2018
Assia A.
Aspect épidémio-clinique de la rougeole chez 131 adultes. Premier congrès de la société algérienne d’infectiologie et 5e congrès de la fédération arabe des sociétés de microbiologie clinique et des maladies infectieuses. 2018.
Bouhadeb CE, MENANI MR, Bouguerra H, Derdous O.
Assessing soil loss using GIS based RUSLE methodology. Case of the Bou Namoussa watershed–North-East of Algeria. Journal of Water and Land DevelopmentJournal of Water and Land Development. 2018.
Belkhiri L, Tiri A, Mouni L.
Assessment of heavy metals contamination in groundwater: a case study of the south of Setif area, East Algeria. Achievements and Challenges of Integrated River Basin ManagementAchievements and Challenges of Integrated River Basin Management. 2018 :17-31.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
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 Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
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.
Abderrahim Y, Aissi S, Bencherif H, Saidi L.
A.Yousfi, Z.Dibi, S.Aissi, H.Bencherif and L.SaidiRF/Analog Performances Enhancement of Short Channel GAAJ MOSFET using Source/Drain Extensions and Metaheuristic Optimization-based Approach. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 No. 2, pp. 81-90.ISSN: 2180 – 1843 e-ISSN: 2289-8131Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 No. 2, pp. 81-90.ISSN: 2180 – 1843 e-ISSN: 2289-8. 2018;10 :81-90.
AbstractThis 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.
Djouima M, Drid S, Mehdi D.
Backstepping glycemic control of type 1 Diabetes for implementation on an embedded system. The International Journal Bioautomation.The International Journal Bioautomation. 2018;22 :117-132.
Boudra S, Yahiaoui I, Behloul A.
Bark identification using improved statistical radial binary patterns. 2018 International conference on content-based multimedia indexing (CBMI). 2018 :1-6.
Belferdi W, Behloul A, Noui L.
A Bayer pattern-based fragile watermarking scheme for color image tamper detection and restoration. Multidimensional Systems and Signal ProcessingMultidimensional Systems and Signal Processing. 2018;30 :1093–1112.
AbstractThe security of multimedia documents becomes an urgent need, especially with the increasing image falsifications provided by the easy access and use of image manipulation tools. Hence, usage of image authentication techniques fulfills this need. In this paper, we propose an effective self-embedding fragile watermarking scheme for color images tamper detection and restoration. To decrease the capacity of insertion, a Bayer pattern is used to reduce the color host image into a gray-level watermark, to further improve the security Torus Automorphism permutation is used to scramble the gray-level watermark. In our algorithm, three copies of the watermark are inserted over three components (R, G, and B channels) of the color host image, providing a high probability of detection accuracy and recovery if one copy is destroyed. In the tamper detection process, a majority voting technique is used to determine the legitimacy of the image and recover the tampered regions after interpolating the extracted gray-level watermark. Using our proposed method, tampering rate can achieve 25% with a high visual quality of recovered image and PSNR values greater than 34 (dB). Experimental results demonstrate that the proposed method affords three major properties: the high quality of watermarked image, the sensitive tamper detection and high localization accuracy besides the high-quality of recovered image.
MCHEBILA.
Bayesian Networks for Frequency Analysis in Dependability. J Fail. Anal. and PrevenJ Fail. Anal. and Preven. 2018;2018 :538–544.
AbstractThe high suppleness of Bayesian networks has led to their wide application in a variety of dependability modeling and analysis problems. The main objective of this paper is to extend the use of such powerful tool to estimate the occurrence frequency of failures and consequences in a straightforward way. Such extension is based on the employment of a transformation operator to substitute the original terms with matrices that hold the full dependability description of the corresponding element. Two simple case studies in reliability and safety contexts are treated using the suggested method whose results are validated through their comparison to the corresponding results of other classical dependability techniques.
Medjghou A, Ghanai M, Chafaa K.
BBO optimization of an EKF for interval type-2 fuzzy sliding mode control. International Journal of Computational Intelligence SystemsInternational Journal of Computational Intelligence Systems. 2018;11 :770–789.
AbstractIn this study, an optimized extended Kalman filter (EKF), and an interval type-2 fuzzy sliding mode control (IT2FSMC) in presence of uncertainties and disturbances are presented for robotic manipulators. The main contribution is the proposal of a novel application of Biogeography-Based Optimization (BBO) to optimize the EKF in order to achieve high performance estimation of states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the performances of EKF. The interval type-2 fuzzy logic system is used to avoid chattering phenomenon in the sliding mode control (SMC). Lyapunov theorem is used to prove the stability of control system. The suggested control approach is demonstrated using a computer simulation of two-link manipulator. Finally, simulations results show the robustness and effectiveness of the proposed scheme, and exhibit a more superior performance than its conventional counterpart.
Karech T, Benseghir A, Bouzid T.
The Behavior of Dam Foundation Reinforced by Stone Columns: Case Study of Kissir Dam-Jijel. International Journal of Civil and Environmental EngineeringInternational Journal of Civil and Environmental Engineering. 2018;11 :1187-1191.
Mechouma R, Mebarki H, Azoui B.
Behavior of nine levels NPC three-phase inverter topology interfacing photovoltaic system to the medium electric grid under variable irradiance. Springer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical EngineeringSpringer-Verlag GmbH Germany, part of Springer Nature 2018, Electrical Engineering. 2018.
Khedidja A, Boudoukha A, Djenba S.
Bibliographic information. 2018.
Zerari N, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional recurrent end-to-end neural network classifier for spoken Arab digit recognition. 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP). 2018 :1-6.
Naima Z, Abdelhamid S, Bouzgou H, Raymond C.
Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition. 2nd International Conference on Natural Language and Speech Processing (ICNLSP) [Internet]. 2018.
Publisher's VersionAbstractAutomatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.
Boubiche S, Boubiche DE, Bilami A, Toral-Cruz H.
Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks. IEEE AccessIEEE Access. 2018;6 :20558 - 20571.
AbstractThe emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.