Meminductor response under periodic current excitations

2014Fouda M.E.JournalRadwan A.G.

Recently, the mem-elements-based circuits have been addressed frequently in the nonlinear circuit theory due to their unique behavior. Thus, the modeling and characterizing of the mem-elements has become essential, especially studying their response under any excitation signal. This paper investigates the response of the meminductor under DC, sinusoidal, and periodic current signals for the first time. Furthermore, a meminductor emulator is developed to fit the obtained formulas which are built using commercial off the shelf components. The proposed analysis offers closed form expressions for the meminductance for each case. Moreover, many fundamentals and properties are derived to understand the responses such as the maximum saturation time in case of the DC response. A general closed form expression for the meminductance is derived under any periodic waveform, and this formula has been validated by applying a square wave as an example. © 2013 Springer Science+Business Media New York.

Memristor-based voltage-controlled relaxation oscillators

2014Fouda M.E.JournalRadwan A.G.

This paper introduces two voltage-controlled memristor-based reactance-less oscillators with analytical and circuit simulations. Two different topologies which are R-M and M-R are discussed as a function of the reference voltage where the generalized formulas of the oscillation frequency and conditions for oscillation for each topology are derived. The effect of the reference voltage on the circuit performance is studied and validated through different examples using PSpice simulations. A memristor-based voltage-controlled oscillator (VCO) is introduced as an application for the proposed circuits which is nano-size and more efficient compared to the conventional VCOs. Copyright © 2013 John Wiley & Sons, Ltd.

Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries

2024Abdelaty A.M.Elwakil A.S.Fouda M.E.JournalRadwan A.G.

The precise identification of electrical model parameters of Li-Ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal model parameter values. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-Ion batteries with different levels of complexity are used to fit the electrochemical impedance spectroscopy (EIS) data operating under different states of charge (SoC) and different operating temperatures. It is found that there is a tradeoff between ECM complexity, identification accuracy, and precision. © 2024 The Electrochemical Society (“ECS”). Published on behalf of ECS by IOP Publishing Limited

Chaotic neural network quantization and its robustness against adversarial attacks

2024Fouda M.E.Gadallah S.I.JournalOsama A.Radwan A.G.Said L.A.

Achieving robustness against adversarial attacks while maintaining high accuracy remains a critical challenge in neural networks. Parameter quantization is one of the main approaches used to compress deep neural networks to have less inference time and less storage memory size. However, quantization causes severe degradation in accuracy and consequently in model robustness. This work investigates the efficacy of stochastic quantization to enhance robustness and accuracy. Noise injection during quantization is explored to understand the impact of noise types and magnitudes on model performance. A comprehensive comparison between different applying scenarios for stochastic quantization and different noise types and magnitudes was implemented in this paper. Compared to the baseline deterministic quantization, chaotic quantization achieves a comparable accuracy, however, it achieves up to a 43% increase in accuracy against various attack scenarios. This highlights stochastic quantization as a promising defense mechanism. In addition, there is a crucial role played by the choice of noise type and magnitude in stochastic quantization. Lorenz and Henon noise distributions in stochastic quantization outperform traditional uniform and Gaussian noise in defending against attacks. A transferability analysis was discussed to understand the generalizability and effectiveness of the proposed stochastic quantization techniques. A cross-validation definition was newly evaluated in this scope to analyse the model’s stability and robustness against attacks. The study outperformed a quantization network technique and improved the model’s robustness and stability against adversarial attacks using chaotic quantization instead of deterministic quantization or even instead of stochastic quantization using traditional noise. © 2024 Elsevier B.V.