant from the current training samples in the feature space. For support vector regression, a specific strategy based on the selection of the samples distant from the support vectors is proposed. Experimental results on three different real data sets are reported and discussed.},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/cem.2443/abstract},
	author = {F. Douak and F. Melgani and N. Alajlan and E. Pasolli and Y. Bazi and N. Benoudjit}
}
@article {14444,
	title = {Color image compression algorithm based on the DCT transform combined to an adaptive block scanning},
	journal = {International Journal of Electronics and communications (AEǗ)},
	volume = {65},
	number = {01},
	year = {2011},
	pages = {16 -26},
	abstract = {This paper considers the design of a lossy image\&nbsp;compression algorithm\&nbsp;dedicated to color still images. After a\&nbsp;preprocessing step\&nbsp;(mean removing and\&nbsp;RGB\&nbsp;to YCbCr transformation), the\&nbsp;DCT\&nbsp;transform is applied and followed by an iterative phase (using the bisection method) including the thresholding, the\&nbsp;quantization, dequantization, the inverse DCT, YCbCr to RGB transform and the mean recovering. This is done in order to guarantee that a desired quality (fixed in advance using the well known\&nbsp;PSNR\&nbsp;metric) is checked. For the aim to obtain the best possible\&nbsp;compression ratio\&nbsp;CR, the next step is the application of a proposed adaptive scanning providing, for each (n,\&nbsp;n) DCT block a corresponding (n{\texttimes}n) vector containing the maximum possible run of zeros at its end. The last step is the application of a modified systematic lossless\&nbsp;encoder. The efficiency of the proposed scheme is demonstrated by results, especially, when faced to the method presented in the recently published paper based on the\&nbsp;block truncation coding\&nbsp;using pattern fitting principle.},
	url = {http://www.sciencedirect.com/science/article/pii/S1434841110000695},
	author = {F. Douak and R. Benzid and N. Benoudjit}
}
@article {14443,
	title = {In Vitro Microemboli Classification using Neural Network Models and RF Signals},
	journal = {Ultrasonics},
	volume = {51},
	number = {04},
	year = {2011},
	pages = {247-252},
	abstract = {
	Emboli classification is of high clinical importance for selecting appropriate treatment for patients. Several ultrasonic (US) methods using Doppler processing have been used for emboli detection and classification as solid or gaseous matter. We suggest in this experimental study exploiting the Radio-Frequency (RF) signal backscattered by the emboli since they contain additional information on the embolus than the Doppler signal. The aim of the study is the analysis of RF signals using Multilayer Perceptron (MLP) and Radial-Basis Function Network (RBFN) in order to classify emboli.



	Anthares scanner with RF access was used with a transmit frequency of 1.82\&nbsp;MHz at two mechanical indices (MI) 0.2 and 0.6. The mechanical index is given as the peak negative pressure (in MPa) divided by the square root of the frequency (in MHz). A Doppler flow phantom was used containing a 0.8\&nbsp;mm diameter vessel surrounded by a tissue mimicking material. To imitate gas emboli US behaviour, Sonovue microbubbles were injected at two different doses (10μl and 5μl) in a nonrecirculating at a constant flow. The surrounding tissue was assumed to behave as a solid emboli. In order to mimic real clinical pathological situations, Sonovue concentration was chosen such that the fundamental scattering from the tissue and from the contrast were identical. The amplitudes and bandwidths of the fundamental and the 2nd harmonic components were selected as input parameters to the MLP and RBFN models. Moreover the frequency bandwidths of the fundamental and the 2nd harmonic echoes were approximated by Gaussian functions and the coefficients were used as a third input parameter to the neural network models. The results show that the Gaussian coefficients provide the highest rate of classification in comparison to the amplitudes and the bandwidths of the fundamental and the 2nd harmonic components. The classification rates reached 89.28\% and 92.85\% with MLP and RBFN models respectively.



	This short communication demonstrates the opportunity to classify emboli based on a RF signals and neural network analysis.

},
	url = {http://www.sciencedirect.com/science/article/pii/S0041624X10001253},
	author = {N. Benoudjit and K. Ferroudji and M. Bahaz and A. Bouakaz}
}
@article {14441,
	title = {Genetic Algorithm Based High Performance Control for Rigid Robot Manipulator},
	journal = {Journal of Computer Sciences and Computer Systems},
	volume = {04},
	number = {02},
	year = {2011},
	pages = {73-84},
	author = {N. Zerrouki and N. Gol{\'e}a and N. Benoudjit}
}
@article {14438,
	title = {Multiple Architecture System for Wind Speed Prediction},
	journal = {Applied Energy},
	volume = {88},
	number = {07},
	year = {2011},
	pages = {463-2471},
	abstract = {A new approach based on multiple architecture system (MAS) for the prediction of wind speed is proposed. The motivation behind the proposed approach is to combine the complementary predictive powers of multiple models in order to improve the performance of the prediction process. The proposed MAS can be implemented by associating the predictions obtained from the different regression algorithms (MLR, MLP, RBF and SVM) making up the ensemble by three fusion strategies (simple, weighted and non-linear). The efficiency of the proposed approach has been assessed on a real data set recorded from seven locations in Algeria during a period of 10\&nbsp;years. The experimental results point out that the proposed MAS approach is capable of improving the precision of the wind speed prediction compared to the traditional prediction methods.},
	url = {http://www.sciencedirect.com/science/article/pii/S0306261911000559},
	author = {H. Bouzgou and N. Benoudjit}
}
@article {14435,
	title = {A Two-Stage Regression Approach for Spectroscopic Quantitative Analysis},
	journal = {Chemometrics and intelligent laboratory systems},
	volume = {109},
	number = {01},
	year = {2011},
	pages = {34-41},
	abstract = {In this paper, we propose a two-stage regression approach, which is based on the residual correction concept. Its underlying idea is to correct any given regressor by analyzing and modeling its residual errors in the input space. We report and discuss results of experiments conducted on three different datasets in infrared spectroscopy and designed in such a way to test the proposed approach by: 1) varying the kind of adopted regression method used to approximate the chemical parameter of interest. Partial least squares regression (PLSR), support vector machines (SVM) and radial basis function neural network (RBF) methods are considered; 2) adopting or not a feature selection strategy to reduce the dimension of the space where to perform the regression task. A comparative study with another approach which exploits differently estimation errors, namely adaptive boosting for regression (AdaBoost.R), is also included. The obtained results point out that the residual-based correction approach (RBC) can improve the accuracy of the estimation process. Not all the improvements are statistically significant but, at the same time, no case of accuracy decrease has been observed.},
	url = {http://www.sciencedirect.com/science/article/pii/S0169743911001559},
	author = {F. Douak and N. Benoudjit and F. Melgani}
}
@article {14463,
	title = {Characterization of harmonic pressure field: Application to medical ultrasound imaging},
	journal = {International Journal of Computer Information Systems and Industrial Management Application (IJCISIM)},
	volume = {1},
	year = {2009},
	pages = {118-124},
	abstract = {Over the last few decades medical ultrasound has become an established diagnostic and therapeutic tool since it produces detailed and high resolution images of tissues in human body. Harmonic imaging is among the recent developments which has brought further improvements to the image quality. Harmonic imaging for tissue or with contrast agent induced a rapid evolution of this modality to diverse clinical uses, among which myocardial perfusion determination seems to be the most important application. This brought the need to understand the physical processes involved in the propagation of finite amplitude sound beams, and the issues for redesigning and optimizing the transducers with higher performances for both tissue imaging and contrast imaging. Concerning tissue harmonic imaging, the advantage of the harmonic beam generated at two times the transmit frequency are translated by reduced reverberations , greater depth of penetration at higher frequencies and improved resolution. In order to characterize the harmonic beam, a time domain solution of the parabolic nonlinear wave equation is used. This equation is traditionally applied in a propagation direction along the central transducer axis, and has been shown to model the pulse propagation satisfactorily. In this work, the characteristics and performances of the second harmonic acoustic beam from a focused piston aperture are described and the physical principles behind tissue harmonic imaging are computed. The field properties are then discussed regarding image quality. Special attention is given to the transmitted and received bandwidths variation and the reception of the pure echo signal.},
	url = {http://www.mirlabs.org/ijcisim/volume_1.html},
	author = {M. Bahaz and K. Benmahammed and N. Benoudjit and A. Bouakaz}
}
@article {14461,
	title = {Multiple Regression Systems for Spectrophotometric Data Analysis},
	journal = {Chemometrics and intelligent laboratory systems},
	volume = {95},
	number = {02},
	year = {2009},
	pages = {144-149},
	abstract = {
	In this paper, we propose a novel approach for the estimation of the concentration of chemical components through spectrophotometric measurements. It is based on the exploitation of the whole spectral information available in the original spectral data space by means of a Multiple Regression System (MRS) whose design is performed in three successive steps. The first one aims at a simple partitioning of the original spectral data space into subspaces of reduced dimensionality. The second step consists in training a (linear or\&nbsp;nonlinear) regression\&nbsp;method in each of the subspaces obtained in the previous step. In the third and final step, the estimates provided by the ensemble of\&nbsp;regressors\&nbsp;are combined in order to produce a global estimate of the concentration of the chemical component of interest. For this purpose, two linear and one nonlinear combination strategies are explored.



	The experimental assessment of the MRS was carried out on two different datasets: 1) a wine dataset for the determination of alcohol concentration by\&nbsp;mid-infrared spectroscopy; and 2) an orange juice dataset where near-infrared reflectance spectroscopy is used to estimate the saccharose concentration. The obtained results suggest that the proposed MRS approach represents a promising alternative to the traditional regression methods.

},
	url = {http://www.sciencedirect.com/science/article/pii/S0169743908001937},
	author = {N. Benoudjit and F. Melgani and H. Bouzgou}
}
@article {14469,
	title = {Improvements for face authentication using color information},
	journal = {WSEAS Transactions on Signal Processing},
	volume = {02},
	number = {03},
	year = {2006},
	pages = {343-350},
	url = {http://www.worldses.org/journals/signal/old.htm},
	author = {D. Saigaa and S. Lelandais and N. Benoudjit and K. Benmahammed}
}
@article {14470,
	title = {Using Enhanced Fisher linear discriminant Model (EFM) for Frontal Face Authentication},
	journal = {WSEAS Transactions on Computers},
	volume = {04},
	number = {12},
	year = {2005},
	pages = {1748-1753},
	url = {http://www.worldses.org/journals/computers/old.htm},
	author = {D. Saigaa and N. Benoudjit and K. Benmahammed and S. Lelandais}
}
@article {14474,
	title = {Spectrophotometric variable selection by mutual information},
	journal = {Chemometrics and intelligent laboratory systems},
	volume = {74},
	number = {02},
	year = {2004},
	pages = {243-251},
	abstract = {Spectrophotometric data often comprise a great number of numerical components or variables that can be used in\&nbsp;calibration models. When a large number of such variables are incorporated into a particular model, many difficulties arise, and it is often necessary to reduce the number of spectral variables. This paper proposes an incremental (Forward{\textendash}Backward) procedure, initiated using an entropy-based criterion (mutual information), to choose the first variable. The advantages of the method are discussed; results in quantitative chemical analysis by\&nbsp;spectrophotometry\&nbsp;show the improvements obtained with respect to traditional and nonlinear calibration models.},
	url = {http://www.sciencedirect.com/science/article/pii/S0169743904001406},
	author = {N. Benoudjit and D. Fran{\c c}ois and M. Meurens and M. Verleysen}
}
@article {14471,
	title = {Chemometric calibration of infrared spectrometers: Selection and validation of variables by non-linear models},
	journal = {Chemometrics and intelligent laboratory systems},
	volume = {70},
	number = {01},
	year = {2004},
	pages = {47-53},
	abstract = {Data from spectrophotometers form spectra that are sets of a great number of exploitable variables in quantitative chemical analysis; calibration models using chemometric methods must be established to exploit these variables. In order to design these calibration models which are specific to each analyzed parameter, it is advisable to select a reduced number of spectral variables. This paper presents a new incremental method (step by step) for the selection of spectral variables, using linear regression or neural networks, and based on an objective validation (external) of the calibration model; this validation is carried out on data that are independent from those used during calibration. The advantages of the method are discussed and highlighted, in comparison to the current calibration methods used in quantitative chemical analysis by spectrophotometry.},
	url = {http://www.sciencedirect.com/science/article/pii/S0169743903001825},
	author = {N. Benoudjit and E. Cools and M. Meurens and M. Verleysen}
}
@article {14475,
	title = {On the kernel widths in Radial-Basis Function Networks},
	journal = {Neural Processing Letters},
	volume = {18},
	number = {2},
	year = {2003},
	pages = {139-154},
	abstract = {RBFN (Radial-Basis Function Networks) represent an attractive alternative to other neural network models. Their learning is usually split into an unsupervised part, where center and widths of the basis functions are set, and a linear supervised part for weight computation. Although available literature on RBFN learning widely covers how basis function centers and weights must be set, little effort has been devoted to the learning of basis function widths. This paper addresses this topic: it shows the importance of a proper choice of basis function widths, and how inadequate values can dramatically influence the approximation performances of the RBFN. It also suggests a one-dimensional searching procedure as a compromise between an exhaustive search on all basis function widths, and a non-optimal a priori choice.},
	url = {https://link.springer.com/article/10.1023/A:1026289910256},
	author = {N. Benoudjit and M. Verleysen}
}
