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Creators/Authors contains: "Alhawari, Mohammad"

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  1. ; (, IEEE Transactions on Very Large Scale Integration (VLSI) Systems)
    Free, publicly-accessible full text available May 1, 2026
  2. ; ; ; (, IEEE)
    This paper explores an energy-efficient resistive random access memory (RRAM) crossbar array framework for predicting epileptic seizures using the CHB-MIT electroencephalogram (EEG) dataset. RRAMs have significant potential for in-memory computing, offering a promising solution to overcome the limitations of the traditional Von Neumann architecture. By integrating a domain-specific feature extraction approach and evaluating the optimal RRAM hardware parameters using the NeuroSim+ benchmarking platform, we assess the performance of RRAM crossbars for predicting epileptic seizures. Our proposed workflow achieves accuracy levels above 80% despite the EEG data being quantized to 1-bit, highlighting the robustness and efficiency of our approach for epileptic seizure prediction 
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  3. ; ; (, IEEE)
    Full Text Available 
  4. ; ; ; (, IEEE)
    Full Text Available 
  5. ; ; ; ; (, IEEE Journal of Biomedical and Health Informatics)
    Full Text Available 
  6. ; ; (, IEEE international Midwest Symposium on Circuits and Systems)
    Accepted Manuscript Full Text Available