This article proposes a unified approach for hidden attractors control in fractional-order chaotic systems. Hidden attractors have small basins of attractions and are very sensitive to initial conditions and parameters. That is, they can be easily drifted from chaotic behavior into another type of dynamics, which is not suitable for encryption applications that require quite wide initial conditions and parameters ranges for encryption key design. Hence, a systematic coordinate affine transformation framework is utilized to construct transformed systems with self-reproducing attractors. Simulation results of two three-dimensional fractional-order chaotic systems with hidden attractors validate that the proposed framework supports attractors geometric structure design and multi-wing generation. Hidden attractor size, polarity, phase, shape and position control while preserving the chaotic dynamics is indicated by strange attractors, spectral entropy, maximum Lyapunov exponent and bifurcation diagrams. Simulations demonstrate the capability of multi-wing generation from fractional-order hidden attractors with no equilibria using non-autonomous parameters as opposed to the classical equilibria extension techniques suitable only for self-excited attractors. The self-reproduced multiple wings can share the same center point or be distributed along an arbitrary line, curve or surface thanks to the non-autonomous translation parameters. Multi-wing attractors widen the basin of attraction and enlarge the state space volume. For practical applications, the proposed technique makes fractional-order systems with hidden attractors suitable for circuit implementations that require specific signal level and polarity conditions. In addition, for digital encryption applications, the relatively wide range of the extra parameters enhances the key space and hence the robustness against brute force attacks. © 2020 IEEE.
Bio-inspired adsorption sheets from waste material for anionic methyl orange dye removal
Abstract: Nano zero-valent iron (nZVI), bimetallic nano zero-valent iron-copper (Fe0–Cu), and Raw algae (sargassum dentifolium) activated carbon-supported bimetallic nano zero-valent iron-copper (AC-Fe0–Cu) are synthesized and characterized using FT-IR, XRD, and SEM. The maximum removal capacity is demonstrated by bimetallic activated carbon AC-Fe0–Cu, which is estimated at 946.5 mg/g capacity at the condition pH = 7, 30 min contact time under shaking at 120 rpm at ambient temperature, 200 ppm of M.O, and 1 g/l dose of raw algae-Fe0–Cu adsorbent. The elimination capability of the H3PO4 chemical AC-Fe0–Cu adsorbent is 991.96 mg/g under the conditions of pH = 3, 120 min contact time under shaking at 120 rpm at room temperature, 200 ppm of M.O, and 2 g/l doses of H3PO4 chemical AC-Fe0–Cu adsorbent. The Bagasse activated carbon adsorbent sheet achieves a removal capacity of 71.6 mg/g MO dye solution. Kinetic and isothermal models are used to fit the results of time and concentration experiments. The intra-particle model yields the best fit for bimetallic Fe0–Cu, AC-Fe0–Cu, H3PO4 chemical AC-Fe0–Cu and bagasse activated carbon(CH), with corrected R-Squared values of 0.9656, 0.9926, 0.964, and 0.951respectively. The isothermal results emphasize the significance of physisorption and chemisorption in concentration outcomes. Response surface methodology (RSM) and artificial neural networks (ANN) are employed to optimize the removal efficiency. RSM models the efficiency and facilitates numerical optimization, while the ANN model is optimized using the moth search algorithm (MSA) for optimal results. Highlights: 1.The Fe0–Cu composite, when combined with activated carbon from Bagasse Pulp (CH), exhibited the most effective decolorization effectiveness for anionic colours present in wastewater.2.The utilization of composites presents a promising opportunity for efficient dye removal due to its cost-effectiveness and environmentally sustainable nature. 3.The utilization of response surface approach and artificial neural network modelling improves the efficacy of removal processes and treatment techniques. © 2023, The Author(s).
Circuit realization and FPGA-based implementation of a fractional-order chaotic system for cancellable face recognition
Biometric security has been developed in recent years with the emergence of cancellable biometric concepts. The idea of the cancellable biometric traits is concerned with creating encrypted or distorted traits of the original ones to protect them from hacking techniques. So, encrypted or distorted biometric traits are stored in databases instead of the original ones. This can be accomplished through non-invertible transforms or encryption schemes. In this paper, a cancellable face recognition algorithm is introduced based on face image encryption through a fractional-order multi-scroll chaotic system. The fundamental concept is to create random keys that will be XORed with the three components of color face images (red, green, and blue) to obtain encrypted face images. These random keys are generated from the Least Significant Bits of all state variables of a proposed fractional-order multi-scroll chaotic system. Lastly, the encrypted color components of face images are combined to produce a single cancellable trait for each color face image. The results of encryption with the proposed system are full-encrypted face images that are suitable for cancellable biometric applications. The strength of the proposed system is that it is extremely sensitive to the user’s selected initial conditions. The numerical simulation of the proposed chaotic system is done with MATLAB. Phase and bifurcation diagrams are used to analyze the dynamic performance of the proposed fractional-order multi-scroll chaotic system. Furthermore, we realized the hardware circuit of the proposed chaotic system on the PSpice simulator. The proposed chaotic system can be implemented on Field Programmable Gate Arrays (FPGAs). To model our generator, we can use Verilog Hardware Description Language HDL, Xilinx ISE 14.7 and Xilinx FPGA Artix-7 XC7A100T based on Grunwald-Letnikov algorithms for mathematical analysis. The numerical simulation, the circuit simulation and the hardware experimental results confirm each other. Cancellable face recognition based on the proposed fractional-order chaotic system has been implemented on FERET, LFW, and ORL datasets, and the results are compared with those of other schemes. Some evaluation metrics containing Equal Error Rate (EER), and Area under the Receiver Operating Characteristic (AROC) curve are used to assess the cancellable biometric system. The numerical results of these metrics show EER levels close to zero and AROC values of 100%. In addition, the encryption scheme is highly efficient. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Commercial Versus Natural Activated Carbon Fabricated Sheets: Applied to Dyes Removal Application
Industrial dyes are considered one of the main causes of increased water pollution of water. Many businesses, such as steel and paper, are located along riverbanks because they require large amounts of water in their manufacturing processes, and their wastes, which contain acids, alkalis, dyes, and other chemicals, are dumped and poured into rivers as effluents. For example, chemical enterprises producing aluminum emit a significant quantity of fluoride into the air and effluents into water bodies. Fertilizer facilities produce a lot of ammonia, whereas steel plants produce cyanide. Many nations consider employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater to address environmental crises. The treated water can be used for targeting the irrigation systems in its majority, as it is biologically acceptable for that specific use, industrial dyes are considered one of the leading causes of increased water pollution of water. Many businesses, such as steel and paper, are located along riverbanks because they require large amounts of water in their manufacturing processes, and their wastes, which contain acids, alkalis, dyes, and other chemicals, are dumped and poured into rivers as effluents. For example, chemical enterprises producing aluminum emit a significant quantity of fluoride into the air and effluents into water bodies. Fertilizer facilities produce much ammonia, whereas steel plants produce cyanide. Chromium salts are used in. Many nations consider employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater to address environmental crises. The treated water can target the majority of irrigation systems, as it is biologically acceptable for that specific use, which economizes the use of freshwater sources for municipal use. This study presents a novel method for fabricating an efficient adsorbent sheet for wastewater treatment. The sheets are fabricated by combining sugarcane bagasse pulp as a scaffold with commercial, naturally activated carbon, and bimetallic-prepared adsorbents. Sugarcane bagasse is utilized in producing activated carbon because of its high carbon contents, availability, and low cost. The prepared composite sheets are synthesized and investigated for pollutants removal of crystal violet (CV), methyl orange (MO), and Chromium (CI) dyes. Different weight ratios of activated carbon are used to form a bio-composite mixed sheet. The formed sheets’ morphology is performed via a high scanning electron microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). To determine the adsorption mechanism, the intra-particle diffuse screening experiment is used to test the experimental data. All the prepared sheets can retain the pollutants, with the best removal efficiency of 98% for methyl orange adsorption onto the bio-composite mixed sheet. The results of the parameter (time, concentration, and dose) sets provided valuable proof that the adsorption of methyl orange onto the bio-composite sheet mixed with naturally activated carbon is an endothermic phenomenon involving physical adsorption. © 2024 Wiley-VCH GmbH.
Secure blind watermarking using Fractional-Order Lorenz system in the frequency domain
This paper investigates two different blind watermarking systems in the frequency domain with the development of a Pseudo Random Number Generator (PRNG), based on a fractional-order chaotic system, for watermark encryption. The methodology is based on converting the cover image to the YCbCr color domain and applying two different techniques of frequency transforms, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), to the Y channel. Then, the encrypted watermark is embedded in the middle-frequency band and HH band coefficients for the DCT and DWT, respectively. For more security and long encryption key size, the fractional-order Lorenz system is used to double the encryption key size and make it secure against brute-force attacks. The proposed algorithms successfully detect the hidden watermark by using the statistical properties of the embedding media, where the PRNG is examined using statistical tests and the watermarking systems are evaluated using standard imperceptibility and robustness measures. Common attacks such as noise-adding attacks, image enhancement attacks and geometric transformation attacks are discussed. Results of the PRNG demonstrate sensitivity to the system parameters, and results of the watermarking systems show good imperceptibility while keeping the robustness measures in a good range. © 2023 Elsevier GmbH
Analysis and Guidelines for Different Designs of Pseudo Random Number Generators
The design of an efficient Pseudo Random Number Generator (PRNG) with good randomness properties is an important research topic because it is a core component in many applications. Based on an extensive study of most PRNGs in the past few decades, this paper categorizes six distinct design scenarios under two primary groups: non-chaotic and chaotic generators. The non-chaotic group comprises Linear Feedback Shift Registers (LFSR) with S-Boxes, primitive roots, and elliptic curves, whereas the chaotic group encompasses discrete, continuous, and fractional-order chaotic generators. This paper delves into the related scientific summaries, equations, flowcharts, and designs with necessary recommendations for each PRNG scenario. Even though the focus is on the basic design characteristics that provide simple, functional and secure PRNGs, it is possible to enhance those designs for additional features and improved efficiency. Simulation outcomes and system key configurations, which produce long random sequences, are also presented and evaluated using leading criteria. The evaluation criteria include the National Institute of Standards and Technology (NIST) SP-800-22 test suite, TestU01 randomness tests, histogram, entropy, autocorrelation, and cross-correlation. Furthermore, key space, key sensitivity, and bit rate indicate that all designed examples meet international standards with high quality. The presented PRNGs are compared and integrated into an image encryption system. Although each PRNG design scenario can have a different key space, simple designs with fixed-length system keys are chosen for the sake of proper comparisons. Statistical and security assessments of the encryption system demonstrate that the PRNGs are cryptographically secure. © 2013 IEEE.
Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries
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
Crystal violet removal using algae-based activated carbon and its composites with bimetallic Fe0-Cu
The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of the pollution of water. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe0-Cu), algae-activated carbon, and their composites (AC-Fe0-Cu), which are employed as adsorbents. In this paper, Synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3-10), time intervals (15-180) min, and initial dye concentrations (50-500 ppm). Raw algae exhibit an impressive 96.64% removal efficiency under the following conditions: pH 7, contact time of 180 min, rotational speed of 120 rpm, temperature range of 25 °C-30 °C, concentration of 300 ppm in the CV dye solution, and a dose of 4 g l?1 of raw algae adsorbent. The best removal efficiencies of Raw algae Fe0-Cu, and H3PO4 chemical AC-Fe0-Cu are 97.61 % and 97.46 %, respectively, at pH = 7, contact time = 150 min, rotational speed = 120 rpm, T = (25-30) °C, concentration = 75 ppm of CV dye solution, and 1.5 g l?1 doses of raw algae F e0-Cu adsorbent and 1 g l?1 dose of H3PO4 chemical AC-Fe0-Cu adsorbent. The maximum amounts (q max) of Bi-RA and RA adsorbed for the adsorption process of CV are 85.92 mg g?1 and 1388 mg g?1, respectively. The Bi-H3A-AC model, optimized using PSO, demonstrates superior performance, with the highest adsorption capacity estimated at 83.51 mg g?1. However, the Langmuir model predicts a maximum adsorption capacity (q e ) of 275.6 mg g?1 for the CV adsorption process when utilizing Bi-H3A-AC. Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM are used to characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the ideal conditions for improving removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency. © 2024 The Author(s). Published by IOP Publishing Ltd.
Chaotic neural network quantization and its robustness against adversarial attacks
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.
Hardware Accelerator of Fractional-Order Operator Based on Phase Optimized Filters With Applications
Hardware accelerators outperform CPUs in terms of performance by parallelizing the algorithm architecture and using the device’s programmable resources. FPGA is a type of hardware accelerator that excels not only in performance but also in energy efficiency. So, it provides a suitable platform for implementing complicated fractional-order systems. This paper proposes a novel phase-based optimization method to implement fractional operators using FIR and IIR filters. We also compare five fractional operator implementation methods on FPGA regarding resource utilization, execution time, power, and accuracy. These methods and the proposed one are evaluated in terms of power consumption, delay, and resources to assist the designer in determining the most suitable implementation method for the given application. The proposed method has a lower phase error of 14.7% in the case of derivative operation and a lower phase error of 18.83% in the case of integration compared to the literature. In addition, the proposed methods decreased the consumed power and area by more than three times compared to the fixed-window GL fractional operator. The proposed approach implements Heaviside’s inductor-terminated lossy line. In addition, it is employed as an edge detection kernel to demonstrate its effectiveness in image processing applications. © 2023 IEEE.