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Publications by Year: 2020

2020
Bentrcia T, Djeffal F, Ferhati H, Dibi Z. A comparative study on scaling capabilities of Si and SiGe nanoscale double gate tunneling FETs. Silicon. 2020;12 (4) :945-953.
Salah SOUDANIM, Elmoundher AOUICHE, Abdelaziz AOUICHE, Mounia TALEB, Kheireddine C, Zaamouche F. Comparison between a Passive and Active Suspension Vehicle using PID and Fuzzy Controllers with Two Entries Applied on Quarter Vehicle Model. system. 2020;8 :9.
Khennaoui A-A, Ouannas A, Momani S, Batiha IM, Dibi Z, Grassi G. On Dynamics of a Fractional-Order Discrete System with Only One Nonlinear Term and without Fixed Points. Electronics. 2020;9 (12) :2179.
Houamed I, Saidi L, Srairi F. ECG signal denoising by fractional wavelet transform thresholding. Research on Biomedical Engineering. 2020;36 (3) :349-360.
Ouali MA, Tinouna A, GHANAI M, CHAFAA K. Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection. International Journal Bioautomation. 2020;24 (4) :323.
Houamed I, Saidi L. Fractional Wavelet-based QRS Detector. International Journal on Electrical Engineering and Informatics. 2020;12 (1) :82-93.
BENCHERIF H, Dehimi L, Pezzimenti F, Yousfi A, Abdi MA, Saidi L, Della Corte FG. Improved InxGa1_xP/GaAs/Ge tandem solar cell using light trapping engineering and multi-objective optimization approach. Optik. 2020;223 :165346.
Yakhelef M, Saidi L. A LINEAR HYBRID MULTI-USER DETECTOR BASED ON SUCCESSIVE INTERFERENCE CANCELLATION AND DECORRELATOR DETECTOR FOR DS/CDMA SYSTEM. Telecommunications and Radio Engineering [Internet]. 2020;79 (1). Publisher's VersionAbstract

A linear hybrid multi-user detector in synchronous direct sequence code division multiple access systems (DS/CDMA) is performed by using linear successive interference cancellation detector (SIC) and linear decorrelator detector in order to exploit the advantages offered by the two detectors. Its main objectives are to reduce the long detection delay times and to improve the performance compared to that of the conventional SIC. Finally, theoretical and simulation results are in perfect agreement demonstrating the effectiveness of the elaborated scheme.

Yakhelef M, Saidi L. Low-complexity iterative detection based on parametric aor for uplink massive mimo systems. Telecommunications and Radio Engineering. 2020;79 (18).
Menacer F, Kadr A, Dibi Z. Modeling of a smart nano force sensor using finite elements and neural networks. International Journal of Automation and Computing. 2020;17 (2) :279-291.
Khezzar ZA, Benzid R, Saidi L. New Thresholding Technique in DCT Domain for Interference Mitigation in GNSS Receivers. Traitement du Signal. 2020;37 (2).
Bounouara N, GHANAI M, MEDJGHOU A, CHAFAA K. Stable and robust control strategy using interval-valued fuzzy systems. International Journal of Applied. 2020;9 (3) :205-217.
Ouali MA, GHANAI M, CHAFAA K. TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising. Traitement du Signal. 2020;37 (4).
Ouali MA, GHANAI M, CHAFAA K. TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising. [Internet]. 2020. Publisher's VersionAbstract

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-modelbased methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.