Yaakoub BERROUCHE

@univ-setif.dz

Department of Electronics Faculty of Technology
Ferhat ABBAS Setif University 1

Yaakoub BERROUCHE


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https://researchid.co/berrouchey

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Mechanical Engineering, Biomedical Engineering
6

Scopus Publications

154

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • A Hyperbolic Secant-Based Pulse for Enhanced FTN Signaling in 5G/6G Systems
    Yaakoub Berrouche, Michel Kulhandjian, Hovannes Kulhandjian
    IEEE Wireless Communications Letters, 2026
  • Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG
    Hassan Ashraf, Asim Waris, Syed Omer Gilani, Uzma Shafiq, Javaid Iqbal, et al.
    Scientific Reports, 2024
    Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human–computer interfaces.
  • Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition
    Yaakoub Berrouche, Govind Vashishtha, Sumika Chauhan, Radoslaw Zimroz
    Knowledge Based Systems, 2024
  • Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects
    Anil Kumar, Yaakoub Berrouche, Radoslaw Zimroz, Govind Vashishtha, Sumika Chauhan, et al.
    Measurement Journal of the International Measurement Confederation, 2023
  • A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis
    Y. Berrouche
    Engineering Technology and Applied Science Research, 2022
    A Non-parametric Ensemble Empirical Mode Decomposition (NCEEMD) method is a novel technique for nonlinear and non-stationary signal analysis to detect a gearbox fault. The NCEEMD method was based on the CEEMD, but the Gaussian white noise was replaced by the fractional Gaussian noise. The NCEEMD method does not need to choose the appropriate SNR and the number of ensemble trials before signal processing, which makes it a non-parametric method. This new approach was evaluated using a simulated malfunction signal representing two typical faults in gearbox systems: modulation and rub-impact. Its performance was evaluated in terms of MSE and computation time. A comparative study between the EMD, EEMD, CEEMD, and NCEEMD methods showed that the latter performed better by improving the computation time and accuracy of CEEMD. The proposed method is a non-parametric method that provides a powerful tool in extracting the modulation and the rub-impact features from a vibration signal. The NCEEMD method helps to track down the gearbox faults and resolve this crucial problem in mechanical machines.
  • Improved multiple description wavelet based image coding using Hadamard Transform
    Yaakoub Berrouche, Raïs El’hadi Bekka
    AEU International Journal of Electronics and Communications, 2014

RECENT SCHOLAR PUBLICATIONS

  • Spectral-Domain Spreading via Hadamard Transform for Robust Downlink Non-Orthogonal Multiple Access
    Y Berrouche, M Kulhandjian, H Kulhandjian
    arXiv preprint arXiv:2603.07836 , 2026
    2026
  • From Noise to Prognosis: A Physics-Grounded, Fractional-Domain Framework for Early Gear Fault Detection in Aviation Drivetrains
    Y Berrouche
    arXiv preprint arXiv:2602.07527 , 2026
    2026
  • Fractional Filtering and Anomaly-Guided Diagnostics: The Local Damage Mode Extractor (LDME) for Early Gear Fault Detection
    Y Berrouche
    arXiv e-prints, arXiv: 2602.07527 , 2026
    2026
  • A Hyperbolic Secant-Based Pulse for Enhanced FTN Signaling in 5G/6G Systems
    Y Berrouche, M Kulhandjian, H Kulhandjian
    IEEE Wireless Communications Letters , 2025
    2025
    Citations: 1
  • Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition
    Y Berrouche, G Vashishtha, S Chauhan, R Zimroz
    Knowledge-Based Systems 301, 112265 , 2024
    2024
    Citations: 38
  • Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG
    H Ashraf, A Waris, SO Gilani, U Shafiq, J Iqbal, EN Kamavuako, ...
    Scientific reports 14 (1), 2020 , 2024
    2024
    Citations: 22
  • Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects
    A Kumar, Y Berrouche, R Zimroz, G Vashishtha, S Chauhan, CP Gandhi, ...
    Measurement 211, 112615 , 2023
    2023
    Citations: 61
  • A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis
    Y Berrouche
    Engineering, Technology & Applied Science Research 12 (1), 8058-8062 , 2022
    2022
    Citations: 5
  • Contribution à l’amélioration du Codage par descriptions multiples
    Y Berrouche
    2017
  • Improved multiple description wavelet based image coding using Hadamard transform
    Y Berrouche, RE Bekka
    AEU-International Journal of Electronics and Communications 68 (10), 976-982 , 2014
    2014
    Citations: 15
  • Improvement of ensemble empirical mode decomposition by over-sampling
    RE Bekka, Y Berrouche
    Advances in Adaptive Data Analysis 5 (03), 1350012 , 2013
    2013
    Citations: 12

MOST CITED SCHOLAR PUBLICATIONS

  • Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects
    A Kumar, Y Berrouche, R Zimroz, G Vashishtha, S Chauhan, CP Gandhi, ...
    Measurement 211, 112615 , 2023
    2023
    Citations: 61
  • Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition
    Y Berrouche, G Vashishtha, S Chauhan, R Zimroz
    Knowledge-Based Systems 301, 112265 , 2024
    2024
    Citations: 38
  • Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG
    H Ashraf, A Waris, SO Gilani, U Shafiq, J Iqbal, EN Kamavuako, ...
    Scientific reports 14 (1), 2020 , 2024
    2024
    Citations: 22
  • Improved multiple description wavelet based image coding using Hadamard transform
    Y Berrouche, RE Bekka
    AEU-International Journal of Electronics and Communications 68 (10), 976-982 , 2014
    2014
    Citations: 15
  • Improvement of ensemble empirical mode decomposition by over-sampling
    RE Bekka, Y Berrouche
    Advances in Adaptive Data Analysis 5 (03), 1350012 , 2013
    2013
    Citations: 12
  • A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis
    Y Berrouche
    Engineering, Technology & Applied Science Research 12 (1), 8058-8062 , 2022
    2022
    Citations: 5
  • A Hyperbolic Secant-Based Pulse for Enhanced FTN Signaling in 5G/6G Systems
    Y Berrouche, M Kulhandjian, H Kulhandjian
    IEEE Wireless Communications Letters , 2025
    2025
    Citations: 1
  • Spectral-Domain Spreading via Hadamard Transform for Robust Downlink Non-Orthogonal Multiple Access
    Y Berrouche, M Kulhandjian, H Kulhandjian
    arXiv preprint arXiv:2603.07836 , 2026
    2026
  • From Noise to Prognosis: A Physics-Grounded, Fractional-Domain Framework for Early Gear Fault Detection in Aviation Drivetrains
    Y Berrouche
    arXiv preprint arXiv:2602.07527 , 2026
    2026
  • Fractional Filtering and Anomaly-Guided Diagnostics: The Local Damage Mode Extractor (LDME) for Early Gear Fault Detection
    Y Berrouche
    arXiv e-prints, arXiv: 2602.07527 , 2026
    2026
  • Contribution à l’amélioration du Codage par descriptions multiples
    Y Berrouche
    2017